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Ann Wagner, Mechanical Engineer.
ERIC Educational Resources Information Center
Bennett, Betsy K.
Presents a profile of Ann Wagner, a mechanical engineer at the Goddard Space Flight Center in Maryland, and her job responsibilities there. Also includes a brief history of mechanical engineering as well as a sample graph and data activity sheet with answers. (AIM)
Estimate product quality with ANNs
Brambilla, A.; Trivella, F.
Artificial neural networks (ANNs) have been applied to predict catalytic reformer octane number (ON) and gasoline splitter product qualities. Results show that ANNs are a valuable tool to derive fast and accurate product quality measurements, and offer a low-cost alternative to online analyzers or rigorous mathematical models. The paper describes product quality measurements, artificial neural networks, ANN structure, estimating gasoline octane numbers, and estimating naphtha splitter product qualities.
ANNs pinpoint underground distribution faults
Glinkowski, M.T.; Wang, N.C.
Many offline fault location techniques in power distribution circuits involve patrolling along the lines or cables. In overhead distribution lines, most of the failures can be located quickly by visual inspection without the aid of special equipment. However, locating a fault in underground cable systems is more difficult. It involves additional equipment (e.g., thumpers, radars, etc.) to transform the invisibility of the cable into other forms of signals, such as acoustic sound and electromagnetic pulses. Trained operators must carry the equipment above the ground, follow the path of the signal, and draw lines on their maps in order to locate the fault. Sometimes, even smelling the burnt cable faults is a way of detecting the problem. These techniques are time consuming, not always reliable, and, as in the case of high-voltage dc thumpers, can cause additional damage to the healthy parts of the cable circuit. Online fault location in power networks that involve interconnected lines (cables) and multiterminal sources continues receiving great attention, with limited success in techniques that would provide simple and practical solutions. This article features a new online fault location technique that: uses the pattern recognition feature of artificial neural networks (ANNs); utilizes new capabilities of modern protective relaying hardware. The output of the neural network can be graphically displayed as a simple three-dimensional (3-D) chart that can provide an operator with an instantaneous indication of the location of the fault.
Phosphorus component in AnnAGNPS
Yuan, Y.; Bingner, R.L.; Theurer, F.D.; Rebich, R.A.; Moore, P.A.
The USDA Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) has been developed to aid in evaluation of watershed response to agricultural management practices. Previous studies have demonstrated the capability of the model to simulate runoff and sediment, but not phosphorus (P). The main purpose of this article is to evaluate the performance of AnnAGNPS on P simulation using comparisons with measurements from the Deep Hollow watershed of the Mississippi Delta Management Systems Evaluation Area (MDMSEA) project. A sensitivity analysis was performed to identify input parameters whose impact is the greatest on P yields. Sensitivity analysis results indicate that the most sensitive variables of those selected are initial soil P contents, P application rate, and plant P uptake. AnnAGNPS simulations of dissolved P yield do not agree well with observed dissolved P yield (Nash-Sutcliffe coefficient of efficiency of 0.34, R2 of 0.51, and slope of 0.24); however, AnnAGNPS simulations of total P yield agree well with observed total P yield (Nash-Sutcliffe coefficient of efficiency of 0.85, R2 of 0.88, and slope of 0.83). The difference in dissolved P yield may be attributed to limitations in model simulation of P processes. Uncertainties in input parameter selections also affect the model's performance.
Katherine Anne Porter on Her Contemporaries.
ERIC Educational Resources Information Center
Bridges, Phyllis
Personal experiences with and critical judgments of leading artists and intellectuals of the twentieth century are recorded in Katherine Anne Porter's essays, letters and conversations which provide snapshots of her attitudes and encounters. Porter's commentaries about such contemporaries as Ernest Hemingway, William Faulkner, Saul Bellow,…
Ann Arbor, Michigan: Solar in Action (Brochure)
Not Available
This brochure provides an overview of the challenges and successes of Ann Arbor, Michigan, a 2007 Solar America City awardee, on the path toward becoming a solar-powered community. Accomplishments, case studies, key lessons learned, and local resource information are given.
Obituary: Anne Barbara Underhill, 1920-2003
NASA Astrophysics Data System (ADS)
Roman, Nancy Grace
Anne was born in Vancouver, British Columbia on 12 June 1920. Her parents were Frederic Clare Underhill, a civil engineer and Irene Anna (née Creery) Underhill. She had a twin brother and three younger brothers. As a young girl she was active in Girl Guides and graduated from high school winning the Lieutenant Governor's medal as one of the top students in the Province. She also excelled in high school sports. Her mother died when Anne was 18 and, while undertaking her university studies, Anne assisted in raising her younger brothers. Her twin brother was killed in Italy during World War II (1944), a loss that Anne felt deeply. Possibly because of fighting to get ahead in astronomy, a field overwhelming male when she started, she frequently appeared combative. At the University of British Columbia, Anne obtained a BA (honors) in Chemistry (1942), followed by a MA in 1944. After working for the NRC in Montreal for a year, she studied at the University of Toronto prior to entering the University of Chicago in 1946 to obtain her PhD. Her thesis was the first model computed for a multi-layered stellar atmosphere (1948). During this time she worked with Otto Struve, developing a lifetime interest in hot stars and the analysis of their high dispersion spectra. She received two fellowships from the University Women of Canada. She received a U.S. National Research Fellowship to work at the Copenhagen Observatory, and upon its completion, she returned to British Columbia to work at the Dominion Astrophysical Observatory as a research scientist from 1949--1962. During this period she spent a year at Harvard University as a visiting professor and at Princeton where she used their advanced computer to write the first code for modeling stellar atmospheres. Anne was invited to the University of Utrecht (Netherlands) as a full professor in 1962. She was an excellent teacher, well liked by the students in her classes, and by the many individuals that she guided throughout her
75 FR 13334 - Notice of Availability of Draft Environmental Assessment; Ann Arbor Municipal Airport, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... Federal Aviation Administration Notice of Availability of Draft Environmental Assessment; Ann Arbor Municipal Airport, Ann Arbor, MI AGENCY: The Federal Aviation Administration is issuing this notice on... extension of runway 6/24 at the Ann Arbor Municipal Airport. While not required for an EA, the FAA...
Obituary: Anne Barbara Underhill, 1920-2003
NASA Astrophysics Data System (ADS)
Roman, Nancy Grace
Anne was born in Vancouver, British Columbia on 12 June 1920. Her parents were Frederic Clare Underhill, a civil engineer and Irene Anna (née Creery) Underhill. She had a twin brother and three younger brothers. As a young girl she was active in Girl Guides and graduated from high school winning the Lieutenant Governor's medal as one of the top students in the Province. She also excelled in high school sports. Her mother died when Anne was 18 and, while undertaking her university studies, Anne assisted in raising her younger brothers. Her twin brother was killed in Italy during World War II (1944), a loss that Anne felt deeply. Possibly because of fighting to get ahead in astronomy, a field overwhelming male when she started, she frequently appeared combative. At the University of British Columbia, Anne obtained a BA (honors) in Chemistry (1942), followed by a MA in 1944. After working for the NRC in Montreal for a year, she studied at the University of Toronto prior to entering the University of Chicago in 1946 to obtain her PhD. Her thesis was the first model computed for a multi-layered stellar atmosphere (1948). During this time she worked with Otto Struve, developing a lifetime interest in hot stars and the analysis of their high dispersion spectra. She received two fellowships from the University Women of Canada. She received a U.S. National Research Fellowship to work at the Copenhagen Observatory, and upon its completion, she returned to British Columbia to work at the Dominion Astrophysical Observatory as a research scientist from 1949--1962. During this period she spent a year at Harvard University as a visiting professor and at Princeton where she used their advanced computer to write the first code for modeling stellar atmospheres. Anne was invited to the University of Utrecht (Netherlands) as a full professor in 1962. She was an excellent teacher, well liked by the students in her classes, and by the many individuals that she guided throughout her
An Overview of ANN Application in the Power Industry
NASA Technical Reports Server (NTRS)
Niebur, D.
The paper presents a survey on the development and experience with artificial neural net (ANN) applications for electric power systems, with emphasis on operational systems. The organization and constraints of electric utilities are reviewed, motivations for investigating ANN are identified, and a current assessment is given from the experience of 2400 projects using ANN for load forecasting, alarm processing, fault detection, component fault diagnosis, static and dynamic security analysis, system planning, and operation planning.
An ANN That Applies Pragmatic Decision on Texts.
ERIC Educational Resources Information Center
Aretoulaki, Maria; Tsujii, Jun-ichi
A computer-based artificial neural network (ANN) that learns to classify sentences in a text as important or unimportant is described. The program is designed to select the sentences that are important enough to be included in composition of an abstract of the text. The ANN is embedded in a conventional symbolic environment consisting of…
Literary Relations: Anne Shirley and Her American Cousins.
ERIC Educational Resources Information Center
Dawson, Janis
Examines similarities between L.M. Montgomery's "Anne of Green Gables" and Kate Douglas Wiggin's "Rebecca of Sunnybrook Farm." Raises questions about the Canadianness of Montgomery's novel and considers the author's literary indebtedness to American authors and contemporary children's literature. Argues that as a literary work, "Anne of Green…
77 FR 75629 - Pramaggiore, Anne R.; Notice of Filing
Federal Register 2010, 2011, 2012, 2013, 2014
... Energy Regulatory Commission Pramaggiore, Anne R.; Notice of Filing Take notice that on December 14, 2012, Anne R. Pramaggiore submitted for filing, an application for authority to hold interlocking positions... the ``eFiling'' link at http://www.ferc.gov . Persons unable to file electronically should submit...
Ann Eliza Young: A Nineteenth Century Champion of Women's Rights.
ERIC Educational Resources Information Center
Cullen, Jack B.
Concentrating on the efforts of such nineteenth century women's rights advocates as Susan B. Anthony and Elizabeth Cady Stanton, communication researchers have largely overlooked the contributions made to the cause by Ann Eliza Young. The nineteenth wife of Mormon leader Brigham Young, Ann Eliza Young left her husband and took to the speaker's…
Applications of Artificial Neural Networks (ANNs) in Food Science
HUang, Yiqun; Kangas, Lars J.; Rasco, Barbara A.
Abstract Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decade, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs have a great deal of promise for modeling complex tasks in process control and simulation, and in applications of machine perception including machine vision and the electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in this field.
Playing tag with ANN: boosted top identification with pattern recognition
NASA Astrophysics Data System (ADS)
Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu; Lee, Seung J.; Perelstein, Maxim
Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a "digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p T in the 1100-1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.
Risk Quantification for ANN Based Short-Term Load Forecasting
NASA Astrophysics Data System (ADS)
Iwashita, Daisuke; Mori, Hiroyuki
A new risk assessment method for short-term load forecasting is proposed. The proposed method makes use of an Artificial Neural Network (ANN) to forecast one-step ahead daily maximum loads and evaluate uncertainty of in load forecasting. As ANN the model, the Radial Basis Function (RBF) network is employed to forecast loads due to the good performance. Sufficient realistic pseudo-scenarios are required to carry out quantitative risk analysis. The multivariate normal distribution with the correlation between input variables is used to give more realistic results to ANN. In addition, the method of Moment Matching is used to improve the accuracy of the multivariate normal distribution. The Peak Over Threshold (POT) approach is used to evaluate risk that exceeds the upper bounds of generation capacity. The proposed method is successfully applied to real data of daily maximum load forecasting.
Decolonizing the Choctaws: Teaching LeAnne Howe's "Shell Shaker"
ERIC Educational Resources Information Center
Hollrah, Patrice
"Shell Shaker" (2001) by LeAnne Howe (Choctaw) is a novel that gives students an opportunity to learn that the history and culture of the Choctaw Nation of Oklahoma are alive today. Winner of the Before Columbus Foundation American Book Award in 2002, the novel deals with two parallel stories that converge in the present, one about the eighteenth…
Anne S. Young: Professor and Variable Star Observer Extraordinaire
NASA Astrophysics Data System (ADS)
Bracher, Katherine
Anne Sewell Young (1871-1961) was one of the eight original members of the AAVSO, to which she contributed more than 6500 observations over 33 years. She also taught astronomy for 37 years at Mount Holyoke College; among her students was Helen Sawyer Hogg. This paper will look at her life and career both at Mount Holyoke and with the AAVSO.
AnnAGNPS Application and Evaluation in NE Indiana
Technology Transfer Automated Retrieval System (TEKTRAN)
The Annualized Agricultural Non-Point Source (AnnAGNPS) pollution model was developed for simulation of runoff, sediment, nutrient, and pesticide losses from ungaged agricultural watersheds. Here, the model was applied to the 707 km2 Cedar Creek Watershed (CCW) and the 45 km2 Matson Ditch Sub-Catchm...
FLOYDS Classification of Gaia16ann as an AGN
NASA Astrophysics Data System (ADS)
Hosseinzadeh, G.; Arcavi, I.; Howell, D. A.; McCully, C.; Valenti, S.
We obtained a spectrum of Gaia16ann on 2016 July 7.5 UT with the robotic FLOYDS instrument mounted on the LCOGT 2-meter telescope on Haleakala, Hawai'i. Using SNID (Blondin & Tonry 2007, ApJ, 666, 1024), we find a good fit to an AGN at redshift z=0.196.
Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)
NASA Astrophysics Data System (ADS)
Ebrahimi, Sh; Kalhor, E. G.; Nabavi, S. R.; Alamiparvin, L.; Pogaku, R.
In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.
Reactions to Ann Arbor: Vernacular Black English and Education.
ERIC Educational Resources Information Center
Whiteman, Marcia Farr, Ed.
The papers in this collection provide a brief state-of-the-art statement on the role of non-standard dialects of English in education and on some implications of the Ann Arbor decision. The following papers are included: (1) "Vernacular Black English: Setting the Issues in Time," by Roger W. Shuy; (2) "Beyond Black English: Implications of the Ann…
Professor Ann De Vaney and a Good Conversation.
ERIC Educational Resources Information Center
Nichols, Randall G.
The author begins this paper by explaining that in examining 10 works by Ann De Vaney his goal is to see what her writing generally reveals about her playing close attention to people-to research subjects, to subjects otherwise addressed in the works, to readers of her works. The author attempts to address each by asking basic questions: (1) What…
Research Review: Ann Markusen's Concept of Artistic Dividend
ERIC Educational Resources Information Center
Hornbacher, Judy, Ed.
This issue of the "Research Review" features the work of Ann Markusen, Director of the Humphrey Institute's Project on Regional and Industrial Economics at the University of Minnesota. Through the six articles reviewed here, Markusen develops the concept of "The Artistic Dividend," examines the impact of Artists' Centers, explores how artists earn…
An Interview with Dr. Anne LaBastille.
ERIC Educational Resources Information Center
Griffin, Elizabeth
Anne LaBastille, a role model for women interested in exploring the wilderness, gives hints on lessening the effects of acid rain, tells outdoor educators to encourage women to explore the wilderness and to take children outdoors to experience nature, and predicts a future economic slump for outdoor education. (LC)
Comparison of Conventional and ANN Models for River Flow Forecasting
NASA Astrophysics Data System (ADS)
Jain, A.; Ganti, R.
Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.
Space partitioning strategies for indoor WLAN positioning with cascade-connected ANN structures.
Borenović, Miloš; Nešković, Aleksandar; Budimir, Djuradj
Position information in indoor environments can be procured using diverse approaches. Due to the ubiquitous presence of WLAN networks, positioning techniques in these environments are the scope of intense research. This paper explores two strategies for space partitioning when utilizing cascade-connected Artificial Neural Networks (ANNs) structures for indoor WLAN positioning. A set of cascade-connected ANN structures with different space partitioning strategies are compared mutually and to the single ANN structure. The benefits of using cascade-connected ANNs structures are shown and discussed in terms of the size of the environment, number of subspaces and partitioning strategy. The optimal cascade-connected ANN structures with space partitioning show up to 50% decrease in median error and up to 12% decrease in the average error with respect to the single ANN model. Finally, the single ANN and the optimal cascade-connected ANN model are compared against other well-known positioning techniques. PMID:21243727
78 FR 70099 - Requested Administrative Waiver of the Coastwise Trade Laws: Vessel LADY ANN; Invitation for...
Federal Register 2010, 2011, 2012, 2013, 2014
... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF TRANSPORTATION Maritime Administration Requested Administrative Waiver of the Coastwise Trade Laws: Vessel LADY ANN... of the vessel LADY ANN is: Intended Commercial Use Of Vessel: ``Charter cruises.'' Geographic...
Groundwater Pollution Source Identification Using Trained ANN Model
NASA Astrophysics Data System (ADS)
Ayaz, M.; Srivastava, R.
Remediation of groundwater contamination is one of the foremost challenges for the present generation. Exact knowledge of the location of the pollution source is essential to tackle this problem. Pollution sources have several important characteristics - location, strength and release period - that can be employed to single out a specific source. Breakthrough curves, which are the temporal distribution of concentration data at a given location, can be utilized to identify the location of an unknown pollution source. However, there is a lag between the time when the readings are taken at the observation well and the time when the source becomes active. In real field situations there is little or no information about this lag. We develop a methodology to identify the location of a pollution source, without using the lag time or the source strength as known parameters, by using an Artificial Neural Network (ANN) based technique. Breakthrough curves are primarily dependent on four variables, namely, source location, strength, release period and lag time. To develop an ANN model, the impact because of strength and lag time has been eliminated in a step-wise fashion. First, the breakthrough curve is normalized, between 0 and 1, by dividing concentration data by the maximum concentration value observed. Then, only the portion of the breakthrough curve near the peak is used as an input to train the ANN model. It has been shown that the breakthrough curve under these conditions is only dependent on the source location and release period, and is unique for a given combination of source strength and release period. An ANN model with one hidden layer is trained using the Levenberg-Marquardt algorithm. The modified breakthrough curve is used as an input to the ANN model while the source strength and release period constitute the output. The number of neurons in the hidden layer has been selected by minimizing the mean squared error for different number of hidden neurons
75 FR 418 - Certificate of Alternative Compliance for the Offshore Supply Vessel KELLY ANN CANDIES
Federal Register 2010, 2011, 2012, 2013, 2014
... SECURITY Coast Guard Certificate of Alternative Compliance for the Offshore Supply Vessel KELLY ANN CANDIES... Alternative Compliance was issued for the offshore supply vessel KELLY ANN CANDIES as required by 33 U.S.C.... SUPPLEMENTARY INFORMATION: Background and Purpose The offshore supply vessel KELLY ANN CANDIES will be used...
Anne S. Young: Professor and Variable Star Observer Extraordinaire
NASA Astrophysics Data System (ADS)
Bracher, K.
One of the original eight members of the AAVSO, but not well known today, was Professor Anne Sewell Young of Mount Holyoke College. Miss Young taught there for thirty-seven years, and trained many women astronomers during the first third of the 20th century. This paper will attempt to present her life as an inspiring teacher, as well as a contributor of more than 6,500 variable star observations to the AAVSO.
Jo Ann Rinaudo, PhD | Division of Cancer Prevention
Dr. Jo Ann Rinaudo is a Program Director in the Cancer Biomarkers Research Group in the Division of Cancer Prevention at the National Cancer Institute. She received a doctoral degree from the University of Toronto, where she studied chemical carcinogenesis in the liver. She was in the pathology department and has a broad background in human disease. Post-graduate training included further studies on the cell cycle during liver regeneration and cancer. |
A Hybrid FEM-ANN Approach for Slope Instability Prediction
NASA Astrophysics Data System (ADS)
Verma, A. K.; Singh, T. N.; Chauhan, Nikhil Kumar; Sarkar, K.
Assessment of slope stability is one of the most critical aspects for the life of a slope. In any slope vulnerability appraisal, Factor Of Safety (FOS) is the widely accepted index to understand, how close or far a slope from the failure. In this work, an attempt has been made to simulate a road cut slope in a landslide prone area in Rudrapryag, Uttarakhand, India which lies near Himalayan geodynamic mountain belt. A combination of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been adopted to predict FOS of the slope. In ANN, a three layer, feed- forward back-propagation neural network with one input layer and one hidden layer with three neurons and one output layer has been considered and trained using datasets generated from numerical analysis of the slope and validated with new set of field slope data. Mean absolute percentage error estimated as 1.04 with coefficient of correlation between the FOS of FEM and ANN as 0.973, which indicates that the system is very vigorous and fast to predict FOS for any slope.
Coupling SWAT and ANN models for enhanced daily streamflow prediction
NASA Astrophysics Data System (ADS)
Noori, Navideh; Kalin, Latif
To improve daily flow prediction in unmonitored watersheds a hybrid model was developed by combining a quasi-distributed watershed model and artificial neural network (ANN). Daily streamflow data from 29 nearby watersheds in and around the city of Atlanta, Southeastern United States, with leave-one-site-out jackknifing technique were used to build the flow predictive models during warm and cool seasons. Daily streamflow was first simulated with the Soil and Water Assessment Tool (SWAT) and then the SWAT simulated baseflow and stormflow were used as inputs to ANN. Out of the total 29 test watersheds, 62% and 83% of them had Nash-Sutcliffe values above 0.50 during the cool and warm seasons, respectively (considered good or better). As the percent forest cover or the size of test watershed increased, the performances of the models gradually decreased during both warm and cool seasons. This indicates that the developed models work better in urbanized watersheds. In addition, SWAT and SWAT Calibration Uncertainty Procedure (SWAT-CUP) program were run separately for each station to compare the flow prediction accuracy of the hybrid approach to SWAT. Only 31% of the sites during the calibration and 34% of validation runs had ENASH values ⩾0.50. This study showed that coupling ANN with semi-distributed models can lead to improved daily streamflow predictions in ungauged watersheds.
Improved Artificial Neural Network-Pedotransfer Functions (ANN-PTFs) for Estimating Soil Hydraulic Parameters
NASA Astrophysics Data System (ADS)
Gautam, M. R.; Zhu, J.; Ye, M.; Meyer, P. D.; Hassan, A. E.
ANN-PTFs have become popular means of mapping easily available soil data into hard-to-measure soil hydraulic parameters in the recent years. These parameters and their distributions are the indispensable inputs to subsurface flow and transport models which provide basis for environmental planning, management and decision making. While improved ANN prediction together with the preservation of probability distributions of hydraulic parameters in ANN training is important, ANN-PTFs have been typically found using conventional ANN training approach with the mean square error as an error function, which may not preserve the probability distribution of the parameters. Moreover, the conventional ANN training can itself introduce correlation among predicted parameters and could not preserve the actual correlation among the measured parameters. The present study describes approaches to deal with such shortcomings of conventional ANN- PTF training algorithms by using new types of error functions and presents a group of improved ANN-PTF models developed on the basis of the new approaches with different levels of data availability. In the study, the bootstrap method is used as part of ANN-PTF development for generating independent training and validation sets, and calculating uncertainty estimates of the ANN predictions. The results demonstrate the merit of the new approaches of the ANN training and the physical significance of various types of less costly soil data in the prediction of soil hydraulic parameters.
Input Variable Selection for Hydrologic Modeling Using Anns
NASA Astrophysics Data System (ADS)
Ganti, R.; Jain, A.
The use of artificial neural network (ANN) models in water resources applications has grown considerably over the last couple of decades. In learning problems, where a connectionist network is trained with a finite sized training set, better generalization performance is often obtained when unneeded weights in the network are eliminated. One source of unneeded weights comes from the inclusion of input variables that provide little information about the output variables. Hence, in the ANN modeling methodology, one of the approaches that has received little attention, is the selection of appropriate model inputs. In the past, different methods have been used for identifying and eliminating these input variables. Normally, linear methods of Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) have been adopted. For nonlinear physical systems e.g. hydrological systems, model inputs selected based on the linear correlation analysis among input and output variables cannot assure to capture the non-linearity in the system. In the present study, two of the non-linear methods have been explored for the Input Variable Selection (IVS). The linear method employing ACF and PACF is also used for comparison purposes. The first non-linear method utilizes a measure of the Mutual Information Criterion (MIC) to characterize the dependence between a potential model input and the output, which is a step wise input selection procedure. The second non-linear method is improvement over the first method which eliminates redundant inputs based on a partial measure of mutual information criterion (PMIC), which is also a step wise procedure. Further, the number of input variables to be considered for the development of ANN model was determined using the Principal Component Analysis (PCA), which previously used to be done by trial and error approach. The daily river flow data derived from Godavari River Basin @ Polavaram, Andhra Pradesh, India, and the daily average
Groundwater Pollution Source Identification using Linked ANN-Optimization Model
NASA Astrophysics Data System (ADS)
Ayaz, Md; Srivastava, Rajesh; Jain, Ashu
Groundwater is the principal source of drinking water in several parts of the world. Contamination of groundwater has become a serious health and environmental problem today. Human activities including industrial and agricultural activities are generally responsible for this contamination. Identification of groundwater pollution source is a major step in groundwater pollution remediation. Complete knowledge of pollution source in terms of its source characteristics is essential to adopt an effective remediation strategy. Groundwater pollution source is said to be identified completely when the source characteristics - location, strength and release period - are known. Identification of unknown groundwater pollution source is an ill-posed inverse problem. It becomes more difficult for real field conditions, when the lag time between the first reading at observation well and the time at which the source becomes active is not known. We developed a linked ANN-Optimization model for complete identification of an unknown groundwater pollution source. The model comprises two parts- an optimization model and an ANN model. Decision variables of linked ANN-Optimization model contain source location and release period of pollution source. An objective function is formulated using the spatial and temporal data of observed and simulated concentrations, and then minimized to identify the pollution source parameters. In the formulation of the objective function, we require the lag time which is not known. An ANN model with one hidden layer is trained using Levenberg-Marquardt algorithm to find the lag time. Different combinations of source locations and release periods are used as inputs and lag time is obtained as the output. Performance of the proposed model is evaluated for two and three dimensional case with error-free and erroneous data. Erroneous data was generated by adding uniformly distributed random error (error level 0-10%) to the analytically computed concentration
ANN expert system screening for illicit amphetamines using molecular descriptors
NASA Astrophysics Data System (ADS)
Gosav, S.; Praisler, M.; Dorohoi, D. O.
The goal of this study was to develop and an artificial neural network (ANN) based on computed descriptors, which would be able to classify the molecular structures of potential illicit amphetamines and to derive their biological activity according to the similarity of their molecular structure with amphetamines of known toxicity. The system is necessary for testing new molecular structures for epidemiological, clinical, and forensic purposes. It was built using a database formed by 146 compounds representing drugs of abuse (mainly central stimulants, hallucinogens, sympathomimetic amines, narcotics and other potent analgesics), precursors, or derivatized counterparts. Their molecular structures were characterized by computing three types of descriptors: 38 constitutional descriptors (CDs), 69 topological descriptors (TDs) and 160 3D-MoRSE descriptors (3DDs). An ANN system was built for each category of variables. All three networks (CD-NN, TD-NN and 3DD-NN) were trained to distinguish between stimulant amphetamines, hallucinogenic amphetamines, and nonamphetamines. A selection of variables was performed when necessary. The efficiency with which each network identifies the class identity of an unknown sample was evaluated by calculating several figures of merit. The results of the comparative analysis are presented.
Hip Fractures: The St Ann's Bay Regional Hospital Experience
O'Connor, I; McDowell, D; Barnes, D
Objectives: To study the outcome of hip fractures in a cohort of patients from two different time periods (2002–2003 and 2006–2008). Methods: Patients treated for hip fractures at the St Ann's Bay Regional Hospital, which provides orthopaedic care for the parishes of St Ann, St Mary and Portland, were retrospectively analysed between 2002–2003 and 2006–2008. Results: A significant increase in the recorded incidence of hip fractures, from 19 in the 2002–2003 time period to 101 in the 2006–2008 time period was noted. There was a drastic fall in the in-hospital mortality rate (43% in the 2002–2003 time period compared to 4.5% in the 2006–2008 time period). In the 2006–2008 period, 82.9% of patients were ambulant at discharge compared to 36% from the 2002–2003 time period. Conclusion: Early surgical fixation is necessary to allow rapid mobilization in these patients for whom the consequences of bed rest would otherwise be devastating. PMID:25303247
Upper Wisconsinan submarine end moraines off Cape Ann, Massachusetts
Oldale, R.N.
Seismic profiles across the southwest end of Jeffreys Ledge, a bathymetric high north of Cape Ann, Massachusetts, reveal two end moraines. The moraines overlie upper Wisconsinan glacialmarine silty clay and are composed mostly of subaqueous ice-contact deposits and outwash. They were formed below sea level in water depths of as much as 120 m during fluctuations of a calving ice front. The moraines are late Wisconsinan in age and were formed after the Cambridge readvance, about 14,000 yr B.P., and before the Kennebunk readvance, about 13,000 yr B.P. They represent fluctuations of the ice front during overall retreat of Laurentide ice from the Gulf of Maine and New England. ?? 1985.
Evaluation of AnnAGNPS in cold and temperate regions.
Das, S; Rudra, R P; Goel, P K; Gharabaghi, B; Gupta, N
Identification of the pollution sources and understanding the processes related to runoff generation and pollution transportation is effective for the water quality management and selection of the Best Management Practices. The ANNualized AGricultural Non-Point Source (AnnAGNPS) model was applied to a watershed in Southern Ontario to evaluate the hydrology and sediment component from the non-point sources. The model was run for two years (1998 to 1999); one year's data was used to calibrate and the second year's data was used for validation purposes. The model has under predicted runoff amount and over predicted the sediment yield. However, the simulated runoff and sediment yield compared fairly well with the observed data indicating that the model had an acceptable performance in simulation of runoff and sediment. The study is still in progress to assess its performance for estimation of TMDL and improvements needed for the model to use under Ontario conditions. PMID:16594345
A drowned Holocene barrier spit off Cape Ann, Massachusetts
Oldale, Robert N.
Seismic profiles and bathymetric contours reveal a drowned barrier spit on Jeffreys Ledge off Cape Ann, Massachusetts. Seaward-dipping internal reflectors indicate that a regressive barrier formed during the early Holocene low sea-level stillstand. Preservation of the barrier spit may have been favored by its large size (as much as 20 m thick), by an ample sediment supply from unconsolidated glacial drift, and by the subsequent rapid sea-level rise. The barrier spit is present in water depths of 50 to 70 m and indicates a low relative sea-level stand of −50 m. This value confirms the low relative sea-level stand of −47 m postulated by Oldale et al. (1983) for northeast Massachusetts and New Hampshire on the basis of the submerged delta of the Merrimack River, and it indicates that the barrier and delta were contemporaneous (Oldale et al., 1983).
Reduction of the number of material parameters by ANN approximation
NASA Astrophysics Data System (ADS)
Sumelka, Wojciech; Łodygowski, Tomasz
Modern industrial standards require advanced constitutive modeling to obtain satisfactory numerical results. This approach however, is causing significant increase in number of material parameters which can not be easily obtained from standard and commonly known experimental techniques. Therefore, it is desirable to introduce procedure decreasing the number of the material parameters. This reduction however, should not lead to misunderstanding the fundamental physical phenomena. This paper proposes the reduction of the number of material parameters by using ANN approximation. Recently proposed viscoplasticity formulation for anisotropic solids (metals) developed by authors is used as an illustrative example. In this model one needs to identify 28 material parameters to handle particular metal behaviour under adiabatic conditions as reported by Glema et al (J Theor Appl Mech 48:973-1001, 2010), (Int J Damage Mech 18:205-231, 2009) and Sumelka (Poznan University of Technology, Poznan, 2009). As a result of proposed approach, authors decreased the number of material parameters to 19.
Applicability of Ann in the ARGO-YBJ experiment
NASA Astrophysics Data System (ADS)
Zhu, Q. Q.; Tan, Y. H.; Yang, X. C.; Argo-Ybj Coll.
We report the applicability of Artificial Neural Networks (ANN) in the ARGO-YBJ data analysis, i.e. inner or outer shower core position identification and γ-proton separation, With the MC samples from Corsika and a standard feed forward neural network, the results indicate that the rejection of outer showers induced by protons is more than 60% and the enhancement in the gamma ray sensitivity is about 37% 1 Motivation The Sino-Italian ARGO-YBJ experiment locates at YangBa-Jing (90o 31'50"E, 30o 6'38"N, 4300m a.s.l.) of Tibet, China. The main goal of the experiment is to search for Very High Energy γ point sources and HE Gamma Ray Bursts. The experimental setup is a coverage RPC carpet with an area of 97m x 103m(D'Ettorre et al., 1999) which consists of 14040 PADs. Each PAD is the detector minimum unit with 8 readout strips (i.e. the maximum number of recorded particles = 8). In order to increase the ratio of signals to noises, we have done a preliminary study on the γ-proton seperation using Artificial Neural Networks(ANN) technique(Bussino 1999). Our further Monte Carlo study indicates that the determination accuracy of event core position will affect the γ-proton identification power significantly and the key point for the determination of event core position is inner or outer event classcification. Here inner (or outer) event means the event real core located inside (or outside) of the central 71m x 74m full coverage carpet. In this note we mainly discuss on the identification power for the two classes of events.
46 CFR 7.10 - Eastport, ME to Cape Ann, MA.
Code of Federal Regulations, 2013 CFR
... 46 Shipping 1 2013-10-01 2013-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of...
33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.
Code of Federal Regulations, 2010 CFR
... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA...
46 CFR 7.10 - Eastport, ME to Cape Ann, MA.
Code of Federal Regulations, 2010 CFR
... 46 Shipping 1 2010-10-01 2010-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of...
33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.
Code of Federal Regulations, 2011 CFR
... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA...
46 CFR 7.10 - Eastport, ME to Cape Ann, MA.
Code of Federal Regulations, 2014 CFR
... 46 Shipping 1 2014-10-01 2014-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of...
33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.
Code of Federal Regulations, 2013 CFR
... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA...
33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.
Code of Federal Regulations, 2012 CFR
... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA...
33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.
Code of Federal Regulations, 2014 CFR
... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA...
Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox
ERIC Educational Resources Information Center
Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima
Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…
75 FR 12563 - Patuxent Research Refuge, Anne Arundel and Prince George's Counties, MD
Federal Register 2010, 2011, 2012, 2013, 2014
... Fish and Wildlife Service Patuxent Research Refuge, Anne Arundel and Prince George's Counties, MD... process for developing a CCP for Patuxent Research Refuge, in Anne Arundel and Prince George's Counties... the Patuxent and Little Patuxent Rivers between Washington, DC, and Baltimore, Maryland, the...
Evaluation of the use of remotely sensed evapotranspiration estimates into AnnAGNPS pollution model
Technology Transfer Automated Retrieval System (TEKTRAN)
The utilization of evapotranspiration (ET) estimates, derived from satellite remote sensing, into the Annualized Agricultural Non-Point Source (AnnAGNPS) pollution model was investigated. Modifications within AnnAGNPS were performed to allow the internal calculations of ET based on climate parameter...
Modification of an RBF ANN-Based Temperature Compensation Model of Interferometric Fiber Optical Gyroscopes
Cheng, Jianhua; Qi, Bing; Chen, Daidai; Jr. Landry, René
This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature product term of fiber loops. Then, the input-modified RBF ANN-based temperature compensation scheme is established, in which temperature relevant terms are transferred to train the RBF ANN. Experimental temperature tests are conducted and sufficient data are collected and post-processed to form the novel RBF ANN. Finally, we apply the modified RBF ANN based on temperature compensation model in two IFOGs with temperature compensation capabilities. The experimental results show the proposed temperature compensation model could efficiently reduce the influence of environment temperature on the output of IFOG, and exhibit a better temperature compensation performance than conventional scheme without proposed improvements. PMID:25985163
ANN Models for Prediction of Sound and Penetration Rate in Percussive Drilling
NASA Astrophysics Data System (ADS)
Kivade, Sangshetty B.; Murthy, Chivukula Surya Naryana; Vardhan, Harsha
In the recent years, new techniques such as; Artificial Neural Network (ANN) were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. In this study, ANN models were developed to predict rock properties of sedimentary rock, by using penetration and sound level produced during percussive drilling. The data generated in the laboratory investigation was utilized for the development of ANN models for predicting rock properties like, uniaxial compressive strength, abrasivity, tensile strength, and Schmidt rebound number using air pressure, thrust, bit diameter, penetration rate and sound level. Further, ANN models were also developed for predicting penetration rate and sound level using air pressure, thrust, bit diameter and rock properties as input parameters. The constructed models were checked using various prediction performance indices. ANN models were more acceptable for predicting rock properties.
iAnn: an event sharing platform for the life sciences
Jimenez, Rafael C.; Albar, Juan P.; Bhak, Jong; Blatter, Marie-Claude; Blicher, Thomas; Brazas, Michelle D.; Brooksbank, Cath; Budd, Aidan; De Las Rivas, Javier; Dreyer, Jacqueline; van Driel, Marc A.; Dunn, Michael J.; Fernandes, Pedro L.; van Gelder, Celia W. G.; Hermjakob, Henning; Ioannidis, Vassilios; Judge, David P.; Kahlem, Pascal; Korpelainen, Eija; Kraus, Hans-Joachim; Loveland, Jane; Mayer, Christine; McDowall, Jennifer; Moran, Federico; Mulder, Nicola; Nyronen, Tommi; Rother, Kristian; Salazar, Gustavo A.; Schneider, Reinhard; Via, Allegra; Villaveces, Jose M.; Yu, Ping; Schneider, Maria V.; Attwood, Teresa K.; Corpas, Manuel
Summary: We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add submitted events, and these are subsequently accessed via web services. Thus, once an iAnn widget is incorporated into a website, it permanently shows timely relevant information as if it were native to the remote site. At the same time, announcements submitted to the repository are automatically disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available. Availability: http://iann.pro/iannviewer Contact: firstname.lastname@example.com PMID:23742982
Daily reservoir inflow forecasting combining QPF into ANNs model
NASA Astrophysics Data System (ADS)
Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian
Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.
Optimization of Nd: YAG Laser Marking of Alumina Ceramic Using RSM And ANN
Peter, Josephine; Doloi, B.; Bhattacharyya, B.
The present research papers deals with the artificial neural network (ANN) and the response surface methodology (RSM) based mathematical modeling and also an optimization analysis on marking characteristics on alumina ceramic. The experiments have been planned and carried out based on Design of Experiment (DOE). It also analyses the influence of the major laser marking process parameters and the optimal combination of laser marking process parametric setting has been obtained. The output of the RSM optimal data is validated through experimentation and ANN predictive model. A good agreement is observed between the results based on ANN predictive model and actual experimental observations.
ANN application for prediction of atmospheric nitrogen deposition to aquatic ecosystems.
Palani, Sundarambal; Tkalich, Pavel; Balasubramanian, Rajasekhar; Palanichamy, Jegathambal
The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variables were nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant Standards Index and meteorological parameters. ANN models predictions were also compared with multiple linear regression model having the same inputs and output. ANN model performance was found relatively more accurate in its predictions and adequate even for high-concentration events with acceptable minimum error. The developed ANN model can be used as a forecasting tool to complement the current TN and ON analysis within the atmospheric deposition-monitoring program in the region. PMID:21481425
ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient
NASA Astrophysics Data System (ADS)
Pektaş, Ali Osman; Kerem Cigizoglu, H.
In this study, monthly runoff coefficients of seven southern large basins are calculated and modeled to forecast a holdout dataset by using univariate autoregressive integrated moving average (ARIMA), multivariate ARIMA (ARIMAX), and Artificial neural network (ANN) models. The applied traditional model performances are found insufficient, since the characteristic behaviors of the time series of direct runoff coefficients are very complicated. Therefore, a new Hybrid approach is adopted by using time series decomposition procedure and ANN. ARIMA, ARIMAX, ANN, and Hybrid models are compared with each other. The results indicate that the new generated Hybrid approach can be generalized to boost the prediction capability of ANNs in complicated time series data. It is seen that the new model captures the physical behavior of the direct runoff coefficient time series. The semi-random spikes of the direct runoff coefficient series are approximated sufficiently.
Monitoring and ANN modeling of coal stockpile behavior under different atmospheric conditions
Ozdeniz, A.H.; Ozbay, Y.; Yilmaz, N.; Sensogut, C.
In this study, an industrial-sized stockpile of 5 m width, 4 m height, and 10 m length was built in a coal stock area to investigate coal stockpile behavior under different atmospheric conditions. The effective parameters on the coal stockpile that were time, weather temperature, atmospheric pressure, air humidity, velocity, and direction of wind values were automatically measured by means of a computer-aided measurement system to obtain Artificial Neural Network (ANN) input data. The coal stockpiles, which should be continuously observed, are capable of spontaneous combustion and then causing serious economical losses due to the mentioned parameters. Afterwards, these measurement values were used for training and testing of the ANN model. Comparison of the experimental and ANN results, accuracy rates of training, and testing were found as 98.6% and 98.7%, respectively. It is shown that possible coal stockpile behavior with this ANN model is powerfully estimated.
A Poem Is a House for Words: NCTE Profiles Mary Ann Hoberman
ERIC Educational Resources Information Center
McClure, Amy A.; Ernst, Shirley B.
A profile of Mary Ann Hoberman, the 13th winner of the NCTE Award for Excellence in Poetry for Children is given. She uses poetic techniques in innovative ways, yet her poems are very accessible to young children.
4. JoAnn SieburgBaker, Photographer, September 1977. VIEW OF POWER BUILDING ...
Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey
4. JoAnn Sieburg-Baker, Photographer, September 1977. VIEW OF POWER BUILDING (ELECTRICAL TRANSFORMER). - Salem Manufacturing Company, Arista Cotton Mill, Brookstown & Marshall Streets, Winston-Salem, Forsyth County, NC
Fiber nonlinearity-induced penalty reduction in CO-OFDM by ANN-based nonlinear equalization.
Giacoumidis, Elias; Le, Son T; Ghanbarisabagh, Mohammad; McCarthy, Mary; Aldaya, Ivan; Mhatli, Sofien; Jarajreh, Mutsam A; Haigh, Paul A; Doran, Nick J; Ellis, Andrew D; Eggleton, Benjamin J
We experimentally demonstrate ∼2 dB quality (Q)-factor enhancement in terms of fiber nonlinearity compensation of 40 Gb/s 16 quadrature amplitude modulation coherent optical orthogonal frequency-division multiplexing at 2000 km, using a nonlinear equalizer (NLE) based on artificial neural networks (ANN). Nonlinearity alleviation depends on escalation of the ANN training overhead and the signal bit rate, reporting ∼4 dBQ-factor enhancement at 70 Gb/s, whereas a reduction of the number of ANN neurons annihilates the NLE performance. An enhanced performance by up to ∼2 dB in Q-factor compared to the inverse Volterra-series transfer function NLE leads to a breakthrough in the efficiency of ANN. PMID:26512532
Application of artificial neural networks (ANN) in the development of solid dosage forms.
Bourquin, J; Schmidli, H; van Hoogevest, P; Leuenberger, H
The application of ANN in pharmaceutical development has been assessed using theoretical as well as typical pharmaceutical technology examples. The aim was to quantitatively describe the achieved data fitting and predicting abilities of the models developed with a view to using ANN in the development of solid dosage forms. The comparison between the ANN and a traditional statistical (i.e., response surface methodology, RSM) modeling technique was carried out using the squared correlation coefficient R2. Using a highly nonlinear arbitrary function the ANN models showed better fitting (R2 = 0.931 vs. R2 = 0.424) as well as predicting (R2 = 0.810 vs. R2 = 0.547) abilities. Experimental data from a tablet compression study were fitted using two types of ANN models (i.e., multilayer perceptrons and a hybrid network composed of a self-organising feature map joined to a multilayer perception). The achieved data fitting was comparable for the three methods (MLP R2 = 0.911, SOFM-MLP R2 = 0.850, and RSM R2 = 0.897). ANN methodology represents a promising modeling technique when applied to pharmaceutical technology data sets. PMID:9552437
RegnANN: Reverse Engineering Gene Networks Using Artificial Neural Networks
Grimaldi, Marco; Visintainer, Roberto; Jurman, Giuseppe
RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems. PMID:22216103
Damage detection and identification in smart structures using SVM and ANN
NASA Astrophysics Data System (ADS)
Farooq, M.; Zheng, H.; Nagabhushana, A.; Roy, S.; Burkett, S.; Barkey, M.; Kotru, S.; Sazonov, E.
A critical part of structural health monitoring is accurate detection of damages in the structure. This paper presents the results of two multi-class damage detection and identification approaches based on classification using Support Vector Machine (SVM) and Artificial Neural Networks (ANN). The article under test was a fiber composite panel modeled by a Finite Element Model (FEM). Static strain data were acquired at 6 predefined locations and mixed with Gaussian noise to simulate performance of real strain sensors. Strain data were then normalized by the mean of the strain values. Two experiments were performed for the performance evaluation of damage detection and identification. In both experiments, one healthy structure and two damaged structures with one and two small cracks were simulated with varying material properties and loading conditions (45 cases for each structure). The SVM and ANN models were trained with 70% of these samples and the remaining 30% samples were used for validation. The objective of the first experiment was to detect whether or not the panel was damaged. In this two class problem the average damage detection accuracy for ANN and SVM were 93.2% and 96.66% respectively. The objective of second experiment was to detect the severity of the damage by differentiating between structures with one crack and two cracks. In this three class problem the average prediction accuracy for ANN and SVM were 83.5% and 90.05% respectively. These results suggest that for noisy data, SVM may perform better than ANN for this problem.
Development and Application of ANN Model for Worker Assignment into Virtual Cells of Large Sized Configurations
NASA Astrophysics Data System (ADS)
Murali, R. V.; Puri, A. B.; Fathi, Khalid
This paper presents an extended version of study already undertaken on development of an artificial neural networks (ANNs) model for assigning workforce into virtual cells under virtual cellular manufacturing systems (VCMS) environments. Previously, the same authors have introduced this concept and applied it to virtual cells of two-cell configuration and the results demonstrated that ANNs could be a worth applying tool for carrying out workforce assignments. In this attempt, three-cell configurations problems are considered for worker assignment task. Virtual cells are formed under dual resource constraint (DRC) context in which the number of available workers is less than the total number of machines available. Since worker assignment tasks are quite non-linear and highly dynamic in nature under varying inputs & conditions and, in parallel, ANNs have the ability to model complex relationships between inputs and outputs and find similar patterns effectively, an attempt was earlier made to employ ANNs into the above task. In this paper, the multilayered perceptron with feed forward (MLP-FF) neural network model has been reused for worker assignment tasks of three-cell configurations under DRC context and its performance at different time periods has been analyzed. The previously proposed worker assignment model has been reconfigured and cell formation solutions available for three-cell configuration in the literature are used in combination to generate datasets for training ANNs framework. Finally, results of the study have been presented and discussed.
Chromatic analysis of burn scar based on ANN by using photoelectrical technology
NASA Astrophysics Data System (ADS)
Wan, Baikun; Qi, Hongzhi; Ming, Dong; Zhang, Mingjian; Wang, Qifang
In this paper a novel method for the chromatic analysis of burn scar is proposed. The aim of the algorithm is to evaluate the curative effect and set up the treatment plan pertinently, because the scar color is an impersonal parameter reflects the degree of scar hypertrophy. The method is based on artificial neural network (ANN) by using photoelectrical technique, and composed of three main parts: firstly capture the digital color images of the burn scar using CCD camera, then change the RGB color data of the burn scar into that of HSB color space and emend it using ANN, lastly judge the degree of burn scar hypertrophy by chromatic analysis using ANN again. The experimental results were good conformed to the degrees of scar hypertrophy given by clinical evaluations. It suggests that the chromatic analysis technique of the burn scar is valuable for further study and apply to the clinical engineering.
Mapping brain circuits of reward and motivation: in the footsteps of Ann Kelley.
Richard, Jocelyn M; Castro, Daniel C; Difeliceantonio, Alexandra G; Robinson, Mike J F; Berridge, Kent C
Ann Kelley was a scientific pioneer in reward neuroscience. Her many notable discoveries included demonstrations of accumbens/striatal circuitry roles in eating behavior and in food reward, explorations of limbic interactions with hypothalamic regulatory circuits, and additional interactions of motivation circuits with learning functions. Ann Kelley's accomplishments inspired other researchers to follow in her footsteps, including our own laboratory group. Here we describe results from several lines of our research that sprang in part from earlier findings by Kelley and colleagues. We describe hedonic hotspots for generating intense pleasure 'liking', separate identities of 'wanting' versus 'liking' systems, a novel role for dorsal neostriatum in generating motivation to eat, a limbic keyboard mechanism in nucleus accumbens for generating intense desire versus intense dread, and dynamic limbic transformations of learned memories into motivation. We describe how origins for each of these themes can be traced to fundamental contributions by Ann Kelley. PMID:23261404
Modeling and forecasting of KLCI weekly return using WT-ANN integrated model
NASA Astrophysics Data System (ADS)
Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi
The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.
Estimating Parameters of Delaminations in GRP Pipes Using Thermal NDE and ANN
Vijayaraghavan, G. K.; Ramachandran, K. P.; Muruganandam, A.; Govindarajan, L.; Majumder, M. C.
Thermographic Non-Destructive Evaluation (TNDE) is one of the techniques that have been widely used over the decades to evaluate the integrity of structures. To meet the increased demand for robust and effective inspection in complex TNDE tasks, Artificial Neural Networks (ANNs) have been recently deployed in many problems. The aim of the paper is to adopt an inverse technique using ANNs in the field of TNDE to estimate various parameters of delamination in Glass Reinforced Polymer (GRP) pipes by supplying thermal contrast evolution data as input. A Radial Basis Network (RBN) is employed with 80 input and 3 output neurons. The estimation capability of the network was evaluated with the data obtained from numerical simulations. The overall absolute errors show that the estimation capability of ANN is good.
Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers.
Rajaee, Taher
In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values. PMID:21546062
ANN-Assisted Planning of Conjunctive Use of Canal and Ground Water in Canal Commands
NASA Astrophysics Data System (ADS)
Kashyap, Deepak; Ghosh, Susmita
Presented herein is an algorithm for ANN-assisted planning of the optimal cropping pattern and the associated groundwater development in a canal command area, followed by an illustration. The planning in the illustration ensures the maximization of the cropped area subject to the constraint of limiting the maximum water table depth to an acceptable limit. A multilayer feed forward ANN model, relating the maximum water table depth to the crop areas, is trained using back-propagation learning algorithm. The data invoked for the training comprise an array of the cropping patterns, and the corresponding maximum water table depths. These data are generated through a pre-calibrated numerical model of groundwater flow. Subsequently, the trained ANN model is linked to a GA based optimizer for arriving at the optimal cropping pattern and the associated pumping pattern.
Mapping brain circuits of reward and motivation: In the footsteps of Ann Kelley
Richard, Jocelyn M.; Castro, Daniel C.; DiFeliceantonio, Alexandra G.; Robinson, Mike J.F.; Berridge, Kent C.
Ann Kelley was a scientific pioneer in reward neuroscience. Her many notable discoveries included demonstrations of accumbens/striatal circuitry roles in eating behavior and in food reward, explorations of limbic interactions with hypothalamic regulatory circuits, and additional interactions of motivation circuits with learning functions. Ann Kelley's accomplishments inspired other researchers to follow in her footsteps, including our own laboratory group. Here we describe results from several lines of our research that sprang in part from earlier findings by Kelley and colleagues. We describe hedonic hotspots for generating intense pleasure `liking', separate identities of `wanting' versus `liking' systems, a novel role for dorsal neostriatum in generating motivation to eat, a limbic keyboard mechanism in nucleus accumbens for generating intense desire versus intense dread, and dynamic limbic transformations of learned memories into motivation. We describe how origins for each of these themes can be traced to fundamental contributions by Ann Kelley. PMID:23261404
PkANN: Non-Linear Matter Power Spectrum Interpolation through Artificial Neural Networks
NASA Astrophysics Data System (ADS)
Agarwal, Shankar
We investigate the interpolation of power spectra of matter fluctuations using artificial neural networks (ANNs). We present a new approach to confront small-scale non-linearities in the matter power spectrum. This ever-present and pernicious uncertainty is often the Achilles' heel in cosmological studies and must be reduced if we are to see the advent of precision cosmology in the late-time Universe. We detail how an accurate interpolation of the matter power spectrum is achievable with only a sparsely sampled grid of cosmological parameters. We show that an optimally trained ANN, when presented with a set of cosmological parameters (Omh2 , Obh2, ns, w0, sigma8, sum mnu and z), can provide a worst-case error ≤ 1 per cent (for redshift z ≤ 2) fit to the non-linear matter power spectrum deduced through large-scale N-body simulations, for modes up to k ≤ 0.9 hMpc-1 . Our power spectrum interpolator, which we label 'PkANN', is designed to simulate a range of cosmological models including massive neutrinos and dark energy equation of state w 0 ≠ -1. PkANN is accurate in the quasi-non-linear regime (0.1 hMpc-1 ≤ k ≤ 0.9 hMpc -1) over the entire parameter space and marks a significant improvement over some of the current power spectrum calculators. The response of the power spectrum to variations in the cosmological parameters is explored using PkANN. Using a compilation of existing peculiar velocity surveys, we investigate the cosmic Mach number statistic and show that PkANN not only successfully accounts for the non-linear motions on small scales, but also, unlike N-body simulations which are computationally expensive and/or infeasible, it can be an extremely quick and reliable tool in interpreting cosmological observations and testing theories of structure-formation.
Multi-output ANN Model for Prediction of Seven Meteorological Parameters in a Weather Station
NASA Astrophysics Data System (ADS)
Raza, Khalid; Jothiprakash, V.
The meteorological parameters plays a vital role for determining various water demand in the water resource systems, planning, management and operation. Thus, accurate prediction of meteorological variables at different spatial and temporal intervals is the key requirement. Artificial Neural Network (ANN) is one of the most widely used data driven modelling techniques with lots of good features like, easy applications, high accuracy in prediction and to predict the multi-output complex non-linear relationships. In this paper, a Multi-input Multi-output (MIMO) ANN model has been developed and applied to predict seven important meteorological parameters, such as maximum temperature, minimum temperature, relative humidity, wind speed, sunshine hours, dew point temperature and evaporation concurrently. Several types of ANN, such as multilayer perceptron, generalized feedforward neural network, radial basis function and recurrent neural network with multi hidden layer and varying number of neurons at the hidden layer, has been developed, trained, validated and tested. From the results, it is found that the recurrent MIMO-ANN having 28 neurons in a single hidden layer, trained using hyperbolic tangent transfer function with a learning rate of 0.3 and momentum factor of 0.7 performed well over the other types of MIMO-ANN models. The MIMO ANN model performed well for all parameters with higher correlation and other performance indicators except for sunshine hours. Due to erratic nature, the importance of each of the input over the output through sensitivity analysis indicated that relative humidity has highest influence while others have equal influence over the output.
A multi-stage methodology for selecting input variables in ANN forecasting flows
NASA Astrophysics Data System (ADS)
Panagoulia, Dionysia; Tsekouras, George; Kousiouris, George
Recently, several methods have more or less efficiently dealt with the selection of input variables to artificial neural networks (ANNs) in the hydrology and water resources domain. While the ultimate purpose is to approximate an effective and computationally parsimonious method accounting for non linear input variables to ANNs, very few approaches could reach this target. Moreover, none of these has considered the seasonality as input to ANNs which may be attributed to the influence of natural or anthropogenic variability to hydro-meteorological time series. To this end, a novel methodology is developed for selecting input variables used in artificial neural network (ANNs) models for flow forecasting. The proposed methodology is generic, multi-stage and makes use of data correlations together with a set of crucial statistical indices for optimizing model performance, both in terms of ANN structure (e.g. neurons, momentum rate, learning rate, activation functions) but also in terms of inputs selection. Daily areal precipitation and temperature data coupled with atmospheric circulation in the form of circulation patterns, observed river flow data, and time expressed via functions of sine and cosine (seasonality) were the potential vectors for inputs selection. The historical data concern the mountainous Mesochora catchment in Central-Western Greece. The proposed methodology revealed the river flow of past four days, the precipitation of past three days and the seasonality as robust input variables. However, the temperature of three past days should be considered as an alternative against the seasonality. The produced models forecasting ability was validated by comparing its one-step ahead flow prediction ability to two other approaches (an auto regressive model and a GA-optimized single input ANN).
How can we deal with ANN in flood forecasting? As a simulation model or updating kernel!
NASA Astrophysics Data System (ADS)
Hassan Saddagh, Mohammad; Javad Abedini, Mohammad
Flood forecasting and early warning, as a non-structural measure for flood control, is often considered to be the most effective and suitable alternative to mitigate the damage and human loss caused by flood. Forecast results which are output of hydrologic, hydraulic and/or black box models should secure accuracy of flood values and timing, especially for long lead time. The application of the artificial neural network (ANN) in flood forecasting has received extensive attentions in recent years due to its capability to capture the dynamics inherent in complex processes including flood. However, results obtained from executing plain ANN as simulation model demonstrate dramatic reduction in performance indices as lead time increases. This paper is intended to monitor the performance indices as it relates to flood forecasting and early warning using two different methodologies. While the first method employs a multilayer neural network trained using back-propagation scheme to forecast output hydrograph of a hypothetical river for various forecast lead time up to 6.0 hr, the second method uses 1D hydrodynamic MIKE11 model as forecasting model and multilayer neural network as updating kernel to monitor and assess the performance indices compared to ANN alone in light of increase in lead time. Results presented in both graphical and tabular format indicate superiority of MIKE11 coupled with ANN as updating kernel compared to ANN as simulation model alone. While plain ANN produces more accurate results for short lead time, the errors increase expeditiously for longer lead time. The second methodology provides more accurate and reliable results for longer forecast lead time.
Understanding Anne Frank's "The Diary of a Young Girl": A Student Casebook to Issues, Sources, and Historical Documents.
ERIC Educational Resources Information Center
Kopf, Hedda Rosner
Anne Frank's "The Diary of a Young Girl" is the most widely read text about the Holocaust, yet it represents only one example of the tragic consequences of the Nazi policy to eliminate the Jews of Europe during World War II. This casebook enriches Anne Frank's remarkable personal account with a variety of historical documents that illuminate the…
Overexpression of a cotton annexin gene, GhAnn1, enhances drought and salt stress tolerance in transgenic cotton.
Zhang, Feng; Li, Shufen; Yang, Shuming; Wang, Like; Guo, Wangzhen
Plant annexins are members of a diverse, multigene protein family that has been associated with a variety of cellular processes and responses to abiotic stresses. GhAnn1, which encodes a putative annexin protein, was isolated from a cotton (Gossypium hirsutum L. acc 7235) cDNA library. Tissue-specific expression showed that GhAnn1 is expressed at differential levels in all tissues examined and strongly induced by various phytohormones and abiotic stress. In vivo and in vitro subcellular localization suggested that GhAnn1 is located in the plasma membrane. In response to drought and salt stress, transgenic cotton plants overexpressing GhAnn1 showed significantly higher germination rates, longer roots, and more vigorous growth than wild-type plants. In addition, plants overexpressing GhAnn1 had higher total chlorophyll content, lower lipid peroxidation levels, increased peroxidase activities, and higher levels of proline and soluble sugars, all of which contributed to increased salt and drought stress tolerance. However, transgenic cotton plants in which the expression of GhAnn1 was suppressed showed the opposite results compared to the overexpressing plants. These findings demonstrated that GhAnn1 plays an important role in the abiotic stress response, and that overexpression of GhAnn1 in transgenic cotton improves salt and drought tolerance. PMID:25330941
The influence of emerging administrative scientists: an interview with Anne Miller.
Miller, Anne; Adams, Jeffrey M
This department highlights emerging nursing leaders and scientists demonstrating promise in advancing innovation and patient care leadership in practice, policy, research, education, and theory. This interview profiles Anne Miller, PhD, BA, assistant professor jointly appointed to the Center of Interdisciplinary Health Workforce Studies and the School of Nursing at Vanderbilt University Medical Center. PMID:25803799
Symbolism--The Main Artistic Style of Katherine Anne Porter's Short Stories
ERIC Educational Resources Information Center
Wang, Ru
The paper takes Katherine Anne Porter's two short stories: "Flowering Judas", "The Grave" as objects of study. It will try to analyze Porter's writing style through her imaginary conception, vivid psychological description and multiple symbolisms so that we can understand her studies and her attitudes to female psychological…
Constructing Anne Frank: Critical Literacy and the Holocaust in Eighth-Grade English
ERIC Educational Resources Information Center
Spector, Karen; Jones, Stephanie
Using the assumption that texts actively work to position readers and readers actively work to position texts, the authors argue that moral lessons emerge from the interactions between texts, readers, and the ideological narratives that inspire both. After differentiating versions of Anne Frank's diary and explicating motives behind their…
Should You Teach "Anne Frank: The Diary of a Young Girl"?
ERIC Educational Resources Information Center
Rochman, Hazel
Considers the value of teaching "Anne Frank" and suggests that the book be just a starting point for exposing readers to other views of the Holocaust. Annotated bibliographies are included for other personal accounts of the Holocaust, with appropriate grade levels indicated. (LRW)
Joseph Campbell, Jung, Anne Tyler, and "The Cards": The Spiritual Journey in "Searching for Caleb."
ERIC Educational Resources Information Center
Thomson, Karen M.
Joseph Campbell, Carl Jung, and Anne Tyler have all dealt with spiritual journeys and card reading in their writings. In his book "Tarot Revelations," Joseph Campbell discusses his first association with tarot cards, dating from 1943, when he was introduced to the symoblism of playing cards by his friend and mentor, Heinrich Zimmer. Carl Jung was…
Friendly Letters on the Correspondence of Helen Keller, Anne Sullivan, and Alexander Graham Bell.
ERIC Educational Resources Information Center
Blatt, Burton
Excerpts from the letters between Alexander Graham Bell and Anne Sullivan and Helen Keller are given to illustrate the educational and personal growth of Helen Keller as well as the educational philosophy of Bell regarding the education of the deaf blind. (DB)
Childhood and Modernity: Dark Themes in Carol Ann Duffy's Poetry for Children
ERIC Educational Resources Information Center
Whitley, David
Carol Ann Duffy's three volumes of children's poetry are important and interesting because they emerge from the work of a writer whose adult poetry has persistently associated childhood with dark and difficult areas of experience. This article explores what happens to such challenging material when a poet of major significance changes the focus of…
33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.
Code of Federal Regulations, 2011 CFR
... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Portland Head, ME to Cape Ann, MA. 80.115 Section 80.115 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY..., MA. (a) Except inside lines specifically described in this section, the 72 COLREGS shall apply on...
33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.
Code of Federal Regulations, 2013 CFR
... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Portland Head, ME to Cape Ann, MA. 80.115 Section 80.115 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY..., MA. (a) Except inside lines specifically described in this section, the 72 COLREGS shall apply on...
33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.
Code of Federal Regulations, 2010 CFR
... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Portland Head, ME to Cape Ann, MA. 80.115 Section 80.115 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY..., MA. (a) Except inside lines specifically described in this section, the 72 COLREGS shall apply on...
33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.
Code of Federal Regulations, 2012 CFR
... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false Portland Head, ME to Cape Ann, MA. 80.115 Section 80.115 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY..., MA. (a) Except inside lines specifically described in this section, the 72 COLREGS shall apply on...
33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.
Code of Federal Regulations, 2014 CFR
... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false Portland Head, ME to Cape Ann, MA. 80.115 Section 80.115 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY..., MA. (a) Except inside lines specifically described in this section, the 72 COLREGS shall apply on...
Master Builder of Bridges between Research and Practice: An Interview with Ann Robinson
ERIC Educational Resources Information Center
Henshon, Suzanna E.
This article presents an interview with Ann Robinson, a professor of educational psychology and founding director of the Center for Gifted Education at the University of Arkansas at Little Rock. She is the president of the National Association for Gifted Children (NAGC), a former editor of the Gifted Child Quarterly (GCQ), and was the first Editor…
USDA-AnnAGNPS Model Capabilities and Applications for Watershed Conservation Planning
Technology Transfer Automated Retrieval System (TEKTRAN)
The US Department of Agriculture (USDA) Annualized Agricultural Non-Point Source Pollutant Loading (AnnAGNPS) model has been developed as a planning tool used in the evaluation of watershed responses to agricultural management practices. Since the first release of the continuous, daily time step, w...
Process Control Strategies for Dual-Phase Steel Manufacturing Using ANN and ANFIS
NASA Astrophysics Data System (ADS)
Vafaeenezhad, H.; Ghanei, S.; Seyedein, S. H.; Beygi, H.; Mazinani, M.
In this research, a comprehensive soft computational approach is presented for the analysis of the influencing parameters on manufacturing of dual-phase steels. A set of experimental data have been gathered to obtain the initial database used for the training and testing of both artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The parameters used in the strategy were intercritical annealing temperature, carbon content, and holding time which gives off martensite percentage as an output. A fraction of the data set was chosen to train both ANN and ANFIS, and the rest was put into practice to authenticate the act of the trained networks while seeing unseen data. To compare the obtained results, coefficient of determination and root mean squared error indexes were chosen. Using artificial intelligence methods, it is not necessary to consider and establish a preliminary mathematical model and formulate its affecting parameters on its definition. In conclusion, the martensite percentages corresponding to the manufacturing parameters can be determined prior to a production using these controlling algorithms. Although the results acquired from both ANN and ANFIS are very encouraging, the proposed ANFIS has enhanced performance over the ANN and takes better effect on cost-reduction profit.
A Case Study of the Ann Sullivan Center in Lima, Peru
ERIC Educational Resources Information Center
Noto, Lori A.
A qualitative study was conducted to describe and explain the educational program at the Ann Sullivan Center, a nationally and internationally recognized program for individuals with disabilities in Peru. The program provides educational programming to individuals with autism, severe disabilities and challenging behaviors across the lifespan. A…
77 FR 61624 - Patuxent Research Refuge, Prince George's and Anne Arundel Counties, MD; Draft Comprehensive...
Federal Register 2010, 2011, 2012, 2013, 2014
... Federal Register (75 FR 12563; March 16, 2010). Patuxent RR was established in 1936 by Executive Order by... Fish and Wildlife Service Patuxent Research Refuge, Prince George's and Anne Arundel Counties, MD... environmental assessment (CCP/EA) for Patuxent Research Refuge (Patuxent RR), located in Prince George's...
The Nation behind the Diary: Anne Frank and the Holocaust of the Dutch Jews
ERIC Educational Resources Information Center
Foray, Jennifer L.
Since its first appearance in 1947, "The Diary of Anne Frank" has been translated into sixty-five different languages, including Welsh, Esperanto, and Faroese. Millions and perhaps even billions of readers, scattered throughout the globe and now spanning multiple generations, are familiar with the life and work of this young Jewish writer. Over…
Audits of Meaning: A Festschrift in Honor of Ann E. Berthoff.
ERIC Educational Resources Information Center
Smith, Louise Z., Ed.
A tribute to the work of Ann Berthoff, this book--a collection of 6 poems and 21 essays--explores issues in the philosophy and teaching of composition. Each of the five sections is introduced with a poem by Marie Ponsot. Essays and their authors consist of (1) "Genre Theory, Academic Discourse, and Writing within Disciplines" (James F. Slevin);…
The Search for Family: Comedy and Pathos in Anne Tyler's Later Novels.
ERIC Educational Resources Information Center
Zoghby, Mary D.
Anne Tyler's rare talent for combining comedy and pathos enables her to create characters whose pain is felt by the reader or student even as that same reader is led into laughter by the ludicrous situations in which Tyler places these characters. In her last three novels, "Dinner at the Homesick Restaurant,""The Accidental Tourist," and…
Bethany Ann Teachman: Award for Distinguished Scientific Early Career Contributions to Psychology
ERIC Educational Resources Information Center
American Psychologist, 2012
Presents a short biography of one of the winners of the American Psychological Association's Award for Distinguished Scientific Early Career Contributions to Psychology. The 2012 winner is Bethany Ann Teachman for transformative, translational research integrating social cognition, life-span, and perceptual approaches to investigating clinical…
78 FR 41993 - Ann Arbor Railroad, Inc.-Lease Exemption-Norfolk Southern Railway Company
Federal Register 2010, 2011, 2012, 2013, 2014
... Surface Transportation Board Ann Arbor Railroad, Inc.--Lease Exemption--Norfolk Southern Railway Company... authority to determine whether to issue notices of exemption under 49 U.S.C. 10502 for lease and operation... for lease and operation of the rail line at issue in this case. The Board determines that this...
78 FR 67086 - Safety Zone, Submarine Cable Replacement Operations, Kent Island Narrows; Queen Anne's County, MD
Federal Register 2010, 2011, 2012, 2013, 2014
...The Coast Guard proposes to establish a temporary safety zone encompassing certain waters of Kent Island Narrows in Queen Anne's County, MD. This action is necessary to provide for the safety of mariners and their vessels on navigable waters during submarine cable replacement operations at the Kent Island Narrows (MD-18B) Bridge. This action is intended to restrict vessel traffic movement to......
Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings
NASA Astrophysics Data System (ADS)
Hakan Arslan, M.
This study investigated the efficiency of an artificial neural network (ANN) in predicting and determining failure load and failure displacement of multi story reinforced concrete (RC) buildings. The study modeled a RC building with four stories and three bays, with a load bearing system composed of columns and beams. Non-linear static pushover analysis of the key parameters in change defined in Turkish Earthquake Code (TEC-2007) for columns and beams was carried out and the capacity curves, failure loads and displacements were obtained. Totally 720 RC buildings were analyzed according to the change intervals of the parameters chosen. The input parameters were selected as longitudinal bar ratio (ρl) of columns, transverse reinforcement ratio (Asw/sc), axial load level (N/No), column and beam cross section, strength of concrete (fc) and the compression bar ratio (ρ'/ρ) on the beam supports. Data from the nonlinear analysis were assessed with ANN in terms of failure load and failure displacement. For all outputs, ANN was trained and tested using of 11 back-propagation methods. All of the ANN models were found to perform well for both failure loads and displacements. The analyses also indicated that a considerable portion of existing RC building stock in Turkey may not meet the safety standards of the Turkish Earthquake Code (TEC-2007).
Optimization of culture medium and modeling of curdlan production from Paenibacillus polymyxa by RSM and ANN.
Rafigh, Sayyid Mahdi; Yazdi, Ali Vaziri; Vossoughi, Manouchehr; Safekordi, Ali Akbar; Ardjmand, Mehdi
Paenibacillus polymyxa ATCC 21830 was used for the production of curdlan gum for first time. A Box-Behnken experimental design was applied to optimize six variables of batch fermentation culture each at three levels. Statistical analyses were employed to investigate the direct and interactive effects of variables on curdlan production. Optimum cultural conditions were temperature (50°C), pH (7), fermentation time (96 h), glucose (100 g/L), yeast extract (3 g/L) and agitation speed (150 rpm). The yield of curdlan production was 6.89 g/L at optimum condition medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to model cultural conditions of curdlan production. The maximum yield of curdlan production were predicted to be 6.68 and 6.85 g/L by RSM and ANN at optimum condition. The prediction capabilities of RSM and ANN were then statistically compared. The results showed that the ANN model is much more accurate in prediction as compared to the RSM. The infrared (IR) and NMR spectra, the thermogram of DSC and pattern of X-ray diffraction for the curdlan of the present study were almost identical to those of the commercial curdlan sample. The average molecular weight of the purified curdlan was determined to be 170 kDa by gel permeation chromatography. PMID:25062991
Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment
NASA Astrophysics Data System (ADS)
Kothari, Mahesh; Gharde, K. D.
The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992-2011) rainfall and other hydrological data were considered, of which 13 years (1992-2004) was for training and rest 7 years (2005-2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.
Development Opportunities: The Effect of UMES on the Town of Princess Anne, Maryland.
ERIC Educational Resources Information Center
Sann, David; Tervala, Victor K.
A study was done of the potential economic effect of the University of Maryland Eastern Shore (UMES) campus on the nearby town of Princess Anne, a small rural community. The study used estimates made by a UMES faculty member which projected that UMES students in 1990 spent over $7 million on goods and services unrelated to educational expenses.…
76 FR 81991 - Tecumseh Products Corporation, Ann Arbor, MI; Notice of Termination of Investigation
Federal Register 2010, 2011, 2012, 2013, 2014
... FR 59176). Workers at the subject firm are engaged in activities related to the production of... Employment and Training Administration Tecumseh Products Corporation, Ann Arbor, MI; Notice of Termination of... Application for Reconsideration applicable to workers and former workers of Tecumseh Products Corporation,...
English for Specific Purposes: The State of the Art (An Online Interview with Ann M. Johns)
ERIC Educational Resources Information Center
Johns, Ann M.; Salmani Nodoushan, M. A.
This forum paper is based on a friendly and informative interview conducted with Professor Ann M. Johns. In providing answers to the interview questions, Professor Johns suggests that all good teaching is ESP, and also distinguishes between EOP and ESP in that the former entails much more "just in time" learning while the latter may be…
A soil moisture index as an auxiliary ANN input for stream flow forecasting
NASA Astrophysics Data System (ADS)
Anctil, François; Michel, Claude; Perrin, Charles; Andréassian, Vazken
This study tests the short-term forecasting improvement afforded by the inclusion of low-frequency inputs to artificial neural network (ANN) rainfall-runoff models that are first optimized by using only fast response components, i.e. using stream flow and rainfall as inputs. Ten low-frequency ANN input candidates are considered: the potential evapotranspiration, the antecedent precipitation index (API i, i=7, 15, 30, 60, and 120 days) and a proposed soil moisture index time series (SMI A, for A=100, 200, 400 and 800 mm). As the ANNs considered are for use in real-time lead-time-L forecasting, forecast performance is expressed in terms of the persistence index, rather than the conventional Nash-Sutcliffe index. The API i are the non-decayed moving average precipitation series, while the SMI A are calculated through the soil moisture accounting reservoir of the lumped conceptual rainfall-runoff model GR4J. Results, based on daily data of the Serein and Leaf rivers, reveal that only the SMI A time series are useful for one-day-ahead stream flow forecasting, with both the potential evapotranspiration and the API itime series failing to improve the ANN performance.
Evaluation of the AnnAGNPS model for atrazine prediction in NE Indiana
Technology Transfer Automated Retrieval System (TEKTRAN)
The Annualized Agricultural Non-Point Source (AnnAGNPS) pollution model was developed for simulation of runoff, sediment, nutrient, and pesticide losses from ungauged agricultural watersheds. Here, the model was applied to the 707 km^2 Cedar Creek Watershed (CCW) and the 45 km^2 Matson Ditch Sub-Cat...
Beneficial application of landfill mining Millersville Landfill, Anne Arundel County, MD
Vanetti, D.J.
Several studies and investigations have been completed for the Millersville Sanitary Landfill in Anne Arundel County, Maryland. The studies and reports range from detailed hydrogeologic investigations through review of closure alternatives for the individual refuse disposal cells located at the landfill. As a result of the evaluations and studies, one recommendation that was put before Anne Arundel County is the excavation and relocation of refuse from Cell 3 to: (1) create an infiltration basin; and (2) reduce the overall refuse footprint at the site, resulting in reduce long term environmental impacts and closure costs. Subsequent to this recommendation, several preliminary reviews have been held between Anne Arundel County, regulatory agencies and their consultants, Stearns & Wheler. These discussions indicated that it would be feasible, and the concept acceptable, to relocate the refuse in Cell 3 to ultimately create an infiltration basin. Subsequent to the preliminary meetings, a project plan and construction Contract Documents and Drawings were developed by Stearns & Wheler. The Project Plan was submitted to the State regulatory agencies (Maryland and Department of Environment (MDE) and Maryland Department of Natural Resources (DNR)), Millersville Landfill Citizen`s Advisory Committee, and Anne Arundel County (Department of public Works (DPW), Permit Acquisition and Code Enforcement (PACE) and Soil Conservation District (SCD)) for review and comment prior to undertaking the relocation of refuse in Cell 3.
78 FR 65360 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains and associated funerary objects, in consultation with the appropriate Indian... American Graves Protection and Repatriation Act (NAGPRA), 25 U.S.C. 3003, of the completion of an...
78 FR 65376 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains and associated funerary objects, in consultation with the appropriate Indian... American Graves Protection and Repatriation Act (NAGPRA), 25 U.S.C. 3003, of the completion of an...
78 FR 65357 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains and associated funerary objects, in consultation with the appropriate Indian... American Graves Protection and Repatriation Act (NAGPRA), 25 U.S.C. 3003, of the completion of an...
78 FR 65371 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains and associated funerary objects, in consultation with the appropriate Indian... American Graves Protection and Repatriation Act (NAGPRA), 25 U.S.C. 3003, of the completion of an...
78 FR 65366 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains, in consultation with the appropriate Indian tribes or Native Hawaiian... Act (NAGPRA), 25 U.S.C. 3003, of the completion of an inventory of human remains under the control...
78 FR 65369 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains, in consultation with the appropriate Indian tribes or Native Hawaiian... Act (NAGPRA), 25 U.S.C. 3003, of the completion of an inventory of human remains under the control...
78 FR 65380 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains, in consultation with the appropriate Indian tribes or Native Hawaiian... Act (NAGPRA), 25 U.S.C. 3003, of the completion of an inventory of human remains under the control...
78 FR 65382 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains, in consultation with the appropriate Indian tribes or Native Hawaiian... Act (NAGPRA), 25 U.S.C. 3003, of the completion of an inventory of human remains under the control...
78 FR 65367 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains and associated funerary objects, in consultation with the appropriate Indian... American Graves Protection and Repatriation Act (NAGPRA), 25 U.S.C. 3003, of the completion of an...
78 FR 65364 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains and associated funerary objects, in consultation with the appropriate Indian... American Graves Protection and Repatriation Act (NAGPRA), 25 U.S.C. 3003, of the completion of an...
78 FR 65375 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI
Federal Register 2010, 2011, 2012, 2013, 2014
... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... inventory of human remains and associated funerary objects, in consultation with the appropriate Indian... inventory of human remains and associated funerary objects under the control of the University of...
Exploring Social Studies through Multicultural Literature: "Legend of the St Ann's Flood"
ERIC Educational Resources Information Center
Fry, Sara Winstead
The search for literature that is of high quality and interest, is written at age-appropriate levels for adolescent readers, addresses social studies topics, and presents multicultural perspectives can be daunting. "Legend of the St Ann's Flood" is a fiction trade book that meets all of these criteria. Its setting in Trinidad and Tobago provides…
Evaluation of alternative management practices with the AnnAGNPS model in the Carapelle Watershed
Technology Transfer Automated Retrieval System (TEKTRAN)
The Annualized Agricultural Non-point Source (AnnAGNPS) model can be used to analyze the effectiveness of management and conservation practices that can control the impact of erosion and subsequent sediment loads in agricultural watersheds. A Mediterranean medium-size watershed (Carapelle) in Apulia...
Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.
Agatonovic-Kustrin, S; Beresford, R
Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine
PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks
NASA Astrophysics Data System (ADS)
Agarwal, Shankar; Abdalla, Filipe B.; Feldman, Hume A.; Lahav, Ofer; Thomas, Shaun A.
We investigate the interpolation of power spectra of matter fluctuations using artificial neural networks (PkANN). We present a new approach to confront small-scale non-linearities in the power spectrum of matter fluctuations. This ever-present and pernicious uncertainty is often the Achilles heel in cosmological studies and must be reduced if we are to see the advent of precision cosmology in the late-time Universe. We show that an optimally trained artificial neural network (ANN), when presented with a set of cosmological parameters (? and redshift z), can provide a worst-case error ≤1 per cent (for z≤ 2) fit to the non-linear matter power spectrum deduced through N-body simulations, for modes up to k≤ 0.7 h Mpc-1. Our power spectrum interpolator is accurate over the entire parameter space. This is a significant improvement over some of the current matter power spectrum calculators. In this paper, we detail how an accurate interpolation of the matter power spectrum is achievable with only a sparsely sampled grid of cosmological parameters. Unlike large-scale N-body simulations which are computationally expensive and/or infeasible, a well-trained ANN can be an extremely quick and reliable tool in interpreting cosmological observations and parameter estimation. This paper is the first in a series. In this method paper, we generate the non-linear matter power spectra using HALOFIT and use them as mock observations to train the ANN. This work sets the foundation for Paper II, where a suite of N-body simulations will be used to compute the non-linear matter power spectra at sub-per cent accuracy, in the quasi-non-linear regime (0.1 ≤k≤ 0.9 h Mpc-1). A trained ANN based on this N-body suite will be released for the scientific community.
Overexpression of Arabidopsis AnnAt8 Alleviates Abiotic Stress in Transgenic Arabidopsis and Tobacco
Yadav, Deepanker; Ahmed, Israr; Shukla, Pawan; Boyidi, Prasanna; Kirti, Pulugurtha Bharadwaja
Abiotic stress results in massive loss of crop productivity throughout the world. Because of our limited knowledge of the plant defense mechanisms, it is very difficult to exploit the plant genetic resources for manipulation of traits that could benefit multiple stress tolerance in plants. To achieve this, we need a deeper understanding of the plant gene regulatory mechanisms involved in stress responses. Understanding the roles of different members of plant gene families involved in different stress responses, would be a step in this direction. Arabidopsis, which served as a model system for the plant research, is also the most suitable system for the functional characterization of plant gene families. Annexin family in Arabidopsis also is one gene family which has not been fully explored. Eight annexin genes have been reported in the genome of Arabidopsis thaliana. Expression studies of different Arabidopsis annexins revealed their differential regulation under various abiotic stress conditions. AnnAt8 (At5g12380), a member of this family has been shown to exhibit ~433 and ~175 fold increase in transcript levels under NaCl and dehydration stress respectively. To characterize Annexin8 (AnnAt8) further, we have generated transgenic Arabidopsis and tobacco plants constitutively expressing AnnAt8, which were evaluated under different abiotic stress conditions. AnnAt8 overexpressing transgenic plants exhibited higher seed germination rates, better plant growth, and higher chlorophyll retention when compared to wild type plants under abiotic stress treatments. Under stress conditions transgenic plants showed comparatively higher levels of proline and lower levels of malondialdehyde compared to the wild-type plants. Real-Time PCR analyses revealed that the expression of several stress-regulated genes was altered in AnnAt8 over-expressing transgenic tobacco plants, and the enhanced tolerance exhibited by the transgenic plants can be correlated with altered expressions of
A calcium-binding protein, rice annexin OsANN1, enhances heat stress tolerance by modulating the production of H2O2.
Qiao, Bei; Zhang, Qian; Liu, Dongliang; Wang, Haiqi; Yin, Jingya; Wang, Rui; He, Mengli; Cui, Meng; Shang, Zhonglin; Wang, Dekai; Zhu, Zhengge
OsANN1 is a member of the annexin protein family in rice. The function of this protein and the mechanisms of its involvement in stress responses and stress tolerance are largely unknown. Here it is reported that OsANN1 confers abiotic stress tolerance by modulating antioxidant accumulation under abiotic stress. OsANN1-knockdown [RNA interference (RNAi)] plants were more sensitive to heat and drought stresses, whereas OsANN1-overexpression (OE) lines showed improved growth with higher expression of OsANN1 under abiotic stress. Overexpression of OsANN1 promoted SOD (superoxide dismutase) and CAT (catalase) activities, which regulate H2O2 content and redox homeostasis, suggesting the existence of a feedback mechanism between OsANN1 and H2O2 production under abiotic stress. Higher expression of OsANN1 can provide overall cellular protection against abiotic stress-induced damage, and a significant accumulation of OsANN1-green fluorescent protein (GFP) signals was found in the cytosol after heat shock treatment. OsANN1 also has calcium-binding and ATPase activities in vitro, indicating that OsANN1 has multiple functions in rice growth. Furthermore, yeast two-hybrid and bimolecular fluorescence complementation (BiFC) assays demonstrated that OsANN1 interacts with OsCDPK24. This cross-talk may provide additional layers of regulation in the abiotic stress response. PMID:26085678
[Estimation of forest volume in Huzhong forest area based on RS, GIS and ANN].
Liu, Zhi-Hua; Chang, Yu; Chen, Hong-Wei
Based on remote sensing (RS) which has integrated and realistic characteristics, geographic information system (GIS) which has powerful spatial analysis ability, and artificial neutral network (ANN) which can optimize nonlinear complex systems, the forest volume in Huzhong forest area was estimated. The results showed that there was an obvious negative correlation between the forest volume and infrared band, indicating that infrared band had definite potential in estimating forest volume. The forest volume also negatively correlated with visible band and PC1. Among the topographic factors, altitude exerted more influence than aspect and slope on the estimation of forest volume. The correlation coefficient of predicted value and actual value reached to 0.973, when the optimal ANN parameter, suitable GIS information, and RS bands were adopted. After principal component transformation, the amount of observation data was effectively reduced, while the predicted precision only had a small decline (R2 = 0.934). PMID:19102299
Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir
NASA Astrophysics Data System (ADS)
Santos, C. A. G.; Freire, P. K. M. M.; Silva, G. B. L.; Silva, R. M.
This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrological data, as there have been few reports in the literature. Finally, the proposed technique is tested using the inflow records to the Três Marias reservoir in São Francisco River basin, Brazil. This transformed signal is used as input for an ANN model to forecast inflows seven days ahead, and the error RMSE decreased by more than 50% (i.e. from 454.2828 to 200.0483).
Development of an ANN optimized mucoadhesive buccal tablet containing flurbiprofen and lidocaine for dental pain.
Hussain, Amjad; Syed, Muhammad Ali; Abbas, Nasir; Hanif, Sana; Arshad, Muhammad Sohail; Bukhari, Nadeem Irfan; Hussain, Khalid; Akhlaq, Muhammad; Ahmad, Zeeshan
A novel mucoadhesive buccal tablet containing flurbiprofen (FLB) and lidocaine HCl (LID) was prepared to relieve dental pain. Tablet formulations (F1-F9) were prepared using variable quantities of mucoadhesive agents, hydroxypropyl methyl cellulose (HPMC) and sodium alginate (SA). The formulations were evaluated for their physicochemical properties, mucoadhesive strength and mucoadhesion time, swellability index and in vitro release of active agents. Release of both drugs depended on the relative ratio of HPMC:SA. However, mucoadhesive strength and mucoadhesion time were better in formulations, containing higher proportions of HPMC compared to SA. An artificial neural network (ANN) approach was applied to optimise formulations based on known effective parameters (i.e., mucoadhesive strength, mucoadhesion time and drug release), which proved valuable. This study indicates that an effective buccal tablet formulation of flurbiprofen and lidocaine can be prepared via an optimized ANN approach. PMID:27279067
ANN based Estimation of Ultra High Energy (UHE) Shower Size using Radio Data
NASA Astrophysics Data System (ADS)
Sinha, Kalpana Roy; Datta, Pranayee; Sarma, Kandarpa Kumar
Size estimation is a challenging area in the field of Ultra High Energy (UHE) showers where actual measurements are always associated with uncertainty of events and imperfections in detection mechanisms. The subtle variations resulting out of such factors incorporate certain random behaviour in the readings provided by shower detectors for subsequent processing. Field strength recorded by radio detectors may also be affected by this statistical nature. Hence there is a necessity of development of a system which can remain immune to such random behaviour and provide resilient readings to subsequent stages. Here, we propose a system based on Artificial Neural Network (ANN) which accepts radio field strength recorded by radio detectors and provides estimates of shower sizes in the UHE region. The ANN in feed-forward form is trained with a range of shower events with which it can effectively handle the randomness observed in the detector reading due to imperfections in the experimental apparatus and related set-up.
An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment
de Cos Juez, Francisco J.; Lasheras, Fernando Sánchez; Roqueñí, Nieves; Osborn, James
In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A). PMID:23012524
Design and improvement of laser tracking control system using ANN technique
NASA Astrophysics Data System (ADS)
Shi, Ying; Zhang, Guoxiong; Li, Xingfei
Laser tracking method for space coordinates measurement is a newly developed technique that possesses the characteristics of high accuracy, large measuring range, flexible and dynamic measurement and so on. Laser tracking interferometer system based on this method has become an important measuring tool in many industrial fields. However, the control system sometimes acts nonlinearly because of long-range measurement and various applications. To solve this problem, artificial neural network (ANN) controller is introduced as an advanced adaptive control configuration in laser tracking interferometer system. The reason is that artificial neural network which is an important branch of intelligent control area has great potential ability in dealing with high nonlinearity and indeterminate factors. Then the simulation of tracking control system based on either conventional PID controller or ANN controller is carried out separately and the result of comparison is given.
Processing RoxAnn sonar data to improve its categorization of lake bed surficial sediments
Cholwek, Gary; Bonde, John; Li, Xing; Richards, Carl; Yin, Karen
To categorize spawning and nursery habitat for lake trout in Minnesota's near shore waters of Lake Superior, data was collected with a single beam echo sounder coupled with a RoxAnn bottom classification sensor. Test areas representative of different bottom surficial substrates were sampled. The collected data consisted of acoustic signals which showed both depth and substrate type. The location of the signals was tagged in real-time with a DGPS. All data was imported into a GIS database. To better interpret the output signal from the RoxAnn, several pattern classifiers were developed by multivariate statistical method. From the data a detailed and accurate map of lake bed bathymetry and surficial substrate types was produced. This map will be of great value to fishery and other natural resource managers.
An ANN-based smart tomographic reconstructor in a dynamic environment.
de Cos Juez, Francisco J; Sánchez Lasheras, Fernando; Roqueñí, Nieves; Osborn, James
In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A). PMID:23012524
Prophecy, patriarchy, and violence in the early modern household: the revelations of Anne Wentworth.
Johnston, Warren
In 1676 the apostate Baptist prophet Anne Wentworth (1629/30-1693?) published "A True Account of Anne Wentworths Being Cruelly, Unjustly, and Unchristianly Dealt with by Some of Those People called Anabaptists," the first in a series of pamphlets that would continue to the end of the decade. Orignially a member of a London Baptist church, Wentworth left the congregation and eventually her own home after her husband used physical force to stop her writing and prophesying. Yet Wentworth persisted in her "revelations." These prophecies increasingly focused on her response to those who were trying to stop her efforts, especially within her own household. This article examines Wentworth's writings as an effort by an early modern woman, using arguments of spiritual agency, to assert ideas about proper gender roles and household responsibilities to denounce her husband and rebut those who criticized and attempted to suppress her. PMID:19999636
Identification of Constitutive Parameters Using Inverse Strategy Coupled to an ANN Model
NASA Astrophysics Data System (ADS)
Aguir, H.; Chamekh, A.; BelHadjSalah, H.; Hambli, R.
This paper deals with the identification of material parameters using an inverse strategy. In the classical methods, the inverse technique is generally coupled with a finite element code which leads to a long computing time. In this work an inverse strategy coupled with an ANN procedure is proposed. This method has the advantage of being faster than the classical one. To validate this approach an experimental plane tensile and bulge tests are used in order to identify material behavior. The ANN model is trained from finite element simulations of the two tests. In order to reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure to identify material parameters for the AISI304. The identified material parameters are the hardening curve and the anisotropic coefficients.
A Practically Validated Intelligent Calibration Circuit Using Optimized ANN for Flow Measurement by Venturi
NASA Astrophysics Data System (ADS)
Venkata, Santhosh Krishnan; Roy, Binoy Krishna
Design of an intelligent flow measurement technique using venturi flow meter is reported in this paper. The objectives of the present work are: (1) to extend the linearity range of measurement to 100 % of full scale input range, (2) to make the measurement technique adaptive to variations in discharge coefficient, diameter ratio of venturi nozzle and pipe (β), liquid density, and liquid temperature, and (3) to achieve the objectives (1) and (2) using an optimized neural network. The output of venturi flow meter is differential pressure. It is converted to voltage by using a suitable data conversion unit. A suitable optimized artificial neural network (ANN) is added, in place of conventional calibration circuit. ANN is trained, tested with simulated data considering variations in discharge coefficient, diameter ratio between venturi nozzle and pipe, liquid density, and liquid temperature. The proposed technique is then subjected to practical data for validation. Results show that the proposed technique has fulfilled the objectives.
Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.
Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza
The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. PMID:26140748
Carbon Cycle 2.0: Mary Ann Piette: Impact of efficient buildings
Mary Ann Piette
Mary Ann Piette speaks at the Carbon Cycle 2.0 kick-off symposium Feb. 2, 2010. We emit more carbon into the atmosphere than natural processes are able to remove - an imbalance with negative consequences. Carbon Cycle 2.0 is a Berkeley Lab initiative to provide the science needed to restore this balance by integrating the Labs diverse research activities and delivering creative solutions toward a carbon-neutral energy future. http://carboncycle2.lbl.gov/
Ann O'Mara, PhD, RN, MPH | Division of Cancer Prevention
Dr. Ann O'Mara is Head of Palliative Research in the NCI Division of Cancer Prevention. She manages a portfolio of symptom management and palliative and end-of-life care research projects. The majority of these projects focus on the more common morbidities associated with cancer and its treatment, e.g., pain, chemotherapy induced neuropathy, fatigue, sleep disturbances, and psychosocial issues, such as distress, anxiety and depression. |
Carbon Cycle 2.0: Mary Ann Piette: Impact of efficient buildings
Mary Ann Piette
Mary Ann Piette speaks at the Carbon Cycle 2.0 kick-off symposium Feb. 2, 2010. We emit more carbon into the atmosphere than natural processes are able to remove - an imbalance with negative consequences. Carbon Cycle 2.0 is a Berkeley Lab initiative to provide the science needed to restore this balance by integrating the Labs diverse research activities and delivering creative solutions toward a carbon-neutral energy future. http://carboncycle2.lbl.gov/
[The embroidery work of the lady at Saint-Anne Hospital].
Thillaud, Pierre L; Postel, Jacques
In July 1974, a 72 old woman had been a patient for forty years in Sainte-Anne Hospital, Ward C. As she had again a violent brawl with her neighbour patient, she revealed being a tremendous artist. She had been confined on account of dementia paralytica in the Mecca of malariotherapy, and passionately devoted herself to embroidery. Her fancy work was rather a matter for Jean Dubuffet's art through its perfect expression and deserved being known. PMID:25230533
Clustering of soybean genotypes via Ward-MLM and ANNs associated with mixed models.
Teodoro, P E; Torres, F E; Corrêa, A M
The objectives of this study were to use mixed models to confirm the presence of genetic variability in 16 soybean genotypes, to compare clusters generated by artificial neural networks (ANNs) with those created by the Ward modified location model (MLM) technique, and to indicate parental combinations that hold promise for obtaining superior segregating populations of soybean. A field trial was conducted between November 2014 and February 2015 at Universidade Estadual de Mato Grosso do Sul, Aquidauana, MS. The experimental design consisted of four replications of randomized blocks, each containing 16 treatments. We assessed the following agronomic traits: plant height, first pod height, number of branches per plant, number of pods per plant, number of grains per pod, hundred-grain weight, and grain yield. Mixed models were used to estimate variance components and genetic parameters, and obtain genotypic values for each trait. After verifying the presence of genetic variability for all traits, genotypic values were submitted to both a Ward-MLM procedure and ANNs to estimate genetic divergence among genotypes. The number of groups formed by both methods was the same, but there were differences in group constitutions. ANN analysis improved soybean genotypes clustering patterns compared to Ward-MLM procedure. Based on these methods, divergent crosses may be made between genotype 97R73 with genotypes AS3797 and SYN9070, whereas convergent crosses may be made between genotypes AS3797 and SYN9070. PMID:27525912
ANN based Performance Evaluation of BDI for Condition Monitoring of Induction Motor Bearings
NASA Astrophysics Data System (ADS)
Patel, Raj Kumar; Giri, V. K.
One of the critical parts in rotating machines is bearings and most of the failure arises from the defective bearings. Bearing failure leads to failure of a machine and the unpredicted productivity loss in the performance. Therefore, bearing fault detection and prognosis is an integral part of the preventive maintenance procedures. In this paper vibration signal for four conditions of a deep groove ball bearing; normal (N), inner race defect (IRD), ball defect (BD) and outer race defect (ORD) were acquired from a customized bearing test rig, under four different conditions and three different fault sizes. Two approaches have been opted for statistical feature extraction from the vibration signal. In the first approach, raw signal is used for statistical feature extraction and in the second approach statistical features extracted are based on bearing damage index (BDI). The proposed BDI technique uses wavelet packet node energy coefficients analysis method. Both the features are used as inputs to an ANN classifier to evaluate its performance. A comparison of ANN performance is made based on raw vibration data and data chosen by using BDI. The ANN performance has been found to be fairly higher when BDI based signals were used as inputs to the classifier.
Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN
Fakhri, Mansour
Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000) four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN) are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation. PMID:24688400
Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification
G. S., Vijay; H. S., Kumar; Pai P., Srinivasa; N. S., Sriram; Rao, Raj B. K. N.
The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher's Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal. PMID:23213323
Dynamics of a mesoscale eddy off Cape Ann, Massachusetts in May 2005
NASA Astrophysics Data System (ADS)
Jiang, Mingshun; Zhou, Meng; Libby, Scott P.; Anderson, Donald M.
Observations and numerical modeling indicate that a mesoscale anti-cyclonic eddy formed south of Cape Ann at the northern entrance of Massachusetts Bay (MB) during May 2005, when large river discharges in the western Gulf of Maine and two strong Nor'easters passing through the regions led to an unprecedented toxic Alexandrium fundyense bloom (red tide). Both model results and field measurements suggest that the western Maine Coastal Current separated from Cape Ann around May 7-8, and the eddy formed on around May 10. The eddy was trapped at the formation location for about a week before detaching from the coastline and moving slowly southward on May 17. Both model results and theoretical analysis suggest that the separation of the coastal current from the coast and subsequent eddy formation were initiated at the subsurface by an adverse pressure gradient between Cape Ann and MB due to the higher sea level set up by onshore Ekman transport and higher density in downstream MB. After the formation, the eddy was maintained by the input of vorticity transported by the coastal current from the north, and local vorticity generation around the cape by the horizontal gradients of wind-driven currents, bottom stress, and water density induced by the Merrimack River plume. Observations and model results indicate that the anti-cyclonic eddy significantly changed the pathway of nutrient and biota transport into the coastal areas and enhanced phytoplankton including Alexandrium abundances around the perimeter of the eddy and in the western coast of MB.
ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining
NASA Astrophysics Data System (ADS)
Chandrasekaran, Muthumari; Tamang, Santosh
Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed (N), feed rate (f) and depth of cut (d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3-5-1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.
[The family relationships of Antoine Brulon, apothecary to the king, and his wife, Anne de Furnes, in Auvergne and Paris in the 17th century. Anne de Furnes and Molière in Paris and the village of Auteuil].
Warolin, Christian
This article presents the biography of Anne de Fumes, wife of Antoine Brulon, the king's apothecary. Due to the successive deaths of her husband and her only daughter, Geneviève, her sisters-in-law, Géraude and Anne Brulon, living in Auvergne, inherited the property of their niece. Anne de Fumes, who inherited the movable assets, carried out a series of transactions to acquire the totality of the property rights, which she obtained, but at the considerable cost of 100,000 pounds. After her death, her five nephews and nieces, her sole legatees, inherited her estate. From 1666, Molière was the tenant of an apartment in a building in the Rue Saint-Thomas-du-Louvre, Place du Palais-Royal, in Paris, which belonged to Anne de Fumes. She lived in the neighbouring house in the Rue Saint-Honoré of which she was also the owner. Three apothecaries, Philibert Boudin, Jean Morel and Pierre Frapin, successively rented the shop and entresol of the house in the Rue Saint-Thomas-du-Louvre. Pierre Frapin, tenant of the shop from 1668, supplied Molière with medicine. Like Molière, Anne de Fumes rented accommodation in a house in the village of Auteuil belonging to Jacques de Grout de Beaufort and his wife Marie Filz. Reports show that the famous actor and Anne de Fumes cohabited in Auteuil during the period 1667 to 1672. PMID:21998974
Assessment of AnnAGNPS model capacity to simulate ephemeral gullies initiation and development
NASA Astrophysics Data System (ADS)
Chahor, Youssef; Giménez, Rafael; Casalí, Javier
The relatively recent recognition of the importance of ephemeral gully erosion in agricultural fields in Europe explains that so far only a few mathematical models have included algorithms to simulate this type of concentrated flow erosion. Precisely, a conceptual and numerical framework was recently incorporated in the Annualized Agricultural Non-Point Source (AnnAGNPS) model to simulate gully initiation and development in order to assess the impact of gully erosion (sediment production) on management practices at watershed scale. More precisely, the Compound Topographic Index (CTI) approach was integrated within the existing AnnAGNPS GIS interface in order to identify the Potential Ephemeral Gully (PEG) mouth throughout a watershed. In addition, the Tillage-Induced Ephemeral Gully Erosion Model (TIEGEM) was also incorporated into AnnAGNPS to estimate ephemeral gully development. The aim of this work was to assess the capability of AnnAGNPS for predicting (i) PEG location and (ii) gully erosion rate and gully geometry. The study was carried out in the region of Pitillas (southern Navarre, Spain; under continental Mediterranean climate), in several field sites cultivated with wheat. First, thirty-one EGs observed in the fields and depicted in aerial photographs were taken as references. A DEM of the study area (5 x 5 m) was processed using AGNPS ArcView interface to determine the CTI values of each raster grid. Then, seven cumulative percentage values of CTI thresholds (94%, 95%, 96%, 97%, 98%, 99% and 99.5%) were used to create seven potential scenarios of PEG mouths locations in the study area. These scenarios were compared with the reference EGs. The CTI cumulative percentage thresholds of 95% presented the best performance in predicting EGs locations. However, the accuracy of the CTI approach notably decreased in low slope areas. On the other hand, four EGs developed and surveyed in the same study area -in different years between 1996 and 2001- were used to
Estimation of Release History of Pollutant Source and Dispersion Coefficient of Aquifer Using Trained ANN Model
NASA Astrophysics Data System (ADS)
Srivastava, R.; Ayaz, M.; Jain, A.
Knowledge of the release history of a groundwater pollutant source is critical in the prediction of the future trend of the pollutant movement and in choosing an effective remediation strategy. Moreover, for source sites which have undergone an ownership change, the estimated release history can be utilized for appropriate allocation of the costs of remediation among different parties who may be responsible for the contamination. Estimation of the release history with the help of concentration data is an inverse problem that becomes ill-posed because of the irreversible nature of the dispersion process. Breakthrough curves represent the temporal variation of pollutant concentration at a particular location, and contain significant information about the source and the release history. Several methodologies have been developed to solve the inverse problem of estimating the source and/or porous medium properties using the breakthrough curves as a known input. A common problem in the use of the breakthrough curves for this purpose is that, in most field situations, we have little or no information about the time of measurement of the breakthrough curve with respect to the time when the pollutant source becomes active. We develop an Artificial Neural Network (ANN) model to estimate the release history of a groundwater pollutant source through the use of breakthrough curves. It is assumed that the source location is known but the time dependent contaminant source strength is unknown. This temporal variation of the strength of the pollutant source is the output of the ANN model that is trained using the Levenberg-Marquardt algorithm utilizing synthetically generated breakthrough curves as inputs. A single hidden layer was used in the neural network and, to utilize just sufficient information and reduce the required sampling duration, only the upper half of the curve is used as the input pattern. The second objective of this work was to identify the aquifer parameters. An
Precipitation Estimation from Remotely Sensed Information using ANN-Cloud Classification System
NASA Astrophysics Data System (ADS)
Hong, Y.; Hsu, K.; Sorooshian, S.
Artificial Neural Network (ANN) models, which contain flexible architectures and are capable of discerning the underlying functional relationships from data, are recognized as very useful tools in geophysical applications. In this study, we demonstrate a hybrid ANN modeling system to estimate surface rainfall from satellite infrared imagery. The proposed network, Precipitation Estimation from Remotely Sensed Information using ANN-Cloud Classification System (PERSIANN-CCS), includes several components: (1) cloud image segmentation, (2) cloud patch feature selection, (3) patch feature classification using a self-organizing feature map network, and (4) patch-based rainfall estimates from a group of multiple nonlinear cloud top temperature and rainfall functions. The PERSIANN-CCS model was first calibrated using observations from Geostationary Operational Environmental Satellite (GOES) infrared imagery and the Next Generation Radar (NEXRAD) rainfall network. To further extend PERSIANN-CCS rainfall estimates over the remote regions, Tropical Rainfall Measurement Mission (TRMM) microwave rainfall estimates (TMI product 2A12) were used to adjust PERSIANN-CCS model parameters. The calibrated nonlinear cloud top temperature and rainfall (Tb-R) functions of classified cloud patches show highly variability, reflecting the complexity of dominant cloud-precipitation processes over various regions. Case studies show that PERSIANN-CCS captures the variability in rain rate at 12kmx12km grid and 3-hour resolutions, with a standard error of 3.0mm/hr and a correlation coefficient around 0.65. Additional insights into the cloud evolution and precipitation process from the classified PERSIANN-CCS cloud patch features and rainfall distributions are discussed.
[Modelling pollutant loads and management alternatives in Jiulong River watershed with AnnAGNPS].
Hong, Hua-Sheng; Huang, Jin-Liang; Zhang, Luo-Ping; Du, Peng-Fei
The modelling package Annualized Agricultural Nonpoint Source Model (AnnAGNPS) was used to predict pollutant loads, and simulate catchment processes and management practices in Jiulong River watershed, a medium-sized mountainous watershed in southeast of China. Four typical sub-watersheds were primarily chosen to calibrate AnnAGNPS model by data collected from storm events during the period of April to September, 2003. The model was further validated in the two biggest branches of Jiulong River watershed, i.e. West river and North river by the data regarding climate, and land using condition in 2002 - 2003. The simulation results show that annual total nitrogen load was 24.76kg/(hm2 x a) and 10.28kg/(hm2 x a) in the West river and North river, respectively, and annual total phosphorus load was 0.67 kg/(hm2 x a) and 0.40 kg/(hm2 x a) in the West river and North river, respectively. With the support of AnnAGNPS model, several management alternatives were separately simulated in the typical sub-watersheds, West river and North river. In the specific cell with cell-ID of 92 in Tianbao and Xiandu sub-watershed, after reforesting in sloping field, runoff surface, sediment yield, total nitrogen load and total phosphorus load cut down with 21.6%, 25.9%, 96% and 79.2%, respectively. In West river, with the cultivation plant changing from banana into rice, the total nitrogen, dissolved nitrogen, total phosphorus and dissolved phosphorus cut down with 23.83%, 25.44%, 9.08% and 19.84%, respectively. In North river, when removing all the hoggerys, nitrogen and dissolved nitrogen cut down with 63.54% and 76.92% , respectively. PMID:16212170
SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients
Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa
Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.
ANN modeling for flood prediction in the upstream Eure's catchment (France)
NASA Astrophysics Data System (ADS)
Kharroubi, Ouissem; masson, Eric; Blanpain, Olivier; Lallahem, Sami
Rainfall-Runoff relationship at basin scale is strongly depending on the catchment complexity including multi-scale interactions. In extreme events cases (i.e. floods and droughts) this relationship is even more complex and differs from average hydrological conditions making extreme runoff prediction very difficult to achieve. However, flood warning, flood prevention and flood mitigation rely on the possibility to predict both flood peak runoff and lag time. This point is crucial for decision making and flood warning to prevent populations and economical stakes to be damaged by extreme hydrological events. Since 2003 in France, a dedicated state service is in charge of producing flood warning from national level (i.e. SCHAPI) to regional level (i.e. SPC). This flood warning service is combining national weather forecast agency (i.e. Meteo France) together with a fully automated realtime hydrological network (i.e. Rainfall-Runoff) in order to produce a flood warning national map online and provide a set of hydro-meteorological data to the SPC in charge of flood prediction from regional to local scale. The SPC is in fact the flood service delivering hydrological prediction at operational level for decision making about flood alert for municipalities and first help services. Our research in collaboration with the SPC SACN (i.e. "Seine Aval et fleuves Côtiers Normands") is focused on the implementation of an Artificial Neural Network model (ANN) for flood prediction in deferent key points of the Eure's catchment and main subcatchment. Our contribution will focus on the ANN model developed for Saint-Luperce gauging station in the upstream part of the Eure's catchment. Prediction of extreme runoff at Saint-Luperce station is of high importance for flood warning in the Eure's catchment because it gives a good indicator on the extreme status and the downstream propagation of a potential flood event. Despite a good runoff monitoring since 27 years Saint Luperce flood
Internal Model Controller of an ANN Speed Sensorless Controlled Induction Motor Drives
NASA Astrophysics Data System (ADS)
Hamed Mouna, Ben; Lassaad, Sbita
This study deals with the performance analysis and implementation of a robust sensorless speed controller. The robustness is guaranteed by the use of the Internal Model Controller (IMC). An intelligent algorithm is evolved to eliminate the mechanical speed. It is based on the Artificial Neural Network (ANN) principle. Verification of the proposed robust sensorless controller is provided by experimental realistic tests on a scalar controlled induction motor drive. Sensorless robust speed control at low speeds and in field weakening region (high speeds) is studied in order to show the robustness of the speed controller under a wide range of load.
Identification of Unknown Groundwater Pollution Source Using Linked ANN-Optimization Model
NASA Astrophysics Data System (ADS)
Ayaz, M.; Srivastava, R.
Identification of groundwater pollution sources is a major step in groundwater pollution remediation, particularly for assigning fractions of the remediation cost to different polluters. Identification of unknown groundwater pollution source is an inverse problem which is generally ill-posed. A pollution source is said to be identified completely when its source characteristics (location, strength and release period) are known. In practice, the lag time between the first reading at observation well and the time at which the source becomes active is not known. For such cases, pollution source identification problem becomes more difficult. We propose a linked ANN-Optimization model for complete identification of unknown groundwater pollution sources. Spatial and temporal data of observed and simulated concentration is used to formulate the objective function. An optimization model is then used to minimize the objective function. We define the lag time as the time from the start of the pollutant release to the peak of the breakthrough curve observed at a monitoring well. Lag time for a particular monitoring well is then dependent only on the source location and release period. An ANN model is trained for different source locations and release periods as input data to determine the lag time for breakthrough curve. In the proposed model, the ANN model is linked externally with the optimization model to identify the pollution sources. The main advantage of the proposed model is to identify the unique solution for pollution sources when lag time is not known. The performance of the model is evaluated for a one dimensional case with error-free and erroneous data. The measurement errors incorporated in the data vary from 0% to 10% of the analytically computed values. The results indicate that the proposed linked ANN-Optimization model is able to predict the source parameters quite well for the error-free data. For the observations subjected to random measurement errors, the
Explosion with a slow-burning fuse: origins of holography in Ann Arbor, Michigan
NASA Astrophysics Data System (ADS)
Johnston, Sean F.
The subject today known as holography emerged from research in three diverse locations and having distinct origins, aims and methods: at a commercial electrical laboratory in Rugby, England, from the late 1940s until the mid 1950s; at the Vavilov State Optical Institute in Leningrad from the late 1950s and again from the mid 1960s; and, from a classified research laboratory operated by the University of Michigan beginning in the mid 1950s and accelerating from the early 1960s. The scientists, engineers, artisans, entrepreneurs and companies in that third location dominated the subject through the 1960s, making Ann Arbor, for a time, the 'holography capital of the world'. Based on extensive unpublished documents, artifacts and interviews with some two-dozen participants (much of it as yet unavailable in publicly accessible archives), this paper focuses on the origins of the subject in Ann Arbor, Michigan. It also explores how the initial explosion of interest was transmitted to other research groups, firms, artists and the wider public.
The Automatic Classification with ANN of sunspot groups using the Solar Feature Catalogue
NASA Astrophysics Data System (ADS)
Zharkov, S. I.; Schetinin, V.; Zharkova, V. V.
The sample 4 months Solar Feature Catalogue of sunspots created from the SOHO/MDI white light images and magnetograms with the automated detection technique is used for a classification of sunspots into groups and production of an automated activity index. The detection techniques are applied to a pair consisting of SOHO/MDI 'white light' continuum image and the magnetogram image from the same source, which are synchronised to the POV of countinuum. As a result we are able to extract the following information from each of sunspot: area, diameter, position (on the disk and on the solar sphere), umbral area, sunspot and umbral intensities and maximum and minimum magnetic flux and some others. The parameters were to classify the detection results by segmenting sunspots detected on a single image into sunspot groups using Artificial Neural Network. The two manual training sets of sunspot detection are used - Locarno Sunspot Drawings and Mount Wilson Classification. Based on them we try to identify the classifiers with the ANN, which have a best fit with the manual classifications above. The trained ANN is tested on a few testing sets from the both sources that revealed a good accuracy of classification. In the future this classification can replace the manual practices in the observatories and can used in solar activity forecast.
An ANN approach in predicting solar radio enhancements at 11 cm wavelength
NASA Astrophysics Data System (ADS)
Molinaro, Marco; Gregorio, Anna; Messerotti, Mauro
The decimetric radio emission from the solar corona is both an effective indicator of solar activity and a proxy for the EUV solar emission, which can cause perturbations in the terrestrial ionosphere. Therefore the 10.7 cm solar radio index is widely used in various ionospheric models and in any models where a reliable quantitative measure of the solar activity level is needed. The time evolution of this radio index closely matches that of the 1-min Soft X-Ray (SXR) measurements, resulting in an effective diagnostics for flaring processes. In fact, the occurrence of decimetric radio flares is associated with that of the correspondent SXR signature. Hence, the capability of predicting decimetric radio flares is a fundamental feature for space weather applications. In this work, we present preliminary results obtained via an Artificial Neural Network (ANN) approach for the prediction of solar radio enhancements at 11 cm wavelength. The data used to feed and run the ANN are the 1-min average radio index provided by the Trieste Solar Radio System, a dedicated multichannel solar radio polarimeters' set operated by the INAF-Astronomical Observatory of Trieste. Different use cases are discussed in the framework of future developments for advanced space weather nowcasting and forecasting via radio diagnostics, based on the unique feature of TSRS which acquires the data with a routinary time cadence of 1 millisecond and this allows to get deeper insights of the events' time evolution and prediction.
Controlling the Twin Wire Arc Spray Process Using Artificial Neural Networks (ANN)
NASA Astrophysics Data System (ADS)
Hartz-Behrend, K.; Schaup, J.; Zierhut, J.; Schein, J.
One approach for controlling the twin wire arc spray process is to use optical properties of the particle beam as input parameters for a process control. The idea is that changes in the process like eroded contact nozzles or variations of current, voltage, and/or atomizing gas pressure may be detected through observation of the particle beam. It can be assumed that if these properties deviate significantly from those obtained from a beam recorded for an optimal coating process, the spray particle and thus the coating properties change significantly. The goal is to detect these deviations and compensate the occurring errors by adjusting appropriate process parameters for the wire arc spray unit. One method for monitoring optical properties is to apply the diagnostic system particle flux imaging (PFI): PFI fits an ellipse to an image of a particle beam thereby defining easy to analyze characteristical parameters by relating optical beam properties to ellipse parameters. Using artificial neural networks (ANN), mathematical relations between ellipse and process parameters can be defined. It will be shown that in the case of a process disturbance through the use of an ANN-based control new process parameters can be computed to compensate particle beam deviations.
Elemental Study on Auscultaiting Diagnosis Support System of Hemodialysis Shunt Stenosis by ANN
NASA Astrophysics Data System (ADS)
Suzuki, Yutaka; Fukasawa, Mizuya; Mori, Takahiro; Sakata, Osamu; Hattori, Asobu; Kato, Takaya
It is desired to detect stenosis at an early stage to use hemodailysis shunt for longer time. Stethoscope auscultation of vascular murmurs is useful noninvasive diagnostic approach, but an experienced expert operator is necessary. Some experts often say that the high-pitch murmurs exist if the shunt becomes stenosed, and some studies report that there are some features detected at high frequency by time-frequency analysis. However, some of the murmurs are difficult to detect, and the final judgment is difficult. This study proposes a new diagnosis support system to screen stenosis by using vascular murmurs. The system is performed using artificial neural networks (ANN) with the analyzed frequency data by maximum entropy method (MEM). The author recorded vascular murmurs both before percutaneous transluminal angioplasty (PTA) and after. Examining the MEM spectral characteristics of the high-pitch stenosis murmurs, three features could be classified, which covered 85 percent of stenosis vascular murmurs. The features were learnt by the ANN, and judged. As a result, a percentage of judging the classified stenosis murmurs was 100%, and that of normal was 86%.
Identification of pain from infant cry vocalizations using artificial neural networks (ANNs)
NASA Astrophysics Data System (ADS)
Petroni, Marco; Malowany, Alfred S.; Johnston, C. Celeste; Stevens, Bonnie J.
The analysis of infant cry vocalizations has been the focus of a number of efforts over the past thirty years. Since the infant cry is one of the only means that an infant has for communicating with its care-giving environment, it is thought that information regarding the state of an infant, such as hunger or pain, can be determined from an infant's cry. To date, research groups have determined that adult listeners can differentiate between different types of cries auditorialy, and at least one group has attempted to automate this classification process. This paper presents the results of another attempt at automating the discrimination process, this time using artificial neural networks (ANNs). The input data consists of successive frames of one or two parametric representations generated from the first second of a cry following the application of either an anger, fear, or pain stimulus. From tests conducted to date, it is determined that ANNs are a useful tool for cry classification and merit further study in this domain.
Prediction of Cutting Forces Using ANNs Approach in Hard Turning of AISI 52100 steel
Makhfi, Souad; Habak, Malek; Velasco, Raphael; Haddouche, Kamel; Vantomme, Pascal
In this study, artificial neural networks (ANNs) was used to predict cutting forces in the case of machining the hard turning of AISI 52100 bearing steel using cBN cutting tool. Cutting forces evolution is considered as the key factors which affect machining. Predicting cutting forces evolution allows optimizing machining by an adaptation of cutting conditions. In this context, it seems interesting to study the contribution that could have artificial neural networks (ANNs) on the machining forces prediction in both numerical and experiment studies. Feed-forward multi-layer neural networks trained by the error back-propagation (BP) algorithm are used. Levenberg-Marquardt (LM) optimization algorithm was used for finding out weights. The training of the network is carried out with experimental machining data.The input dataset used are cutting speed, feed rate, cutting depth and hardness of the material. The output dataset used are cutting forces (Ft-cutting force, Fa- feed force and Fr- radial force).Results of the neural networks approach, in comparison with experimental data are discussed in last part of this paper.
Clearance Rate and BP-ANN Model in Paraquat Poisoned Patients Treated with Hemoperfusion
Hu, Lufeng; Hong, Guangliang; Ma, Jianshe; Wang, Xianqin; Lin, Guanyang; Zhang, Xiuhua; Lu, Zhongqiu
In order to investigate the effect of hemoperfusion (HP) on the clearance rate of paraquat (PQ) and develop a clearance model, 41 PQ-poisoned patients who acquired acute PQ intoxication received HP treatment. PQ concentrations were determined by high performance liquid chromatography (HPLC). According to initial PQ concentration, study subjects were divided into two groups: Low-PQ group (0.05–1.0 μg/mL) and High-PQ group (1.0–10 μg/mL). After initial HP treatment, PQ concentrations decreased in both groups. However, in the High-PQ group, PQ levels remained in excess of 0.05 μg/mL and increased when the second HP treatment was initiated. Based on the PQ concentrations before and after HP treatment, the mean clearance rate of PQ calculated was 73 ± 15%. We also established a backpropagation artificial neural network (BP-ANN) model, which set PQ concentrations before HP treatment as input data and after HP treatment as output data. When it is used to predict PQ concentration after HP treatment, high prediction accuracy (R = 0.9977) can be obtained in this model. In conclusion, HP is an effective way to clear PQ from the blood, and the PQ concentration after HP treatment can be predicted by BP-ANN model. PMID:25695058
Solving Capelin Time Series Ecosystem Problem Using Hybrid ANN-GAs Model and Multiple Linear Regression Model
NASA Astrophysics Data System (ADS)
Eghnam, Karam M.; Sheta, Alaa F.
Development of accurate models is necessary in critical applications such as prediction. In this paper, a solution to the stock prediction problem of the Barents Sea capelin is introduced using Artificial Neural Network (ANN) and Multiple Linear model Regression (MLR) models. The Capelin stock in the Barents Sea is one of the largest in the world. It normally maintained a fishery with annual catches of up to 3 million tons. The Capelin stock problem has an impact in the fish stock development. The proposed prediction model was developed using an ANNs with their weights adapted using Genetic Algorithm (GA). The proposed model was compared to traditional linear model the MLR. The results showed that the ANN-GA model produced an overall accuracy of 21% better than the MLR model.
QSPR modeling of soil sorption coefficients (K(OC)) of pesticides using SPA-ANN and SPA-MLR.
Goudarzi, Nasser; Goodarzi, Mohammad; Araujo, Mario Cesar Ugulino; Galvão, Roberto Kawakami Harrop
A quantitative structure-property relationship (QSPR) study was conducted to predict the adsorption coefficients of some pesticides. The successive projection algorithm feature selection (SPA) strategy was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and adsorption coefficient data was achieved by linear (multiple linear regression; MLR) and nonlinear (artificial neural network; ANN) methods. The QSPR models were validated by cross-validation as well as application of the models to predict the K(OC) of external set compounds, which did not contribute to model development steps. Both linear and nonlinear methods provided accurate predictions, although more accurate results were obtained by the ANN model. The root-mean-square errors of test set obtained by MLR and ANN models were 0.3705 and 0.2888, respectively. PMID:19722589
The Use of ANN to Predict the Hot Deformation Behavior of AA7075 at Low Strain Rates
NASA Astrophysics Data System (ADS)
Jenab, A.; Karimi Taheri, A.; Jenab, K.
In this study, artificial neural network (ANN) was used to model the hot deformation behavior of 7075 aluminum alloy during compression test, in the strain rate range of 0.0003-1 s-1 and temperature range of 200-450 °C. The inputs of the model were temperature, strain rate, and strain, while the output of the model was the flow stress. The feed-forward back-propagation network with two hidden layers was built and successfully trained at different deformation domains by Levenberg-Marquardt training algorithm. Comparative analysis of the results obtained from the hyperbolic sine, the power law constitutive equations, and the ANN shows that the newly developed ANN model has a better performance in predicting the hot deformation behavior of 7075 aluminum alloy.
Assessment of maximum likelihood (ML) and artificial neural network (ANN) algorithms for classification of remote sensing data
NASA Astrophysics Data System (ADS)
Gupta, R. K.; Prasad, T. S.; Vijayan, D.; Balamanikavelu, P. M.
Due to mix-up of contributions from varied features on the ground surface, getting back of individual feature in remote sensing data using pattern recognition techniques is an ill-defined inverse problem. By placing maximum likelihood (ML) constraint, the available operational softwares classify the image. Without placing any parametric constraint, the image could also be classified using artificial neural networks (ANN). As GIS overlay, developed professionally by forest officials, was available for Antilova reserve forest in Andhra Pradesh, India (170 50^' to 170 56^' N, 810 45^' to 810 54^' E), the IRS-1C LISS-III image of February 11, 1999 was used for assessing the limits of classification accuracy attainable from ML and ANN classifiers. In ML classifier, full GIS overlay was used to give training sets over whole of the image (approach `a') and in approach `b', a priori probability (normally taken equal for all the classes in operational softwares) was assigned (in addition to full spectral signature) based on the fraction areas under each class in GIS overlay. Under such ideal situation of inputs, the achieved accuracy, i.e. Kappa coefficients were 0.709 and 0.735 for approaches `a' and `b' , respectively (called iteration `0'). Using fraction area under each class in the classified output to assign a priori probability for the next iteration, the convergence (within 2% variation) was achieved for 2nd and 3rd iterations with Kappa coefficient values of 0.773 and 0.797 for approaches `a' and `b', respectively. The non-attaining of 100% classification accuracy under ideal inputs situation could be due to assumption of guassian distribution in spectral signatures. In back propagation technique based ANN classifier, spectral signatures for training were identified from GIS overlay. The number of learning iterations were 20,000 with momentum and learning rate of 0.7 and 0.25, respectively. With one hidden layer the Kappa coefficient for ANN classifier was 0
Queer paradox/paradoxical queer: Anne Garréta's Pas un jour (2002).
Cairns, Lucille
This paper shows how Anne Garréta's Pas un jour (2002) is a decidedly queer text, in both the new and the old sense of that contested epithet. I examine three interrelated concerns central to Pas un jour. First, I analyze Garréta's mediation of desire in general: her own experiences of it; modalities thereof which subvert more 'normative' models of lesbianism; and her convergences with other gay, but male writers and theorists of desire such as Guy Hocquenghem, Gilles Deleuze and Michel Foucault. Second, I interrogate Garréta's dichotomy between desire and friendship, and adumbrate contrasts with Foucauldian theory. Finally, I scrutinize the meaning and value attributed to the particular body of desire with which Garréta is most commonly associated-homosexuality- and their links with those of a contemporary gay male writer, Dominique Fernandez. PMID:17804371
Ali, Cunich: Halley's Churches: Halley and the London Queen Anne Churches
NASA Astrophysics Data System (ADS)
Ali, Jason R.; Cunich, Peter
Edmond Halley's enormous contribution to science has received much attention. New research adds an intriguing chapter to his story and concerns his hitherto unexplored association with the baroque architectural visionary Nicholas Hawksmoor, and some important Temple-inspired churches that were built in London in the early 1700s. We argue that Christchurch Spitalfields and St Anne's Limehouse, which were both started in the summer of 1714, were aligned exactly eastwards using ``corrected'' magnetic-compass bearings and that Halley influenced or aided Hawksmoor. By this time the men had probably known each other for 30 years and had recently worked together on the Clarendon Building in Oxford. Despite there being more than 1500 years of Chinese and about 500 years of Western compass technology at the time, these probably represent the first constructions planned using a modern-day ``scientific'' technique. The research also throws light on Halley's contended religious position.
STS-103 Commander Curtis L. Brown Jr. and fiancee Ann Brickert at Pad 39B
NASA Technical Reports Server (NTRS)
STS-103 Commander Curtis L. Brown Jr. and his fiancee, Ann Brickert, pose for a photograph at Launch Pad 39B during a meeting of the STS-103 crew with their family and friends. The lights in the background are on the Fixed Service Structure next to Space Shuttle Discovery. The mission, to service the Hubble Space Telescope, is scheduled for launch Dec. 17 at 8:47 p.m. EST from Launch Pad 39B. Mission objectives include replacing gyroscopes and an old computer, installing another solid state recorder, and replacing damaged insulation in the telescope. The mission is expected to last about 8 days and 21 hours. Discovery is expected to land at KSC Sunday, Dec. 26, at about 6:25 p.m. EST.
Processing/formulation parameters determining dispersity of chitosan particles: an ANNs study.
Esmaeilzadeh-Gharehdaghi, Elina; Faramarzi, Mohammad Ali; Amini, Mohammad Ali; Moazeni, Esmaeil; Amani, Amir
Although a great number of studies may be found in literature about the parameters affecting the size of chitosan nanoparticles, no systematic work so far has detailed the factors affecting the polydispersity of chitosan as an important factor determining the quality of many preparations. Herein, using artificial neural networks (ANNs), four independent variables, namely, pH and concentration of chitosan solution as well as time and amplitude of sonication of the solution were studied to determine their influence on the polydispersity of solution. We found that in an ultrasound prepared nanodispersion of chitosan, all the four input parameters have reverse but non-linear relation with the polydispersity of the nanoparticles. PMID:23795904
[The utility boiler low NOx combustion optimization based on ANN and simulated annealing algorithm].
Zhou, Hao; Qian, Xinping; Zheng, Ligang; Weng, Anxin; Cen, Kefa
With the developing restrict environmental protection demand, more attention was paid on the low NOx combustion optimizing technology for its cheap and easy property. In this work, field experiments on the NOx emissions characteristics of a 600 MW coal-fired boiler were carried out, on the base of the artificial neural network (ANN) modeling, the simulated annealing (SA) algorithm was employed to optimize the boiler combustion to achieve a low NOx emissions concentration, and the combustion scheme was obtained. Two sets of SA parameters were adopted to find a better SA scheme, the result show that the parameters of T0 = 50 K, alpha = 0.6 can lead to a better optimizing process. This work can give the foundation of the boiler low NOx combustion on-line control technology. PMID:14768567
Assessing the Long Term Impact of Phosphorus Fertilization on Phosphorus Loadings Using AnnAGNPS
Yuan, Yongping; Bingner, Ronald L.; Locke, Martin A.; Stafford, Jim; Theurer, Fred D.
High phosphorus (P) loss from agricultural fields has been an environmental concern because of potential water quality problems in streams and lakes. To better understand the process of P loss and evaluate the effects of different phosphorus fertilization rates on phosphorus losses, the USDA Annualized AGricultural Non-Point Source (AnnAGNPS) pollutant loading model was applied to the Ohio Upper Auglaize watershed, located in the southern portion of the Maumee River Basin. In this study, the AnnAGNPS model was calibrated using USGS monitored data; and then the effects of different phosphorus fertilization rates on phosphorus loadings were assessed. It was found that P loadings increase as fertilization rate increases, and long term higher P application would lead to much higher P loadings to the watershed outlet. The P loadings to the watershed outlet have a dramatic change after some time with higher P application rate. This dramatic change of P loading to the watershed outlet indicates that a "critical point" may exist in the soil at which soil P loss to water changes dramatically. Simulations with different initial soil P contents showed that the higher the initial soil P content is, the less time it takes to reach the "critical point" where P loadings to the watershed outlet increases dramatically. More research needs to be done to understand the processes involved in the transfer of P between the various stable, active and labile states in the soil to ensure that the model simulations are accurate. This finding may be useful in setting up future P application and management guidelines. PMID:21776225
Evaluation of a non-point source pollution model, AnnAGNPS, in a tropical watershed
Polyakov, V.; Fares, A.; Kubo, D.; Jacobi, J.; Smith, C.
Impaired water quality caused by human activity and the spread of invasive plant and animal species has been identified as a major factor of degradation of coastal ecosystems in the tropics. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized Non-Point Source Pollution Model), in simulating runoff and soil erosion in a 48 km2 watershed located on the Island of Kauai, Hawaii. The model was calibrated and validated using 2 years of observed stream flow and sediment load data. Alternative scenarios of spatial rainfall distribution and canopy interception were evaluated. Monthly runoff volumes predicted by AnnAGNPS compared well with the measured data (R2 = 0.90, P < 0.05); however, up to 60% difference between the actual and simulated runoff were observed during the driest months (May and July). Prediction of daily runoff was less accurate (R2 = 0.55, P < 0.05). Predicted and observed sediment yield on a daily basis was poorly correlated (R2 = 0.5, P < 0.05). For the events of small magnitude, the model generally overestimated sediment yield, while the opposite was true for larger events. Total monthly sediment yield varied within 50% of the observed values, except for May 2004. Among the input parameters the model was most sensitive to the values of ground residue cover and canopy cover. It was found that approximately one third of the watershed area had low sediment yield (0-1 t ha-1 y-1), and presented limited erosion threat. However, 5% of the area had sediment yields in excess of 5 t ha-1 y-1. Overall, the model performed reasonably well, and it can be used as a management tool on tropical watersheds to estimate and compare sediment loads, and identify "hot spots" on the landscape. ?? 2007 Elsevier Ltd. All rights reserved.
Assessing the long term impact of phosphorus fertilization on phosphorus loadings using AnnAGNPS.
Yuan, Yongping; Bingner, Ronald L; Locke, Martin A; Stafford, Jim; Theurer, Fred D
High phosphorus (P) loss from agricultural fields has been an environmental concern because of potential water quality problems in streams and lakes. To better understand the process of P loss and evaluate the effects of different phosphorus fertilization rates on phosphorus losses, the USDA Annualized AGricultural Non-Point Source (AnnAGNPS) pollutant loading model was applied to the Ohio Upper Auglaize watershed, located in the southern portion of the Maumee River Basin. In this study, the AnnAGNPS model was calibrated using USGS monitored data; and then the effects of different phosphorus fertilization rates on phosphorus loadings were assessed. It was found that P loadings increase as fertilization rate increases, and long term higher P application would lead to much higher P loadings to the watershed outlet. The P loadings to the watershed outlet have a dramatic change after some time with higher P application rate. This dramatic change of P loading to the watershed outlet indicates that a "critical point" may exist in the soil at which soil P loss to water changes dramatically. Simulations with different initial soil P contents showed that the higher the initial soil P content is, the less time it takes to reach the "critical point" where P loadings to the watershed outlet increases dramatically. More research needs to be done to understand the processes involved in the transfer of P between the various stable, active and labile states in the soil to ensure that the model simulations are accurate. This finding may be useful in setting up future P application and management guidelines. PMID:21776225
Feasibility of a Cooperative Processing Center for Anne Arundel, Baltimore, Montgomery and Prince Georges Counties in Maryland.
ERIC Educational Resources Information Center
Pfefferle, Richard A.; Hines, Theodore C.
A study was conducted for the public library systems of Anne Arundel, Baltimore, Montgomery and Prince Georges Counties to determine what aspects of their acquisitions, cataloging and processing operations, if any, might be carried on cooperatively for lower costs and improved services. Conclusions and recommendations were that: (1) the growth of…
A Report to the Board of Education of Anne Arundel County on the Status of the Schools.
ERIC Educational Resources Information Center
Anne Arundel County Board of Education, Annapolis, MD.
This annual report for the 1979-1980 school year summarizes and gives the status of the main objectives of the major programs within the public school system of Anne Arundel County. Project objectives for the 1981-82 school year as well as the one immediately following are also presented and explained. In his statement, the Superintendent of…
Evaluation and Assessment of Conservation Management Practice Effects on Water Quality – AnnAGNPS Watershed Modeling Studies
Technology Transfer Automated Retrieval System (TEKTRAN)
The United States Department of Agriculture (USDA)–Annualized Agricultural Non-Point Source Pollutant Loading model (AnnAGNPS) is a watershed scale, continuous simulation, daily time step, conservation management planning tool that is currently utilized in many field and watershed-scale locations ar...
78 FR 72745 - Bureau of Political-Military Affairs; Administrative Debarment of LeAnne Lesmeister Under the...
Federal Register 2010, 2011, 2012, 2013, 2014
... of Political-Military Affairs; Administrative Debarment of LeAnne Lesmeister Under the Arms Export... CONTACT: Sue Gainor, Director, Office of Defense Trade Controls Compliance, Bureau of Political-Military... services. Section 127.7(a) of the ITAR authorizes the Assistant Secretary of State for...
DeAnnIso: a tool for online detection and annotation of isomiRs from small RNA sequencing data.
Zhang, Yuanwei; Zang, Qiguang; Zhang, Huan; Ban, Rongjun; Yang, Yifan; Iqbal, Furhan; Li, Ao; Shi, Qinghua
Small RNA (sRNA) Sequencing technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent variations from their canonical sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). However, integrated tool to precisely detect and systematically annotate isomiRs from sRNA sequencing data is still in great demand. Here, we present an online tool, DeAnnIso (Detection and Annotation of IsomiRs from sRNA sequencing data). DeAnnIso can detect all the isomiRs in an uploaded sample, and can extract the differentially expressing isomiRs from paired or multiple samples. Once the isomiRs detection is accomplished, detailed annotation information, including isomiRs expression, isomiRs classification, SNPs in miRNAs and tissue specific isomiR expression are provided to users. Furthermore, DeAnnIso provides a comprehensive module of target analysis and enrichment analysis for the selected isomiRs. Taken together, DeAnnIso is convenient for users to screen for isomiRs of their interest and useful for further functional studies. The server is implemented in PHP + Perl + R and available to all users for free at: http://mcg.ustc.edu.cn/bsc/deanniso/ and http://mcg2.ustc.edu.cn/bsc/deanniso/. PMID:27179030
Bodies in Space/Bodies in Motion/Bodies in Character: Adolescents Bear Witness to Anne Frank
ERIC Educational Resources Information Center
Chisholm, James S.; Whitmore, Kathryn F.
Situated at the intersection of research on Holocaust education and embodied literacies this study examines how an arts-based instructional approach engaged middle school learners in developing empathetic perspectives on the Anne Frank narrative. We addressed the research question: What can adolescents who are using their bodies to gain empathy…
Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based, ANN and Multiple Regression Methods
NASA Astrophysics Data System (ADS)
Saeidi, Omid; Torabi, Seyed Rahman; Ataei, Mohammad
Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.
Bridge-Builders and Storytellers: Anne M. Dunn, Ojibwe Storyteller, Shares in the Revival of Teaching and Learning Stories.
ERIC Educational Resources Information Center
Merritt, Judy
Based on her belief that all of our lives are stories that are pieces to a puzzle forming the truth behind the sacredness of life, Anne Dunn--Ojibwe storyteller and author--seeks to build bridges between cultures, between generations, and between oral and written storytelling. Includes a review of her book "When Beaver Was Very Great." (SV)
A computation ANN model for quantifying the global solar radiation: A case study of Al-Aqabah-Jordan
NASA Astrophysics Data System (ADS)
Abolgasem, I. M.; Alghoul, M. A.; Ruslan, M. H.; Chan, H. Y.; Khrit, N. G.; Sopian, K.
In this paper, a computation model is developed to predict the global solar radiation (GSR) in Aqaba city based on the data recorded with association of Artificial Neural Networks (ANN). The data used in this work are global solar radiation (GSR), sunshine duration, maximum & minimum air temperature and relative humidity. These data are available from Jordanian meteorological station over a period of two years. The quality of GSR forecasting is compared by using different Learning Algorithms. The decision of changing the ANN architecture is essentially based on the predicted results to obtain the best ANN model for monthly and seasonal GSR. Different configurations patterns were tested using available observed data. It was found that the model using mainly sunshine duration and air temperature as inputs gives accurate results. The ANN model efficiency and the mean square error values show that the prediction model is accurate. It is found that the effect of the three learning algorithms on the accuracy of the prediction model at the training and testing stages for each time scale is mostly within the same accuracy range.
"Anne of Green Gables": A One-Act Musical Based on Lucy Maud Montgomery's Novel. Cue Sheet for Teachers.
ERIC Educational Resources Information Center
Flynn, Rosalind
This performance guide is designed for teachers to use with students before and after a performance of the one-act musical based on Lucy Maud Montgomery's novel, "Anne of Green Gables," with music by Richard DeRosa and book and lyrics by Greg Gunning. The guide is designed to help teachers foster students' appreciation of theatre, dance, and…
Quantifying the impact of conservation practices at the Choptank watershed in Maryland using AnnAGNPS Model
Technology Transfer Automated Retrieval System (TEKTRAN)
This study is being conducted at the Choptank watershed under the USDA-CEAP program with the objective of quantifying the environmental benefits of conservation practices such as cover crops using AnnAGNPS (Annualized Agricultural Non-Point Source) model. Choptank is nearly 800 square miles watersh...
Boundary Crosser: Anne Whitelaw and Her Leadership Role in Girls' Secondary Schooling in England, New Zealand and East Africa
ERIC Educational Resources Information Center
Matthews, Kay Morris
This paper highlights the intersections of history, gender and educational administration through a case study of an influential woman educator, Anne Whitelaw. It draws upon manuscripts, school archives, school histories, official files and periodicals from "both sides" of the world. Although known in New Zealand as the first Headmistress of…
Ann Hutchinson (as subject), Dr. Joan Vernikos (R), Dee O'Hara (L), J. Evans and E. Lowe pose for
NASA Technical Reports Server (NTRS)
Ann Hutchinson (as subject), Dr. Joan Vernikos (R), Dee O'Hara (L), J. Evans and E. Lowe pose for pictures in the NASA Magazine aritcle 'How it Feels to be a Human Test Subject' as they prepare for a bed rest study to simulate the efects of microgravity on the human body.
78 FR 10172 - Lisa Anne Cornell and G. Ware Cornell, Jr. v. Princess Cruise Lines, Ltd. (Corp), Carnival PLC...
Federal Register 2010, 2011, 2012, 2013, 2014
... Lisa Anne Cornell and G. Ware Cornell, Jr. v. Princess Cruise Lines, Ltd. (Corp), Carnival PLC, and..., Jr., hereinafter ``Complainants,'' against Princess Cruise Lines, Ltd (Corp), Carnival plc, and... common carrier for hire of passengers from ports in the United States;'' Respondent Carnival plc ``is...
Development of experimental design approach and ANN-based models for determination of Cr(VI) ions uptake rate from aqueous solution onto the solid biodiesel waste residue.
Shanmugaprakash, M; Sivakumar, V
In the present work, the evaluation capacities of two optimization methodologies such as RSM and ANN were employed and compared for predication of Cr(VI) uptake rate using defatted pongamia oil cake (DPOC) in both batch and column mode. The influence of operating parameters was investigated through a central composite design (CCD) of RSM using Design Expert 22.214.171.124 software. The same data was fed as input in ANN to obtain a trained the multilayer feed-forward networks back-propagation algorithm using MATLAB. The performance of the developed ANN models were compared with RSM mathematical models for Cr(VI) uptake rate in terms of the coefficient of determination (R(2)), root mean square error (RMSE) and absolute average deviation (AAD). The estimated values confirm that ANN predominates RSM representing the superiority of a trained ANN models over RSM models in order to capture the non-linear behavior of the given system. PMID:24080294
Using an Artificial Neural Networks (ANNs) Model for Prediction of Intensive Care Unit (ICU) Outcome and Length of Stay at Hospital in Traumatic Patients
Gholipour, Changiz; Rahim, Fakher; Fakhree, Abolghasem
Introduction Currently applications of artificial neural network (ANN) models in outcome predicting of patients have made considerable strides in clinical medicine. This project aims to use a neural network for predicting survival and length of stay of patients in the ward and the intensive care unit (ICU) of trauma patients and to obtain predictive power of the current method. Materials and Methods We used Neuro-Solution software (NS), a leading-edge neural network software for data mining to create highly accurate and predictive models using advanced preprocessing techniques, intelligent automated neural network topology through cutting-edge distributed computing. This ANN model was used based on back-propagation, feed forward, and fed by Trauma and injury severity score (TRISS) components, biochemical findings, risk factors and outcome of 95 patients. In the next step a trained ANN was used to predict outcome, ICU and ward length of stay for 30 test group patients by processing primary data. Results The sensitivity and specificity of an ANN for predicting the outcome of traumatic patients in this study calculated 75% and 96.26%, respectively. 93.33% of outcome predictions obtained by ANN were correct. In 3.33% of predictions, results of ANN were optimistic and 3.33% of cases predicted ANN results were worse than the actual outcome of patients. Neither difference in average length of stay in the ward and ICU with predicted ANN results, were statistically significant. Correlation coefficient of two variables of ANN prediction and actual length of stay in hospital was equal to 0.643. Conclusion Using ANN model based on clinical and biochemical variables in patients with moderate to severe traumatic injury, resulted in satisfactory outcome prediction when applied to a test set. PMID:26023581
ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.
Singh, Nandita; Chakrapani, G J
The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, the source of Bhagirathi River in the Himalaya. Discharge, rainfall and temperature have been considered as the main controlling factors of variations in sediment concentration in the dynamic glacial environment of Gangotri. Fourteen feed forward neural networks with error back propagation algorithm have been created, trained and tested for prediction of sediment concentration. Seven models (T1-T7) have been trained and tested in the non-updating mode whereas remaining seven models (T1a-T7a) have been trained in the updating mode. The non-updating mode refers to the scenario where antecedent time (previous time step) values are not used as input to the model. In case of the updating mode, antecedent time values are used as network inputs. The inputs applied in the models are either the variables mentioned above as individual factors (single input networks) or a combination of them (multi-input networks). The suitability of employing antecedent time-step values as network inputs has hence been checked by comparative analysis of model performance in the two modes. The simple feed forward network has been improvised with a series parallel non-linear autoregressive with exogenous input (NARX) architecture wherein true values of sediment concentration have been fed as input during training. In the glacial scenario of Gangotri, maximum sediment movement takes place during the melt period (May-October). Hence, daily data of discharge, rainfall, temperature and sediment concentration for five consecutive melt periods (May-October, 2000-2004) have been used for modelling. High Coefficient of determination values [0.77-0.88] have been obtained between observed and ANN-predicted values of sediment concentration. The study has brought out relationships between variables that are not reflected in normal statistical analysis. A
Long-Term Precipitation Forecasting Using Large Scale Climate Indices: An ANN Approach
NASA Astrophysics Data System (ADS)
Jaw, T. C.; Hsu, K.; Sorooshian, S.
Seasonal to interannual forecasting for precipitation and streamflow is needed for water resources planning. Many studies have indicated that statistical-based approaches show better skills than numerical weather models do for long-term climate forecasting. In this study an ANN model, named Self-Organizing Linear Output map (SOLO), is applied to predict long-term, watershed-scale mean areal precipitation (MAP). Several climate indices (e.g. ENSO, Nino3.4, NAO, PDO, etc.) representing large-scale climate anomalies relevant to long-term precipitation are used as the input variables of the SOLO. The SOLO classifies the selected multiple climate indices into suitable categories and maps the input variables into the MAP forecasts by the multivariate linear regressions. Two watersheds located at Northern and Southern California (the Feather River and the Santa Ana River Watershed respectively) are evaluated by the SOLO. During the test period from 1951 to 2008, the hindcast results show the SOLO forecast has more substantial skills over wet seasons than over dry seasons, while the climatology is viewed as the comparing benchmark. Moreover, the varying degrees of improvement due to the different climate index sets and forecast seasons reflect the impact of the selected indices on the regional precipitation variation.
An ANN Approach to Classification of Galaxy Spectra for the 2DF Galaxy Redshift Survey
NASA Astrophysics Data System (ADS)
Folkes, S. R.; Lahav, O.; Maddox, S. J.
We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey, and with these simulations we investigate the technique of Principal Component Analysis when applied specifically to spectra of low S/N. We relate the objective principal components to features in the spectra and use a small number of components to successfully reconstruct the underlying signal from the low quality spectra. Using the principal components as input, we train an Artificial Neural Network (ANN) to classify the noisy simulated spectra into morphological classes, revealing the success of the classification against the observed bJ magnitude of the source, which we compare with alternative methods of classification. We find that more than 90% of our sample of normal galaxies are correctly classified into one of five broad morphological classes for simulations at bJ = 19.7. We also show the application of these methods to spectra from other sources.
The Anne Frank Haven: A case of an alternative educational program in an integrative Kibbutz setting
NASA Astrophysics Data System (ADS)
Ben-Peretz, Miriam; Giladi, Moshe; Dror, Yuval
The essential features of the programme of the Anne Frank Haven are the complete integration of children from low SES and different cultural backgrounds with Kibbutz children; a holistic approach to education; and the involvement of the whole community in an "open" residential school. After 33 years, it is argued that the experiment has proved successful in absorbing city-born youth in the Kibbutz, enabling at-risk populations to reach significant academic achievements, and ensuring their continued participation in the dominant culture. The basic integration model consists of "layers" of concentric circles, in dynamic interaction. The innermost circle is the class, the learning community. The Kibbutz community and the foster parents form a supportive, enveloping circle, which enables students to become part of the outer community and to intervene in it. A kind of meta-environment, the inter-Kibbutz partnership and the Israeli educational system, influence the program through decision making and guidance. Some of the principles of the Haven — integration, community involvement, a year's induction for all new students, and open residential settings — could be useful for cultures and societies outside the Kibbutz. The real "secret" of success of an alternative educational program is the dedicated, motivated and highly trained staff.
Spatio-temporal analysis of soil erosion risk and runoff using AnnAGNPS
NASA Astrophysics Data System (ADS)
Yeshaneh, Eleni; Wagner, Wolfgang; Blöschl, Günter
Soil erosion is one form of land degradation in Ethiopia deteriorating the fertility and productivity of the land. This fact indicates the need to delineate high erosion risk areas for appropriate soil and conservation measures. Land use/cover change is one of the important factors in soil erosion. This study attempts test and implement AnnAGNPS model to estimate the spatio-temporal patterns of soil erosion and runoff associated with land use changes in the past 50 years in the 9900 ha upstream part of the Koga catchment. High erosion risk areas will then be delineated for simulation of the appropriate soil and water conservation measures that would reduce the soil loss. The study is based on two years high temporal resolution data on discharge, sediment, and rain fall accompanied by historical land use/cover data generated from satellite imagery. In addition, it uses several documented physical parameters of the study area. The Koga catchment is one of the agriculture dominated typical catchments in the North Western Ethiopian highlands with high population density that lead to increased pressure on natural resources.
ANN modeling of water consumption in the lead-acid batteries
NASA Astrophysics Data System (ADS)
Karimi, Mohammad Ali; Karami, Hassan; Mahdipour, Maryam
Due to importance of the quantity of water loss in the life cycle of lead-acid batteries, water consumption tests were performed on 72 lead-acid batteries with low antimony grid alloy at different charge voltages and temperatures. Weight loss of batteries was measured during a period of 10 days. The behavior of batteries in different charge voltages and temperatures were modeled by artificial neural networks (ANNs) using MATLAB 7 media. Four temperatures were used in the training set, out of which three were used in prediction set and one in validation set. The network was trained by training and prediction data sets, and then was used for predicting water consumption in all three temperatures of prediction set. Finally, the network obtained was verified while being used in predicting water loss in defined temperatures of validation set. To achieve a better evaluation of the model ability, three models with different validation temperatures were used (model 1 = 50 °C, model 2 = 60 °C and model 3 = 70 °C). There was a good agreement between predicted and experimental results at prediction and validation sets for all the models. Mean prediction errors in modeling charge voltage-temperature-time behavior in the water consumption quantity for models 1-3 were below 0.99%, 0.03%, and 0.76%, respectively. The model can be simply used by inexpert operators working in lead-acid battery industry.
Comparing ANNs, EAs, and Trees: a basic machine-learning approach to predictive environmental models.
NASA Astrophysics Data System (ADS)
Williams, J.; Poff, N.
Machine learning techniques for ecological applications or "eco-informatics" are becoming increasingly useful and accessible for ecologists. We evaluated the predictive ability of three commercially available (i.e. user-friendly) software packages for artificial neural networks (ANNs), evolutionary algorithms (EAs), and classification/regression trees (Trees). We analyzed fish and habitat data for streams in the mid-Atlantic region of the U.S., which was collected by the U.S. Environmental Protection Agency (EPA). The data includes over 200 environmental descriptors summarizing watershed, stream, and water chemistry characteristics in addition to derived fish community metrics (i.e. richness, IBI scores, % exotics). In our analysis we predicted individual species presence/absence and fish community metrics as a function of these local and regional scale habitat variables. Predictive ability is evaluated with independent validation data. These approaches could prove especially useful for conservation or management applications where ecologists seek to utilize the most comprehensive data to make predictions at various scales. By employing "user-friendly" software we hope to show that ecologists, without extensive knowledge of computational science, can benefit from these techniques by extracting more information about complex ecosystems. Relative strengths and weaknesses of these three approaches are compared and recommendations for their use in conservation applications are presented.
[Simulation of nitrogen and phosphorus loss in Siling Reservoir watershed with AnnAGNPS].
Bian, Jin-yun; Wang, Fei-er; Yang, Jia; Yu, Jie; Lou, Li-ping; Yu, Dan-ping
By using annual agricultural non-point source model (AnnAGNPS), this study simulated the export loading of nitrogen and phosphorus in Siling Reservoir watershed in Tiaoxi Basin, and integrated with the simulation results, the spatial distribution characteristics of non-point source pollution in the watershed was analyzed. The result showed that the export loading of nitrogen and phosphorus had similar characteristics: in the study area, the export loading of nutrients were higher in southern and western regions and lower in northern and eastern regions. Forest land mainly made up of bamboo was the main export source of nitrogen and phosphorus loading with the contribution above 90% of nutrient load of whole watershed. Three fertilization practices such as no fertilizer (CK), site-specific nutrient management (SSNM) and farmers' fertilizaction practice (FFP) were used in the scenario analysis. The scenario analysis showed that to a certain degree, SSNM could reduce the nitrogen and phosphorus loss. Comparing with FFP, the reduction of SSNM in dissolved nitrogen (DN), particle nitrogen (PN), dissolved phosphorus (DP) and particle phosphorus (PP) was 8.17%, 4.33%, 9.08% and 1.02%, respectively. PMID:23213887
ANN implementation of stereo vision using a multi-layer feedback architecture
Mousavi, M.S.; Schalkoff, R.J.
An Artificial Neural Network (ANN), consisting of three interacting neural modules, is developed for stereo vision. The first module locates sharp intensity changes in each of the images. The edge detection process is basically a bottom-up, one-to-one input-output mapping process with a network structure which is time-invariant. In the second module, a multilayered connectionist network is used to extract the features or primitives for disparity analysis (matching). A similarity measure is defined and computed for each pair of primitive matches and is passed to the third module. The third module solves the difficult correspondence problem by mapping it into a constraint satisfaction problem. Intra- and inter-scanline constraints are used in order to restrict possible feature matches. The inter-scanline constraints are implemented via interconnections of a three-dimensional neural network. The overall process is iterative. At the end of each network iteration, the output of the third constraint satisfaction module feeds back updated information on matching pairs as well as their corresponding location in the left and right images to the input of the second module. This iterative process continues until the output of the third module converges to an stable state. Once the matching process is completed, the disparity can be calculated, and camera calibration parameters can be used to find the three-dimensional location of object points. Results using this computational architecture are shown. 26 refs.
Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN).
Delnavaz, M; Ayati, B; Ganjidoust, H
In this study, the results of 1-year efficiency forecasting using artificial neural networks (ANN) models of a moving bed biofilm reactor (MBBR) for a toxic and hard biodegradable aniline removal were investigated. The reactor was operated in an aerobic batch and continuous condition with 50% by volume which was filled with light expanded clay aggregate (LECA) as carrier. Efficiency evaluation of the reactors was obtained at different retention time (RT) of 8, 24, 48 and 72 h with an influent COD from 100 to 4000 mg/L. Exploratory data analysis was used to detect relationships between the data and dependent evaluated one. The appropriate architecture of the neural network models was determined using several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting MBBR performance for treating aromatic amine compounds. PMID:20399558
Prediction of fatigue crack propagation rate on the interface of wood-FRP using the artificial neural network (ANN)
NASA Astrophysics Data System (ADS)
Zhang, Liang; Jia, Junhui; Liu, Yongjun
Crack propagation rate of the interface of fiber reinforced polymer (FRP) bonded to red maple wood, is analyzed and predicted using an artificial neural network (ANN) method. The performance of Multilayer Perceptron (MLP) and Modular Neural Network (MNN) is compared to obtain an optimal ANN model to predict the crack propagation rate. The effect of various parameters of the MNN and MLP models are investigated. The number of input vectors of MLP and MNN models is studied to see if this will affect the training and predicting performance by the scatter of input vectors. At last, a new method called sensitivity analysis is adopted to explore the influenced proportion of the input vectors and the effect of load ratio, frequency, et al., on the crack propagation rate.
William Henry, Duke of Gloucester (1689-1700), son of Queen Anne (1665-1714), could have ruled Great Britain.
Holmes, G E F; Holmes, F F
Hope for continuation of the Stuart dynasty in Britain ended with the death, from pneumonia in 1700, of the 11-year-old son of Princess Anne and Prince George, William Henry Duke of Gloucester. Considered by some to have been physically and mentally unfit to reign, careful examination of primary source materials shows him to have been a bright and interesting boy with mild hydrocephalus. Had he lived, he could have ruled. PMID:18463064
Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose
Zou, Hui-Qin; Li, Shuo; Huang, Ying-Hua; Liu, Yong; Bauer, Rudolf; Peng, Lian; Tao, Ou; Yan, Su-Rong; Yan, Yong-Hong
Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area. Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers' safety and efficacy. In recent decades, electronic nose (E-nose) has been studied as an alternative approach. In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN) classification model. Feature selection algorithms, including principal component analysis (PCA) and BestFirst + CfsSubsetEval (BC), were applied in the improvement of RBF-ANN models. Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100%) remained the same as in the original model with higher-dimension data. It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants. This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model. PMID:25214873
AE index forecast at different time scales through an ANN algorithm based on L1 IMF and plasma measurements
NASA Astrophysics Data System (ADS)
Pallocchia, G.; Amata, E.; Consolini, G.; Marcucci, M. F.; Bertello, I.
The AE index is known to have two main components, one directly driven by the solar wind and the other related to the magnetotail unloading process. As regards the role played by the IMF and solar wind parameters, recently several authors used artificial neural networks (ANN) to forecast AE from solar wind data. Following this track, in this paper we present a study of the AE forecast at different time scales, from 5 min to 1 h, in order to check whether the performance of the ANN prediction varies significantly as a function of the AE time resolution.The study is based on a new ANN Elman network with Bz (in GSM) and Vx as inputs, one hidden layer containing four neurons, four context units and one output neuron. We find that the forecast AE values, during disturbed AE periods, result to be always smaller than the experimental values; on the other hand, the algorithm performance improves as the time scale increases, i.e. the total standard deviation (calculated over a test data set) between the forecast and the Kyoto AE decreases as the averaging time increases. Under the hypothesis that this decrease follows an exponential law, we find that the 1 h scale normalised standard deviation is 0.975, very close to the asymptotic value of 0.95 for an infinite averaging time. We interpret our results in the sense that the unloading component of the AE variations cannot be predicted from IMF and solar wind parameters only.
Development of LC-MS determination method and back-propagation ANN pharmacokinetic model of corynoxeine in rat.
Ma, Jianshe; Cai, Jinzhang; Lin, Guanyang; Chen, Huilin; Wang, Xianqin; Wang, Xianchuan; Hu, Lufeng
Corynoxeine(CX), isolated from the extract of Uncaria rhynchophylla, is a useful and prospective compound in the prevention and treatment for vascular diseases. A simple and selective liquid chromatography mass spectrometry (LC-MS) method was developed to determine the concentration of CX in rat plasma. The chromatographic separation was achieved on a Zorbax SB-C18 (2.1 mm × 150 mm, 5 μm) column with acetonitrile-0.1% formic acid in water as mobile phase. Selective ion monitoring (SIM) mode was used for quantification using target ions m/z 383 for CX and m/z 237 for the carbamazepine (IS). After the LC-MS method was validated, it was applied to a back-propagation artificial neural network (BP-ANN) pharmacokinetic model study of CX in rats. The results showed that after intravenous administration of CX, it was mainly distributed in blood and eliminated quickly, t1/2 was less than 1h. The predicted concentrations generated by BP-ANN model had a high correlation coefficient (R>0.99) with experimental values. The developed BP-ANN pharmacokinetic model can be used to predict the concentration of CX in rats. PMID:24732215
Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements
NASA Technical Reports Server (NTRS)
Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael
A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular
Assessment of strawberry aroma through SPME/GC and ANN methods. Classification and discrimination of varieties.
Urruty, Louise; Giraudel, Jean-Luc; Lek, Sovan; Roudeillac, Philippe; Montury, Michel
To provide an efficient and running analytical tool to strawberry plant breeders who have to characterize and compare the aromatic properties of new cultivars to those already known, a HS-SPME/GC-MS analysis method has been coupled with a statistical treatment method issued from the current development of artificial neuron networks (ANN), and more specifically, the unsupervised learning systems called Kohonen self-organizing maps (SOMs). So, 70 strawberry samples harvested at CIREF from 17 known varieties have been extracted by using a DVB/Carboxen/PDMS SPME fiber according to the headspace procedure, and then chromatographed. A panel of 23 characteristic aromatic constituents has been selected according to published results relative to strawberry aroma. The complex resulting matrix, collecting the relative abundance of the 23 selected constituents for each sample, has been input into the SOM software adapted and optimized from the Kohonen approach described by one of the authors. After a period of training, the self-organized system affords a map of virtual strawberries to which real samples are compared and plotted in the best matching unit (BMU) of the map. The efficiency for discriminating the real samples according to their variety is dependent on the number of units selected to define the map. In this case, a 24-unit map allowed the complete discrimination of the 17 selected varieties. Moreover, to test the validity of this approach, two additional samples were blind-analyzed and the results were computed according to the same procedure. At the end of this treatment, both samples were plotted into the same unit as those of the same variety used for training the map. PMID:12009974
Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study
Grossi, Enzo; Buscema, Massimo P; Snowdon, David; Antuono, Piero
Background Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old. The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis Methods The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts. Results By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same. Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus
Classification of input vectors of ANN model into "regular event" and "extreme event" subsets with fuzzy c-means algorithm
NASA Astrophysics Data System (ADS)
Kentel, Elcin
Estimating future river flows is essential in water resources planning and management. Artificial neural network (ANN) models have been extensively utilized for rainfall-runoff modeling in the last decade. One of the major weaknesses of artificial neural network models is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training set. If reliable estimates can be distinguished from unreliable ones, the former can be used with greater confidence in planning and management of the water resources. A fuzzy c-means algorithm is developed to cluster the estimates of the artificial neural networks into reliable and less-reliable river flow values (Kentel, 2009). The proposed algorithm is only tested for a single case (i.e. Güvenç River, Turkey) and produced promising results. In this study, applicability of the fuzzy c-means algorithm for different catchments in Turkey is tested. Three flow gauging stations are selected at four different catchments in mid and south Turkey. First, an ANN is developed for each gauging station; then fuzzy c-means algorithm is used together with the outputs of ANN models to test the success of the clustering algorithm in identifying input vectors that are susceptible to produce unreliable estimates. Results obtained for 12 gauging stations are used to identify the drawbacks of fuzzy c-means algorithm and to suggest modifications to improve the algorithm. Key words: Future river flow estimation; Artificial Neural Network; fuzzy c-means clustering Kentel, E. (2009) "Estimation of River Flow by Artificial Neural Networks and Identification of Input Vectors Susceptible to Producing Unreliable Flow Estimates," Journal of Hydrology, 375, 481-488.
Selection Site for Artificial Recharge of Groundwater in Hard Rocks Using ANN with Special Reference to India
NASA Astrophysics Data System (ADS)
Banerjee, Pallavi
In the recent years there has been overall development in the field of agriculture and industry in the Asian countries, particularly in India. The growth in urbanization has also been increasing. All these have lead to ever increasing demand for water. It has resulted into indiscriminate exploitation of groundwater resources, which is major source of fresh water in hard rock terrain. The hard rocks pose special problems for artificial recharge due to the limited extent of aquifer horizons, heterogeneity and low hydraulic conductivity. The fracture system may have good storativity. One such area is Kurmapalli watershed covering an area of about 108 sq km in Nalgonda district (Andhra Pradesh), India. It is drought prone area. This basin is characterized by poor land soil, scarce vegetation, erratic rainfall, heavy runoff and lack of soil moisture for most part of the year. Recurring drought coupled with increase in groundwater exploitation results in decline in the groundwater levels. Artificial Neural Network (ANN) method is a paradigm shift towards research methodology in the field of hydrology. An approach based on Back Propagation Neural Network (BPNN) algorithm is developed to estimate the best location for artificial recharge. The proposed technique is applied to climatic and hydrological data of wells gathered from the different locations of the study area. Feed Forward BPNN is used to train the predefined inputs and outputs. After successful completion of training with appropriate data, different ANN models are developed to estimate the proper site for artificial recharge. High degree of predictive accuracy of the Feed-Forward Network based predictive model proves ANN techniques is a potential tool for hydrological studies.
Using ANN to predict E. coli accumulation in coves based on interaction amongst various physical, chemical and biological factors
NASA Astrophysics Data System (ADS)
Dwivedi, D.; Mohanty, B. P.; Lesikar, B. J.
The accumulation of Escherichia Coli (E. coli) in canals, coves and streams is the result of a number of interacting processes operating at multiple spatial and temporal scales. Fate and transport of E. coli in surface water systems is governed by different physical, chemical, and biological processes. Various models developed to quantify each of these processes occurring at different scales are not so far pooled into a single predictive model. At present, very little is known about the fate and transport of E. coli in the environment. We hypothesize that E. coli population heterogeneity in canals and coves is affected by physical factors (average stream width and/ depth, secchi depth, flow and flow severity, day since precipitation, aquatic vegetation, solar radiation, dissolved and total suspended solids etc.); chemical factors (basic water quality, nutrients, organic compounds, pH, and toxicity etc.); and biological factors (type of bacterial strain, predation, and antagonism etc.). The specific objectives of this study are to: (1) examine the interactions between E. coli and various coupled physical, chemical and biological factors; (2) examine the interactions between E. coli and toxic organic pollutants and other pathogens (viruses); and (3) evaluate qualitatively the removal efficiency of E. coli. We suggest that artificial neural networks (ANN) may be used to provide a possible solution to this problem. To demonstrate the application of the approach, we develop an ANN representing E. coli accumulation in two polluted sites at Lake Granbury in the upper part of the Brazos River in North Central Texas. The graphical structure of ANN explicitly represents cause- and-effect relationship between system variables. Each of these relationships can then be quantified independently using an approach suitable for the type and scale of information available. Preliminary results revealed that E. coli concentrations in canals show seasonal variations regardless of change
The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data
Behrang, M.A.; Assareh, E.; Ghanbarzadeh, A.; Noghrehabadi, A.R.
The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32 16'N, 48 25'E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I)Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output. (II)Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III)Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV)Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V)Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI)Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)). (author)
AnnAGNPS model as a potential tool for seeking adequate agriculture land management in Navarre (Spain)
NASA Astrophysics Data System (ADS)
Chahor, Y.; Giménez, R.; Casalí, J.
Nowadays agricultural activities face two important challenges. They must be efficient from an economic point of view but with low environment impacts (soil erosion risk, nutrient/pesticide contamination, greenhouse gases emissions, etc.). In this context, hydrological and erosion models appear as remarkable tools when looking for the best management practices. AnnAGNPS (Annualized Agricultural Non Point Source Pollution) is a continuous simulation watershed-scale model that estimates yield and transit of surface water, sediment, nutrients, and pesticides through a watershed. This model has been successfully evaluated -in terms of annual runoff and sediment yield- in a small (around 200 ha) agricultural watershed located in central eastern part of Navarre (Spain), named Latxaga. The watershed is under a humid Sub-Mediterranean climate. It is cultivated almost entirely with winter cereals (wheat and barley) following conventional soil and tillage management practices. The remaining 15% of the watershed is covered by urban and shrub areas. The aim of this work is to evaluate in Latxga watershed the effect of potential and realistic changes in land use and management on surface runoff and sediment yield by using AnnAGNPS. Six years (2003 - 2008) of daily climate data were considered in the simulation. This dataset is the same used in the model evaluation previously made. Six different scenarios regarding soil use and management were considered: i) 60% cereals25% sunflower; ii) 60% cereals, 25% rapeseed; iii) 60% cereals, 25% legumes; iv) 60% cereals, 25% sunflower + rapeseed+ legumes, in equal parts; v) cereals, and alternatively different amount of shrubs (from 20% to 100% ); vi) only cereal but under different combinations of conventional tillage and no-tillage management. Overall, no significant differences in runoff generation were observed with the exception of scenario iii (in which legume is the main alternative crops), whit a slight increase in predicted
Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique
NASA Astrophysics Data System (ADS)
Yadav, Ravindra Nath; Yadava, Vinod; Singh, G. K.
The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness ( R a). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGAII gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.
Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)
NASA Astrophysics Data System (ADS)
Wang, Sheng; Fu, Zhi-yong; Chen, Hong-song; Nie, Yun-peng; Wang, Ke-lin
Nonlinear complexity is a characteristic of hydrologic processes. Using fewer model parameters is recommended to reduce error. This study investigates, and compares, the ability of gene expression programming (GEP) and artificial neural network (ANN) techniques in modeling ET0 by using fewer meteorological parameters in the karst area of northwest Guangxi province, China. Over a 5-year period (2008-2012), meteorological data consisting of maximum and minimum air temperature, relative humidity, wind speed, and sunshine duration were collected from four weather stations: BaiSe, DuAn, HeChi, and RongAn. The ET0 calculated by the FAO-56 PM equation was used as a reference to evaluate results for GEP, ANN, and Hargreaves models. The coefficient of determination (R 2) and the root mean square error (RMSE) were used as statistical indicators. Evaluations revealed that GEP, and ANN, can be used to successfully model ET0. In most cases, when using the same input variables, ANN models were superior to GEP. We then established ET0 equations with fewer parameters under various conditions. GEP can produce simple explicit mathematical formulations which are easier to use than the ANN models.
A Characterization of Hot Flow Behaviors Involving Different Softening Mechanisms by ANN for As-Forged Ti-10V-2Fe-3Al Alloy
NASA Astrophysics Data System (ADS)
Quan, Guo-zheng; Zou, Zhen-yu; Wen, Hai-rong; Pu, Shi-ao; Lv, Wen-quan
The isothermal compressions of as-forged Ti-10V-2Fe-3Al alloy at the deformation temperature range of 948-1,123 K and the strain rates in the range of 0.001-10 s-1 with a height reduction of 60% were conducted on a Gleeble-3500 thermo-mechanical simulator. The flow behaviors show nonlinear sensitivity to strain, strain rate and temperature. Based on the experimental data, an artificial neural network (ANN) with back-propagation algorithm was developed to deal with the complex deformation behavior characteristics. In the present ANN model, strain, strain rate and temperature were taken as inputs, and flow stress as output. A comparative study on the constitutive relationships based on regression and ANN methods was conducted. According to the predicted and experimental results, the predictabilities of the two models have been evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). The R-value and the AARE-value at strain of 0.5 from the ANN model is 0.9998 and 0.572%, respectively, better than 0.9902 and 6.583% from the regression model. The predicted strain-stress curves outside of experimental conditions indicate similar characteristics with experimental curves. The results have sufficiently articulated that the well-trained ANN model with back-propagation algorithm has excellent capability to deal with the complex flow behaviors of as-forged Ti-10V-2Fe-3Al alloy.
Optimization of delignification of two Pennisetum grass species by NaOH pretreatment using Taguchi and ANN statistical approach.
Mohaptra, Sonali; Dash, Preeti Krishna; Behera, Sudhanshu Shekar; Thatoi, Hrudayanath
In the bioconversion of lignocelluloses for bioethanol, pretreatment seems to be the most important step which improves the elimination of the lignin and hemicelluloses content, exposing cellulose to further hydrolysis. The present study discusses the application of dynamic statistical techniques like the Taguchi method and artificial neural network (ANN) in the optimization of pretreatment of lignocellulosic biomasses such as Hybrid Napier grass (HNG) (Pennisetum purpureum) and Denanath grass (DG) (Pennisetum pedicellatum), using alkali sodium hydroxide. This study analysed and determined a parameter combination with a low number of experiments by using the Taguchi method in which both the substrates can be efficiently pretreated. The optimized parameters obtained from the L16 orthogonal array are soaking time (18 and 26 h), temperature (60°C and 55°C), and alkali concentration (1%) for HNG and DG, respectively. High performance liquid chromatography analysis of the optimized pretreated grass varieties confirmed the presence of glucan (47.94% and 46.50%), xylan (9.35% and 7.95%), arabinan (2.15% and 2.2%), and galactan/mannan (1.44% and 1.52%) for HNG and DG, respectively. Physicochemical characterization studies of native and alkali-pretreated grasses were carried out by scanning electron microscopy and Fourier transformation Infrared spectroscopy which revealed some morphological differences between the native and optimized pretreated samples. Model validation by ANN showed a good agreement between experimental results and the predicted responses. PMID:26584152
Design of an Experiment to Measure ann Using 3H(γ, pn)n at HIγS★
NASA Astrophysics Data System (ADS)
Friesen, F. Q. L.; Ahmed, M. W.; Crowe, B. J.; Crowell, A. S.; Cumberbatch, L. C.; Fallin, B.; Han, Z.; Howell, C. R.; Malone, R. M.; Markoff, D.; Tornow, W.; Witała, H.
We provide an update on the development of an experiment at TUNL for determining the 1S0 neutron-neutron (nn) scattering length (ann) from differential cross-section measurements of three-body photodisintegration of the triton. The experiment will be conducted using a linearly polarized gamma-ray beam at the High Intensity Gamma-ray Source (HIγS) and tritium gas contained in thin-walled cells. The main components of the planned experiment are a 230 Ci gas target system, a set of wire chambers and silicon strip detectors on each side of the beam axis, and an array of neutron detectors on each side beyond the silicon detectors. The protons emitted in the reaction are tracked in the wire chambers and their energy and position are measured in silicon strip detectors. The first iteration of the experiment will be simplified, making use of a collimator system, and silicon detectors to interrogate the main region of interest near 90° in the polar angle. Monte-Carlo simulations based on rigorous 3N calculations have been conducted to validate the sensitivity of the experimental setup to ann. This research supported in part by the DOE Office of Nuclear Physics Grant Number DE-FG02-97ER41033
Optoelectronic performance and artificial neural networks (ANNs) modeling of n-InSe/p-Si solar cell
NASA Astrophysics Data System (ADS)
Darwish, A. A. A.; Hanafy, T. A.; Attia, A. A.; Habashy, D. M.; El-Bakry, M. Y.; El-Nahass, M. M.
Nanostructure thin film of InSe deposited on p-Si single crystal to fabricate n-InSe/p-Si heterojunction. Electrical and photoelectrical have been studied by the current density-voltage (J-V). The fabricated cell exhibited rectifying characteristics. Analyzing the results of dark forward J-V shows that there are differrent conduction mechanisms. At low voltages, the current density is controlled by a Schottky emission mechanism. While at a relatively high voltage, a space charge-limited-conduction mechanism is observed with a single trap level. The cell also exhibited a photovoltaic characteristic with a power conversion efficiency of 3.42%. Moreover, artificial neural networks (ANNs) are adopted to model the J-V through the obtained functions. Different network configurations and many runs were trying to achieve good performance and finally obtained the current density, J, as a function of the junction temperature, T, and applied voltage, V. In all cases studied, we compared our obtained functions produced by the ANN technique with the corresponding experimental data and the excellent matching was so clear.
A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions
NASA Astrophysics Data System (ADS)
Yoon, Heesung; Hyun, Yunjung; Ha, Kyoochul; Lee, Kang-Kun; Kim, Gyoo-Bum
The prediction of long-term groundwater level fluctuations is necessary to effectively manage groundwater resources and to assess the effects of changes in rainfall patterns on groundwater resources. In the present study, a weighted error function approach was utilised to improve the performance of artificial neural network (ANN)- and support vector machine (SVM)-based recursive prediction models for the long-term prediction of groundwater levels in response to rainfall. The developed time series models were applied to groundwater level data from 5 groundwater-monitoring stations in South Korea. The results demonstrated that the weighted error function approach can improve the stability and accuracy of recursive prediction models, especially for ANN models. The comparison of the model performance showed that the recursive prediction performance of the SVM was superior to the performance of the ANN in this case study.
Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester
Basri, Mahiran; Rahman, Raja Noor Zaliha Raja Abd; Ebrahimpour, Afshin; Salleh, Abu Bakar; Gunawan, Erin Ryantin; Rahman, Mohd Basyaruddin Abd
Background Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes. Results The coefficient of determination (R2) and absolute average deviation (AAD) values between the actual and estimated responses were determined as 1 and 0.002844 for ANN training set, 0.994122 and 1.289405 for ANN test set, and 0.999619 and 0.0256 for RSM training set respectively. The predicted optimum condition was: reaction time 7.38 h, temperature 53.9°C, amount of enzyme 0.149 g, and substrate molar ratio 1:3.41. The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9%) and RSM (85.4%). The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively. Conclusion Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities. PMID:17760990
Forward Greedy ANN input selection in a stacked framework with Adaboost.RT - A streamflow forecasting case study exploiting radar rainfall estimates
NASA Astrophysics Data System (ADS)
Brochero, D.; Anctil, F.; Gagné, C.
In input selection (or feature selection), modellers are interested in identifying k of the d dimensions that provide the most information. In hydrology, this problem is particularly relevant when dealing with temporally and spatially distributed data such as radar rainfall estimates or meteorological ensemble forecasts. The most common approaches for input determination of artifitial neural networks (ANN) in water resources are cross-correlation, heuristics, embedding window analysis (chaos theory), and sensitivity analyses. We resorted here to Forward Greedy Selection (FGS), a sensitivity analysis, for identifying the inputs that maximize the performance of ANN forecasting. It consists of a pool of ANNs with different structures, initial weights, and training data subsets. The stacked ANN model was setup through the joint use of stop training and a special type of boosting for regression known as AdaBoost.RT. Several ANN are then used in series, each one exploiting, with incremental probability, data with relative estimation error higher than a pre-set threshold value. The global estimate is then obtained from the aggregation of the estimates of the models (here the median value). Two schemes are compared here, which differ in their input type. The first scheme looks at lagged radar rainfall estimates averaged over entire catchment (the average scenario), while the second scheme deals with the spatial variation fields of the radar rainfall estimates (the distributed scenario). Results lead to three major findings. First, stacked ANN response outperforms the best single ANN (in the same way as many others reports). Second, a positive gain in the test subset of around 20%, when compared to the average scenario, is observed in the distributed scenario. However, the most important result from the selecting process is the final structure of the inputs, for the distributed scenario clearly outlines the areas with the greatest impact on forecasting in terms of the
Dating of pollen samples from the sediment core of Lake St Anne in the East Carpathian Mountains, Romania
NASA Astrophysics Data System (ADS)
Hubay, Katalin; Katalin Magyari, Enikö; Braun, Mihály; Schabitz, Frank; Molnár, Mihály
Lake St Anne (950 m a.s.l.) is situated in the Ciomadul volcano crater, the youngest volcano in the Carpathians. Aims driving forward the studies there are twofold, one is dating the latest eruption of the Ciomadul volcano and the other is the multi-proxy palaeoenvironmental reconstruction of this region. The sediment of Lake St Anne was sampled several times already, but never reached the bottom of the lake before. During the winter of 2013 at a new core location drilling started at 600 cm water depth and finally reached the bottom of the lake sediment at approximately 2300 cm including water depth. As for all multi-proxy studies essential requirement was to build a reliable chronology. Sediments were dated by radiocarbon method. Previous radiocarbon dates were measured on plant macrofossils, charcoal, Cladocera eggs, chironomid head capsules and bulk lake sediments. Lake St Anne has volcanic origin and there is intensive upwelling of CO2it is important to study and take into consideration, whether there is any local reservoir effect at the case of samples where it could be problematic. Furthermore the late part of the sediment section (between 15,000 and 30,000 cal. yr BP) has low organic matter content (less than 2-4%) with scarcity of datable plant macrofossil material. In this review a different fraction of pollen samples with terrestrial origin was tested and studied as a novel sample type for the radiocarbon dating. Pollen samples were extracted from the lake sediment cores. This type of organic material could be an ideal candidate for radiocarbon based chronological studies as it has terrestrial source and is present in the whole core in contrast with the terrestrial macrofossils. Although the pollen remains were present in the whole core, in many cases their amount give a challenge even for the AMS technic. Samples were measured with EnvironMICADAS AMS and its gas ion source in the HEKAL laboratory (Debrecen, Hungary). We examine the reliability the
Estimation of runoff and sediment yield in the Redrock Creek watershed using AnnAGNPS and GIS
Tsou, M.-S.; Zhan, X.-Y.
Sediment has been identified as a significant threat to water quality and channel clogging that in turn may lead to river flooding. With the increasing awareness of the impairment from sediment to water bodies in a watershed, identifying the locations of the major sediment sources and reducing the sediment through management practices will be important for an effective watershed management. The annualized agricultural non-point source pollution (AnnAGNPS) model and newly developed GIS interface for it were applied in a small agricultural watershed, Redrock Creek watershed, Kansas, in this pilot study for exploring the effectiveness of using this model as a management tool. The calibrated model appropriately simulated monthly runoff and sediment yield through the practices in this study and potentially suggested the ways of sediment reduction through evaluating the changes of land use and field operation in the model for the purpose of watershed management.
Estimating sediment, nitrogen, and phosphorous loads from the Pipestem Creek watershed, North Dakota, using AnnAGNPS
NASA Astrophysics Data System (ADS)
Pease, Lyndon M.; Oduor, P.; Padmanabhan, G.
Agricultural pollution is a significant problem in North Dakota. Water quality in the Pipestem Creek watershed upstream of Pingree, North Dakota, USA, has been a major environmental concern amongst other adjacent watersheds within the region. The annualized agricultural non-point source (AnnAGNPS) model, a large-scale watershed model designed to predict sediment and nutrient loads, was used to evaluate non-point source pollution in a typical agricultural watershed. The best available data were assembled and used in the analysis. The model predicted runoff of 0.31 m 3/s, compared to a measured value of 0.46 m 3/s. A poor correlation was observed when comparing the model's predicted nitrogen, phosphorus, and sediment with the observed counterparts. The model's poor performance was most likely a result of the large size of the study area and the high variability in land use and management practices.
Modeling and prediction of surface roughness for running-in wear using Gauss-Newton algorithm and ANN
NASA Astrophysics Data System (ADS)
Hanief, M.; Wani, M. F.
In this paper, surface roughness model for running-in and steady state of the wear process is proposed. In this work monotonously decreasing trend of surface roughness during running-in was assumed. The model was developed by considering the surface roughness as an explicit function of time during running-in, keeping other system parameters (velocity, load, hardness, etc.) constant. The proposed model being non-linear, optimal values of the model parameters were evaluated by Gauss-Newton (GN) algorithm. The experimental results adopted from the literature, for steel and Cu-Zn alloy specimens, were used for validation of the model. Artificial neural network (ANN) based model was also developed and was compared with the proposed model on the basis of statistical methods (coefficient of determination (R2), mean square error (MSE) and mean absolute percentage error (MAPE)).
Predicting spatial kelp abundance in shallow coastal waters using the acoustic ground discrimination system RoxAnn
NASA Astrophysics Data System (ADS)
Mielck, F.; Bartsch, I.; Hass, H. C.; Wölfl, A.-C.; Bürk, D.; Betzler, C.
Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not
The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP.
Mustafa, Yasmen A; Jaid, Ghydaa M; Alwared, Abeer I; Ebrahim, Mothana
The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H2O2/Fe(+2)) process. The reaction is influenced by the input concentration of hydrogen peroxide H2O2, amount of the iron catalyst Fe(+2), pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H2O2 = 400 mg/L, Fe(+2) = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72%. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R (2) = 0.9949). The sensitivity analysis showed that all studied variables (H2O2, Fe(+2), pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6%. PMID:24595749
Women's translations of scientific texts in the 18th century: a case study of Marie-Anne Lavoisier.
Kawashima, Keiko
In the 18th century, many outstanding translations of scientific texts were done by women. These women were important mediators of science. However, I would like to raise the issue that the 'selection,' which is the process by which intellectual women chose to conduct translation works, and those 'selections' made by male translators, would not be made at the same level. For example, Émilie du Châtelet (1706-1749), the only French translator of Newton's "Principia," admitted her role as participating in important work, but, still, she was not perfectly satisfied with the position. For du Châtelet, the role as a translator was only an option under the current conditions that a female was denied the right to be a creator by society. In the case of Marie-Anne Lavoisier (1743-1794), like du Châtelet, we find an acute feeling in her mind that translation was not the work of creators. Because of her respect toward creative geniuses and her knowledge about the practical situation and concrete results of scientific studies, the translation works done by Marie-Anne Lavoisier were excellent. At the same time, the source of this excellence appears paradoxical at a glance: this excellence of translation was related closely with her low self-estimation in the field of science. Hence, we should not forget the gender problem that is behind such translations of scientific works done by women in that era. Such a possibility was a ray of light that was grasped by females, the sign of a gender that was eliminated from the center of scientific study due to social systems and norms and one of the few valuable opportunities to let people know of her own existence in the field of science. PMID:22606747
[Discriminating and quantifying potential adulteration in virgin olive oil by near infrared spectroscopy with BP-ANN and PLS].
Weng, Xin-Xin; Lu, Feng; Wang, Chuan-Xian; Qi, Yun-Peng
In the present paper, the use of near infrared spectroscopy (NIR) as a rapid and cost-effective classification and quantification techniques for the authentication of virgin olive oil were preliminarily investigated. NIR spectra in the range of 12 000 - 3 700 cm(-1) were recorded for pure virgin olive oil and virgin olive oil samples adulterated with varying concentrations of sesame oil, soybean oil and sunflower oil (5%-50% adulterations in the weight of virgin olive oil). The spectral range from 12 000 to 5 390 cm(-1) was adopted to set up an analysis model. In order to handle these data efficiently, after pretreatment, firstly, principal component analysis (PCA) was used to compress thousands of spectral data into several variables and to describe the body of the spectra, and the analysis suggested that the cumulate reliabilities of the first six components was more than 99.999%. Then ANN-BP was chosen as further research method. The six components were secondly applied as ANN-BP inputs. The experiment took a total of 100 samples as original model examples and left 52 samples as unknown samples to predict. Finally, the results showed that the 52 test samples were discriminated accurately. And the calibration models of quantitative analysis were built using partial-least-square (PLS). The R values for PLS model are 98.77, 99.37 and 99.44 for sesame oil, soybean oil and sunflower oil respectively, the root mean standard errors of cross validation (RMSECV) are 1.3, 1.1 and 1.04 respectively. Overall, the near infrared spectroscopic method in the present paper played a good role in the discrimination and quantification, and offered a new approach to the rapid discrimination of pure and adulterated virgin olive oil. PMID:20210151
Proceedings of Workshop on Priority Great Lakes Environmental Research Initiatives (Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan, October 10-11, 1974).
ERIC Educational Resources Information Center
Pinsak, Arthur P., Ed.
This publication contains the proceedings of a workshop held in Ann Arbor, Michigan to identify the priority Great Lakes environmental research initiatives. The five major objectives of the workshop include the determination of research initiatives, opportunities for university research communities to discuss and recommend future research…
Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain)
Technology Transfer Automated Retrieval System (TEKTRAN)
AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model) is a system of computer models developed to predict non-point source pollutant loadings within agricultural watersheds. It contains a daily time step distributed parameter continuous simulation surface runoff model designed to assis...
Comparison of artificial neural network (ANN) and response surface methodology (RSM) in optimization of the immobilization conditions for lipase from Candida rugosa on Amberjet(®) 4200-Cl.
Fatiha, Benamia; Sameh, Bouchagra; Youcef, Saihi; Zeineddine, Djeghaba; Nacer, Rebbani
Candida rugosa lipase (CRL) is an important industrial enzyme that is successfully utilized in a variety of hydrolysis and esterification reactions. This work describes the optimization of immobilization conditions (enzyme/support ratio, immobilization temperature, and buffer concentration) of CRL on the anionic resin Amberjet® 4200-Cl, using enantioselectivity (E) as the reference parameter. The model reaction used for this purpose is the acylation of (R,S)-1-phenylethanol. Optimal conditions for immobilization have been investigated through a response surface methodology (RSM) and artificial neural network (ANN). The coefficient of determination (R(2)) and the root mean square error (RMSE) values between the calculated and estimated responses were respectively equal to 0.99 and 0.06 for the ANN training set, 0.97 and 0.2 for the ANN testing set, and 0.94 and 0.4 for the RSM training set. Both models provided good quality predictions, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities. PMID:23215653
Artificial neural network (ANN) modeling of adsorption of methylene blue by NaOH-modified rice husk in a fixed-bed column system.
Chowdhury, Shamik; Saha, Papita Das
In this study, rice husk was modified with NaOH and used as adsorbent for dynamic adsorption of methylene blue (MB) from aqueous solutions. Continuous removal of MB from aqueous solutions was studied in a laboratory scale fixed-bed column packed with NaOH-modified rice husk (NMRH). Effect of different flow rates and bed heights on the column breakthrough performance was investigated. In order to determine the most suitable model for describing the adsorption kinetics of MB in the fixed-bed column system, the bed depth service time (BDST) model as well as the Thomas model was fitted to the experimental data. An artificial neural network (ANN)-based model was also developed for describing the dynamic dye adsorption process. An extensive error analysis was carried out between experimental data and data predicted by the models by using the following error functions: correlation coefficient (R(2)), average relative error, sum of the absolute error and Chi-square statistic test (χ(2)). Results show that with increasing bed height and decreasing flow rate, the breakthrough time was delayed. All the error functions yielded minimum values for the ANN model than the traditional models (BDST and Thomas), suggesting that the ANN model is the most suitable model to describe the fixed-bed adsorption of MB by NMRH. It is also more rational and reliable to interpret dynamic dye adsorption data through a process of ANN architecture. PMID:22562342
Application of calibrated AnnAGNPS model to assess stream flow, pesticide loading, and conservation tillage effects in the Cedar Creek Watershed
Technology Transfer Automated Retrieval System (TEKTRAN)
AnnAGNPS was applied to the Cedar Creek Watershed and its Matson Ditch sub-catchment in the St. Joseph River Watershed to assess its effectiveness at predicting monthly hydrology and pesticide loading at different scales. The model was also utilized to assess the effects that conservation tillage ha...
Down-regulating annexin gene GhAnn2 inhibits cotton fiber elongation and decreases Ca2+ influx at the cell apex.
Tang, Wenxin; He, Yonghui; Tu, Lili; Wang, Maojun; Li, Yang; Ruan, Yong-Ling; Zhang, Xianlong
Cotton fiber is a single cell that differentiates from the ovule epidermis and undergoes synchronous elongation with high secretion and growth rate. Apart from economic importance, cotton fiber provides an excellent single-celled model for studying mechanisms of cell-growth. Annexins are Ca(2+)- and phospholipid-binding proteins that have been reported to be localized in multiple cellular compartments and involved in control of vesicle secretions. Although several annexins have been found to be highly expressed in elongating cotton fibers, their functional roles in fiber development remain unknown. Here, 14 annexin family members were identified from the fully sequenced diploid G. raimondii (D5 genome), half of which were expressed in fibers of the cultivated tetraploid species G. hirsutum (cv. YZ1). Among them, GhAnn2 from the D genome of the tetraploid species displayed high expression level in elongating fiber. The expression of GhAnn2 could be induced by some phytohormones that play important roles in fiber elongation, such as IAA and GA3. RNAi-mediated down-regulation of GhAnn2 inhibited fiber elongation and secondary cell wall synthesis, resulting in shorter and thinner mature fibers in the transgenic plants. Measurement with non-invasive scanning ion-selective electrode revealed that the rate of Ca(2+) influx from extracellular to intracellular was decreased at the fiber cell apex of GhAnn2 silencing lines, in comparison to that in the wild type. These results indicate that GhAnn2 may regulate fiber development through modulating Ca(2+) fluxes and signaling. PMID:24890373
Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed
NASA Astrophysics Data System (ADS)
Chahor, Youssef; Casalí, Javier; Goñi, Mikel; Giménez, Rafael; Campo, Miguel A.; Del Valle de Lersundi, Jokin
Four experimental watersheds with contrasting land uses located in Navarre (Spain), and maintained by the local government have been monitored and studied since 1996 (La Tejería and Latxaga) and 2001 (Oskotz principal and Oskotz woodland). As a result, a detailed description and a general characterization of the hydrological and erosion behaviour of these watersheds were published recently by the same authors of this current research. This information is of great utility for evaluation of modelling tools; however, we have been done few efforts until now in this research line. The Annualized Agricultural Non Point Source Pollution Model (AnnAGNPS) is a well known and widely used model developed by the USDA-ARS and the USDA- NRCS, to assess the hydrologic and water quality responses of watersheds. More precisely, it is a distributed parameter, physically based, continuous simulation, and daily time step model. The purpose of this study is then to evaluate the AnnAGNPS model capability to simulate runoff and sediment yield with data sets from one of our agricultural watershed: Latxaga. Latxaga watershed covers an area of 207 ha and is located in the central eastern part of Navarre. Its climate is humid Sub-Mediterranean, with an average annual precipitation of 835 mm, and an average annual temperature of 12.8 °C. Geologically, the area is underlined by clay marls and sandstones. Prevailing soils are alkaline with a fine texture top-layer. Regarding land use, 80-90% of the total area is cultivated with winter grain crops. The model was calibrated using two years (2003 and 2004) of continuous 10-min/daily data, whereas another whole year (2005) was used for model validation. The calibration process was carried out by modifying Curve Number (CN) values obtained by standard procedure. CN represents a key factor in obtaining accurate prediction of runoff and sediment yield; besides it is the most important input parameter to which the runoff is sensitive. The target
BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass
Pi, Erxu; Mantri, Nitin; Ngai, Sai Ming; Lu, Hongfei; Du, Liqun
Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, 'Savannah' and 'Princess VII'). Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low) temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments). Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R2 values of germination prediction function could be significantly improved from about 0.6940–0.8177 (DQEM approach) to 0.9439–0.9813 (BP-ANN-QE). These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale of
Carol Anne Bond v the United States of America: how a woman scorned threatened the Chemical Weapons Convention.
Muldoon, Anna; Kornblet, Sarah; Katz, Rebecca
The case of Carol Anne Bond v the United States of America stemmed from a domestic dispute when Ms. Bond attempted to retaliate against her best friend by attacking her with chemical agents. What has emerged is a much greater issue--a test of standing on whether a private citizen can challenge the Tenth Amendment. Instead of being prosecuted in state court for assault, Ms. Bond was charged and tried in district court under a federal criminal statute passed as part of implementation of the Chemical Weapons Convention (CWC). Ms. Bond's argument rests on the claim that the statute exceeded the federal government's enumerated powers in criminalizing her behavior and violated the Constitution, while the government contends legislation implementing treaty obligations is well within its purview. This question remains unanswered because there is dispute among the lower courts as to whether Ms. Bond, as a citizen, even has the right to challenge an amendment guaranteeing states rights when a state is not a party to the action. The Supreme Court heard the case on February 22, 2011, and, if it decides to grant Ms. Bond standing to challenge her conviction, the case will be returned to the lower courts. Should the court decide Ms. Bond has the standing to challenge her conviction and further questions the constitutionality of the law, it would be a significant blow to implementation of the CWC in the U.S. and the effort of the federal government to ensure we are meeting our international obligations. PMID:21819227
The collaboration of Antoine and Marie-Anne Lavoisier and the first measurements of human oxygen consumption.
West, John B
Antoine Lavoisier (1743-1794) was one of the most eminent scientists of the late 18th century. He is often referred to as the father of chemistry, in part because of his book Elementary Treatise on Chemistry. In addition he was a major figure in respiratory physiology, being the first person to recognize the true nature of oxygen, elucidating the similarities between respiration and combustion, and making the first measurements of human oxygen consumption under various conditions. Less well known are the contributions made by his wife, Marie-Anne Lavoisier. However, she was responsible for drawings of the experiments on oxygen consumption when the French revolution was imminent. These are of great interest because written descriptions are not available. Possible interpretations of the experiments are given here. In addition, her translations from English to French of papers by Priestley and others were critical in Lavoisier's demolition of the erroneous phlogiston theory. She also provided the engravings for her husband's textbook, thus documenting the extensive new equipment that he developed. In addition she undertook editorial work, for example in preparing his posthumous memoirs. The scientific collaboration of this husband-wife team is perhaps unique among the giants of respiratory physiology. PMID:24097559