uuid
int64 0
6k
| title
stringlengths 8
285
| abstract
stringlengths 22
4.43k
|
|---|---|---|
5,100 | Art Market Investment Bubble during COVID-19-Case Study of the Rare Books Market in Poland | The literature provides information on the impact of the pandemic on various markets. Many articles suggest the necessity of diversifying investment portfolios during financial market turmoil. The article in a unique way analyzed the rare books market in Poland during the COVID-19 period. The results of rare books auctions (the largest players with 85% of the market share were taken into account) over the 2018-2022 period were considered. The authors presented the hypothesis that COVID-19 triggered an investment bubble which may burst quite suddenly after the pandemic period. The data presented confirm the hypothesis, showing sharp increases (up to 50%) in sales revenues and a subsequent collapse. Less spectacular results were obtained when analyzing the number of books offered and sold in a given period. The authors used descriptive measures as well as statistical tests. A simple model calculating possible revenue in conjunction with the WPDI (World Pandemics Discussion Index) indicator was also presented. |
5,101 | Software Architecture Reconstruction: A Process-Oriented Taxonomy | To maintain and understand large applications, it is important to know their architecture. The first problem is that unlike classes and packages, architecture is not explicitly represented in the code. The second problem is that successful applications evolve over time, so their architecture inevitably drifts. Reconstructing the architecture and checking whether it is still valid is therefore an important aid. While there is a plethora of approaches and techniques supporting architecture reconstruction, there is no comprehensive software architecture reconstruction state of the art and it is often difficult to compare the approaches. This paper presents a state of the art in software architecture reconstruction approaches. |
5,102 | Tomato POLLEN DEFICIENT 2 encodes a G-type lectin receptor kinase required for viable pollen grain formation | Pollen development is a crucial biological process indispensable for seed set in flowering plants and for successful crop breeding. However, little is known about the molecular mechanisms regulating pollen development in crop species. This study reports a novel male-sterile tomato mutant, pollen deficient 2 (pod2), characterized by the production of non-viable pollen grains and resulting in the development of small parthenocarpic fruits. A combined strategy of mapping-by-sequencing and RNA interference-mediated gene silencing was used to prove that the pod2 phenotype is caused by the loss of Solanum lycopersicum G-type lectin receptor kinase II.9 (SlG-LecRK-II.9) activity. In situ hybridization of floral buds showed that POD2/SlG-LecRK-II.9 is specifically expressed in tapetal cells and microspores at the late tetrad stage. Accordingly, abnormalities in meiosis and tapetum programmed cell death in pod2 occurred during microsporogenesis, resulting in the formation of four dysfunctional microspores leading to an aberrant microgametogenesis process. RNA-seq analyses supported the existence of alterations at the final stage of microsporogenesis, since we found tomato deregulated genes whose counterparts in Arabidopsis are essential for the normal progression of male meiosis and cytokinesis. Collectively, our results revealed the essential role of POD2/SlG-LecRK-II.9 in regulating tomato pollen development. |
5,103 | Relevant topics in wire electrical discharge machining control | A relationship between the dynamics of the wire electrode and the state of the art in wire electrical discharge machining (WEDM) control is established through wire modeling, listing the control issues related to WEDM and providing a catalogue of corresponding solutions. The results are to be used for identifying promising R&D directions in terms of customer convenience, and set up cost reduction by an improved process mastering. (C) 2004 Elsevier B.V. All rights reserved. |
5,104 | Social Network and Place: The Inheritance and Development of Beijing Crosstalk Performing Art | In order to protect the traditional performing arts, we need to analyze the factors that sustain its inheritance and development. Some of the factors are embedded in the place. This paper takes Beijing Crosstalk as an example to explore its relationship with the place Beijing. The authors interviewed and surveyed the crosstalk performers, and analyzed data of crosstalk performers from the Sina Weibo social media platform. The study found that Beijing crosstalk can be particularly successful because there are three levels of social networks embedded in Beijing: The first is the mentor-apprentice relationship within the crosstalk group. The second is communication with other performing groups or performers (such as other crosstalk groups, performers from opera, drama, etc.) in Beijing. The third is their cooperating relationship with the media. These three networks are not available in any other cities of China, which is the key to the inheritance and development of Beijing crosstalk as intangible cultural heritage. Therefore, the protection and transmission of local intangible cultural heritage needs not only to protect the intangible cultural heritage itself, but also to protect its related social networks and social resources that make up such networks. |
5,105 | A robust salient object detection using edge enhanced global topographical saliency | Complex salient object detection is the most challenging task in clutter background images. In this prevailing problem, global contrast-based methods are comprehensively preferred. But these methods fail in preserving the structure, shape and broader related geometrical information. Aiming at these limitations, the proposed method uses global contrast and iterative Laplacian of Gaussian to generate initial global topographical saliency. In this topographical saliency, iterative Laplacian of Gaussian is used to preserve the structural, shape and broader related geometrical information. This global topographical saliency is used as a reference plane for integrating regional saliencies. The color, spatial and distance based regional saliencies are integrated into the boundary enhanced global topographical saliency to improve the substantial information of the object. Boundary-based Gaussian weighted, background suppression model, is used to remove the background and edge-effects. Finally, central saliency addition is used to enhance the final saliency. The proposed method is compared with recent six global contrasts based state-of-art methods, two deep learning based methods and four publicly available datasets. The experimental result presented here shows that the proposed method performs better in comparison to the state-of-the-art methods. |
5,106 | Smartphone processor architecture, operations, and functions: current state-of-the-art and future outlook: energy performance trade-off Energy-performance trade-off for smartphone processors | Balancing energy-performance trade-offs for smartphone processor operations is undergoing intense research considering the challenges with the evolving technology of mobile computing. However, to guarantee energy-efficient processor operation, layout, and architecture, it is necessary to identify and integrate optimization techniques and parameters influencing energy-performance trade-off in various processor activity domains. Existing literature on energy optimization in smartphones focuses primarily on individual sub-domains such as OS, GPU, and cloud offloading methods. It reflects multiple smartphone processor activities domains as being the most discussed but less integrated. Through this study, we intend to provide the current state-of-the-art energy optimization techniques for smartphone processor operations. It also classifies multiple energy-draining processor operations along with their thorough discussion of methodologies and popular optimization techniques. The study models smartphone processor sub-components highlighting conventional techniques and performance parameters among its varied domains affecting the device's energy performance along with significant energy drain minimization without any serious performance degradation. The study analyzes these approaches in the context of applicability, performance, and expected future demands along with revealing limitations of those approaches and open research issues prevailing in the available literature. Finally, we conclude our study by summarizing the current state of the art for smartphone processor activities power consumption. |
5,107 | Design of Antennas through Artificial Intelligence: State of the Art and Challenges | The antenna is a critical part of the RF front end of a communication system. In this study, we present some of the major applications of artificial intelligence (AI) to antenna design. We review the previous research and applications of several AI techniques such as evolutionary algorithms, machine learning, and knowledge representation ontologies. Applications may vary from antenna design to antenna features evaluation in a research field, which is rapidly growing. Finally, we summarize the challenges of new AI techniques in antenna design based on the current state of the art and predict its future research directions. |
5,108 | The velocity of postglacial migration of fire-adapted boreal tree species in eastern North America | The Earth's climate has been warming rapidly since the beginning of the industrial era, forcing terrestrial organisms to adapt. Migration constitutes one of the most effective processes for surviving and thriving, although the speed at which tree species migrate as a function of climate change is unknown. One way to predict latitudinal movement of trees under the climate of the twenty-first century is to examine past migration since the Last Glacial Maximum. In this study, radiocarbon-dated macrofossils were used to calculate the velocity of past migration of jack pine (Pinus banksiana) and black spruce (Picea mariana), two important fire-adapted conifers of the North American boreal forest. Jack pine migrated at a mean rate of 19 km per century (km-cent) from unglaciated sites in the central and southeastern United States to the northern limit of the species in subarctic Canada. However, the velocity increased between unglaciated and early deglaciated sites in southern Quebec and slowed from early to mid-Holocene in central and eastern Quebec. Migration was at its lowest speed in late-Holocene times, when it stopped about 3,000 y ago. Compared with jack pine, black spruce migrated at a faster mean rate of 25 km-cent from the ice border at the last interstadial (Bølling/Allerød) to the species tree limit. The modern range of both species was nearly occupied about 6,000 y ago. The factors modulating the changing velocity of jack pine migration were closely associated with the warm-dry climate of the late Pleistocene-Holocene transition and the more humid climate of the mid- and late-Holocene. |
5,109 | An Adaptive Group of Density Outlier Removal Filter: Snow Particle Removal from LiDAR Data | Light Detection And Ranging (LiDAR) is an important technology integrated into self-driving cars to enhance the reliability of these systems. Even with some advantages over cameras, it is still limited under extreme weather conditions such as heavy rain, fog, or snow. Traditional methods such as Radius Outlier Removal (ROR) and Statistical Outlier Removal (SOR) are limited in their ability to detect snow points in LiDAR point clouds. This paper proposes an Adaptive Group of Density Outlier Removal (AGDOR) filter that can remove snow particles more effectively in raw LiDAR point clouds, with verification on the Winter Adverse Driving Dataset (WADS). In our proposed method, an intensity threshold combined with a proposed outlier removal filter was employed. Outstanding performance was obtained, with higher accuracy up to 96% and processing speed of 0.51 s per frame in our result. In particular, our filter outperforms the state-of-the-art filter by achieving a 16.32% higher Precision at the same accuracy. However, our method archive is lower in recall than the state-of-the-art method. This clearly indicates that AGDOR retains a significant amount of object points from LiDAR. The results suggest that our filter would be useful for snow removal under harsh weathers for autonomous driving systems. |
5,110 | Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM) | To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method. |
5,111 | Role of Automated Volume, Conductivity and Scatter (VCS) Parameters of Neutrophils as Indicators of Sepsis | This study was done to evaluate the role of automated volume, conductivity and scatter (VCS) parameters of neutrophils as indicators of sepsis and its differentiation from other inflammatory disorders. In this cross-sectional study, 225 patients with culture proven or with clinical evidence of sepsis were included along with an equal number of healthy controls. In addition, 138 patients with non-infective inflammatory conditions-acute pancreatitis (50), burns (45) and acute myocardial infarction (43) were also included. Complete blood count was done on LH750 automated hematology analyser (Beckman Coulter). VCS data; mean neutrophil volume (MNV), mean neutrophil conductivity (MNC) and mean neutrophil scatter (MNS) for all patients was recorded. MNV was high (p < .0001) while MNS was lower (p < .0001) in patients with sepsis compared to the control group. MNC was comparable between the two groups (p = .4735). On subgroup analysis of patients with sepsis, significant difference in MNV (p = .0009) and MNS (p = .0210) was observed in patients with leukopenia, normal TLC and leucocytosis. Youden Index was maximum (71%) at MNV of 144.6 (sensitivity-82.7%; specificity-88.5%) and MNV of 147.9 (sensitivity-75.6%; specificity-95.6%) for sepsis. On comparing patients with sepsis with acute pancreatitis and myocardial infarction, MNV and MNC were significantly higher in patients with sepsis. MNV is a useful, inexpensive parameter which can be accessed during a routine CBC run from the raw data. It can be utilized as an early indicator of sepsis as an adjunct to the clinical diagnosis in suspect patients. However, its availability in only select hematology analyzers may limit its use. |
5,112 | Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms | Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the individual needs of people with disabilities. This paper describes the computer vision algorithms part of the multimodal interface developed inside the AIDE European project. The main contribution of this computer vision part is the integration with the robotic system and with the other sensory systems (electrooculography (EOG) and electroencephalography (EEG)). The technical achievements solved herein are the algorithm for the selection of objects using the gaze, and especially the state-of-the-art algorithm for the efficient detection and pose estimation of textureless objects. These algorithms were tested in real conditions, and were thoroughly evaluated both qualitatively and quantitatively. The experimental results of the object selection algorithm were excellent (object selection over 90%) in less than 12 s. The detection and pose estimation algorithms evaluated using the LINEMOD database were similar to the state-of-the-art method, and were the most computationally efficient. |
5,113 | Rock art interpretive approaches - Devising frameworks to maximally utilise independent lines of evidence | Past cultures in all the major continents have, for millennia, developed symbolic systems and iconographies whose depictions spanned a multiplicity of media, including rock art as well as ceramic decoration and other physical forms. This is an area which has long been recognised, but requires further investigation and research for any meaningful conclusions to be drawn - particularly regarding its potential to assist in the meaning and chronology of rock art. Taken together with other data such as the contrasting distribution of archaeological sites and rock art locations, we may form independent lines of evidence, not conclusive in their own right, but when combined can provide convincing arguments toward specific chronology and attribution. Indeed, each rock art site needs to have its own tailored interpretive framework, devised to maximally exploit the available archaeological and other data to assist in the understanding of the rock art. This paper will describe a multidimensional methodology to rock art chronological and cultural attribution using an approach applied at the site of Hierakonpolis, Upper Egypt which is known for its late Predynastic and Early Dynastic settlement and funerary localities, occurring adjacent to rock beds and hills incorporating rock art and inscriptions from a span of ages. (C) 2016 Elsevier Ltd. All rights reserved. |
5,114 | Extending co-citation using sections of research articles | The excessive amount of digital information has made it crucial to extract the relevant information. This hinders researchers in finding documents pertaining to their research. There exist various state-of-the-art techniques, such as co-citation, bibliographic coupling, and their recent extensions like citation proximity analysis and citation order analysis, that recommend the relevant documents against the posed query. Most of these approaches are statistical in nature and thus can further be extended by incorporating some semantics to enhance the results. In this paper, we present an extension of a co-citation-based technique to identify the most relevant documents to co-cited document(s). The proposed system explores in-text citation frequencies and in-text citation patterns of co-cited documents within the different logical sections of cited-by papers. Furthermore, we have evaluated the proposed approach with the co-citation approach and citation proximity analysis (CPA) approach on a dataset acquired from CiteSeer. The outcomes revealed that most of the time the proposed approach outperformed other state-of-the-art techniques. The average correlation of the proposed approach is increased by 68% as compared to the co-citation-based approach. In comparison with CPA approach, the average correlation of the proposed approach is increased by 39% with respect to gold-standard rankings. |
5,115 | Single-cell RNA sequencing analysis dissected the osteo-immunology microenvironment and revealed key regulators in osteoporosis | Osteoporoticfractures become increasingly common in postmenopausal women over age 55 years and men after age 65 years, bringing about substantial bone-associated morbidities, and augmented mortality and health-care costs. Advanced researches have led to a more accurate assessment of osteoporosis (OP) and have broadened the range of therapeutic approaches available to prevent osteoporotic fractures. Single-cell RNA sequencing (scRNA-seq) analysis is an evolutionary method that quantifies the majority of transcripts in individual cells at isoform resolution, paving the way for more detailed analyses of gene regulation in biology and medicine. We have extracted 19,102 cells and 4097 dynamical genes with significant expression changes. Several new subtypes of macrophages and differentially over-expressed genes were discovered in the trajectory of osteoclasts formation. The zinc finger protein 36, C3H type-like 1 (ZFP36L1) and defensin alpha 3 (DEFA3) were identified as novel bone metabolism-related genes. RETN-CAP1 was newly found to be involved in the interaction between osteoclasts and immunocytes, indicating that osteo-immunology microenvironment substantially contributed to the pathology of osteoporosis or osteopenia. In this research, we have performed Single-cell RNA sequencing analysis to display the trajectory of osteoclast formation and reveal the possible gene targets and signaling pathways that probably play an important role in osteoporosis. |
5,116 | Intention to Breastfeed and Paternal Influence on Pregnant Mothers Exclusively Using Marijuana Compared with Other Substances | Objective: To determine intention to breastfeed (ITBF) rates among mothers exclusively using marijuana (eMJ) compared with electronic cigarettes (eEcig), tobacco products (eTob), or multisubstances (MS), nonusers (NU), and the influence of paternal presence and paternal substance use. Study Design: Cross-sectional study of parental survey responses merged with electronic birth certificates. Accounting for clinical and social determinants of health, analyses of ITBF included (1) all mothers, (2) single mothers, and (3) mothers with fathers. Results: Among all mothers (n = 1,073), eMJ, eTob, and MS users had lower odds of ITBF compared with NU. Only eMJ users had lower odds of ITBF for those without paternal presence. However, in those mothers with a paternal presence, odds of ITBF were similar to NU for eMJ, eTob, and MS users when accounting for paternal factors, including paternal substance use. Conclusion: Women exclusively using MJ have lower ITBF compared with NU. However, paternal presence mitigated this effect, independent of parental MJ use. The presence of fathers may represent a unique predictor for increased ITBF in MJ using mothers. |
5,117 | Lineage-Specific Patterns of Genome Deterioration in Obligate Symbionts of Sharpshooter Leafhoppers | Plant sap-feeding insects (Hemiptera) rely on obligate bacterial symbionts that provision nutrients. Some of these symbionts are ancient and have evolved tiny genomes, whereas others are younger and retain larger, dynamic genomes. Baumannia cicadellinicola, an obligate symbiont of sharpshooter leafhoppers, is derived from a relatively recent symbiont replacement. To better understand evolutionary decay of genomes, we compared Baumannia from three host species. A newly sequenced genome for Baumannia from the green sharpshooter (B-GSS) was compared with genomes of Baumannia from the blue-green sharpshooter (B-BGSS, 759 kilobases [kb]) and from the glassy-winged sharpshooter (B-GWSS, 680 kb). B-GSS has the smallest Baumannia genome sequenced to date (633 kb), with only three unique genes, all involved in membrane function. It has lost nearly all pathways involved in vitamin and cofactor synthesis, as well as amino acid biosynthetic pathways that are redundant with pathways of the host or the symbiotic partner, Sulcia muelleri. The entire biosynthetic pathway for methionine is eliminated, suggesting that methionine has become a dietary requirement for hosts. B-GSS and B-BGSS share 33 genes involved in bacterial functions (e.g., cell division, membrane synthesis, metabolite transport, etc.) that are lost from the more distantly related B-GWSS and most other tiny genome symbionts. Finally, pairwise divergence estimates indicate that B-GSS has experienced a lineage-specific increase in substitution rates. This increase correlates with accelerated protein-level changes and widespread gene loss. Thus, the mode and tempo of genome reduction vary widely among symbiont lineages and result in wide variation in metabolic capabilities across hosts. |
5,118 | Identification of a necroptosis-related prognostic gene signature associated with tumor immune microenvironment in cervical carcinoma and experimental verification | Cervical carcinoma (CC) has been associated with high morbidity, poor prognosis, and high intratumor heterogeneity. Necroptosis is the significant cellular signal pathway in tumors which may overcome tumor cells' apoptosis resistance. To investigate the relationship between CC and necroptosis, we established a prognostic model based on necroptosis-related genes for predicting the overall survival (OS) of CC patients. The gene expression data and clinical information of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) patients were obtained from The Cancer Genome Atlas (TCGA). We identified 43 differentially expressed necroptosis-related genes (NRGs) in CESC by examining differential gene expression between CESC tumors and normal tissues, and 159 NRGs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Gene ontology (GO) and KEGG enrichment analysis illustrated that the genes identified were mainly related to cell necrosis, extrinsic apoptosis, Influenza A, I - kappaB kinase/NF - kappaB, NOD - like receptor, and other signaling pathways. Subsequently, least absolute shrinkage and selection operator (LASSO) regression and univariate and multivariate Cox regression analyses were used to screen for NRGs that were correlated with patient prognosis. A prognostic signature that includes CAMK2A, CYBB, IL1A, IL1B, SLC25A5, and TICAM2 was established. Based on the prognostic model, patients were stratified into either the high-risk or low-risk subgroups with distinct survival. Receiver operating characteristic (ROC) curve analysis was used to identify the predictive accuracy of the model. In relation to different clinical variables, stratification analyses were performed to demonstrate the associations between the expression levels of the six identified NRGs and the clinical variables in CESC. Immunohistochemical (IHC) validation experiments explored abnormal expressions of these six NRGs in CESC. We also explored the relationship between risk score of this necroptosis signature and expression levels of some driver genes in TCGA CESC database and Gene Expression Omnibus (GEO) datasets. Significant relationships between the six prognostic NRGs and immune-cell infiltration, chemokines, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoints in CESC were discovered. In conclusion, we successfully constructed and validated a novel NRG signature for predicting the prognosis of CC patients and might also play a crucial role in the progression and immune microenvironment in CC. |
5,119 | Analysis on spatial development mode of eco-sports tourism in Grand Canal landscape environment culture belt | The development mode of sports tourism is based on the ecological environment. In order to reduce the negative impact of sports tourism on the environment, the development of ecological sports tourism based on the perspective of ecology is the top priority for the healthy and sustainable development of sports tourism. On the basis of summarizing and analyzing the previous research results, this paper expounds the elements and value of ecological sports culture tourism resources, introduces the ecological sports tourism complex model of landscape environmental culture belt, and expounds the cultural connotation characteristics of ecological sports tourism environment. Environmental science is mainly reflected in the promotion of the benign cycle of natural ecological environment, the protection of living things, the protection of water bodies and the treatment of water pollution, and the good protection of atmospheric environment, geology, and geomorphology. Based on the development of ecological sports tourism space with landscape environment culture belt, this paper introduces the resource development system of Beijing-Hangzhou Canal ecological sports culture tourism, constructs the landscape space development mode of ecological sports tourism, and analyzes the landscape space structure of ecological sports tourism. Based on landscape culture with the basis of sports tourism resources, analyzes the influence factors of sports tourism spatial structure from the direction of the depth and breadth of two dimensions expand sports events and the ecological sports cultural tourism, the depth of the fusion depth aspects emphatically at sporting events, and the ecological sports cultural tourism into the connotation of mining, breadth aspect focuses on the formats of the extension of rich and associated industries. To study the important carrier of promoting the development of ecological sports culture resources and sports industry in the Canal, to plan the layout and establish the sports culture tourism industry base reasonably, and to form the sports culture tourism industry ecosphere. The results show that the scale and speed of the development of sports tourism not only depend on the attractive sports tourism resources but also highly depend on the level of economic and social development in the region. The supply of sports tourism must take the demand of sports tourism market as the guide, combine sports activities with tourism activities in an ecological and organic way, develop various sports items to meet the needs of tourists, and realize the ecological, economic, and social benefits of ecological sports tourism. The research results of this paper provide reference for the further development of eco-sports tourism in landscape environment and culture belt. |
5,120 | Sustainability in manufacturing operations scheduling: A state of the art review | Sustainability in manufacturing systems is an urgent requirement for today's manufacturing companies. This paper focuses on sustainable manufacturing operations scheduling, a subject which has been attracting increasing interest from researchers in recent years. This paper presents a state of the art review of this field. First, it characterizes what can be considered as sustainable manufacturing operations scheduling, and introduces the relevant challenges and issues. An analysis of the literature is then proposed, and organized according to three keys. The shortcomings in the literature are then discussed in depth, and subsequently urgent problems that must be solved through research in order to meet industry requirements are pointed out. (C) 2015 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved. |
5,121 | LEARN: Learned Experts' Assessment-Based Reconstruction Network for Sparse-Data CT | Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold the state-of-the- art "fields of experts"-based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a learned experts' assessment-based reconstruction network (LEARN) for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a superior performance with the well-known Mayo Clinic low-dose challenge data set relative to the several state-of- the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 50, reducing the computational complexity of typical iterative algorithms by orders of magnitude. |
5,122 | Contactless Heterogeneous 3-D ICs for Smart Sensing Systems | A heterogeneous contactless transceiver circuit is designed to provide inter-tier signalling for a 3-D system considering specific bonding constraints. The system is composed of two tiers, a 65 nm processing tier and a 0.35 mu m sensing tier. Face-to-back integration is chosen to support fluidic sensing. Half duplex communication between the tiers is provided through inductive links. Each tier is considered to be fabricated in a different technology to enable low manufacturing cost and benefit from the advantages each technology offers. Both the uplink and downlink transceivers achieve data rates that reach 1 Gbps with non-return-to-zero data encoding. Energy efficiency is the predominant objective, with the uplink dissipating 5.28 pJ/b and 8.67 pJ/b for the downlink. A 6.8x power reduction is demonstrated when using heterogeneous technologies, compared to a state-of-the-art 0.35 mu m transceiver, while the dissipated energy is decreased by 37.5% as compared to a state-of-the-art 65 nm transceiver. Process variation analysis is also performed to ensure the proposed circuit operates correctly across several process corners, covering a broad design space. To improve system robustness, an overhead of 2.3% on the peak power and <1% on the average power is shown, respectively. |
5,123 | Universal platform for accurately damage-free mapping of sEVs cargo information | SEVs (small extracellular vesicles) contents signatures appear to mirror pathological changes of diseases, and mapping sEVs contents profile is a promising approach for non-invasive diagnosis of the disease. Herein, we propose a universal system for accurately and damage-freely mapping of sEVs content profile using dual-recognition triggered CHA (catalytic hairpin assembly) and DNAzyme based signal amplification strategy. After immunoassay based capture of CD63 positive sEVs by anti-CD63 lgG coated on the surface of polystyrene plates, probes are incubated with fixed sEVs to penetrate sEVs membrane and act to sense sEVs contents. In detection step, integrated CHA and DNAzyme based strategy is initiated by released initiator from capture probe after recognizing targets, forming a dual circle signal recycling process, realizing signal amplification for high sensitivity. Given the attractive analytical features that i) a universal platform for indistinctive sEVs nucleic acids and protein molecules detection; ii) high sensitivity derived from dual circle signal recycling process; iii) enzyme-free characteristic of integrated CHA and DNAzyme minimizes the interference to sEVs biological activity; iv) mapping of sEVs contents profiles indicates a brand-new strategy for non-invasive diagnosis of the disease, the present approach shows great promise for analyzing additional different analytes in clinical and experimental researches. |
5,124 | Theoretical Analysis of AM and FM Interference Robustness of Integrating DDR Receiver for Human Body Communication | Prolific growth of miniaturized devices has led to widespread use of wearable devices and physiological sensors. The state-of-art technique for connecting these devices and sensors is through wireless radio waves. However, wireless body area wireless body area network (WBAN) suffers from limited security (wireless signals from energy-constrained sensors can be snooped by nearby attackers), poor energy-efficiency (up conversion and down conversion), and self-interference. Human body communication (HBC), which uses human body as a conducting medium, has emerged as a new alternative physical layer for WBAN, as it can enable communication with better energy efficiency and enhanced security. Broadband (BB) HBC uses the human body channel as a broadband communication medium and can enable higher energy efficiency compared to narrowband HBC. However, due to the antenna effect of human body, ambient interferences get picked up from the environment, proving to be one of the primary bottlenecks for BB-HBC systems. In this paper, we analyze the performance of an integrating dual data rate (I-DDR) receiver, which enables interference robust BB-HBC, under continuous wave (CW), amplitude modulated (AM), and frequency modulated (FM) interferences. Theoretical derivations along with simulations provide key insights into the behavior of I-DDR receiver under different interference scenarios, highlighting the efficacy (>22 dB improvement in SIR tolerance for both FM and AM) of the technique. Finally, measurements are carried out by applying the I-DDR principle on signals transmitted through the human body and captured on an oscilloscope. Measurements from an I-DDR receiver fabricated in TSMC 65 nm technology shows <10(-4) BER in presence of CW, AM, and FM interference with -21 dB SIR further demonstrating the efficacy of the I-DDR method in interference rejection. |
5,125 | Coal mining and environmental sustainability in South Africa: do institutions matter? | This study investigates the effects of coal mining on environmental sustainability in South Africa in relation to the moderating role of institutions. To achieve the study's objectives, the fully modified least square (FMOLS), dynamic least squares (DOLS), canonical cointegrating regression (CCR), Bayer-Hanck cointegration and Toda-Yamamoto causality test are employed for the period 1984-2018. Results from the study show that coal mining contributes to environmental degradation in South Africa, while its interaction with institutional quality mitigates the severity of this negative impact. Furthermore, there is evidence that economic growth has a bidirectional causality with ecological footprint and coal mining, while institutional quality also Granger causes ecological footprint. To promote a sustainable environment, there is a need for the government and the institutions to form the foundation for the shift toward environmental sustainability, with particular attention paid to the development and implementation of greenhouse policy. |
5,126 | Bees swarm optimization guided by data mining techniques for document information retrieval | This paper explores advances in the data mining field to solve the fundamental Document Information Retrieval problem. In the proposed approach, useful knowledge is first discovered by using data mining techniques, then swarms use this knowledge to explore the whole space of documents intelligently. We have investigated two data mining techniques in the preprocessing step. The first one aims to split the collection of documents into similar clusters by using the k-means algorithm, while the second one extracts the most closed frequent terms on each cluster already created using the DCI_Closed algorithm. For the solving step, BSO (Bees Swarm Optimization) is used to explore the cluster of documents deeply. The proposed approach has been evaluated on well-known collections such as CACM (Collection of ACM), TREC (Text REtrieval Conference), Webdocs, and Wikilinks, and it has been compared to state-of-the-art data mining, bio-inspired and other documents information retrieval based approaches. The results show that the proposed approach improves the quality of returned documents considerably, with a competitive computational time compared to state-of-the-art approaches. (C) 2017 Elsevier Ltd. All rights reserved. |
5,127 | Challenges in Anaesthesia Management of a 15-Year-Old Female With Ovarian Teratoma for Exploratory Laparotomy: A Case Report | Rarely, an ovarian tumour will develop the growing teratoma syndrome. Growing teratoma syndrome of the cystic type has been linked to difficulties with anaesthesia because of the abdominal pressure the tumour exerts on the thorax. There haven't been any reports of this kind of ovarian tumour associated with ascites and bilateral pleural effusion in a paediatric age group. Here, we describe our anaesthetic experience in a case of developing solid-type ovarian teratoma syndrome with deranged lung status and haemodynamics. The patient was a 15-year-old female who was diagnosed with ovarian teratoma. She was scheduled for surgery when she arrived at our hospital with a 13 cm solid mass and respiratory distress. The patient's liver profile was abnormal; she had ascites, pleural effusion and a severely worsened lung condition. The patient was planned for an exploratory laparotomy and debulking surgery after preoperative optimisation. To prevent the re-expansion pulmonary oedema (RPO) following the excision of the tumour, a volume-restricted postoperative ventilation strategy was planned. Following enhanced recovery after surgery (ERAS) protocol and specific anaesthetic measures, we successfully managed the anaesthesia in a case of teratoma syndrome with a large abdominal tumour with successful recovery and early discharge from hospital. |
5,128 | Self-Supervised Vision Transformers for Malware Detection | Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in these attacks and it is becoming inevitable to find a solution that can self-learn from unlabeled sample data. This paper presents SHERLOCK, a self-supervision based deep learning model to detect malware based on the Vision Transformer (ViT) architecture. SHERLOCK is a novel malware detection method which learns unique features to differentiate malware from benign programs with the use of image-based binary representation. Experimental results using 1.2 million Android applications across a hierarchy of 47 types and 696 families, shows that self-supervised learning can achieve an accuracy of 97% for the binary classification of malware which is higher than existing state-of-the-art techniques. Our proposed model is also able to outperform state-of-the-art techniques for multi-class malware classification of types and family with macro-F1 score of.497 and.491 respectively. |
5,129 | What about Your Body Ornament? Experiences of Tattoo and Piercing among Italian Youths | Background: tattooing and piercing are increasingly common, especially among youths. However, several health complications may be associated with these practices if basic hygiene rules are not respected. This multicenter study was aimed at exploring tattoo and piercing experiences reported by a large sample of Italian undergraduate students through a public health perspective. Methods: tattooed and/or pierced students attending 12 Italian universities were asked to complete a web-based questionnaire regarding their body art experience. Results: out of 1472 respondents, 833 (56.6%) were tattooed and 1009 (68.5%) were pierced. The greatest proportion of tattooed students (93.9%) got her/his first tattoo in a tattoo studio, while most of the pierced were serviced in a jewelry store (48.0%). The pierced ones were less informed on health issues related to the procedure (56.0% versus 77.8% of tattooed p < 0.001), and tattooists were reportedly more attentive to hygiene rules (instrument sterilization 91.5% versus 79.1% of piercers, p < 0.001; use of disposable gloves 98.2% versus 71% of piercers, p < 0.001). Conclusions: educational interventions for both professionals and communities are needed to improve the awareness and the control of health risks related to body art throughout the Italian territory. |
5,130 | Light bullets in transparent dielectrics | This paper presents a retrospective analysis of the development of notions in nonlinear optics: from beam self-focusing and pulse filamentation to light bullets ??? wave packets extremely compressed in space and time during laser light propagation in the bulk of a transparent medium. We describe the state of the art in studies of mid-IR light bullets in condensed media and air. |
5,131 | Current progress in the biology of members of the Sporothrix schenckii complex following the genomic era | Sporotrichosis has been attributed for more than a century to one single etiological agent, Sporothrix schencki. Only eight years ago, it was described that, in fact, the disease is caused by several pathogenic cryptic species. The present review will focus on recent advances to understand the biology and virulence of epidemiologically relevant pathogenic species of the S. schenckii complex. The main subjects covered are the new clinical and epidemiological aspects including diagnostic and therapeutic challenges, the development of molecular tools, the genome database and the perspectives for study of virulence of emerging Sporothrix species. |
5,132 | Spodoptera frugiperda in Togo 5 years on: early impact of the invasion and future developments | The infestation of the fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) in Africa since 2016 has been a major threat to maize production. Previous studies in Togo and Ghana from 2016 to 2018 did not correlate FAW infestation to yield losses. Thus, the aim of this study which assesses the impact of FAW infestation by inspecting 150 maize farms throughout the five Agro-Ecological Zones (AEZs) of Togo for FAW plant damage, and third instar larvae were used to infest 10-day-old maize plants in netted plots under controlled conditions at an experiment station (Station d'Expérimentations Agronomiques de Lomé) in 2019 and 2020. As control plots at the experiment station, plots were both netted and treated with emamectin benzoate, simply netted, or open to natural infestation. The number of larvae, egg masses, percent damaged plants, and damage proportions of leaves and ears were scored until harvest. Infestations and damages on maize plant throughout Togo were similar between the two years but were higher in the southern part of the county (AEZ5). At the experiment station, the yield losses were significantly considerable and increased from 25% infestation. The losses were 0.37 t ha-1 for 25% infestation, 0.34 t ha-1 for 30%, 0.59 t ha-1 for the open plots, 0.70 t ha-1 for simple netted and 50% infestation, 1.03 t ha-1 for 75%, and 1.27 t ha-1 for 100% infestation. This current study suggested thorough inspection on maize farms to set off management practices from 25% of infestation. |
5,133 | In vitro antifungal synergy between amphiphilic aminoglycoside K20 and azoles against Candida species and Cryptococcus neoformans | Several azoles are widely used to treat human fungal infections. Increasing resistance to these azoles has prompted exploration of their synergistic antifungal activities when combined with other agents. The amphiphilic aminoglycoside, K20, was recently shown to inhibit filamentous fungi, yeasts and heterokonts, but not bacteria. In this study, in vitro synergistic growth inhibition by combinations of K20 and azoles (fluconazole, itraconazole, voriconazole, clotrimazole, or posaconazole) were examined against Candida species and Cryptococcus neoformans. Checkerboard microbroth dilution, time-kill curve, and disk diffusion assays revealed that K20 has synergistic inhibitory activities with all five azoles against C. albicans including azole-resistant C. albicans strains ATCC 64124 and ATCC 10231. Four (fluconazole, itraconazole, clotrimazole, posaconazole) and three (fluconazole, itraconazole, voriconazole) azoles were synergistically inhibitory with K20 against C. lusitaniae and C. tropicalis, respectively. Only posaconazole showed synergy with K20 against two Cryptococcus neoformans strains (90-26 and VR-54). Time-kill curves with azole-resistant C. albicans 64124 and azole-sensitive C. albicans MYA-2876 confirmed the K20-azole synergistic interactions with a ≥ 2 log10 decrease in colony-forming units (CFU)/ml compared with the corresponding azoles alone. These results suggest that combinations of K20 and azoles offer a possible strategy for developing therapies against candidiasis. |
5,134 | De-ghosted HDR video acquisition for embedded systems Ghost-free HDR video of motion objects from stationary cameras | This paper proposes a novel ghost-free High Dynamic Range (HDR) multi-exposure video acquisition suitable for real-time implementation in embedded systems. While the method is limited to stationary cameras, it achieves, with low requirements on resources, results comparable to state-of-the-art de-ghosting methods that are often very computationally expensive and almost impossible to implement in smart cameras and embedded systems. The paper describes the method itself and includes an evaluation of the performance on selected embedded platforms and a comparison of the results to the state of the art using HDR datasets. |
5,135 | Distilling and transferring knowledge via cGAN-generated samples for image classification and regression | Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student model based on the knowledge from a teacher model. However, applying KD in image regression with a scalar response variable is also important (e.g., age estimation) yet has been rarely studied. Besides, existing KD methods often require a practitioner to carefully select or adjust the teacher and student architectures, making these methods less flexible in practice. To address the above problems in a unified way, we propose a comprehensive KD framework based on conditional generative adversarial networks (cGANs), termed cGAN-KD. Fundamentally different from existing KD methods, cGAN-KD distills and transfers knowledge from a teacher model to a student model via specifically processed cGAN-generated samples. This novel mechanism makes cGAN-KD suitable for both classification and regression tasks, compatible with other KD methods, and insensitive to the teacher and student architectures. An error bound for a student model trained in the cGAN-KD framework is derived in this work, providing a theory for why cGAN-KD is effective as well as guiding the practical implementation of cGAN-KD. Extensive experiments on CIFAR-100 and ImageNet-100 (a subset of ImageNet with only 100 classes) datasets show that the cGAN-KD framework can leverage state-of-the-art KD methods to yield a new state of the art. Moreover, experiments on Steering Angle and UTKFace datasets demonstrate the effectiveness of cGAN-KD in image regression tasks. Notably, in classification, incorporating cGAN-KD into training improves the state-of-the-art SSKD by an average of 1.32% in test accuracy on ImageNet-100 across five different teacher-student pairs. In regression, cGAN-KD decreases the test mean absolute error of a WRN16 x 1 student model from 5.74 to 1.79 degrees (i.e., 68.82% drop) on Steering Angle. |
5,136 | Development of duplex real-time PCR for quick detection of cryptosporidiosis in goats | Cryptosporidium spp. is the most important foodborne and waterborne pathogens and a leading cause of mortality from foodborne and waterborne gastrointestinal diseases. In neonates of domestic animals, it is associated with consistent diarrhea and dehydration. Cryptosporidium infection begins with the ingestion of sporulated oocytes disseminated by carrier animals that consistently contaminate the environment. Many diagnostic tests are available including microscopy and antigen trap-ELISA, but none of the diagnostic tests available currently cannot differentiate between active and passive infection in the host. In the current study, to address this challenge an mRNA-based duplex TaqMan® probe PCR was developed to target the Cryptosporidium oocyst wall protein gene and 18SSU rRNA gene in a single tube that can detect metabolically active cryptosporidial oocysts. The mRNA transcripts are the direct indicator of any actively replicating cell and they will help decipher the active stages of its lifecycle in a host. This diagnostic assay was standardized by computing transcript copy number-based limit of detection (LOD). For COWP and 18SSU rRNA genes, the LOD was 7.08 × 1004 and 5.95 × 1005 , respectively. During active infections, the oocyst wall protein will be active and so its COWP gene transcripts will act as a marker for active infection. While transcripts for 18SSU rRNA are constitutively expressed in cryptosporidial life cycle. This current diagnostic assay will be a quantitative marker that will help assess the active stages of Cryptosporidium infection in neonates. The disease dynamics will help better understand to formulate the control strategies and contain infection among healthy animals. |
5,137 | METTL3-mediated N6-methyladenosine modification and HDAC5/YY1 promote IFFO1 downregulation in tumor development and chemo-resistance | Ovarian cancer (OC) is a malignant tumor that seriously threatens women's health. Due to the difficulty of early diagnosis, most patients exhibit advanced disease or peritoneal metastasis at diagnosis. We discovered that IFFO1 is a novel tumor suppressor, but its role in tumorigenesis, development and chemoresistance is unknown. In this study, IFFO1 levels were downregulated across cancers, leading to the acceleration of tumor development, metastasis and/or cisplatin resistance. Overexpression of IFFO1 inhibited the translocation of β-catenin to the nucleus and decreased tumor metastasis and cisplatin resistance. Furthermore, we demonstrated that IFFO1 was regulated at both the transcriptional and posttranscriptional levels. At the transcriptional level, the recruitment of HDAC5 inhibited IFFO1 expression, which is mediated by the transcription factor YY1, and the METTL3/YTHDF2 axis regulated the mRNA stability of IFFO1 in an m6A-dependent manner. Mice injected with IFFO1-overexpressing cells had lower ascites volumes and tumor weights throughout the peritoneal cavity than those injected with parental cells expressing the vector control. In conclusion, we demonstrated that IFFO1 is a novel tumor suppressor that inhibits tumor metastasis and reverses drug resistance in ovarian cancer. IFFO1 was downregulated at both the transcriptional level and posttranscriptional level by histone deacetylase and RNA methylation, respectively, and the IFFO1 signaling pathway was identified as a potential therapeutic target for cancer. |
5,138 | Thickness- and Twist-Angle-Dependent Interlayer Excitons in Metal Monochalcogenide Heterostructures | Interlayer excitons, or bound electron-hole pairs whose constituent quasiparticles are located in distinct stacked semiconducting layers, are being intensively studied in heterobilayers of two-dimensional semiconductors. They owe their existence to an intrinsic type-II band alignment between both layers that convert these into p-n junctions. Here, we unveil a pronounced interlayer exciton (IX) in heterobilayers of metal monochalcogenides, namely, γ-InSe on ε-GaSe, whose pronounced emission is adjustable just by varying their thicknesses given their number of layers dependent direct band gaps. Time-dependent photoluminescense spectroscopy unveils considerably longer interlayer exciton lifetimes with respect to intralayer ones, thus confirming their nature. The linear Stark effect yields a bound electron-hole pair whose separation d is just (3.6 ± 0.1) Å with d being very close to dSe = 3.4 Å which is the calculated interfacial Se separation. The envelope of IX is twist-angle-dependent and describable by superimposed emissions that are nearly equally spaced in energy, as if quantized due to localization induced by the small moiré periodicity. These heterostacks are characterized by extremely flat interfacial valence bands making them prime candidates for the observation of magnetism or other correlated electronic phases upon carrier doping. |
5,139 | Source Printer Classification Using Printer Specific Local Texture Descriptor | The knowledge of the source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. The development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, the state-of-the-art systems require that the font of letters present in the test documents of unknown origin must be available in those used for training the classifier. In this paper, we attempt to take the first step toward overcoming this limitation. Specifically, we introduce a novel printer specific local texture descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font and reduces the confusion between the printers of same brand and model on another dataset having documents printed in four different fonts, the proposed method outperforms state-of-the-art methods for cross font experiments. |
5,140 | New Cerebellar Ataxia, Neuropathy, Vestibular Areflexia Syndrome cases are caused by the presence of a nonsense variant in compound heterozygosity with the pathogenic repeat expansion in the RFC1 gene | The biallelic pathogenic repeat (AAGGG)400-2000 intronic expansion in the RFC1 gene has been recently described as the cause of cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS) and as a major cause of late-onset ataxia. Since then, many heterozygous carriers have been identified, with an estimated allele frequency of 0.7% to 4% in the healthy population. Here, we describe in two affected CANVAS sisters the presence of the nonsense c.724C > T p.(Arg242*) variant in compound heterozygosity with the pathogenic repeat expansion in the RFC1 gene. Further RNA analysis demonstrated a reduced expression of the p.Arg242* allele in patients confirming an efficient nonsense-mediated mRNA decay. We also highlight the importance of considering the sequencing of the RFC1 gene for the diagnosis, especially in patients with CANVAS diagnosis carriers of the AAGGG repeat expansion. |
5,141 | Fruit classification based on weighted score-level feature fusion | We describe an object classification method based on weighted score-level feature fusion using learned weights. Our method is able to recognize 20 object classes in a customized fruit dataset. Although the fusion of multiple features is commonly used to distinguish variable object classes, the optimal combination of features is not well defined. Moreover, in these methods, most parameters used for feature extraction are not optimized and the contribution of each feature to an individual class is not considered when determining the weight of the feature. Our algorithm relies on optimizing a single feature during feature selection and learning the weight of each feature for an individual class from the training data using a linear support vector machine before the features are linearly combined with the weights at the score level. The optimal single feature is selected using cross-validation. The optimal combination of features is explored and tested experimentally using a customized fruit dataset with 20 object classes and a variety of complex backgrounds. The experiment results show that the proposed feature fusion method outperforms four state-of-the-art fruit classification algorithms and improves the classification accuracy when compared with some state-of-the-art feature fusion methods. (C) 2016 SPIE and IS&T |
5,142 | Concordance between nurses' perception of their ability to provide spiritual care and the identified spiritual needs of hospitalized patients: A cross-sectional observational study | Spiritual care is essential to the well-being of patients, and nurses provide spiritual care as a fundamental part of nursing practice. In this study, we investigated the spiritual care needs of hospitalized patients to determine whether the perceived knowledge of nurses corresponded with these spiritual care needs. A cross-sectional study was conducted on 1351 hospitalized patients and 200 registered nurses recruited from a medical center in central Taiwan. A questionnaire, including the 21-item Spiritual Care Needs Inventory (patient and nurse version) and basic demographic information, was distributed to eligible participants. The top three items of the spiritual care needs expressed by the hospitalized patients were respect for privacy and dignity, showing concern, and guidance in gaining a sense of hope in life; the percentages of nurses not knowing how to provide these spiritual care needs were 0%, 1%, and 15%, respectively. The spiritual care needs of patients showed a significant relationship with the knowledge of nurses, suggesting that the perceived knowledge of the nurses generally corresponded with the spiritual care items that the patients required most. |
5,143 | Impact of the Hydrogen-Bonding Functional Group on Hydrogelation of Amphiphilic Naphthalene-diimide Derivatives and Nonspecific Protein Adsorption | This manuscript reports the effect of hydrogen-bonding functionality on the supramolecular assembly of naphthalene-diimide (NDI)-derived amphiphilic building blocks in water. All the molecules contain a central NDI chromophore, functionalized with a hydrophilic oligo-oxyethylene (OE) wedge in one arm and a phenyl group on the opposite arm. They differ by a single H-bonding functionality, which links the NDI chromophore and the phenyl moiety. The H-bonding functionalities are amide, thioamide, urea, and urethane in NDI-A, NDI-TA, NDI-U, and NDI-UT, respectively. All of these molecules exhibit π-stacking in water, as evident from their distinct UV/vis absorption spectra when compared to that of the monomeric dye in THF. However, among these four, only NDI-A and NDI-TA show hydrogelation, while the other two precipitate out of the medium. The NDI-A hydrogel also exhibits transient stability and leads to a crystalline precipitate within ∼5 h. Only NDI-TA produces stable transparent hydrogel with the entangled fibrillar morphology that is typical for gelators. Both NDI-A and NDI-TA showed a thermoresponsive property with a lower critical solution temperature of about 41-42 °C. Powder XRD studies show a parallel orientation for NDI-A and an antiparallel orientation for NDI-TA. Computational studies support this experimental observation and indicate that the NDI-A assembly is highly stabilized by strong H-bonding among the amide groups and π-stacking interaction in the parallel orientation. On the other hand, due to weak H-bonding among the thioamide groups, the binding energy of the parallelly oriented NDI-TA was significantly lower and the optimized structure was disordered. Instead, its antiparallel orientation was more stable, with criss-cross aligned H-bonding interactions and π-π interactions between adjacent aromatic rings. The NDI-TA hydrogel with less ordered OE chains on the surface showed prominent adsorption of serum protein BSA. In sharp contrast, NDI-A did not exhibit any notable interaction with BSA, as evident from the ITC studies. |
5,144 | Pole-Restraining Control of three-phase Active Front End - a comparison to state-of-the art controls and its performance under fault-ride-through conditions | State-of-the-art control systems often neglect the time-variant characteristics of power-electronic systems in favour of a time-averaged approach. In consequence, the resulting system is partly instable, thus not optimally controlled. The novel pole-restraining control (PRC) concept removes the inherent instability considerably improving dynamic behaviour. This enhancement is demonstrated at hand of a three-phase Active Front End (AFE). The essentials of the pole-restraining control approach are explained, the advanced transient behaviour is illustrated by comparison to other state-of-the-art control schemes. |
5,145 | Assessment of Position Repeatability Error in an Electromagnetic Tracking System for Surgical Navigation | In this paper we present a study of the repeatability of an innovative electromagnetic tracking system (EMTS) for surgical navigation, developed to overcome the state of the art of current commercial systems, allowing for the placement of the magnetic field generator far from the operating table. Previous studies led to the development of a preliminary EMTS prototype. Several hardware improvements are described, which result in noise reduction in both signal generation and the measurement process, as shown by experimental tests. The analysis of experimental results has highlighted the presence of drift in voltage components, whose effect has been quantified and related to the variation of the sensor position. Repeatability in the sensor position measurement is evaluated by means of the propagation of the voltage repeatability error, and the results are compared with the performance of the Aurora system (which represents the state of the art for EMTS for surgical navigation), showing a repeatability error about ten times lower. Finally, the proposed improvements aim to overcome the limited operating distance between the field generator and electromagnetic (EM) sensors provided by commercial EM tracking systems for surgical applications and seem to provide a not negligible technological advantage. |
5,146 | Internet-based intelligent and sustainable manufacturing: developments and challenges | In recent years, the nations of the world have presented the development strategy of manufacturing. Manufacturing is the foundation of a country. However, with the increasing global resource constraints and market heterogeneity, the variety of individual demands, and the long-term goals of sustainable development, with the support of emerging information and communication technologies such as Internet, cyber-physical system, Internet of Things, cloud computing, and big data, industrial value creation is causing a paradigm shift in manufacturing. This paper studies a range of new manufacturing paradigms and presents a state-of-the-art survey of published works. It explores the corresponding current manufacturing concepts, technologies, framework features, application effects, resource optimization, and future challenges in these new paradigms. The integration of various manufacturing paradigms is also analyzed. Through this survey, the developments of these new manufacturing paradigms are explained and the future prospects are also discussed. |
5,147 | Synergistic Effects of Zanubrutinib Combined With CD19 CAR-T Cells in Raji Cells in Vitro and in Vivo | Background and Objects: Bruton's tyrosine kinase inhibitors are commonly used and effective for lymphoma and chronic lymphocytic leukemia (CLL). Ibrutinib might improve the effect of anti-cluster of differentiation 19 (CD19) chimeric antigen receptor (CD19 CAR) T-cell therapy in lymphoma, but the effects of zanubrutinib combined with CAR-T cells is unclear. Methods: We selected a low effect-target ratio (E:T = 1:3) to study this synergistic effect in vitro. The programed cell death protein 1 (PD-1) expression in CD19 CAR-T cells and immune phenotype of T lymphocytes were analyzed by flow cytometry (FCM). We selected CD19 CAR-T cells of a patient with diffuse large B cell lymphoma (DLBCL) to study the synergistic effect of zanubrutinib with CAR-T cells by bioluminescence imaging monitoring. The CD19 CAR-T cells expansion in mice was compared by FCM. Results: Zanubrutinib and ibrutinib had dose-dependent toxicity on both CAR-T cells and lymphoma cells. But there was no significant synergistic effect of the CD19 CAR-T cells combined with zanubrutinib/ibrutinib in vitro. The PD-1 expression in CD19 CAR-T cells increased when the CD19 CAR-T cells were co-cultured with Raji cells and decreased when ibrutinib was added in culture, but zanubrutinib had no such effect. The extinction of luciferase expression was more obvious in the polytherapy group of ibrutinib and CD19 CAR-T cell than that in the other groups. Moreover, the proportion of CAR-T cells in the combination therapy group of CD19 CAR-T cells and ibrutinib was higher than that of the polytherapy group of CD19 CAR-T cells with zanubrutinib group. The synergistic effect could be observed obviously in mice receiving ibrutinib combined with CD19 CAR-T cells. But zanubrutinib cannot perform joint therapy effect either in vitro or in mice. Conclusion: Zanubrutinib might have no joint therapy effect with CD19 CAR-T cells neither in vitro nor in mice, but the mechanism of different curative effects requires our further research and exploration. |
5,148 | Analytical Design and Experimental Verification of Geofencing Control for Aerial Applications | Keep-in operational envelopes are essential to maintain the safety of unmanned aerial vehicles (UAVs). System properties and constraints, including underactuated dynamics and actuator saturation, dramatically affect the system's maneuverability inside the operational envelope. Moreover, sate-of-the-art safety control depends heavily on the specifications of the operational envelope. Thus, this article focuses on creating a scalable technique to transform safety envelopes into input-constrained barriers along each axis of motion. Then, it is shown that the proposed class of operational envelopes simultaneously guarantees safety and asymptotic stability. The closed-form solution for the safety rule is derived as allowable low and high bounds of the control command, which are calculated in real time. Furthermore, it is shown that the proposed safety design seamlessly integrates with an existing motion control algorithm with minimum modification. The super-twisting control is used to handle the nonlinear complexity of the UAV and parametric uncertainties and achieve a desirable robust behavior for trajectory and attitude control. The control calibration and tuning are carried out on a state-of-the-art experimental system. The experimental results verify the effectiveness of the proposed safety control. |
5,149 | Robust Offset-Cancellation Sensing-Circuit-Based Spin-Transfer-Torque Nonvolatile Flip-Flop | Nonvolatile flip-flop (NV-FF) based systems can be effectively implemented in battery-limited internet of things (IoT) applications due to their zero standby power consumption and instant ON/OFF characteristics. Among various NV-FFs, spin-transfer-torque magnetic tunnel junction based NV-FFs are the most applicable due to their nonvolatility, high endurance, complementary metal-oxide-semiconductor (CMOS) compatibility, scalability, and rapid sensing and write speed due to its low resistance. However, they are subject to a degraded sensing margin for restoring operation with technology shrinks because of the increased process variation and reduced supply voltage. This paper proposes a novel NV-FF with a significantly superior offset-tolerance and reduction in read current (I-read) that produces read disturbance in comparison to state-of-the-art NV-FFs. Monte Carlo HSPICE simulation results based on industry-compatible 65-nm model parameters revealed that the proposed NV-FF can achieve a three-order improvement in the restore yield and a two-order reduction in the I-read when compared with state-of-the-art NV-FFs. |
5,150 | Testing the Effect of Histone Acetyltransferases on Local Chromatin Compaction | Experiments determining the chromatin association of histone acetylases (HATs) and deacetylases (HDACs) at the genome-wide level provide precise maps of locus occupancy, but do not allow conclusions on the functional consequences of this locus-specific enrichment. Here we describe a protocol that allows tethering of HATs or HDACs to specific genomic loci upon fusion with a fluorescent protein and a DNA-binding protein such as the E. coli Lac repressor (LacI), which binds to genomically inserted lac operon sequences (lacO) via DNA/protein interactions. Integration of these lacO sequences into a genomic region of interest allows to monitor the functional consequences of HAT/HDAC targeting on chromatin (de)compaction, histone modification, and interaction with other proteins by quantitative light microscopy, as described here. As DNA-binding of LacI can be tightly controlled by the addition of galactose-derivatives, this method also allows to monitor the effects of locus-specific recruitment in a time-resolved manner. |
5,151 | Interpretability-Driven Sample Selection Using Self Supervised Learning for Disease Classification and Segmentation | In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and with minimal expert interactions (e.g. label querying in an active learning setup). In this article we propose a novel sample selection methodology based on deep features leveraging information contained in interpretability saliency maps. In the absence of ground truth labels for informative samples, we use a novel self supervised learning based approach for training a classifier that learns to identify the most informative sample in a given batch of images. We demonstrate the benefits of the proposed approach, termed Interpretability-Driven Sample Selection (IDEAL), in an active learning setup aimed at lung disease classification and histopathology image segmentation. We analyze three different approaches to determine sample informativeness from interpretability saliency maps: (i) an observational model stemming from findings on previous uncertainty-based sample selection approaches, (ii) a radiomics-based model, and (iii) a novel data-driven self-supervised approach. We compare IDEAL to other baselines using the publicly available NIH chest X-ray dataset for lung disease classification, and a public histopathology segmentation dataset (GLaS), demonstrating the potential of using interpretability information for sample selection in active learning systems. Results show our proposed self supervised approach outperforms other approaches in selecting informative samples leading to state of the art performance with fewer samples. |
5,152 | Color Image Denoising via Cross-Channel Texture Transferring | Image denoising can reduce the perturbation inevitably generated during image signal acquisition and its subsequent processing. While the utilization of nonlocal properties can enhance the performance of the state-of-the-art denoising methods, a heavy computational burden is incurred especially for color images. Inspired by the high correlation in the texture information over color channels, for a reduction of the computational burden, this letter proposes denoising the luma channel first, and then, performing a patch-wise linear prediction to transfer the texture information of the denoised luma channel to the other two channels. The texture transferring is adapted to local characteristic (i.e., variance of the local patches) for a reduction of color smearing caused by large prediction error especially along edges. Experimental results confirm that the proposed method achieves performance improvement over the state-of-the-art color image denoising methods only at a slightly increased complexity of single-channel denoising. |
5,153 | Fixed-Point NLMS and IPNLMS VLSI Architectures for Accurate FECG and FHR Processing | Capturing signals without noise and interference while monitoring the maternal abdomen's fetal electrocardiogram (FECG) is a challenging task. This method can provide fetal monitoring for long hours, not harming the pregnant woman or the fetus. Such non-invasive FECG raw signal suffers from various interference sources as the bio-electric maternal potentials include her ECG component. Therefore, a critical step in the non-invasive FECG is to design the filtering of components derived from the maternal ECG. There is an increasing demand for portable devices to extract a pure FECG signal and to detect fetal heart rate (FHR) with precision. Dedicated CMOS architectures enable higher energy efficiency in portable devices. This paper proposes VLSI architectures dedicated to FECG extraction and FHR processing. Fixed-point architectures for the FECG detection exploring the NLMS (normalized least mean square), IPNLMS (improved proportional NLMS), and three different division VLSI CMOS architectures are designed herein. An architecture based on the Pan-Tompkins algorithm that processes the FECG for extracting the FHR, extending the functionally of the system, is also proposed. The results show that the NLMS and IPNLMS based architectures effectively detect the R-peaks of FECG with a detection accuracy of 92.86% and 93.75%, respectively. The synthesis results shows that our NLMS architecture proposal saves 13.3 % energy, due to a reduction of 279 clock cycles, compared to the state of the art. On the other hand, the IPNLMS algorithm results in +0.89% detection accuracy at the price of 42% additional energy consumption w.r.t NLMS. |
5,154 | A numerical investigation of the mechanics of intracranial aneurysms walls: Assessing the influence of tissue hyperelastic laws and heterogeneous properties on the stress and stretch fields | Numerical simulations have been extensively used in the past two decades for the study of intracranial aneurysms (IAs), a dangerous disease that occurs in the arteries that reach the brain and affect overall 3.2% of a population without comorbidity with up to 60% mortality rate, in case of rupture. The majority of those studies, though, assumed a rigid-wall model to simulate the blood flow. However, to also study the mechanics of IAs walls, it is important to assume a fluid-solid interaction (FSI) modeling. Progress towards more reliable FSI simulations is limited because FSI techniques pose severe numerical difficulties, but also due to scarce data on the mechanical behavior and material constants of IA tissue. Additionally, works that have investigated the impact of different wall modeling choices for patient-specific IAs geometries are a few and often with limited conclusions. Thus our present study investigated the effect of different modeling approaches to simulate the motion of an IA. We used three hyperelastic laws - the Yeoh law, the three-parameter Mooney-Rivlin law, and a Fung-like law with a single parameter - and two different ways of modeling the wall thickness and tissue mechanical properties - one assumed that both were uniform while the other accounted for the heterogeneity of the wall by using a "hemodynamics-driven" approach in which both thickness and material constants varied spatially with the cardiac-cycle-averaged hemodynamics. Pulsatile numerical simulations, with patient-specific vascular geometries harboring IAs, were carried out using the one-way fluid-solid interaction solution strategy implemented in solids4foam, an extension of OpenFOAM®, in which the blood flow is solved and applied as the driving force of the wall motion. We found that different wall morphology models yield smaller absolute differences in the mechanical response than different hyperelastic laws. Furthermore, the stretch levels of IAs walls were more sensitive to the hyperelastic and material constants than the stress. These findings could be used to guide modeling decisions on IA simulations, since the computational behavior of each law was different, for example, with the Yeoh law being the fastest to converge. |
5,155 | Differences in Insulin Sensitivity, Secretion, and the Metabolic Clearance Rate of Glucose in Newly Diagnosed Type 2 Diabetes Mellitus Patients: The Influences of Body Mass Index and Fatty Liver | Background: Obesity and nonalcoholic fatty liver disease are strongly associated with type 2 diabetes mellitus (T2DM), affecting insulin sensitivity and β-cell function. They interact, exacerbating the development of hyperinsulinemia to T2DM. Methods: Through oral glucose tolerance and insulin secretion tests, the relationships between insulin sensitivity and secretion, glucose clearance, body mass index (BMI), and fatty liver were studied in newly diagnosed T2DM patients. The homeostasis model assessment of insulin resistance (HOMA-IR), homeostasis model assessment of β-cell function (HOMA-β), insulin sensitivity index (ISI), and metabolic clearance rate (MCR) of glucose were calculated to analyze insulin sensitivity and β-cell function. Results: There were no differences in HOMA-IR, HOMA-β, first-phase insulin secretion (1st PH), second-phase insulin secretion (2nd PH), ISI, or MCR between lean fatty liver and lean nonfatty liver patients. Both overweight/obesity (ow/ob) and patients with fatty liver increased HOMA-IR, and decreased ISI and MCR. In the ow/ob subgroup, patients with fatty liver had severe insulin resistance but greater HOMA-β, 1st PH, and 2nd PH than individuals with nonfatty liver. The difference in MCR between fatty liver and nonfatty liver groups was not significant. Conclusion: BMI and hepatic steatosis are independent determinants of increased insulin resistance and decreased MCR. However, it is steatosis, not BMI, related to the increase in insulin secretion. |
5,156 | GPU Sparse Ray-Traced Segmentation | This paper introduces a real-time region growing segmentation algorithm, designed for graphics processing units (GPUs), which labels only a fraction of the input elements. Instead of searching locally around each element for strong similarity, like state-of-the-art segmentation and pre-segmentation methods do, the proposed algorithm searches both locally and remotely, using a unique ray tracing-based search strategy, which quickly covers the segmentation search space. The presented algorithm fully exploits the parallelism of the GPUs, sparsely segmenting high-resolution images (4K) in real-time on low range laptops and other mobile devices, approximately 5x times faster than the state-of-the-art simple linear iterative clustering (SLIC). While this paper demonstrates the results with images, the algorithm is trivially modifiable to work with input sets of any dimension. In contrast to the state-of-the-art real-time GPU methods, this algorithm doesn't require additional merging steps, as pre-segmentation methods do, and it produces complete segmentation. Additionally, post-segmentation optional stages for complete labeling and region merging on the GPU are also provided, although they are not always necessary. |
5,157 | Restoring our senses, restoring the Earth. Fostering imaginative capacities through the arts for envisioning climate transformations | Humanity has never lived in a world of global average temperature above two degrees of current levels. Moving towards such High-End Climate Change (HECC) futures presents fundamental challenges to current governance structures and involves the need to confront high uncertainties, non-linear dynamics and multiple irreversibilities in global social-ecological systems. In order to face HECC, imaginative practices able to support multiple ways of learning about and experiencing the future are necessary. In this article we analysed a set of arts-based activities conducted within the five-year EU-funded project IMPRESSIONS aimed at identifying transformative strategies to high-end climate change. The exploratory artistic activities were carried out alongside a science-led participatory integrated assessment process with stakeholders from the Iberian Peninsula. Our arts-based approach combined a range of performative, visual and reflexive practices with the ambition to reach out to more-than-rational but also practical elements of HECC futures. Our study suggests that the arts-based approach helped to bring out new ways of seeing, feeling and interpreting the world which may support the development of individual and collective sensibilities needed to address HECC. |
5,158 | Role of Defects on the Particle Size-Capacitance Relationship of Zn-Co Mixed Metal Oxide Supported on Heteroatom-Doped Graphenes as Supercapacitors | Supercapacitors are considered among the most promising electrical energy storage devices, there being a need to achieve the highest possible energy storage density. Herein small mixed Zn-Co metal oxide nanoparticles are grown on doped graphene (O-, N- and, B-doped graphenes). The electrochemical properties of the resulting mixed Zn-Co metal oxide nanoparticles (4 nm) grown on B-doped graphene exhibit an outstanding specific capacitance of 2568 F g-1 at 2 A g-1 , ranking this B-doped graphene composite among the best performing electrodes. The energy storage capacity is also remarkable even at large current densities (i.e., 640 F g-1 at 40 A g-1 ). In contrast, larger nanoparticles are obtained using N- and O-doped graphenes as support, the resulting materials exhibiting lower performance. Besides energy storage, the Zn-Co oxide on B-doped graphene shows notable electrochemical performance and stability obtaining a maximum energy density of 77.6 W h Kg-1 at 850 W Kg-1 , a power density of 8500 W Kg-1 at 28.3 W h Kg-1 , and a capacitance retention higher than 85% after 5000 cycles. The smaller nanoparticle size and improved electrochemical performance on B-doped graphene-based devices are attributed to the higher defect density and nature of the dopant element on graphene. |
5,159 | Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks | Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate (equal error rate) of 0.7 percent an improvement ratio of 371 percent compared to previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and to provide insights of the feature learning process. |
5,160 | Shape autotuning activation function | The choice of activation function is essential for building state-of-the-art neural networks. At present, the most widely-used activation function with effectiveness is ReLU. However, ReLU suffers from the weakness including non-zero mean, negative missing, and unbounded output, thus it has potential disadvantages in the optimization process. In this paper, we propose a novel activation function, namely ?Shape Autotuning Activation Function? (SAAF), to overcome these three challenges simultaneously. The SAAF inherits merits of smooth activation functions (such as Sigmoid and Tanh) and piecewise activation functions (such as ReLU and its variants), and avoids their deficiencies. Specifically, the SAAF adaptively adjusts a pair of independent trainable parameters to capture negative information and provide a near-zero mean output, resulting in better generalization performance and faster learning speed. At the same time, it provides bounded outputs to ensure a more stable distribution of output during network training. We evaluated SAAF on deep networks applied to a variety of tasks, including image classification, machine translation, and generative modeling. Comprehensive comparison study shows that the proposed SAAF is superior to state-of-the-art activation functions. |
5,161 | Modeling multiple dependent variables in meta-analysis of single-case experimental design using multilevel modeling | Although meta-analyses of single-case experimental design (SCED) often include multiple types of dependent variables (DVs), multiple DVs are rarely considered within models in the analysis. Baek et al. (Journal of Experimental Education, 90(4), 934-961, 2022) identified several statistical issues that arise when researchers fail to model multiple DVs in meta-analyses of SCED data. However, the degree to which non-modeling of multiple DVs impacts the results of the meta-analysis of SCED has not been fully examined. In this simulation study, we have systematically investigated the impact of non-modeling of multiple DVs when analyzing meta SCED data using multilevel modeling. The result demonstrates that modeling multiple DVs has advantages over the non-modeling option for meta-analysis of SCED. Modeling multiple DVs enables the determination of precise effects from different DVs in addition to the unbiased and accurate average effect and accurate estimates and inferences for the error variances at the study level as well as the observation level. The current study also reveals potential factors (i.e., the number of DVs, degree of heterogeneity in the level-1 error variances and autocorrelation, and presence of the moderator effect) that impact the precision and accuracy of the variance parameters. |
5,162 | Use of the CuFe2O4/biochar composite to remove methylene blue, methyl orange and tartrazine dyes from wastewater using photo-Fenton process | In this study, CuFe2O4 ferrite was supported on biochar produced from malt biomass residues as a photocatalyst for degradation of methylene blue (MB), methyl orange (MO), and tartrazine (TZ) dyes. XRD, FT-IR, and FE-SEM were used to characterize the crystallinity and morphology of the samples. The characterization showed that the ferrite was uniformly supported on the surface of the biochar, confirming the formation of the composite. Degradation tests showed that CuFe2O4 degraded approximately 50, 47, and 62% of MB, MO, and TZ dyes, respectively, after 60 min of reaction. On the other hand, the CuFe2O4/biochar composite showed a significant increase in dye degradation, ~ 100%, for all three dyes. This increase in degradation efficiency may be due to less agglomeration of supported particles and due to decreased recombination of electron/hole pairs. Thus, results showed that the photocatalyst composite produced in this study is an effective alternative for removing dyes from wastewater. |
5,163 | [Diagnostics and treatment of clinically relevant paraneoplastic syndromes] | Paraneoplastic syndromes (PS) are rare disorders with often complex clinical manifestations that occur in association with a tumor without being triggered by direct tumor invasion or compression. They arise from tumor secretions of hormones, peptides or cytokines or from immune cross-reactivity between malignant and healthy tissue. They are categorized into endocrine, neurological, dermatological, rheumatological, and hematological PS. The PS most commonly occurs in small cell lung carcinoma but also in association with other respiratory tract tumors, gynecological tumors, and hematological malignancies. The PS can precede a tumor diagnosis, therefore timely diagnosis can improve the prognosis of a malignant disease. The diagnostics are based on the clinical presentation as well as diagnostic methods depending on the underlying pathogenesis. The most important treatment approach involves the best possible treatment of the tumor and a targeted treatment is only sometimes possible. This review focuses on the clinically most frequently encountered PS. |
5,164 | Environmental management in Ramsar designated wetland areas in Vietnam: studies from U Minh Thuong and Tram Chim national parks (Mekong Delta) | This study investigated the possibility of using remotely sensed data and field surveys for understanding the environmental management practices in two Ramsar sites - U Minh Thong and Tram Chim national parks - in the Vietnamese Mekong Delta. Enhanced agriculture, infrastructure development, changes in hydrological regime, forest fires, and natural resources exploitation are the key variables that caused the depletion of these two wetland areas. Land cover, particularly vegetation coverage, has been changed considerably during the post-war period and agriculture has been intensified in the surrounding areas of U Minh Thuong and Tram Chim wetlands. The current water management strategies in U Minh Thuong and Tram Chim were designated to ensure proper water circulation during the dry and wet seasons in a way helpful to agriculture in the buffer zones and to prevent forest fires during the dry season. It is found that the water management strategies to prevent forest fires in both the parks resulted in the accumulation of toxic agrochemicals within the park during the wet season. Both U Minh Thuong and Tram Chim wetlands are invaded by alien plant species which is threatening the natural biodiversity of the area. Proper monitoring and control of invasive species is necessary for protecting the natural biodiversity of these wetland ecosystems. Proper law enforcement and an interactive and inclusive wetland management should be practiced in order to conserve these valuable wetland ecosystems. |
5,165 | Autonomous near-field communication (NFC) sensors for long-term preventive care of fine art objects | We present the design and pilot trial of near-field communication (NFC) sensors for the long-term preventive care of fine art objects. This work was undertaken to address the unmet need for a permanent and unified object specific sensory and digital data labelling system for fine art objects that does not require large-scale wireless infrastructure. The sensor-tags are demonstrated in a six-month pilot study to evaluate the temperature and humidity buffering performance of a microclimate enclosure (MCE) constructed to protect a late C16th panel painting. The framed painting fitted with NFC sensor-tags was hung in a busy, and environmentally uncontrolled, Cambridge college dining hall to mimic a typically large, semipublic exhibition space. The resulting visibility of climatic conditions and the detailed analytics made possible from the data sets harvested from the NFC sensor-tags have provided invaluable information to the painting conservators. This study provides the first steps in understanding how object specific electronic sensor-tags will play a critical role in improving the display management, storage and long-term preventive care of fine art objects. (C) 2018 Elsevier B.V. All rights reserved. |
5,166 | Multi-view hypergraph neural networks for student academic performance prediction | Academic performance prediction is a fundamental and hot issue in educational data mining (EDM). Recently, researchers have proposed a series of effective machine learning (ML) based classification strategies to predict students' academic performance. However, prior arts are typically concerned about individual models but neglect the association among students, which might considerably have an effect on the integrity of the academic performance-related representations. Meanwhile, students' multi-viewing behavior contains complex relations among students. Therefore, we propose a Multi-View Hypergraph Neural Network (MVHGNN) for predicting students' academic performance. MVHGNN uses hypergraphs to construct high-order relations among students. The semantic information implied by multiple behaviors is consolidated through meta-paths. Further, a Cascade Attention Transformer (CAT) module is introduced to mine the weight of different behaviors by the self-attention mechanism. Our method is evaluated on real campus student behavioral datasets. The experimental results demonstrate that our method outperforms the state-of-the-art ones. |
5,167 | Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages | Multi-modal neuroimages, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), can provide complementary structural and functional information of the brain, thus facilitating automated brain disease identification. Incomplete data problem is unavoidable in multi-modal neuroimage studies due to patient dropouts and/or poor data quality. Conventional methods usually discard data-missing subjects, thus significantly reducing the number of training samples. Even though several deep learning methods have been proposed, they usually rely on pre-defined regions-of-interest in neuroimages, requiring disease-specific expert knowledge. To this end, we propose a spatially-constrained Fisher representation framework for brain disease diagnosis with incomplete multi-modal neuroimages. We first impute missing PET images based on their corresponding MRI scans using a hybrid generative adversarial network. With the complete (after imputation) MRI and PET data, we then develop a spatially-constrained Fisher representation network to extract statistical descriptors of neuroimages for disease diagnosis, assuming that these descriptors follow a Gaussian mixture model with a strong spatial constraint (i.e., images from different subjects have similar anatomical structures). Experimental results on three databases suggest that our method can synthesize reasonable neuroimages and achieve promising results in brain disease identification, compared with several state-of-the-art methods. |
5,168 | Secure Integrated Circuit with Physical Attack Detection based on Reconfigurable Top Metal Shield | Invasive physical attacks on integrated circuits (ICs), such as de-packaging, focused ion beam (FIB) chip editing, and micro-probing attempts, constitute security threats for chips with potentially valuable information, such as smart cards. Using a state-of-the-art circuit-editing technique, an attacker can remove an IC's top metal layer, leaving its secure information exposed to micro-probing attacks. Security ICs can be seriously threatened by such attacks and thus require on-chip countermeasures. Conventional active shields, however, have difficulty coping with physical attacks based on FIB chip editing (i.e., bypassing the top metal shield). This study presents a novel countermeasure against physical attacks based on the use of a reconfigurable metal shield for both top metal removal and micro-probing attack detection. This shield consists of two circuits: an FIB chip editing detection circuit consisting of a random number generator and a micro-probing attempt detection circuit consisting of two conditionally synchronized ring oscillators. Both circuits share a randomly reconfigured top metal shield, which represents a promising solution for security against state-of-the-art invasive attacks. |
5,169 | Multi-level learning counting via pyramid vision transformer and CNN | Severe scale variation has become a challenging issue for hindering the improvement of accuracy in crowd counting task. To tackle the problem, we propose a Pyramid Transformer CNN Network (PTCNet), an effective combination of the transformer and the CNN, which possesses both the global receptive fields and the locality to deal with the severe scale variation problems and boost the prediction accuracy. Firstly, we utilize the pyramid vision transformer to extract multi-level global context information of the crowd, aiming at different head scales. And then, the multi-level information is fully fused in the multi-level feature aggregation module where detailed crowd characteristics from all feature spaces are preserved to be further processed. Finally, we design a multi-branch regression head to enrich the crowd features for strong representations and regress the density maps. Extensive experiments on challenging datasets with complex scenarios and multiple scales demonstrate the effectiveness of the our method. The proposed method achieves competitive results comparing with the state-of-the-art approaches and achieves state-of-the-art results(MAE:51.7, RMSE:79.6) on ShanghaiTech Part_A dataset. |
5,170 | Review of 3-D Endoscopic Surface Imaging Techniques | This paper provides an overview of state-of-the-art 3-D endoscopic imaging technologies. Physical objects in the world are 3-D, yet traditional endoscopes can only acquire 2-D images that lack depth information. This fundamental restriction greatly limits our ability to perceive and understand the complexity of real world objects. Lack of 3-D information also hinders our ability to quantitatively measure 3-D objects. In both medical imaging and industrial inspection applications, 3-D surface imaging capability would add one more dimension, literally and figuratively, to the existing imaging technologies. Over the past decades, tremendous new technologies and methods emerged in the 3-D surface imaging field. In this paper, we first provide a classification of these technologies. We then describe each category in detail, with representative designs and examples. This overview would be useful to researchers in the field since it provides a snapshot of the current state-of-the-art, from which subsequent research in meaningful directions is encouraged. This overview also contributes to the efficiency of research by preventing unnecessary duplication of already performed research. |
5,171 | Scale Sequence Joint Deep Learning (SS-JDL) for land use and land cover classification | Choosing appropriate scales for remotely sensed image classification is extremely important yet still an open question in relation to deep convolutional neural networks (CNN), due to the impact of spatial scale (i.e., input patch size) on the recognition of ground objects. Currently, the optimal scale selection processes are extremely cumbersome and time-consuming requiring repetitive experiments involving trial-and-error procedures, which significantly reduce the practical utility of the corresponding classification methods. This issue is crucial when trying to classify large-scale land use (LU) and land cover (LC) jointly (Zhang et al., 2019). In this paper, a simple and parsimonious Scale Sequence Joint Deep Learning (SS-JDL) method is proposed for joint LU and LC classification, in which a sequence of scales is embedded in the iterative process of fitting the joint distribution implicit in the joint deep learning (JDL) method, thus, replacing the previous paradigm of scale selection. The sequence of scales, derived autonomously and used to define the CNN input patch sizes, provides consecutive information transmission from small-scale features to large-scale representations, and from simple LC states to complex LU characterisations. The effectiveness of the novel SS-JDL method was tested on aerial digital photography of three complex and heterogeneous landscapes, two in Southern England (Bournemouth and Southampton) and one in North West England (Manchester). Benchmark comparisons were provided in the form of a range of LU and LC methods, including the state-of-the-art joint deep learning (JDL) method. The experimental results demonstrated that the SS-JDL consistently outperformed all of the state-of-the-art baselines in terms of both LU and LC classification accuracies, as well as computational efficiency. The proposed SS-JDL method, therefore, represents a fast and effective implementation of the state-of-the-art JDL method. By creating a single, unifying joint distribution framework for classifying higher order feature representations, including LU, the SS-JDL method has the potential to transform the classification paradigm in remote sensing, and in machine learning more generally. |
5,172 | Overexpressing NeuroD1 reprograms Müller cells into various types of retinal neurons | The onset of retinal degenerative disease is often associated with neuronal loss. Therefore, how to regenerate new neurons to restore vision is an important issue. NeuroD1 is a neural transcription factor with the ability to reprogram brain astrocytes into neurons in vivo. Here, we demonstrate that in adult mice, NeuroD1 can reprogram Müller cells, the principal glial cell type in the retina, to become retinal neurons. Most strikingly, ectopic expression of NeuroD1 using two different viral vectors converted Müller cells into different cell types. Specifically, AAV7m8 GFAP681::GFP-ND1 converted Müller cells into inner retinal neurons, including amacrine cells and ganglion cells. In contrast, AAV9 GFAP104::ND1-GFP converted Müller cells into outer retinal neurons such as photoreceptors and horizontal cells, with higher conversion efficiency. Furthermore, we demonstrate that Müller cell conversion induced by AAV9 GFAP104::ND1-GFP displayed clear dose- and time-dependence. These results indicate that Müller cells in adult mice are highly plastic and can be reprogrammed into various subtypes of retinal neurons. |
5,173 | A secured internet of robotic things (IoRT) for long-term care services in a smart building | Long-term care refers to any support, both medical and non-medical, provided to the elderly with a chronic illness or disability due to physical or mental conditions. Since the cost of long-term care insurance is not inexpensive, low-cost devices and sensors can be used to create medical assistance systems to reduce human maintenance costs. The requirement of security and privacy under healthcare information protection is a critical issue for internet of medical things (IoMT) data transmission. In this paper, we designed an IoMT security robot for a long-term care system. The goal of this IoMT security robot is to provide secure transmission of the residents' private information. It is composed of three layers, namely, collection, encryption, and transmission. The function of the IoMT security robot is to first collect data from the patient or the elderly, then provide efficient data encryption, and deliver secured data transmission mechanisms to send the valuable data to the cloud. This IoMT security robot also has a server authentication mechanism, and a support IoT and IoMT devices inspection function. Our evaluation results showed that even when we utilized a low power consumption device like Raspberry Pi, AES algorithm achieved an encrypt and decrypt of 100-100 K bytes under 9 ms, which is a lot better than ECC, which takes about 104 ms. Further, we found that the AES only takes 0.00015 s to decrypt 100 Bytes data, which is way faster than the ECC algorithm, which takes 0.09 s. |
5,174 | Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images | Digital pathology has entered a new era with the availability of whole slide scanners that create the high-resolution images of full biopsy slides. Consequently, the uncertainty regarding the correspondence between the image areas and the diagnostic labels assigned by pathologists at the slide level, and the need for identifying regions that belong to multiple classes with different clinical significances have emerged as two new challenges. However, generalizability of the state-of-the-art algorithms, whose accuracies were reported on carefully selected regions of interest (ROIs) for the binary benign versus cancer classification, to these multi-class learning and localization problems is currently unknown. This paper presents our potential solutions to these challenges by exploiting the viewing records of pathologists and their slide-level annotations in weakly supervised learning scenarios. First, we extract candidate ROIs from the logs of pathologists' image screenings based on different behaviors, such as zooming, panning, and fixation. Then, we model each slide with a bag of instances represented by the candidate ROIs and a set of class labels extracted from the pathology forms. Finally, we use four different multi-instance multi-label learning algorithms for both slide-level and ROI-level predictions of diagnostic categories in whole slide breast histopathology images. Slide-level evaluation using 5-class and 14-class settings showed average precision values up to 81% and 69%, respectively, under different weakly labeled learning scenarios. ROI-level predictions showed that the classifier could successfully perform multi-class localization and classification within whole slide images that were selected to include the full range of challenging diagnostic categories. |
5,175 | Application Analysis of Traditional Cultural Elements in the Environmental Art Design of Coastal Cities | In the design of urban modern environmental art, traditional cultural elements are widely used, and many environmental art designers can integrate traditional cultural elements with modern environmental art perfectly, so that traditional cultural elements can be better inherited and carried forward. This paper summarizes the connotation of traditional cultural elements and modern environmental art design, briefly analyzes the role of traditional cultural elements in the design of modern environmental art in coastal cities and the current situation of modern environmental art design, and focuses on how to strengthen the use of traditional cultural elements in the modern environmental art design in coastal cities. |
5,176 | Molecular transformation of dissolved organic matter and formation pathway of humic substances in dredged sludge under aerobic composting | Using Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) and molecular reaction network analysis, this study investigated molecular transformation of dissolved organic matter (DOM) and formation pathway of humic substances (HS) in dredged sludge during aerobic composting. The results showed that macromolecular N-containing compounds in dredged sludge are abundantly transformed into unsaturated and aromatic oxygenated compounds, exhibiting physicochemical properties similar to those of humus. Especially, N-containing compounds with one nitrogen atom are susceptible to oxidative deamination. Furthermore, assemblages of reactive fragments (e.g., -C7H8O2, -C10H12O2, -C2H2O2, and -C4H6O2) were identified as potential precursors to HS formed by the following reactions: starting with protein deamination and desulfurization, lignin delignification cascaded, finally decarbonylation occurred. This work provides novel insight for optimizing the process of stabilization and humification of dredged sludge. |
5,177 | On the Efficiency of Supernodal Factorization in Interior-Point Method Using CPU-GPU Collaboration | Primal-dual interior-point method (PDIPM) is the most efficient technique for solving sparse linear programming (LP) problems. Despite its efficiency, PDIPM remains a compute-intensive algorithm. Fortunately, graphics processing units (GPUs) have the potential to meet this requirement. However, their peculiar architecture entails a positive relationship between problem density and speedup, conversely implying a limited affinity of GPUs for problem sparsity. To overcome this difficulty, the state-of-the-art hybrid (CPU-GPU) implementation of PDIPM exploits presence of supernodes in sparse matrices during factorization. Supernodes are groups of similar columns that can be treated as dense submatrices. Factorization method used in the state-of-the-art solver performs only selected operations related to large supernodes on GPU. This method is known to underutilize GPU's computational power while increasing CPU-GPU communication overhead. These shortcomings encouraged us to adapt another factorization method, which processes sets of related supernodes on GPU, and introduce it to the PDIPM implementation of a popular open-source solver. Our adaptation enabled the factorization method to better mitigate the effects of round-off errors accumulated over multiple iterations of PDIPM. To augment performance gains, we also used an efficient CPU-based matrix multiplication method. When tested for a set of well-known sparse problems, the adapted solver showed average speed-ups of approximately 55X, 1.14X and 1.05X over the open-source solver's original version, the state-of-the-art solver, and a highly optimized proprietary solver known as CPLEX, respectively. These results strongly indicate that our proposed hybrid approach can lead to significant performance gains for solving large sparse problems. |
5,178 | Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection of Wind Turbines | Blade icing detection is critical to maintaining the health of wind turbines, especially in cold climates. Rapid and accurate icing detection allows proper control of wind turbines, including shutting down and clearing the ice, thus ensuring turbine safety. This article presents a wavelet-driven multiscale graph convolutional network (MWGCN), which is a supervised deep learning model for blade icing detection. The proposed model first uses wavelet decomposition to capture multivariate information in the time and frequency domains, and then employs a temporal graph convolutional network (GCN) to model the intervariable correlations of the decomposed multiscale wavelets and their temporal dynamics. In addition, this article introduces scale attention to the MWGCN for a further improvement of the model and proposes the method to address the class imbalance problem of the training data sets. Finally, the article conducts comprehensive experiments to evaluate the proposed model, and the results demonstrate the effectiveness of the model in blade icing detection and its better performance over eight state-of-the-art algorithms, with 17.2% and 11.3% higher F1 scores over the best state-of-the-art baseline on the labeled datasets. |
5,179 | Improving the robustness of variational optical flow through tensor voting | Differential optical flow methods allow the estimation of optical flow fields based on the first-order and even higher-order spatio-temporal derivatives (gradients) of sequences of input images. If the input images are noisy, for instance because of the limited quality of the capturing devices or due to poor illumination conditions, the use of partial derivatives will amplify that noise and thus end up affecting the accuracy of the computed flow fields. The typical approach in order to reduce that noise consists of smoothing the required gradient images with Gaussian filters, for instance by applying structure tensors. However, that filtering is isotropic and tends to blur the discontinuities that may be present in the original images, thus likely leading to an undesired loss of accuracy in the resulting flow fields. This paper proposes the use of tensor voting as an alternative to Gaussian filtering, and shows that the discontinuity preserving capabilities of the former yield more robust and accurate results. In particular, a state-of-the-art variational optical flow method has been adapted in order to utilize a tensor voting filtering approach. The proposed technique has been tested upon different datasets of both synthetic and real image sequences, and compared to both well known and state-of-the-art differential optical flow methods. (C) 2012 Elsevier Inc. All rights reserved. |
5,180 | Alteration of Upstream Autophagy-Related Proteins (ULK1, ULK2, Beclin1, VPS34 and AMBRA1) in Lewy Body Disease | Autophagy is associated with the pathogenesis of Lewy body disease, including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). It is known that several downstream autophagosomal proteins are incorporated into Lewy bodies (LBs). We performed immunostaining and Western blot analysis using a cellular model of PD and human brain samples to investigate the involvement of upstream autophagosomal proteins (ULK1, ULK2, Beclin1, VPS34 and AMBRA1), which initiate autophagy and form autophagosomes. Time course analysis of cultured cells transfected with flag-α-synuclein and synphilin-1 revealed upregulation of these upstream proteins with accumulation of LB-like inclusions. In human specimens, only mature LBs were positive for upstream autophagosomal proteins. Western blotting of fractionated brain lysates showed that upstream autophagosomal proteins were detected in the soluble and insoluble fraction in DLB, corresponding to the bands of phosphorylated α-synuclein. However, Western blot analysis of total brain lysates in PD and DLB showed that the increase of upstream autophagosomal proteins was only partial. The quantitative, qualitative and locational alteration of upstream autophagosomal proteins in the present study indicates their involvement in the pathogenesis of LB disease. Our data also suggest that misinduction or impairment of upstream autophagy might occur in the disease process of LB disease. |
5,181 | Advances in microbial electrochemistry-enhanced constructed wetlands | Constructed wetland (CW) is an effective ecological technology to treat water pollution and has the significant advantages of high impact resistance, simple construction process, and low maintenance cost. However, under extreme conditions such as low temperature, high salt concentration, and multiple types of pollutants, some bottlenecks exist, including the difficulty in improving operating efficiency and the low pollutant removal rate. Microbial electrochemical technology is an emerging clean energy technology and has the similar structure and pollutant removal mechanism to CW. Microbial electrochemistry combined with CW can improve the overall removal effect of pollutants in wetlands. This review summarizes characterization methods of microbial electrochemistry-enhanced constructed wetland systems, construction methods of different composite systems, mechanisms of single and composite systems, and removal effects of composite systems on different pollutants in water bodies. Based on the shortcomings of existing studies, the potential breakthroughs in microbial electrochemistry-enhanced constructed wetlands are proposed for developing the optimization solution of constructed wetlands. |
5,182 | Disruption of proteostasis causes IRE1 mediated reprogramming of alveolar epithelial cells | Disruption of alveolar type 2 cell (AEC2) protein quality control has been implicated in chronic lung diseases, including pulmonary fibrosis (PF). We previously reported the in vivo modeling of a clinical surfactant protein C (SP-C) mutation that led to AEC2 endoplasmic reticulum (ER) stress and spontaneous lung fibrosis, providing proof of concept for disruption to proteostasis as a proximal driver of PF. Using two clinical SP-C mutation models, we have now discovered that AEC2s experiencing significant ER stress lose quintessential AEC2 features and develop a reprogrammed cell state that heretofore has been seen only as a response to lung injury. Using single-cell RNA sequencing in vivo and organoid-based modeling, we show that this state arises de novo from intrinsic AEC2 dysfunction. The cell-autonomous AEC2 reprogramming can be attenuated through inhibition of inositol-requiring enzyme 1 (IRE1α) signaling as the use of an IRE1α inhibitor reduced the development of the reprogrammed cell state and also diminished AEC2-driven recruitment of granulocytes, alveolitis, and lung injury. These findings identify AEC2 proteostasis, and specifically IRE1α signaling through its major product XBP-1, as a driver of a key AEC2 phenotypic change that has been identified in lung fibrosis. |
5,183 | Fuzzy knowledge evaluation model as a methodological basis for automation of pedagogical testing | Implementation of the mathematical methods and state-of-the-art information technologies into such an informal and descriptive subject as pedagogics is considered to be one of the demands of the times, resulting from the necessity to adapt educational systems to a modern, information-oriented society as fast as possible. The presented study considers a trainee's knowledge evaluation model intended for the procedure of testing. This fuzzy knowledge evaluation model is based on the methods of fuzzy algebra. |
5,184 | Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction | Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing trend, but usually a large number of annotated data are required for training, which is very labor-intensive and time-consuming. In this paper, we consider a partially supervised setting, where cheap image-level annotations are provided for all the training data, and the costly per-voxel annotations are only available for a subset of them. We propose an Inductive Attention Guidance Network (IAG-Net) to jointly learn a global image-level classifier for normal/PDAC classification and a local voxel-level classifier for semi-supervised PDAC segmentation. We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier. Experimental results show that our IAG-Net boosts PDAC segmentation accuracy by more than 5% compared with the state-of-the-arts. |
5,185 | Developing a digital game for excel skills learning in higher education - a comparative study analyzing differences in learning between digital games and textbook learning | In higher education, many universities in Taiwan let college students learn excel in a self-directed way. The current axle of the Excel curriculum mainly relies on self-directed learning. In the study, we designed the digital game "Legendary Wizard Excel" and took a certified Excel textbook as the research tool. The game we designed integrated the role-play with cognitive scaffolding to help learners learn Excel skills, whereas the textbook we used was "Excel Expert" in the Microsoft Office Specialist. We compared the Learning Effectiveness, Flow Status, and Technology Acceptance Model with 187 college students between two tools, and found that: (1) The game reached a high Technology Acceptance Model; (2) Both groups of learners had significant improvements in learning effectiveness and were engaged in the activity; (3) On learning effectiveness, learners in game-based learning groups achieved higher than learners in textbook groups; (4) Learners in game-based learning groups engaged better in the activity than learners in textbook groups. Therefore, in the future, we looked forward to bringing our results to higher education levels and workplace training to enhance the Excel skills. |
5,186 | Long-Term Non Anesthetic Preclinical Study Available Extra-Cranial Brain Activator (ECBA) System for the Future Minimally Invasive Human Neuro Modulation | In recent years, electroceuticals have been spotlighted as an emerging treatment for various severe chronic brain diseases, owing to their intrinsic advantage of electrical interaction with the brain, which is the most electrically active organ. However, the majority of research has verified only the short-term efficacy through acute studies in laboratory tests owing to the lack of a reliable miniaturized platform for long-term animal studies. The construction of a sufficient integrated system for such a platform is extremely difficult because it requires multi-disciplinary work using state-of-the-art technologies in a wide range of fields. In this study, we propose a complete system of an implantable platform for long-term preclinical brain studies. Our proposed system, the extra-cranial brain activator (ECBA), consists of a titanium-packaged implantable module and a helmet-type base station that powers the module wirelessly. The ECBA can also be controlled by a remote handheld device. Using the ECBA, we performed a long-term non-anesthetic study with multiple canine subjects, and the resulting PET-CT scans demonstrated remarkable enhancement in brain activity relating to memory and sensory skills. Furthermore, the histological analysis and high-temperature aging test confirmed the reliability of the system for up to 31 months. Hence, the proposed ECBA system is expected to lead a new paradigm of human neuromodulation studies in the near future. |
5,187 | The Benefits of Using State-Of-The-Art Digital Soil Properties Maps to Improve the Modeling of Soil Moisture in Land Surface Models | This study assesses the added value of using emerging maps of soil properties to improve surface soil moisture simulations using the HydroBlocks land surface model with different soil hydraulic parameterization schemes. Simulations were run at an hourly 30-m resolution between 2012 and 2019 and evaluated against U.S. Climate Reference Network measurements. The results show that state-of-the-art soil properties maps (POLARIS and SoilGrids250m V2.0) improve the accuracy of simulated surface soil moisture when compared to the STATSGO-derived CONUS-SOIL map. Contemporary pedotransfer functions (multi-linear regression and Artificial Neural Networks-based) also improve model performance in comparison to the lookup table-derived soil parameterization schemes. The addition of vertical heterogeneity to the soil properties further improves the mean Kling-Gupta efficiency by 0.04 and lowers the mean Root mean square error by 0.003 over the CONUS. This study demonstrates that land surface modeling can be improved by using state-of-the-art maps of soil properties, accounting for the vertical heterogeneity of soils, and advancing the use of contemporary pedotransfer functions. |
5,188 | A lightweight authentication scheme with privacy preservation for vehicular networks | The vehicular ad-hoc network relies on wireless communication, thus exposing security and privacy-related issues. The primary security requirements in a vehicular network are vehicle authentication, message integrity, and privacy preservation. Most of the existing state-of-the-art security schemes heavily relied on infrastructural entities such as roadside units and trusted authorities to achieve authentication. Typically, frequent authentication involving infrastructural entities increases communication overhead. Besides, the existing solutions assume the usage of ideal tamper-proof devices for storing sensitive data, which is prone to physical and cloning attacks. In this context, we present a Physically Unclonable Function based Lightweight Authentication Scheme with privacy preservation for vehicle-to-vehicle communication in the vehicular network. Physically unclonable function adds an extra layer of security from cloning and physical attacks. The proposed method uses a two-tier approach consisting of trusted authority and vehicles for achieving authentication using lightweight bitwise XOR operations and one-way hash functions. The proposed scheme reduces the burden on infrastructural entities by performing authentication through vehicle-to-vehicle communication. Compared with state-of-the-art schemes, the proposed scheme's security and performance analysis demonstrates its effectiveness in meeting various security requirements while ensuring resilience against various known attacks and competitive communication and computation cost. |
5,189 | A Dilemma of Self-interest vs. Ethical Responsibilities in Political Insider Trading | Political insider trading has brought substantial attention to ethical considerations in the academic literature. While the Stop Trading on Congressional Knowledge (STOCK) Act prohibits members of Congress and their staff from leveraging non-public information to make investment decisions, political insider trading still prevails. We discuss political ethics and social contract theory to re-engage the debate on whether political insider trading is unethical and raises the issues of conflict of interest and social distrust. Empirically, using a novel measure of information risk, we find that senator trades are associated with substantially high levels of information asymmetry. Moreover, based on inside political information, senators earn significant market-adjusted returns (4.9% over 3 months). Thus, our results do not support the prediction made by social contract theory and thereby provide a potential resolution to the ongoing debate on banning stock trading for members of Congress. |
5,190 | Examining the perceived stress and body image in burn patients: A cross-sectional study | This study aimed to examine the perceived stress and body image in burn patients and the relationship between these two variables. This is a descriptive and cross-sectional study. The study included total of 144 patients who had burn injuries, received treatment in a research and training hospital and were scheduled to be discharged. The data were collected prospectively by the researchers, using descriptive methods, Kruskal Wallis test, paired samples t test, and Pearson's correlation analysis. Of the patients, 59% were between the ages of 18 and 35 years, 68.1% were male, 65.3% had second-degree burns, 77.1% had burn surfaces ranging between 10% and 20% of their body, and 54.9% had autograft surgery. The burn patients aged 51 years and over had higher perceived stress than younger patients, and the difference between them was statistically significant (P < 0.05). As the percentage of burn surface increased, the perceived stress increased, and the perceived body image weakened (P < 0.05). The burn patients with autograft surgery had lower perceived stress and higher perceived body image than those without autograft surgery, and the difference between them was statistically significant (P < 0.01). This study found an inverse relationship between perceived stress and body image in burn patients, which was affected by the percentage of burn surface and autograft surgery. Relevant interventions are suggested to increase perceived body image in burn patients and reduce their perceived stress. |
5,191 | PuVAE: A Variational Autoencoder to Purify Adversarial Examples | Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise networks at inference time by applying elaborately designed perturbations to input data. Although several defense methods have been proposed to address specific attacks, other types of attacks can circumvent these defense mechanisms. Therefore, we propose Purifying Variational AutoEncoder (PuVAE), a method to purify adversarial examples. The proposed method eliminates an adversarial perturbation by projecting an adversarial example on the manifold of each class and determining the closest projection as a purified sample. We experimentally illustrate the robustness of PuVAE against various attack methods without any prior knowledge about the attacks. In our experiments, the proposed method exhibits performances that are competitive with state-of-the-art defense methods, and the inference time is approximately 130 times faster than that of Defense-GAN which is a state-of-the art purifier method. |
5,192 | Open internet gateways to archives of media art | The EU-projects OASIS Archive and GAMA aimed to develop systems for the common presentation of distributed Media Art works, independent of their location. The paper presents the technical solutions implemented during projects, and contextualise the work in the related media art scene. In order to ensure the preservation and availability (sustainability) of cultural heritage, the metadata systems of all participating institutions were interlinked and they can now be accessed by individual users (both researchers and general public) through an on-line interface. The developed multimedia archive servers enable the exchange of metadata within a distributed system and enable various play-out sources and media. The decentralised architecture, where data can remain at their physical location and are linked at the metadata level, is the key concept of the presented system. |
5,193 | Image-guided fine-needle aspiration cytology or core biopsy - A key to definitive diagnosis of tuberculous mastitis | Despite advances in the treatment, tuberculosis (TB) is still a global health problem. The diagnosis of extrapulmonary TB in their primary form is very challenging. Breast TB is very uncommon and accounts for < 0.1% of all breast lesions. Due to rarity of the disease and difficulty in diagnosis, we report a case of a 40-year-old female who had a hard lump in the right breast. Full-field digital mammography suggested the lesion as American College of Radiology Breast Imaging Reporting and Data System-5 (ACR BIRADS-5) (highly suggestive of carcinoma). Histopathological examination of multiple cores of the breast tissue showed lymphocytic inflammatory infiltrates confined to breast lobules. Fungal stains and Ziehl-Neelsen (ZN) stain were negative. A diagnosis of chronic mastitis with the possibility of autoimmune lobular mastitis was suggested. Subsequent image-guided fine needle aspiration smears showed epithelioid granulomas mixed with lymphocytes. Areas of amorphous-to-granular eosinophilic material (caseous necrosis) were seen at places. ZN stain showed acid-fast bacilli. A diagnosis of tuberculous mastitis was given. |
5,194 | Exploring Convolution Neural Network for Branch Prediction | Recently, there have been significant advances in deep neural networks (DNNs) and they have shown distinctive performance in speech recognition, natural language processing, and image recognition. In this paper, we explore DNNs to push the limit for branch prediction. We treat branch prediction as a classification problem and employ both deep convolutional neural networks (CNNs), ranging from LeNet to ResNet-50, and deep belief network (DBN) for branch prediction. We compare the effectiveness of DNNs with the state-of-the-art branch predictors, including the perceptron, our prior work, Multi-poTAGE+SC, and MTAGE+SC branch predictors. The last two are the most recent winners of championship branch prediction (CBP) contests. Several interesting observations emerged from our study. First, for branch prediction, the DNNs outperform the perceptron model as high as 60-80%. Second, we analyze the impact of the depth of CNNs (i.e., number of convolutional layers and pooling layers) on the misprediction rates. The results confirm that deeper CNN structures can lead to lower misprediction rates. Third, the DBN could outperform our prior work, but not outperform the state-of-the-art TAGE-like branch predictor; the ResNet-50 could not only outperform our prior work, but also the Multi-poTAGE+SC and MTAGE+SC. |
5,195 | Exercise Dependence in Practitioners of Martial Arts and Combat Sports | Background: The aim of this study was to analyse prevalence exercise dependence among practicing martial arts and combat sports. Methods: There were 166 respondents. The Exercise Dependence Scale-EDS was used. Results: The martial arts practitioners obtained a lower result in the 'intention effects' (p < 0.05; eta(2) = 0.03), 'continuance' (p = 0.04; eta(2) = 0.03), 'lack of control' (p < 0.05; eta(2) = 0.03), 'reduction in other activities' (p = 0.04; eta(2) = 0.03), and 'total score' (p = 0.04; eta(2) = 0.03) than the combat sports athletes. Both the respondents with a high training rank (p < 0.05) and subjects with above 5 years of training experience (p = 0.03; eta(2) = 0.03) achieved the higher mean in the 'time' subscale. Women obtained lower results in the case of 'tolerance' (p = 0.04; eta(2) = 0.04). The regression coefficient indicates that the higher respondent's age, the lower total score she/he will achieve in the EDS. Conclusions: The findings have practical implications for identifying subjects 'at-risk for exercise dependence' symptoms, and may aid coaches and individuals in the implementation of a prevention program, to seek suitable support. |
5,196 | Nursing Students and the Human Body: Application of an Ethics Pilot Project | This manuscript offers findings from a pilot project which prepares nursing students for embodied professional practice through the lens of ethics. Four undergraduate nursing students were mentored by two nursing faculty in the Dundon-Berchtold Institute Faculty Fellowship Program in the Application of Ethics through an exploration on the ethics of embodiment using an arts pedagogy across one academic year. Inspired by the intersection of nature and health, this project explores the impact of an arts-integrated pedagogy on the human body. The findings from this project provide a natural first step for nursing students to consider multiple interpretations of the human body and to facilitate the students' development of an embodied ethical practice that is perceptive, empathic, and attuned to themselves as natural beings as well as diverse individuals and populations. The findings from this pilot project presents a pivotal opportunity to guide future nursing curricular development toward holistic, nature-inspired, and mindful-based interventions in order to increase resilience, decrease risk factors of compassion fatigue and burnout, and support nursing students to develop strength-based skills to use in their professional nursing practice. |
5,197 | Useful signs for the assessment of vascular rings on cross-sectional imaging | Vascular rings can be challenging to diagnose because they can contain atretic portions not detectable with current imaging modalities. In these cases, where the compressed airway and esophagus are not encircled by patent, opacified vessels, there are useful secondary signs that should be considered and should raise suspicion for the presence of a vascular ring. These signs include a double aortic arch, the four-vessel sign, the distorted subclavian artery sign, a diverticulum of Kommerell, a ductal diverticulum contralateral to the aortic arch, and a descending aorta contralateral to the arch or circumflex aorta. If none of these findings is present, a ring can be excluded with confidence. |
5,198 | Radiological risk estimation from indoor radon, thoron, and their progeny concentrations using nuclear track detectors | In this paper, we report the results of seasonal variations of indoor radon and thoron concentrations, equilibrium factors for gas progeny, and radiological risks to dwellers in the hilly area of Guwahati City, Assam, India. Twin-cup dosemeters with LR-115 (II) nuclear track detectors were used in this study. The findings show that values vary significantly, with winter having the highest values and summer having the lowest, with spring and autumn having moderate values. In winter, radon concentrations range from 61.6 ± 11.2 Bq m-3 (Mud) to 115.3 ± 34.3 Bq m-3 (AT), with geometric mean values of 69.2 ± 13.8 Bq m-3 and 109.4 ± 27.9 Bq m-3, and in summer, they range from 21.1 ± 5.9 Bq m-3 (Mud) to 28.4 ± 8.3 Bq m-3 (AT), with geometric mean values of 22.7 ± 6.3 Bq m-3 and 26.1 ± 7.1 Bq m-3, whereas thoron concentrations range from 13.1 ± 5.1 Bq m-3 (Mud) to 58.8 ± 12.6 Bq m-3 (AT), with geometric mean values of 27.6 ± 7.0 Bq m-3 and 52.9 ± 10.1 Bq m-3 in winter, respectively, and in summer, from 8.8 ± 2.3 Bq m-3 (Mud) to 13.0 ± 5.5 Bq m-3 (Mud), with a geometric mean value of 1.87 ± 1.29 Bq m-3. Radon and thoron progeny levels are reported to vary from 4.1 ± 0.3 mWL (Mud) to 15.1 ± 4.3 mWL (AT) and 2.6 ± 0.9 mWL (Mud) to 14.3 ± 4.2 mWL (AT) in winter and from 1.5 ± 0.7 mWL (AT) to 3.0 ± 2.5 mWL (Mud) and 0.9 ± 0.3 mWL (AT) to 2.7 ± 0.5 mWL (Mud) in summer, respectively. The equilibrium factors for radon and its progeny have been reported to range from 0.23 ± 0.1 (Mud) to 0.51 ± 0.3 (AT) in winter, whereas from 0.23 ± 0.1 (AT) to 0.48 ± 0.4 (Mud) in summer, respectively. The equilibrium factors for thoron and its progeny have been estimated in the range of 0.02 ± 0.01 (Mud) to 0.09 ± 0.06 (AT) in winter, whereas 0.02 ± 0.02 (AT) to 0.07 ± 0.05 (Mud) in summer, respectively. The inhalation dose rates differed from house to house, having values in the range of 1.2 ± 0.2 mSv year-1 (Mud) to 4.6 ± 1.3 mSv year-1 (AT) in winter, whereas 0.5 ± 0.3 mSv year-1 (AT) to 0.9 ± 0.5 mSv year-1 (Mud) in summer, respectively. The effective doses (EDs) due to the exposure of radon and thoron in the study area have been found to range from 2.5 ± 0.3 mSv (Mud) to 9.1 ± 2.7 mSv (AT) in winter and 0.9 ± 0.4 mSv (AT) to 1.8 ± 1.3 mSv (Mud) in summer, respectively. The levels of radon and thoron in similar types of construction were found to be significantly different from one house to another. The estimated radon and thoron concentrations in the houses of that region during winter are found to be substantially higher than the global averages as reported by UNSCEAR. |
5,199 | Virtual classroom proficiency-based progression for robotic surgery training (VROBOT): a randomised, prospective, cross-over, effectiveness study | Robotic surgery training has lacked evidence-based standardisation. We aimed to determine the effectiveness of adjunctive interactive virtual classroom training (VCT) in concordance with the self-directed Fundamentals of Robotic Surgery (FRS) curriculum. The virtual classroom is comprised of a studio with multiple audio-visual inputs to which participants can connect remotely via the BARCO weConnect platform. Eleven novice surgical trainees were randomly allocated to two training groups (A and B). In week 1, both groups completed a robotic skills induction. In week 2, Group A received training with the FRS curriculum and adjunctive VCT; Group B only received access to the FRS curriculum. In week 3, the groups received the alternate intervention. The primary outcome was measured using the validated robotic-objective structured assessment of technical skills (R-OSAT) at the end of week 2 (time-point 1) and 3 (time-point 2). All participants completed the training curriculum and were included in the final analyses. At time-point 1, Group A achieved a statistically significant greater mean proficiency score compared to Group B (44.80 vs 35.33 points, p = 0.006). At time-point 2, there was no significant difference in mean proficiency score in Group A from time-point 1. In contrast, Group B, who received further adjunctive VCT showed significant improvement in mean proficiency by 9.67 points from time-point 1 (95% CI 5.18-14.15, p = 0.003). VCT is an effective, accessible training adjunct to self-directed robotic skills training. With the steep learning curve in robotic surgery training, VCT offers interactive, expert-led learning and can increase training effectiveness and accessibility. |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.