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3,600 | A Wireless Communications Laboratory on Cellular Network Planning | The field of radio network planning and optimization (RNPO) is central for wireless cellular network design, deployment, and enhancement. Wireless cellular operators invest huge sums of capital on deploying, launching, and maintaining their networks in order to ensure competitive performance and high user satisfaction. This work presents a lab course composed of 10 experiments that cover the different phases of RNPO for various state-ofthe-art wireless technologies such as GSM, UMTS, and WiMAX. Each experiment constitutes a complete entity including the necessary theoretical background and references, the lab tasks based on real-world scenarios, and a research component consisting of general questions. The proposed lab course represents a novel initiative to increase interactive learning by integrating communications theory fundamental knowledge with state-of-the-art wireless communications software tools and measurement equipment. The experiments are carefully designed to enhance the analytical skills and to advance the academic and practical knowledge of the students. |
3,601 | Hospitalizations, active component, U.S. Armed Forces, 2021 | The hospitalization rate in 2021 was 48.0 per 1,000 person-years (p-yrs), the second lowest rate of the most recent 10 years. For hospitalizations limited to military facilities, the rate in 2021 was the lowest for the entire period. As in prior years, the majority (71.2%) of hospitalizations were associated with diagnoses in the categories of mental health disorders, pregnancy-related conditions, injury/poisoning, and digestive system disorders. |
3,602 | Concavity and convexity of illumination | When estimating the convexity of illuminated surfaces the human mind uses schematic patterns. Discovering and investigating these mechanisms is very important in the work of a lighting designer, as inappropriate illumination of architecture can cause unsuitable distortion of surface's texture and give a caricatural look to the illuminated building. In this article we present the mechanisms of observing and estimating the concavity-convexity supported by "real life" examples. |
3,603 | Secret-Fragment-Visible Mosaic Image-A New Computer Art and Its Application to Information Hiding | A new type of computer art image called secret-fragment-visible mosaic image is proposed, which is created automatically by composing small fragments of a given image to become a target image in a mosaic form, achieving an effect of embedding the given image visibly but secretly in the resulting mosaic image. This effect of information hiding is useful for covert communication or secure keeping of secret images. To create a mosaic image of this type from a given secret color image, the 3-D color space is transformed into a new 1-D colorscale, based on which a new image similarity measure is proposed for selecting from a database a target image that is the most similar to the given secret image. A fast greedy search algorithm is proposed to find a similar tile image in the secret image to fit into each block in the target image. The information of the tile image fitting sequence is embedded into randomly-selected pixels in the created mosaic image by a lossless LSB replacement scheme using a secret key; without the key, the secret image cannot be recovered. The proposed method, originally designed for dealing with color images, is also extended to create grayscale mosaic images which are useful for hiding text-type grayscale document images. An additional measure to enhance the embedded data security is also proposed. Good experimental results show the feasibility of the proposed method. |
3,604 | Magnetic Sensors: Taxonomy, Applications, and New Trends | In this paper, we present the current state of the art in magnetic sensors. The three major magnetic effects that may be used in sensing devices and applications are: the magnetic induction, based on the characteristics and properties of the magnetization loop and its dependence on control parameters such as magnetic field, temperature, and stress; the magnetoelastic coupling, based on the magnetostiction loop and its dependence on stress, temperature, and magnetic field; the magnetotransport phenomena and their macroscopic characteristics, namely, the anisotropic magnetoresistance, the giant magnetoresistance, and the magnetoimpedance. Based on the above taxonomy, we present a variety of sensing elements materials and analyze their characteristics, focusing on sensitivity and uncertainty. We also demonstrate applications in field, stress, and positioning measurements, using state-of-the-art sensor designs. Finally, we discuss the outlook in this field, focusing on the needs of industrial and medical sectors. |
3,605 | Examining the Scholarly Literature: A Bibliometric Study of Journal Articles Related to Sustainability and the Arts | The Arts shows great promise in working toward a sustainable future as they can have a significant influence on the development of cultural norms. Using bibliometrics, this study uncovers the current body of scholarly literature related to the intersection of sustainability and the Arts. The results show that while there are very few articles (n = 77) published in scholarly journals related to this area, the number of manuscripts and the number of journals publishing manuscripts related to this subject area is increasing. Further, while there is no one individual who stands out to date as a leader in this field, the results show that Australia and Canada have produced the most published articles. Finally, this study demonstrates that scholarly articles related to the Arts and sustainability are mostly being published in well-established interdisciplinary sustainability-related journals and journals associated with the field of education for sustainable development. The results of this study give a more definitive answer to the question: what scholarly literature resources currently exist on the intersection of the Arts and sustainability and offers the scholarly community a better idea of what and how those involved in this area are publishing and mobilizing knowledge regarding their work. |
3,606 | Data-driven evaluation framework for the effectiveness of rural vitalization in China: an empirical case study of Hubei Province | Rural vitalization (RV) has attracted more and more attention in China, especially since the Rural Vitalization Strategy (RVS) was proposed to restrict rural decline in 2017. The evaluation of RV is an effective means to objectively identify the characteristics and problems of rural development, so exploring scientific and rational evaluation methods is important for sustainable rural development. Therefore, this study builds a data-driven evaluation framework from a "bottom-up" perspective, and selects Hubei Province as the object to evaluate the effectiveness of RV. The evaluation index system is formed based on the concept and connotation of RV, which contains six dimensions, namely thriving businesses (TB), pleasant living environments (PLE), social etiquette and civility (SEC), effective governance (EG), living in prosperity (LP), and organization system (OS). The empirical results indicate that there is a low level of variation of the total scores but an obvious disparity in the dimensional scores in 13 prefecture-level and 83 county-level regions. At county-level, the regional development stage has an impact on the effectiveness of RV, and regions with a higher economy or endowed with better resources perform better. The results of spatial analysis further reveal that there is regional agglomeration as well as differences in various dimensions, and regions with characteristic industries or policy support perform better. Compared with the traditional evaluation method, differentiated evaluation objectives and diversified data are considered in the evaluation process of this study. The results and discussion shown in this study could provide empirical evidence for policymakers to effectively promote RV in the future. |
3,607 | Ischemic accumulation of succinate induces Cdc42 succinylation and inhibits neural stem cell proliferation after cerebral ischemia/reperfusion | Ischemic accumulation of succinate causes cerebral damage by excess production of reactive oxygen species. However, it is unknown whether ischemic accumulation of succinate affects neural stem cell proliferation. In this study, we established a rat model of cerebral ischemia/reperfusion injury by occlusion of the middle cerebral artery. We found that succinate levels increased in serum and brain tissue (cortex and hippocampus) after ischemia/reperfusion injury. Oxygen-glucose deprivation and reoxygenation stimulated primary neural stem cells to produce abundant succinate. Succinate can be converted into diethyl succinate in cells. Exogenous diethyl succinate inhibited the proliferation of mouse-derived C17.2 neural stem cells and increased the infarct volume in the rat model of cerebral ischemia/reperfusion injury. Exogenous diethyl succinate also increased the succinylation of the Rho family GTPase Cdc42 but repressed Cdc42 GTPase activity in C17.2 cells. Increasing Cdc42 succinylation by knockdown of the desuccinylase Sirt5 also inhibited Cdc42 GTPase activity in C17.2 cells. Our findings suggest that ischemic accumulation of succinate decreases Cdc42 GTPase activity by induction of Cdc42 succinylation, which inhibits the proliferation of neural stem cells and aggravates cerebral ischemia/reperfusion injury. |
3,608 | Teledentistry and oral health in older adults - aspects for implementation of the "Patient centric solution for smart and sustainable healthcare (ACESO)" project | P u r p o s e: Oral health and diseases are significant components of general health. However, oral health-care remains at the lowest of older patients' priorities. The inability to obtain dental care can result in progression of dental disease, leading to a diminished quality of life and overall health. Teledentistry (TD) provides an opportunity to improve the quality of oral health services. The aim of our narrative review was to analyze the usefulness of teledentistry as a part of telemedicine to improve oral health in the elderly. Materials/Methods: The PubMed database search was done for: teledentistry, oral health, oral- health related diseases, elderly, older adults. R e s u l t s: The applicability of TD has been demonstrated from children to older adults. Older adults have many obstacles in getting oral health care, including low income, lack health insurance, frailty, anxiety, depression, mobility problems or other handicaps. Available data suggests that the usefulness of TD in the provision of oral care in elderly people living in residential aged care facilities. Moreover, TD procedures were found to be as accurate as traditional face-to-face dental examinations, they was cost-effective and well accepted among patients and caregivers. C o n c l u s i o n s: TD might be a very useful tool for professional education, improving access and patient satisfaction of dental care. However, such TD modes would be difficult to widely implementation in community-dwelling older people who cannot access dental care. The ongoing "Patient centric solution for smart and sustainable healthcare (ACESO)" project will add to the intelligent oral health solutions. |
3,609 | A Local Perturbation Generation Method for GAN-Generated Face Anti-Forensics | Although the current generative adversarial networks (GAN)-generated face forensic detectors based on deep neural networks (DNNs) have achieved considerable performance, they are vulnerable to adversarial attacks. In this paper, an effective local perturbation generation method is proposed to expose the vulnerability of state-of-the-art forensic detectors. The main idea is to mine the fake faces' areas of common concern in multiple-detectors' decision-making, then generate local anti-forensic perturbations by GANs in these areas to enhance the visual quality and transferability of anti-forensic faces. Meanwhile, in order to improve the anti-forensic effect, a double- mask (soft mask and hard mask) strategy and a three-part loss (the GAN training loss, the adversarial loss consisting of ensemble classification loss and ensemble feature loss, and the regularization loss) are designed for the training of the generator. Experiments conducted on fake faces generated by StyleGAN demonstrate the proposed method's advantage over the state-of-the-art methods in terms of anti-forensic success rate, imperceptibility, and transferability. The source code is available at https://github.com/imagecbj/A-Local-Perturbation-Generation-Method-for-GAN-generated-Face-Anti-forensics. |
3,610 | Fast large-scale object retrieval with binary quantization | The objective of large-scale object retrieval systems is to search for images that contain the target object in an image database. Where state-of-the-art approaches rely on global image representations to conduct searches, we consider many boxes per image as candidates to search locally in a picture. In this paper, a feature quantization algorithm called binary quantization is proposed. In binary quantization, a scale-invariant feature transform (SIFT) feature is quantized into a descriptive and discriminative bit-vector, which allows itself to adapt to the classic inverted file structure for box indexing. The inverted file, which stores the bit-vector and box ID where the SIFT feature is located inside, is compact and can be loaded into the main memory for efficient box indexing. We evaluate our approach on available object retrieval datasets. Experimental results demonstrate that the proposed approach is fast and achieves excellent search quality. Therefore, the proposed approach is an improvement over state-of-the-art approaches for object retrieval. (C) 2015 SPIE and IS&T |
3,611 | SLC44A1-PRKCA fusion in papillary and rosette-forming glioneuronal tumors | We investigated the fused protein of solute carrier family 44 choline transporter member 1 (SLC44A1) and protein kinase C alpha (PRKCA) in three patients with papillary glioneuronal tumors (PGNT). PGNT and rosette-forming glioneuronal tumors (RGNT) are recently identified, unusual glioneuronal tumor variants which were categorized as novel tumor entities in the 2007 World Health Organization classification system. The molecular background of these tumors remains poorly understood due to the paucity of studies. The SLC44A1-PRKCA fusion was recently detected in three cases of PGNT. We invesitgated for the SLC44A1-PRKCA fusion protein in the three PGNT patients and a further two with RGNT using fluorescence in situ hybridization. Two out of the three PGNT patients had a fused signal (paired red-green signal) representing a rearrangement on chromosomes 9 and 17. A normal signal pattern was observed in the third PGNT patient. Neither of the two RGNT patients demonstrated a fused signal. This suggests that the SLC44A1-PRKCA fusion is a characteristic alteration in PGNT but not RGNT. Therefore, it is a potential biomarker of PGNT. The paired red-green signal that was observed in the PGNT patients implies the presence of a different breakpoint than that previously reported in the 9q31 and 17q24 genes. |
3,612 | The Segmentation of 3D Images Using the Random Walking Technique on a Randomly Created Image Adjacency Graph | This paper considers the problem of image segmentation using the random walker algorithm. In the case of 3D images, the method uses an extreme amount of memory and time resources. These are required in order to represent the corresponding enormous image graph and to solve the resulting sparse linear system. Having in mind these limitations, this paper proposes techniques for the optimization of the random walker approach. The optimization is obtained by processing supervoxels representing homogeneous image regions rather than single voxels. A fast and efficient method for supervoxel determination is introduced. A method for the creation of an image adjacency graph from an irregular grid of supervoxels is also proposed. The results of applying the introduced approach to segmentation of 3D CT data sets are presented and compared with the results of the original random walker approach and other state-of-the-art methods. The accuracy and the computational overhead is regarded in the comparison. The analysis of results shows that the modified method can be successfully applied for the segmentation of volumetric images and provides results in a reasonable time without a significant loss in the image segmentation accuracy. It also outperforms the state-of-the-art methods considered in the comparison. |
3,613 | The Value of Active Arts Engagement on Health and Well-Being of Older Adults: A Nation-Wide Participatory Study | An emerging body of research indicates that active arts engagement can enhance older adults' health and experienced well-being, but scientific evidence is still fragmented. There is a research gap in understanding arts engagement grounded in a multidimensional conceptualization of the value of health and well-being from older participants' perspectives. This Dutch nation-wide study aimed to explore the broader value of arts engagement on older people's perceived health and well-being in 18 participatory arts-based projects (dance, music, singing, theater, visual arts, video, and spoken word) for community-dwelling older adults and those living in long term care facilities. In this study, we followed a participatory design with narrative- and arts-based inquiry. We gathered micro-narratives from older people and their (in)formal caregivers (n = 470). The findings demonstrate that arts engagement, according to participants, resulted in (1) positive feelings, (2) personal and artistic growth, and (3) increased meaningful social interactions. This study concludes that art-based practices promote older people's experienced well-being and increase the quality of life of older people. This study emphasizes the intrinsic value of arts engagement and has implications for research and evaluation of arts engagement. |
3,614 | Contrastive Self-Supervised Learning With Smoothed Representation for Remote Sensing | In remote sensing, numerous unlabeled images are continuously accumulated over time, and it is difficult to annotate all the data. Therefore, a self-supervised learning technique that can improve the recognition rate using unlabeled data will be useful for remote sensing. This letter presents contrastive self-supervised learning with smoothed representation for remote sensing based on the SimCLR framework. In self-supervised learning for remote sensing, the well-known characteristic that images within a short distance might be semantically similar is usually used. Our algorithm is based on this knowledge, and it simultaneously utilizes several neighboring images as a positive pair of the anchor image, unlike existing methods such as Tile2Vec. Furthermore, MoCo and SimCLR, which are among the state-of-the-art self-supervised learning approaches, only use two augmented views of the single-input image, but our proposed approach uses multiple-input images and averages their representations (e.g., smoothed representation). Consequently, the proposed approach outperforms state-of-the-art self-supervised learning methods, such as Tile2Vec, MoCo, and SimCLR, in the cropland data layer (CDL), RESISC-45, UCMerced, and EuroSAT data sets. The proposed approach is comparable to the pretrained ImageNet model in the CDL classification task. |
3,615 | Joint Reconstruction of Tracer Distribution and Background in Magnetic Particle Imaging | Magnetic particle imaging (MPI) is a novel tomographic imaging technique, which visualizes the distribution of a magnetic nanoparticle-based tracer material. However, reconstructed MPI images often suffer from an insufficiently compensated image background caused by rapid non-deterministic changes in the background signal of the imaging device. In particular, the signal-to-background ratio (SBR) of the images is reduced for lower tracer concentrations or longer acquisitions. The state-of-the-art procedure in MPI is to frequently measure the background signal during the sample measurement. Unfortunately, this requires a removal of the entire object from the scanner's field of view (FOV), which introduces dead time and repositioning artifacts. To overcome these considerable restrictions, we propose a novel method that uses two consecutive image acquisitions as input parameters for a simultaneous reconstruction of the tracer distribution, as well as the background signal. The two acquisitions differ by just a small spatial shift, while keeping the object always within the focus of a slightly reduced FOV. A linearly interpolated background between the initial and final background measurement is used to seed the iterative reconstruction. The method has been tested with simulations and phantom measurements. Overall, a substantial reduction of the image background was observed, and the image SBR is increased by a factor of 2(7) for the measurement (simulation) data. |
3,616 | Spatio-Temporal Representation Matching-Based Open-Set Action Recognition by Joint Learning of Motion and Appearance | In this paper, we propose the spatio-temporal representation matching (STRM) for video-based action recognition under the open-set condition. Open-set action recognition is a more challenging problem than closed-set action recognition since samples of the untrained action class need to be recognized and most of the conventional frameworks are likely to give a false prediction. To handle the untrained action classes, we propose STRM, which involves jointly learning both motion and appearance. STRM extracts spatio-temporal representations from video clips through a joint learning pipeline with both motion and appearance information. Then, STRM computes the similarities between the ST-representations to find the one with highest similarity. We set the experimental protocol for open-set action recognition and carried out experiments on UCF101 and HMDB51 to evaluate STRM. We first investigated the effects of different hyper-parameter settings on STRM, and then compared its performance with existing state-of-the-art methods. The experimental results showed that the proposed method not only outperformed existing methods under the open-set condition, but also provided comparable performance to the state-of-the-art methods under the closed-set condition. |
3,617 | Caffeic Acid-Grafted PLGA as a Novel Material for the Design of Fluvastatin-Eluting Nanoparticles for the Prevention of Neointimal Hyperplasia | Drug-eluting nanoparticles (NPs) administered by an eluting balloon represent a novel tool to prevent restenosis after angioplasty, even if the selection of the suitable drug and biodegradable material is still a matter of debate. Herein, we provide the proof of concept of the use of a novel material obtained by combining the grafting of caffeic acid or resveratrol on a poly(lactide-co-glycolide) backbone (g-CA-PLGA or g-RV-PLGA) and the pleiotropic effects of fluvastatin chosen because of its low lipophilic profile which is challenging for the encapsulation in NPs and delivery to the artery wall cells. NPs made of such materials are biocompatible with macrophages, human smooth muscle cells (SMCs), and endothelial cells (ECs). Their cellular uptake is demonstrated and quantified by confocal microscopy using fluorescent NPs, while their distribution in the cytoplasm is verified by TEM images using NPs stained with an Ag-PVP probe appositely synthetized. g-CA-PLGA assures the best control of the FLV release from NP sizing around 180 nm and the faster SMC uptake, as demonstrated by confocal analyses. Interestingly and surprisingly, g-CA-PLGA improves the FLV efficacy to inhibit the SMC migration, without altering its effects on EC proliferation and migration. The improved trophism of NPs toward SMCs, combined with the excellent biocompatibility and low modification of the microenvironment pH upon polymer degradation, makes g-CA-PLGA a suitable material for the design of drug-eluting balloons. |
3,618 | Understanding the hydrochemical functioning of glacierized catchments of the Upper Indus Basin in Ladakh, Indian Himalayas | Recent studies have endorsed that surface water chemical composition in the Himalayas is impacted by climate change-induced accelerated melting of glaciers. Chemical weathering dynamics in the Ladakh region is poorly understood, due to unavailability of in situ dataset. The aim of the present study is to investigate how the two distinct catchments (Lato and Stok) drive the meltwater chemistry of the Indus River and its tributary, in the Western Himalayas. Water samples were collected from two glaciated catchments (Lato and Stok), Chabe Nama (tributary) and the Indus River in Ladakh. The mildly alkaline pH (range 7.3-8.5) and fluctuating ionic trend of the meltwater samples reflected the distinct geology and weathering patterns of the Upper Indus Basin (UIB). Gibbs plot and mixing diagram revealed rock weathering outweighed evaporation and precipitation. The strong associations between Ca2+-HCO3-, Mg2+-HCO3-, Ca2+-Mg2+, Na+-HCO3-, and Mg2+-Na+ demonstrated carbonate rock weathering contributed to the major ion influx. Principal component analysis (PCA) marked carbonate and silicates as the most abundant minerals respectively. Chemical weathering patterns were predominantly controlled by percentage of glacierized area and basin runoff. Thus, Lato with the larger glacierized area (~ 25%) and higher runoff contributed low TDS, HCO3-, Ca2+, and Na+ and exhibited higher chemical weathering, whereas lower chemical weathering was evinced at Stok with the smaller glacierized area (~ 5%). In contrast, the carbonate weathering rate (CWR) of larger glacierized catchments (Lato) exhibits higher average value of 15.7 t/km2/year as compared to smaller glacierized catchment (Stok) with lower average value 6.69 t/km2/year. However, CWR is high in both the catchments compared to silicate weathering rate (SWR). For the first time, in situ datasets for stream water chemical characteristics have been generated for Lato and Stok glaciated catchments in Ladakh, to facilitate healthy ecosystems and livelihoods in the UIB. |
3,619 | The extended analogy of extraembryonic development in insects and amniotes | It is fascinating that the amnion and serosa/chorion, two extraembryonic (EE) tissues that are characteristic of the amniote vertebrates (mammals, birds and reptiles), have also independently evolved in insects. In this review, we offer the first detailed, macroevolutionary comparison of EE development and tissue biology across these animal groups. Some commonalities represent independent solutions to shared challenges for protecting the embryo (environmental assaults, risk of pathogens) and supporting its development, including clear links between cellular properties (e.g. polyploidy) and physiological function. Further parallels encompass developmental features such as the early segregation of the serosa/chorion compared to later, progressive differentiation of the amnion and formation of the amniotic cavity from serosal-amniotic folds as a widespread morphogenetic mode across species. We also discuss common developmental roles for orthologous transcription factors and BMP signalling in EE tissues of amniotes and insects, and between EE and cardiac tissues, supported by our exploration of new resources for global and tissue-specific gene expression. This highlights the degree to which general developmental principles and protective tissue features can be deduced from each of these animal groups, emphasizing the value of broad comparative studies to reveal subtle developmental strategies and answer questions that are common across species. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'. |
3,620 | SA-LuT-Nets: Learning Sample-Adaptive Intensity Lookup Tables for Brain Tumor Segmentation | In clinics, the information about the appearance and location of brain tumors is essential to assist doctors in diagnosis and treatment. Automatic brain tumor segmentation on the images acquired by magnetic resonance imaging (MRI) is a common way to attain this information. However, MR images are not quantitative and can exhibit significant variation in signal depending on a range of factors, which increases the difficulty of training an automatic segmentation network and applying it to new MR images. To deal with this issue, this paper proposes to learn a sample-adaptive intensity lookup table (LuT) that dynamically transforms the intensity contrast of each input MR image to adapt to the following segmentation task. Specifically, the proposed deep SA-LuT-Net framework consists of a LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific nonlinear intensity mapping function through communication with the segmentation module, aiming at improving the final segmentation performance. In order to make the LuT learning sample-adaptive, we parameterize the intensity mapping function by exploring two families of non-linear functions (i.e., piece-wise linear and power functions) and predict the function parameters for each given sample. These sample-specific parameters make the intensity mapping adaptive to samples. We develop our SA-LuT-Nets separately based on two backbone networks for segmentation, i.e., DMFNet and the modified 3D Unet, and validate them on BRATS2018 and BRATS2019 datasets for brain tumor segmentation. Our experimental results clearly demonstrate the superior performance of the proposed SA-LuT-Nets using either single or multiple MR modalities. It not only significantly improves the two baselines (DMFNet and the modified 3D Unet), but also wins a set of state-of-the-art segmentation methods. Moreover, we show that, the LuTs learnt using one segmentation model could also be applied to improving the performance of another segmentation model, indicating the general segmentation information captured by LuTs. |
3,621 | Deep Q-CapsNet Reinforcement Learning Framework for Intrauterine Cavity Segmentation in TTTS Fetal Surgery Planning | Fetoscopic laser photocoagulation is the most effective treatment for Twin-to-Twin Transfusion Syndrome, a condition affecting twin pregnancies in which there is a deregulation of blood circulation through the placenta, that can be fatal to both babies. For the purposes of surgical planning, we design the first automatic approach to detect and segment the intrauterine cavity from axial, sagittal and coronal MRI stacks. Our methodology relies on the ability of capsule networks to successfully capture the part-whole interdependency of objects in the scene, particularly for unique class instances (i.e., intrauterine cavity). The presented deep Q-CapsNet reinforcement learning framework is built upon a context-adaptive detection policy to generate a bounding box of the womb. A capsule architecture is subsequently designed to segment (or refine) the whole intrauterine cavity. This network is coupled with a strided nnU-Net feature extractor, which encodes discriminative feature maps to construct strong primary capsules. The method is robustly evaluated with and without the localization stage using 13 performance measures, and directly compared with 15 state-of-the-art deep neural networks trained on 71 singleton and monochorionic twin pregnancies. An average Dice score above 0.91 is achieved for all ablations, revealing the potential of our approach to be used in clinical practice. |
3,622 | ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples | From recent research work, it has been shown that neural network (NN) classifiers are vulnerable to adversarial examples which contain special perturbations that are ignored by human eyes while can mislead NN classifiers. In this paper, we propose a practical black-box adversarial example generator, dubbed ManiGen. ManiGen does not require any knowledge of the inner state of the target classifier. It generates adversarial examples by searching along the manifold, which is a concise representation of input data. Through extensive set of experiments on different datasets, we show that (1) adversarial examples generated by ManiGen can mislead standalone classifiers by being as successful as the state-of-the-art white-box generator, Carlini, and (2) adversarial examples generated by ManiGen can more effectively attack classifiers with state-of-the-art defenses. |
3,623 | Acquired Immune Deficiency Syndrome Cholangiopathy: Case Series of Three Patients and Literature Review | Cholangiopathy in acquired immune deficiency syndrome (AIDS) is being less frequently reported since antiretroviral therapy (ART) is available. It is associated with an advanced disease and seen in situations with poor access or non-compliance with ART. Liver biopsy is thought to have low yield in cases of AIDS cholangiopathy, but it can be an important tool in diagnosis, especially early in the course of the disease. The prognosis of AIDS cholangiopathy is generally not favorable, the therapy for opportunistic infections is mostly ineffective and restoration of immune system with ART remains the therapy of choice. We are sharing our experience of diagnosing and managing three cases of AIDS cholangiopathy. |
3,624 | A Tensor B-Spline Approach for Solving the Diffusion PDE With Application to Optical Diffusion Tomography | Optical Diffusion Tomography (ODT) is amodern non-invasive medical imaging modality which requires mathematical modelling of near-infrared light propagation in tissue. Solving the ODT forward problem equation accurately and efficiently is crucial. Typically, the forward problem is represented by a Diffusion PDE and is solved using the Finite Element Method (FEM) on a mesh, which is often unstructured. Tensor B-spline signal processing has the attractive features of excellent interpolation and approximation properties, multiscale properties, fast algorithms and does not require meshing. This paper introduces Tensor B-spline methodology with arbitrary spline degree tailored to solve the ODT forward problem in an accurate and efficient manner. We show that our Tensor B-spline formulation induces efficient and highly parallelizable computational algorithms. Exploitation of B-spline properties for integration over irregular domains proved valuable. The TensorB-spline solverwas tested on standard problems and on synthetic medical data and compared to FEM, including state-of-the art ODT forward solvers. Results show that 1) a significantly higher accuracy can be achieved with the same number of nodes, 2) fewer nodes are required to achieve a prespecified accuracy, 3) the algorithm converges in significantly fewer iterations to a given error. These findings support the value of Tensor Bsplinemethodology for high-performanceODT implementations. This may translate into advances in ODT imaging for biomedical research and clinical application. |
3,625 | Simulation Tools for Electromagnetic Transients in Power Systems: Overview and Challenges | This paper presents an overview on available tools and methods for the simulation of electromagnetic transients in power systems. Both off-line and real-time simulation tools are presented and discussed. The first objective is to give the reader an overview on the modeling and simulation capabilities in currently available state-of-the-art tools. The second objective is to provide perspectives on research topics and needed enhancements. |
3,626 | Luminescence investigation of Sm3+ ,Eu2+ -activated/co-activated Ba2 AlB4 O7 Cl phosphors: novel red to blue colour tuning in chloroborates | Borochlorate phosphors have shown their worth in modern day lighting over the last few years. Colour tunability of the phosphor is a modern techniques used to obtain white-light-emitting diodes (WLEDs). In the proposed work, Sm3+ ,Eu2+ -activated/co-activated Ba2 AlB4 O7 Cl phosphors were investigated for WLED applications as well as display devices. A convectional solid-state diffusion method was used to synthesize the proposed phosphors. X-ray diffraction (XRD) of the proposed phosphors confirmed the crystalline nature of the sample. Morphological studies on the samples were carried out using scanning electron microscopy analysis. A photoluminescence study of the colour tunable phosphor showed the characteristic peak of Sm3+ together with one broad peak for Eu2+ ions. Red to blue colour tunability was observed in the proposed phosphor with enhancement of Eu2+ ions. All these results showed the worth of this sample in WLED applications as well as in display devices. |
3,627 | Cloning Strategy for HDAC1/HDAC2 Hybrid Protein Expression in Mammalian Cells | Dynamic deacetylation of non-histone proteins by histone deacetylases (HDACs) is a key regulator of protein functions, interactions, and turnover. Among class I HDACs, human HDAC1 and HDAC2 share more than 80% global homology at the amino acid level. However, despite the high redundancy, there are examples for differential substrate specificities of HDAC1 and HDAC2. Until now it remains quite unclear how specific and overlapping functions of HDAC1/HDAC2 are regulated in different contexts. Here, we describe molecular cloning techniques for the generation of HDAC1/HDAC2 hybrid proteins, HDAC1/HDAC2 mutants lacking known interaction domains, and HDAC1/HDAC2 hybrid proteins with interchanged N-terminal domains. These proteins are tools for the analysis of specific protein interactions and functions in mammalian cells. |
3,628 | Specialised ribosomes as versatile regulators of gene expression | The ribosome has long been thought to be a homogeneous cellular machine that constitutively and globally synthesises proteins from mRNA. However, recent studies have revealed that ribosomes are highly heterogeneous, dynamic macromolecular complexes with specialised roles in translational regulation in many organisms across the kingdoms. In this review, we summarise the current understanding of ribosome heterogeneity and the specialised functions of heterogeneous ribosomes. We also discuss specialised translation systems that utilise orthogonal ribosomes. |
3,629 | Theoretical analysis for the fluctuation in the electric parameters of the electroporated cells before and during the electrofusion | An electric pulse with a sufficient amplitude can lead to electroporation of intracellular organelles. Also, the electric field can lead to electrofusion of the neighboring cells. In this paper, a finite element mathematical model was used to simulate the distribution, radius, and density of the pores. We simulated a mathematical model of the two neighbor cells to analyze the fluctuation in the electroporation parameters before the electrofusion under the ultra-shorted electric field pulse (i.e., impulse signal) for each cell separately and after the electrofusion under the ultra-shorted pulse. The analysis of the temporal and spatial distribution can lead to improving the mathematical models that are used to analyze both electroporation and electrofusion. The study combines the advantages of the nanosecond pulse to avoid the effect of the cell size on the electrofusion and the large-pore radius at the contact point between the cells. |
3,630 | Optimization and kinetic modeling of phosphate recovery as struvite by electrocoagulation from source-separated urine | Phosphorus recovery is indispensable due to the rapid depletion of its natural reserves and excessive utility in agriculture. Though human urine has high nutrient content including phosphate, nitrogen and potassium; direct use as a fertilizer is restricted due to hygienic, environmental, social and ethical issues. To overcome these limitations, the nutrients are precipitated by the external addition of magnesium (Mg) to form a slow-releasing fertilizer called struvite. The present study aims to maximize phosphate recovery through optimizing struvite production by an emerging electrocoagulation technique. A maximum of 95% phosphate recovery was achieved using inter-electrode distance of 0.5 cm, 2 A current from undiluted urine using Mg-Mg electrodes in a reaction time of 30 min. Further, kinetic modeling of phosphate recovery through electrocoagulation was conducted to comprehend the intended mechanism through the order of kinetics. The results revealed that the data best correlated with first-order kinetics with a correlation coefficient of 0.95. Electrocoagulation improved the supernatant quality by reducing the ion concentrations other than phosphate (30-50%), salinity (40-45%), and microbial population (99%). Qualitative assessment of the precipitate through sophisticated analysis further confirmed the presence of struvite crystals. |
3,631 | Orthogonally Integrating Programmable Structural Color and Photo-Rewritable Fluorescence in Hydrazone Photoswitch-bonded Cholesteric Liquid Crystalline Network | Design and fabrication of advanced security label showing superior performance in data encryption has attracted tremendous scientific interests. Especially, multifunctional optical labels capable of storing distinct information in different modes are highly demanded. Here, a fluorescent cholesteric liquid crystalline network (CLCN) film with programmable visible patterns and photo-rewritable fluorescent patterns was designed and prepared. The visible patterns were fixed by helical network and the colors of visible patterns were tunable by changing relative humidity (RH). The fluorescent patterns originated from dynamic isomerization of fluorescent hydrazones, exhibiting highly thermal stability and switching-ability controlled by light. The orthogonal construction of visible and fluorescent pattern enabled the novel CLCN to encrypt distinct information in reflective mode and in emissive mode, indicating its potential in anti-counterfeiting and information encryptions. |
3,632 | An optimal circular antenna array design considering mutual coupling using heuristic approaches | This article shows the design of a non-uniformly excited single ring circular antenna array (CAA) for the synthesis of optimal far-field radiation characteristics. A recently proposed meta-heuristic based optimization algorithm called gray wolf optimization (GWO) and state-of-the-art swarm intelligence based evolutionary optimization technique known as particle swarm optimization with a distribution based update mechanism (PSOd) are individually applied to determine the optimum set of current excitation amplitude weights and the inter-element spacing among the array elements to reduce the side lobe level and 3-dB beamwidth considering the mutual coupling. The results obtained by employing PSOd and GWO are compared to those of the uniform radiation pattern and the recently published results of state-of-the-art literature having equal sets of elements to show the superiority of employed approaches. Three different design examples of 8, 10, and 12 elements CAA are reported in this article to study the performances of PSOd and GWO algorithm-based results over the results of other recently reported literature. |
3,633 | An Improved Describing Function With Applications for OTA-Based Circuits | Electronic systems make extensive use of operational transconductance amplifiers (OTAs) to build filters and oscillators. Studying the effects of the saturation nonlinearity on these OTA-based circuits is difficult and often requires lengthy simulations to check the system's performance under large-signal operation. The describing function (DF) theory allows to circumvent these simulations by deriving a signal-dependent linearized gain, which predicts the effects of the nonlinearity. However, its use is limited since the state-of-the-art DFs deviate significantly from the real saturating behavior of OTAs. This paper proposes an improved DF, which can be directly derived from the static nonlinear characteristic of the transconductance amplifier. The performance of the proposed methodology is demonstrated for both an OTA-based filter and oscillator. It is shown that the proposed DF has a better nonlinear prediction capability than the state-of-the-art solutions. |
3,634 | Optimization Reduces Knee-Joint Forces During Walking and Squatting: Validating the Inverse Dynamics Approach for Full Body Movements on Instrumented Knee Prostheses | Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies, a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a) predict forces measured in knee prostheses during walking and squatting and (b) study the effect of different motor control strategies (i.e., minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1 BW in the medial and anterior directions. Generally, minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4 BW for walking and 2.5 BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25 BW and 2.12 BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion. |
3,635 | Techno-economic evaluation of an ammonia-based post-combustion process integrated with a state-of-the-art coal-fired power plant | A techno-economic evaluation of the application of an ammonia-based post-combustion CO2 capture system to an existing, state-of-the-art, coal-fired power plant. The study comprised an assessment of the ammonia based capture process together with a detailed cost analysis, based on which the overall design of the capture process is presented, including a power plant integration strategy and estimates of the specific CO2 capture cost ((sic)/tCO(2)). The evaluations of the power plant and the CO2 capture plant were based on process modeling. The cost analysis was based on the installed cost of each unit in the equipment list derived from the process simulation, which was determined using detailed-factor estimation. We show that the steam required for a CO2 capture efficiency of 90% lowers the electric output from 408.0 MWel to 341.8 MWel. The capital expenditure related to the retrofit of the reference power plant with CO2 capture is 230M(sic) and the operating expenditure is determined to be 66.5M(sic)/year, corresponding to a relative capture cost of 35(sic)/tCO(2). Furthermore, the present work proposes design improvements that may reduce the cost of capture to 31(sic)/tCO(2). (C) 2014 Elsevier Ltd. All rights reserved. |
3,636 | The need for standards and certification for investigative genetic genealogy, and a notice of action | As investigative genetic genealogy (IGG) becomes a more common tool for investigating agencies to resolve cold cases and provide names to unidentified human remains, there is an urgent need for standards and a certification process for IGG practitioners. There are four broad concerns that give rise to this need: data privacy, public trust, proficiency (and agency trust), and accountability. Yet, while the need is clear, the few discussions of standards and certification thus far have been plagued by misunderstandings of IGG and poor analogs for the profession. Thus, in addition to describing the need, this article analyzes three relevant analogs for IGG standards and certification and describes the strengths and weaknesses of each. Finally, this article announces the creation of a non-profit Board of Certification for Investigative Genetic Genealogy and a framework for standards and a certification process for IGG. |
3,637 | Practical target values of Shockley-Read-Hall recombination rates in state-of-the-art triple-junction solar cells for realizing conversion efficiencies within 1% of the internal radiative limit | In order to identify the cause of the difference between actual efficiency and the theoretical limit in state-of-the-art triple-junction solar cells, we investigate the internal luminescence efficiency in the depletion region ( eta intdep). The average internal luminescence efficiency of the whole subcell volume ( eta int over bar ) is obtained experimentally, and the eta intdep is deduced by numerical calculations using rate equations. We find that eta int over bar and eta intdep agree well in the low-recombination current regime including the maximum power point. This indicates that the non-radiative recombination loss at the maximum power point strongly depends on the recombination in the depletion region. Furthermore, we determine the actual Shockley-Read-Hall recombination coefficient in the depletion region, A(dep), which is proportional to the effective density of recombination centers. Our analysis reveals the target values of A(dep) required for realizing conversion efficiencies that are within 1% of the internal radiative limit. The analysis also clarifies the extent of reduction of the effective density of recombination centers (in the depletion region) that is required to realize a given target efficiency. |
3,638 | Approaches to investigating metabolism in human neurodevelopment using organoids: insights from intestinal and cancer studies | Interrogating the impact of metabolism during development is important for understanding cellular and tissue formation, organ and systemic homeostasis, and dysregulation in disease states. To evaluate the vital functions metabolism coordinates during human brain development and disease, pluripotent stem cell-derived models, such as organoids, provide tractable access to neurodevelopmental processes. Despite many strengths of neural organoid models, the extent of their replication of endogenous metabolic programs is currently unclear and requires direct investigation. Studies in intestinal and cancer organoids that functionally evaluate dynamic bioenergetic changes provide a framework that can be adapted for the study of neural metabolism. Validation of in vitro models remains a significant challenge; investigation using in vivo models and primary tissue samples is required to improve our in vitro model systems and, concomitantly, improve our understanding of human development. |
3,639 | Regulation of the Ca(2+)-ATPase by cholesterol: a specific or non-specific effect? | Like other integral membrane proteins, the activity of the Sarco/Endoplasmic Reticulum Ca(2+)-ATPase (SERCA) is regulated by the membrane environment. Cholesterol is present in the endoplasmic reticulum membrane at low levels, and it has the potential to affect SERCA activity both through direct, specific interaction with the protein or through indirect interaction through changes of the overall membrane properties. There are experimental data arguing for both modes of action for a cholesterol-mediated regulation of SERCA. In the current study, coarse-grained molecular dynamics simulations are used to address how a mixed lipid-cholesterol membrane interacts with SERCA. Candidates for direct regulatory sites with specific cholesterol binding modes are extracted from the simulations. The binding pocket for thapsigargin, a nanomolar inhibitor of SERCA, has been suggested as a cholesterol binding site. However, the thapsigargin binding pocket displayed very little cholesterol occupation in the simulations. Neither did atomistic simulations of cholesterol in the thapsigargin binding pocket support any specific interaction. The current study points to a non-specific effect of cholesterol on SERCA activity, and offers an alternative interpretation of the experimental results used to argue for a specific effect. |
3,640 | A robust JPEG compression detector for image forensics | Identification of JPEG compressed images saved in uncompressed format (JPEG-U images) is an important issue in forensic analysis. The state-of-the-art JPEG compression detection methods fail to identify such images when subjected to post-processing/anti-forensic operations. In this paper, we propose a novel JPEG compression detector which is robust to post-processing and anti-forensic operations. The detector is based on the difference in the discrete cosine transform (DCT) coefficient distributions in the ac subbands of uncompressed images and JPEG-U images. We show theoretically and empirically that the probability of subband DCT coefficients which lie in the interval (-0.5,0.5) is significantly different for a JPEG-U and the corresponding uncompressed image. This difference is exploited to derive a detection statistic which is compared with a threshold to detect JPEG-U images. The detector makes use of calibration, a technique used in steganalysis, to obtain the detection statistic. The experimental results show that the proposed detector significantly outperforms the state-of-the-art detectors, especially in the presence of post-processing and anti-forensic operations. |
3,641 | Lenvatinib rechallenge in a patient with advanced thymic carcinoma: A case report | Advanced thymic carcinomas have limited treatment options. Recently, lenvatinib was approved for advanced thymic carcinoma treatment. However, the clinical benefit of lenvatinib re-administration in patients with advanced thymic carcinoma who developed prior lenvatinib treatment resistance (lenvatinib rechallenge) remains unclear. Here, we present a case treated with lenvatinib rechallenge for advanced thymic carcinoma who was previously treated with lenvatinib as the second-line treatment followed by multiple cytotoxic agents. Disease control rapidly deteriorated after the eighth line of treatment because of uncontrollable right pleural and pericardial effusion, which required repeated thoracic and pericardial drainage. Shortly after lenvatinib re-administration, rapid pleural and pericardial effusion reduction was observed. Thereafter, the patient achieved sustained clinical response with good pleural and pericardial effusion control for approximately 7 months. Our case might suggest lenvatinib rechallenge as a treatment option for patients with advanced thymic carcinoma, especially those with poor pleural and pericardial effusion control. |
3,642 | Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy | Adjuvant and salvage radiotherapy after radical prostatectomy requires precise delineations of prostate bed (PB), i.e., the clinical target volume, and surrounding organs at risk (OARs) to optimize radiotherapy planning. Segmenting PB is particularly challenging even for clinicians, e.g., from the planning computed tomography (CT) images, as it is an invisible/virtual target after the operative removal of the cancerous prostate gland. Very recently, a few deep learning-based methods have been proposed to automatically contour non-contrast PB by leveraging its spatial reliance on adjacent OARs (i.e., the bladder and rectum) with much more clear boundaries, mimicking the clinical workflow of experienced clinicians. Although achieving state-of-the-art results from both the clinical and technical aspects, these existing methods improperly ignore the gap between the hierarchical feature representations needed for segmenting those fundamentally different clinical targets (i.e., PB and OARs), which in turn limits their delineation accuracy. This paper proposes an asymmetric multi-task network integrating dynamic cross-task representation adaptation (i.e., DyAdapt) for accurate and efficient co-segmentation of PB and OARs in one-pass from CT images. In the learning-to-learn framework, the DyAdapt modules adaptively transfer the hierarchical feature representations from the source task of OARs segmentation to match up with the target (and more challenging) task of PB segmentation, conditioned on the dynamic inter-task associations learned from the learning states of the feed-forward path. On a real-patient dataset, our method led to state-of-the-art results of PB and OARs co-segmentation. |
3,643 | Martial Arts Tourism of the "Europe-Far East" Direction, in the Opinion of Grand Masters | Martial arts tourism is a form of cultural, sports and educational tourism that requires special recognition; particularly important is knowledge about martial arts. The sources of this practical knowledge are especially high-ranking masters. The scientific problem raised here involves the issue of high-ranking martial arts teachers taking trips for their own studies (to acquire knowledge and skills) and teaching others. Some of the questions addressed include how often the trips occur (single, sporadic, or multiple, regular), what their effects are, and what their meaning is-in the opinion of these experts. The "Martial Arts Tourism" questionnaire was addressed to N = 12 people, masters/teachers of high-rank in martial arts (level 7-10 dan/toan) who live and teach in Europe and the USA, but come from Europe. They are the holders of the highest degrees in Chinese, Japanese and Korean styles. Further questions were asked through direct correspondence. The collected statements were usedby means of qualitative analysis-as in the method of 'expert courts'/'competent judges'. The respondents in most cases undertook trips from Europe to East Asia for their own learning. They teach themselves mainly in their own countries and in Europe. Stays rarely lasted over two weeks. The respondents are convinced of the legitimacy of this type of trip, and believe that the trips are very helpful on the way to mastery. None of the respondents mentioned the material forms of cultural heritage pertaining to martial arts as motives for the trips. Therefore, the ability to visit historic places is a marginal concern. The trips were directly linked to a career path and self-improvement in martial arts, learning or teaching. |
3,644 | Simultaneous Sparsity Model for Histopathological Image Representation and Classification | The multi-channel nature of digital histopathological images presents an opportunity to exploit the correlated color channel information for better image modeling. Inspired by recent work in sparsity for single channel image classification, we propose a new simultaneous sparsity model for multi-channel histopathological image representation and classification (SHIRC). Essentially, we represent a histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints. Classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. A practical challenge is the correspondence of image objects (cellular and nuclear structures) at different spatial locations in the image. We propose a robust locally adaptive variant of SHIRC (LA-SHIRC) to tackle this issue. Experiments on two challenging real-world image data sets: 1) mammalian tissue images acquired by pathologists of the animal diagnostics lab (ADL) at Pennsylvania State University, and 2) human intraductal breast lesions, reveal the merits of our proposal over state-of-the-art alternatives. Further, we demonstrate that LA-SHIRC exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training per class is often not available. |
3,645 | Rasiella rasia gen. nov. sp. nov. within the family Flavobacteriaceae isolated from seawater recirculating aquaculture system | A novel bacterium designated RR4-40T was isolated from a biofilter of seawater recirculating aquaculture system in Busan, South Korea. Cells are strictly aerobic, Gram-negative, irregular short rod, non-motile, and oxidase- and catalase-negative. Growth was observed at 15-30°C, 0.5-6% NaCl (w/v), and pH 5.0-9.5. The strain grew optimally at 28°C, 3% salinity (w/v), and pH 8.5. The phylogenetic analysis based on 16S rRNA gene sequences showed that strain RR4-40T was most closely related to Marinirhabdus gelatinilytica NH83T (94.16% of 16S rRNA gene similarity) and formed a cluster with genera within the family Flavobacteriaceae. The values of the average nucleotide identity (ANI), digital DNA-DNA hybridization (dDDH), and average amino acid identity (AAI) between genomes of strain RR4-40T and M. gelatinilytica NH83T were 72.91, 18.2, and 76.84%, respectively, and the values against the strains in the other genera were lower than those. The major fatty acids were iso-C15:0 (31.34%), iso-C17:0 3-OH (13.65%), iso-C16:0 3-OH (10.61%), and iso-C15:1 G (10.38%). The polar lipids comprised phosphatidylglycerol, diphosphatidylglycerol, aminophospholipid, aminolipid, glycolipid, and sphingolipid. The major respiratory quinone was menaquinone-6 (MK-6) and the DNA G + C content of strain RR4-40T was 37.4 mol%. According to the polyphasic analysis, strain RR4-40T is considered to represent a novel genus within the family Flavobacteriaceae, for which the name Rasiella rasia gen. nov, sp. nov. is proposed. The type strain is RR4-40T (= KCTC 52650T = MCCC 1K04210T). |
3,646 | Improving Code Completion by Solving Data Inconsistencies in the Source Code with a Hierarchical Language Model | In the field of software engineering, applying language models to the token sequence of source code is the state-of-the-art approach to building a code recommendation system. When applying language models to source code, it is difficult for state-of-the-art language models to deal with the data inconsistency problem, which is caused by the free naming conventions of source code. It is common for user-defined variables or methods with similar semantics in source code, to have different names in different projects. This means that a model trained on one project may encounter many words the model has never seen before during another project. Those freely named variables or functions in the code will bring difficulties to the processes of training and prediction and cause a data inconsistency problem between projects. However, we discover that the syntax tree of source code has hierarchical structures. This code structure has strong regularity in different projects and can be used to combat data inconsistency. In this paper, we propose a novel Hierarchical Language Model (HLM) to improve the robustness of the state-of-the-art recurrent language model, in order to be able to deal with data inconsistency between training and testing. The newly proposed HLM takes the hierarchical structure of the code tree into consideration to predict code. The proposed HLM method generates the embedding for each sub-tree according to hierarchies and collects the embedding of each sub-tree in context, to predict the next piece of code. The experiments on inner-project and cross-project datasets indicate that the newly proposed HLM method performs better than the state-of-the-art recurrent language model in dealing with the data inconsistency between training and testing, and achieves an average improvement in prediction accuracy of 11.2%. |
3,647 | An efficient biosorbent for the removal of arsenic from a typical urban-generated wastewater | The arousal of environmental concerns due to spike in environmental degradation has necessitated proper waste management and disposal. Arsenic, a potentially toxic element in cassava wastewater, requires treatment prior to the wastewater disposal to minimize environmental pollution and associated health implications. The present study thus addressed the treatment of As5+ heavy metal in cassava wastewater using an efficient biosorbent from chemically pretreated unshelled Moringa oleifera seeds. The effect of various factors influencing the biosorption process for arsenate removal was studied including pH, contact time, biosorbent dosage, and biosorbent pretreatment concentration. The results of Fourier transform infrared spectroscopy clearly suggested that additional functional groups attributed to esters were formed in the pretreated biosorbent, which is responsible for improvement in biosorption. It was found that contact time, biosorbent dosage, and biosorbent pretreatment concentration had statistically significant effect (p values < 0.05) on arsenate removal. A maximum percentage removal of 99.9% was achieved in the synthetic solution at pH 4.0, contact time of 30 min, and dosage of 2 g for biosorbent pretreated with 1 M of chemical solution. Furthermore, through isotherm and kinetics studies, it was discovered that the biosorption process for untreated biosorbent is by ion exchange, while that for treated biosorbents indicated a multifarious adsorption mechanism. Moreover, the biosorption process was exothermic and spontaneous. Also, it is noted that the sorption capability of the biosorbent increases with pretreatment concentration. A statistical model has been developed with prediction R2 of 0.898, which incorporates the effect of treatment concentration on the percentage removal of As5+ from cassava wastewater. |
3,648 | No-Reference JPEG Image Quality Assessment Based on Blockiness and Luminance Change | When scoring the quality of JPEG images, the two main considerations for viewers are blocking artifacts and improper luminance changes, such as blur. In this letter, we first propose two measures to estimate the blockiness and the luminance change within individual blocks. Then, a no-reference image quality assessment (NR-IQA) method for JPEG images is proposed. Our method obtains the quality score by considering the blocking artifacts and the luminance changes from all nonoverlapping 8 x 8 blocks in one JPEG image. The proposed method has been tested on five public IQA databases and compared with five state-of-the-art NR-IQA methods for JPEG images. The experimental results show that our method is more consistent with subjective evaluations than the state-of-the-art NR-IQA methods. The MATLAB source code of our method is available at http://image.ustc.edu.cn/IQA.html. |
3,649 | Inferring Optimal Species Trees in the Presence of Gene Duplication and Loss: Beyond Rooted Gene Trees | Estimating species trees from multiple genes is complicated and challenging due to gene tree-species tree discordance. One of the basic approaches to understanding differences between gene trees and species trees is gene duplication and loss events. Minimize Gene Duplication and Loss (MGDL) is a popular technique for inferring species trees from gene trees when the gene trees are discordant due to gene duplications and losses. Previously, exact algorithms for estimating species trees from rooted, binary trees under MGDL were proposed. However, gene trees are usually estimated using time-reversible mutation models, which result in unrooted trees. In this article, we propose a dynamic programming (DP) algorithm that can be used for an exact but exponential time solution for the case when gene trees are not rooted. We also show that a constrained version of this problem can be solved by this DP algorithm in time that is polynomial in the number of gene trees and taxa. We have proved important structural properties that allow us to extend the algorithms for rooted gene trees to unrooted gene trees. We propose a linear time algorithm for finding the optimal rooted version of an unrooted gene tree given a rooted species tree so that the duplication and loss cost is minimized. Moreover, we prove that the optimal rooting under MGDL is also optimal under the MDC (minimize deep coalescence) criterion. The proposed methods can be applied to both orthologous genes and gene families that by definition include both paralogs and orthologs. Therefore, we hope that these techniques will be useful for estimating species trees from genes sampled throughout the whole genome. |
3,650 | ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation | Semantic segmentation is a challenging task that addresses most of the perception needs of intelligent vehicles (IVs) in an unified way. Deep neural networks excel at this task, as they can be trained end-to-end to accurately classify multiple object categories in an image at pixel level. However, a good tradeoff between high quality and computational resources is yet not present in the state-of-the-art semantic segmentation approaches, limiting their application in real vehicles. In this paper, we propose a deep architecture that is able to run in real time while providing accurate semantic segmentation. The core of our architecture is a novel layer that uses residual connections and factorized convolutions in order to remain efficient while retaining remarkable accuracy. Our approach is able to run at over 83 FPS in a single Titan X, and 7 FPS in a Jetson TX1 (embedded device). A comprehensive set of experiments on the publicly available Cityscapes data set demonstrates that our system achieves an accuracy that is similar to the state of the art, while being orders of magnitude faster to compute than other architectures that achieve top precision. The resulting tradeoff makes our model an ideal approach for scene understanding in IV applications. The code is publicly available at: https://github.com/Eromera/erfnet |
3,651 | Highly chlorinated toxic contaminants in pesticide-treated wooden art objects | Although the contamination of wooden art objects with pesticides is well known, to the authors' knowledge Do attempt has yet been made to investigate the eventual presence of other toxic compounds that have been produced during the degradation of pesticides or that may be present in the technical formulations. Here, the authors report on the presence of polychlorinated biphenyls (PCBs) and polychlorinated terphenyls (PCTs) in scrapings from wooden antique art objects, namely printing blocks, sculptures, and masks. These antiques belong to 2 fine art museums in Belgium-Antwerp's Ethnographic Museum and the Plantin-Moretus Museum. It is documented that these art objects were treated with pesticides in the 1950s. In addition, 2-heptachlorodibenzo-p-dioxin (HpCDD) isomers and octachlorodibenzo-p-dioxin (OCDD) were also identified. The presence of these toxic compounds in these antiques requires a better understanding of safety for the persons (conservators, museum employees, restorers, and visitors) coming in contact with these objects. |
3,652 | MADET: a Manually Curated Knowledge Base for Microbiomic Effects on Efficacy and Toxicity of Anticancer Treatments | A plethora of studies have reported the associations between microbiota and multiple diseases, leading to the development of at least four databases to demonstrate microbiota-disease associations, i.e., gutMDisorder, mBodyMap, Gmrepo, and Amadis. Moreover, gut microbiota mediates drug efficacy and toxicity, whereas a comprehensive database to elucidate the microbiota-drug associations is lacking. Here, we report an open-access knowledge base, MADET (Microbiomics of Anticancer Drug Efficacy and Toxicity), which harbors 483 manually annotated microbiota-drug associations from 26 studies. MADET provides user-friendly functions allowing users to freely browse, search, and download data conveniently from the database. Users can customize their search filters in MADET using different types of keywords, including bacterial name (e.g., Akkermansia muciniphila), anticancer treatment (e.g., anti-PD-1 therapy), and cancer type (e.g., lung cancer) with different types of experimental evidence of microbiota-drug association and causation. We have also enabled user submission to further enrich the data documented in MADET. The MADET database is freely available at https://www.madet.info. We anticipate that MADET will serve as a useful resource for a better understanding of microbiota-drug associations and facilitate the future development of novel biomarkers and live biotherapeutic products for anticancer therapies. IMPORTANCE Human microbiota plays an important role in mediating drug efficacy and toxicity in anticancer treatment. In this work, we developed a comprehensive online database, which documents over 480 microbiota-drug associations manually curated from 26 research articles. Users can conveniently browse, search, and download the data from the database. Search filters can be customized using different types of keywords, including bacterial name (e.g., Akkermansia muciniphila), anticancer treatment (e.g., anti-PD-1 therapy), and cancer type (e.g., lung cancer), with different types of experimental evidence of microbiota-drug association. We anticipate that this database will serve as a convenient platform for facilitating research on microbiota-drug associations, including the development of novel biomarkers for predicting drug outcomes as well as novel live biotherapeutic products for improving the outcomes of anticancer drugs. |
3,653 | Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection | Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark localization, leading to unreliable diagnosis results. In this work, we propose a novel Structure-Aware Long Short-Term Memory framework (SA-LSTM) for efficient and accurate 3D landmark detection. To reduce the computational burden, SA-LSTM is designed in two stages. It first locates the coarse landmarks via heatmap regression on a down-sampled CBCT volume and then progressively refines landmarks by attentive offset regression using multi-resolution cropped patches. To boost accuracy, SA-LSTM captures global-local dependence among the cropping patches via self-attention. Specifically, a novel graph attention module implicitly encodes the landmark's global structure to rationalize the predicted position. Moreover, a novel attention-gated module recursively filters irrelevant local features and maintains high-confident local predictions for aggregating the final result. Experiments conducted on an in-house dataset and a public dataset show that our method outperforms state-of-the-art methods, achieving 1.64 mm and 2.37 mm average errors, respectively. Furthermore, our method is very efficient, taking only 0.5 seconds for inferring the whole CBCT volume of resolution 768 x 768 x 576. |
3,654 | 4D printing - revolution or fad? | Purpose - This feature article aims to review state-of-the-art developments in additive manufacture, in particular, 4D printing. It discusses what it is, what research has been carried out and maps potential applications and its future impact. Design/methodology/approach - The article first defines additive manufacturing technologies and goes on to describe the state-of-the-art. Following which the paper examines several case studies and maps a trend that shows an emergence of 4D printing. Findings - The case studies highlight a particular specialization within additive manufacture where the use of adaptive, biomimetic composites can be programmed to reshape, or have embedded properties or functionality that transform themselves when subjected to external stimuli. Originality/value - This paper discusses the state-of-the-art of additive manufacture, discussing strategies that can be used to reduce the print process (such as through kinematics); and the use of smart materials where parts adapt themselves in response to the surrounding environment supporting the notion of self-assemblies. |
3,655 | Fertilizing ability and survivability of rooster sperm diluted with a novel semen extender supplemented with serine for practical use on smallholder farms | Semen extenders are essential for maintaining the quality of sperm during storage and assuring the success of fertility after insemination. The objective of this study was to examine the effect of a new rooster semen extender (hereinafter, NCAB) supplemented with serine on the survivability and fertilizing ability of sperm following storage. NaCl solution, the NCAB extender, and IGGKPh extender were used as treatments. In Experiment 1, different storage temperatures (5°C and 25°C) and durations of storage (0, 12, and 24 h) were used to compare the semen quality and determine the suitable storage temperature. The fertility test was performed in in-station tests and on-farm experimental trials in Experiments 2 and 3, respectively. The results indicated that the interaction effect of duration, treatment, and storage temperature on all sperm parameters was highly significant. The NCAB extender significantly improved rooster semen quality during storage for 24 h at 5°C (P < 0.01). The sperm diluted in saline solution could not survive 24 h of storage at 25°C. The fertility and hatchability rates obtained for sperm diluted with the NCAB extender were higher than those diluted with other extenders. In addition, the fertilizing capacity of the NCAB extender-diluted sperm under field conditions was significantly higher than that of the saline solution-treated sperm. In conclusion, the NCAB extender supplemented with serine and stored at a low temperature (5°C) positively affects sperm quality and fertilization. |
3,656 | Morphological markers of chromosomal instability in bone marrow aspiration and trephine biopsy of acute leukemia and myelodysplastic syndrome | The role of chromosomal instability (CI) in oncogenesis is very well described in solid tumours, but there are a lack of studies on haematology malignancy, especially with multiple morphological markers. The study aims to analyze seven morphological markers of CI- chromatin bridges (CB), multipolar mitosis (MPM), nuclear budding (NB), micronuclei (MN), nuclear heterogeneity (NH), laggards, chromatin strings (CS) in bone marrow aspirate (BMA) and biopsy of acute leukaemia (AL), and myelodysplastic syndrome (MDS). It is a retrospective cross-sectional analytical study where BMA and biopsy were reviewed for CI markers. We compared CI markers in five categories. CI markers were further correlated with clinical manifestations and outcomes of patients. The study included 54 samples of 37 patients. Overall, the median (IQR) of markers were as follows: MN 3.5 (1,7), NB 5 (1,18), MPM 1 (0,4), CB 1(0,2), Laggards 0 (0,1), and CS 2.5 (0,6). NH was noted in 65.4% of samples. All CI markers except laggards were significantly increased in B-ALL, AML, and MDS compared to other categories. Many CI markers were significantly raised with a few clinical features. The MN, MPM, Laggard, and NH markers were significantly increased in the dead patients compared to those who survived. The study, one of the first to analyze multiple CI markers, revealed that the CI markers were significantly increased in AL and MDS patients and significantly associated with clinical manifestations and outcomes. Morphology markers of CI are valuable and cost-effective in diagnostic strategy, type of malignancies, and assessing prognosis. |
3,657 | Uniform Color Space-Based High Dynamic Range Video Compression | Recently, there has been a significant progress in the research and development of the high dynamic range (HDR) video technology and the state-of-the-art video pipelines are able to offer a higher bit depth support to capture, store, encode, and display HDR video content. In this paper, we introduce a novel HDR video compression algorithm, which uses a perceptually uniform color opponent space, a novel perceptual transfer function to encode the dynamic range of the scene, and a novel error minimization scheme for accurate chroma reproduction. The proposed algorithm was objectively and subjectively evaluated against four state-of-the-art algorithms. The objective evaluation was conducted across a set of 39 HDR video sequences, using the latest x265 10-bit video codec along with several perceptual and structural quality assessment metrics at 11 different quality levels. Furthermore, a rating-based subjective evaluation (n = 40) was conducted with six sequences at two different output bitrates. Results suggest that the proposed algorithm exhibits the lowest coding error amongst the five algorithms evaluated. Additionally, the rate-distortion characteristics suggest that the proposed algorithm outperforms the existing state-of-the-art at bitrates >= 0.4 bits/pixel. |
3,658 | Control of parallelized bioreactors I: dynamic scheduling software for efficient bioprocess management in high-throughput systems | The shift towards high-throughput technologies and automation in research and development in industrial biotechnology is highlighting the need for increased automation competence and specialized software solutions. Within bioprocess development, the trends towards miniaturization and parallelization of bioreactor systems rely on full automation and digital process control. Thus, mL-scale, parallel bioreactor systems require integration into liquid handling stations to perform a range of tasks stretching from substrate addition to automated sampling and sample analysis. To orchestrate these tasks, the authors propose a scheduling software to fully leverage the advantages of a state-of-the-art liquid handling station (LHS) and to enable improved process control and resource allocation. Fixed sequential order execution, the norm in LHS software, results in imperfect timing of essential operations like feeding or Ph control and execution intervals thereof, that are unknown a priori. However, the duration and control of, e.g., the feeding task and their frequency are of great importance for bioprocess control and the design of experiments. Hence, a software solution is presented that allows the orchestration of the respective operations through dynamic scheduling by external LHS control. With the proposed scheduling software, it is possible to define a dynamic process control strategy based on data-driven real-time prioritization and transparent, user-defined constraints. Drivers for a commercial 48 parallel bioreactor system and the related sensor equipment were developed using the SiLA 2 standard greatly simplifying the integration effort. Furthermore, this paper describes the experimental hardware and software setup required for the application use case presented in the second part. |
3,659 | Exploiting Color Volume and Color Difference for Salient Region Detection | Foreground and background cues can assist humans in quickly understanding visual scenes. In computer vision, however, it is difficult to detect salient objects when they touch the image boundary. Hence, detecting salient objects robustly under such circumstances without sacrificing precision and recall can be challenging. In this paper, we propose a novel model for salient region detection, namely, the foreground-center background (FCB) saliency model. Its main highlights as follows. First, we use regional color volume as the foreground, together with perceptually uniform color differences within regions to detect salient regions. This can highlight salient objects robustly, even when they touched the image boundary, without greatly sacrificing precision and recall. Second, we employ center saliency to detect salient regions together with foreground and background cues, which improves saliency detection performance. Finally, we propose a novel and simple yet efficient method that combines foreground, center, and background saliency. Experimental validation with three well-known benchmark data sets indicates that the FCB model outperforms several stateof-the-art methods in terms of precision, recall, F-measure, and particularly, the mean absolute error. Salient regions are brighter than those of some existing state-of-the-art methods. |
3,660 | One stage lesion detection based on 3D context convolutional neural networks | Lesion detection from Computed Tomography (CT) scans is a challenge because non-lesions and true lesions always have similar appearances. Therefore, the performance of mainstream 2D image-based object detection algorithms is not promising since the texture and shape of inner-classes are always different. To detect lesions, we propose a novel deep convolutional feature fusion scheme, 3D Context Feature Fusion (3DCFF). Motivated by state-of-the-art object detection algorithms, we use a one-stage framework, rather than a Region Proposal Network, to extract lesions. In addition, because 3D context provides texture, contour, and shape information that are helpful for generating distinguishable lesion features, 3D context is used as the input for the proposed network. Furthermore, the network adopts a multi-resolution fusion scheme among different scales of feature maps. Results of experiments, conducted with the Deeplesion database, show that the proposed 3DCFF performs better and faster than state-of-the-art algorithms, such as Faster R-CNN, RetinaNet, and 3DCE. (C) 2019 Elsevier Ltd. All rights reserved. |
3,661 | Advances in numerical ditching simulation of flexible aircraft models | This paper deals with explicit numerical simulation of fixed-wing aircraft ditching using a coupled approach of Smoothed Particle Hydrodynamics and Finite Elements. Particular focus is put on recent advances toward simulation of flexible full aircraft models, which comprises significant challenges with respect to the numerical efficiency as well as the model complexity. First, the pursued numerical approach is briefly presented. In order to deal with aforementioned challenges, an automated modular tool, which generates numerical models and launches ditching simulations, is presented. The paper provides a brief explanation of state-of-the-art aircraft as well as fluid modelling techniques used and furthermore presents an integrated model that computes aerodynamic loads during the simulation. The developed tool is used to conduct various numerical studies. First, the improved efficiency of such simulations over the state of the art is shown. Next, results of parameter studies are presented, demonstrating the effects of impact conditions on the aircraft motion. Finally, the structural deformation experienced during ditching of a detailed finite element aircraft model is analysed and its effects on the aircraft motion are discussed. |
3,662 | RECISTSup: Weakly-Supervised Lesion Volume Segmentation Using RECIST Measurement | Lesion volume segmentation in medical imaging is an effective tool for assessing lesion/tumor sizes and monitoring changes in growth. Since manually segmentation of lesion volume is not only time-consuming but also requires radiological experience, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Although RECIST measurement is coarse compared with voxel-level annotation, it can reflect the lesion's location, length, and width, resulting in a possibility of segmenting lesion volume directly via RECIST measurement. In this study, a novel weakly-supervised method called RECISTSup is proposed to automatically segment lesion volume via RECIST measurement. Based on RECIST measurement, a new RECIST measurement propagation algorithm is proposed to generate pseudo masks, which are then used to train the segmentation networks. Due to the spatial prior knowledge provided by RECIST measurement, two new losses are also designed to make full use of it. In addition, the automatically segmented lesion results are used to supervise the model training iteratively for further improving segmentation performance. A series of experiments are carried out on three datasets to evaluate the proposed method, including ablation experiments, comparison of various methods, annotation cost analyses, visualization of results. Experimental results show that the proposed RECISTSup achieves the state-of-the-art result compared with other weakly-supervised methods. The results also demonstrate that RECIST measurement can produce similar performance to voxel-level annotation while significantly saving the annotation cost. |
3,663 | State-of-the-art of reverse osmosis desalination pretreatment | Pretreatment plays the critical role of removing source water constituents, like sediment and microbes, which could hinder the downstream reverse osmosis (RO) desalination process. While some source waters require negligible pretreatment, others like surface waters, require rigorous treatment to protect the RO process operation. The RO industry grew rapidly between 1995 and 2010. Growth pushed the industry to find cost-effective and robust large-scale pretreatment solutions. Over the last two decades, RO manufacturers have also developed membranes with greater fouling resistance and advocated system designs that reduce fouling potential. As a result, the state of the art in pretreatment has progressed significantly since the mid 1990s. Many of the improvements in pretreatment were enabled by a better understanding of fouling processes. Because fouling is complex and dynamic, with biofouling contributing to its complexity, significant research and development have been necessary to identify improvements. This paper provides a basis to understand the various fouling mechanisms found in RO systems and to describe the current state-of-the-art of the pretreatment technologies for fouling control. The paper addresses pretreatment of the myriad water sources in which RO technology is applied, with greater emphasis on seawater RO SWRO pretreatment as the largest single pretreatment market segment. (C) 2014 Elsevier B.V. All rights reserved. |
3,664 | fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs | Since neural networks renaissance, convolutional neural networks (ConvNets) have demonstrated a state-of-the-art performance in several emerging artificial intelligence tasks. The deployment of ConvNets in real-life applications requires power-efficient designs that meet the application-level performance needs. In this context, field-programmable gate arrays (FPGAs) can provide a potential platform that can be tailored to application-specific requirements. However, with the complexity of ConvNet models increasing rapidly, the ConvNet-to-FPGA design space becomes prohibitively large. This paper presents fpgaConvNet, an end-to-end framework for the optimized mapping of ConvNets on FPGAs. The proposed framework comprises an automated design methodology based on the synchronous dataflow (SDF) paradigm and defines a set of SDF transformations in order to efficiently navigate the architectural design space. By proposing a systematic multiobjective optimization formulation, the presented framework is able to generate hardware designs that are cooptimized for the ConvNet workload, the target device, and the application's performance metric of interest. Quantitative evaluation shows that the proposed methodology yields hardware designs that improve the performance by up to 6.65x over highly optimized graphics processing unit designs for the same power constraints and achieve up to 2.94x higher performance density compared with the state-of-the-art FPGA-based ConvNet architectures. |
3,665 | Single-Event Multiple Effect Tolerant RHBD14T SRAM Cell Design for Space Applications | Static Random Access Memory (SRAM) is primarily used as a memory storage element, which is susceptible to radiation-induced Single Event Upsets (SEUs). Hence, a robust SRAM bit-cell design is primarily a difficult task to address the space radiation environment. Furthermore, as the transistor's size moves into nanometer regimes, a new challenge like Single Event Multiple Effects (SEME's) evolved in SRAMs. SEME's make the design of SRAM a serious challenge. In this article, a novel Radiation Hardened By Design (RHBD) SRAM bit-cell is proposed based on the polarity upset mechanism of SEUs. This work shows that the proposed RHBD14T SRAM bit-cell is SEU immune and delivers higher SEME critical charge than state-of-the-art RHBD SRAM bit-cells. The Monte Carlo (MC) simulations further show that the proposed RHBD14T SRAM delivers the lower Probability of Failure when compared to reported RHBD SRAM cells. Consequently, the proposed bit cell's sensitive area is 128% lower with respect to the recently reported state-of-the-art RHBD RSP14T SRAM bit-cells. |
3,666 | A framework for advancing sustainable magnetic resonance imaging access in Africa | Magnetic resonance imaging (MRI) technology has profoundly transformed current healthcare systems globally, owing to advances in hardware and software research innovations. Despite these advances, MRI remains largely inaccessible to clinicians, patients, and researchers in low-resource areas, such as Africa. The rapidly growing burden of noncommunicable diseases in Africa underscores the importance of improving access to MRI equipment as well as training and research opportunities on the continent. The Consortium for Advancement of MRI Education and Research in Africa (CAMERA) is a network of African biomedical imaging experts and global partners, implementing novel strategies to advance MRI access and research in Africa. Upon its inception in 2019, CAMERA sets out to identify challenges to MRI usage and provide a framework for addressing MRI needs in the region. To this end, CAMERA conducted a needs assessment survey (NAS) and a series of symposia at international MRI society meetings over a 2-year period. The 68-question NAS was distributed to MRI users in Africa and was completed by 157 clinicians and scientists from across Sub-Saharan Africa (SSA). On average, the number of MRI scanners per million people remained at less than one, of which 39% were obsolete low-field systems but still in use to meet daily clinical needs. The feasibility of coupling stable energy supplies from various sources has contributed to the growing number of higher-field (1.5 T) MRI scanners in the region. However, these systems are underutilized, with only 8% of facilities reporting clinical scans of 15 or more patients per day, per scanner. The most frequently reported MRI scans were neurological and musculoskeletal. The CAMERA NAS combined with the World Health Organization and International Atomic Energy Agency data provides the most up-to-date data on MRI density in Africa and offers a unique insight into Africa's MRI needs. Reported gaps in training, maintenance, and research capacity indicate ongoing challenges in providing sustainable high-value MRI access in SSA. Findings from the NAS and focused discussions at international MRI society meetings provided the basis for the framework presented here for advancing MRI capacity in SSA. While these findings pertain to SSA, the framework provides a model for advancing imaging needs in other low-resource settings. |
3,667 | Deep Spatial Interpolation of Rain Field for UK Satellite Networks | This article presents two new state-of-the-art spatial rain field interpolation convolutional neural networks (SRFICNNs), referred to as learned deviation (LD) and learned interpolation (LI) models, for predicting the point rain rate at finer spatial scales. The main contribution is the successful introduction of the prior-art deep learning technique into high-resolution (HR) rainfall rate prediction with significant improvement in accuracy. This is very important for the effective implementation of fade mitigation techniques for both terrestrial and satellite networks. The comparison of the models' perfor-mances with ground truth (radar measurements) shows that the proposed models give an excellent mean square error (MSE) and structural SIMilarity (SSIM) in rainfall field reconstruction if the network depth falls in the range of 15-25 weight layers. The final model uses 20 layers for HR point rain rate prediction. Further study shows that the LD model offers a faster convergence and yields a more accurate rain rate prediction. In particular, this article compares the rain rate exceedance distribution and Log-Normality property from the model estimates with values calculated from measured data. Results show that the LD model gives a highly accurate estimate of these two indices with the corresponding root mean square (rms) error of 5.1709 x 10(-4) and 0.0013, respectively. |
3,668 | Improved Faster R-CNN With Multiscale Feature Fusion and Homography Augmentation for Vehicle Detection in Remote Sensing Images | Vehicle detection in remote sensing images has attracted remarkable attention for its important role in a variety of applications in traffic, security, and military fields. Motivated by the stunning success of region convolutional neural network (R-CNN) techniques, which have achieved the state-of-the-art performance in object detection task on benchmark data sets, we propose to improve the Faster R-CNN method with better feature extraction, multiscale feature fusion, and homography data augmentation to realize vehicle detection in remote sensing images. Extensive experiments on representative remote sensing data sets related to vehicle detection demonstrate that our method achieves better performance than the state-of-the-art approaches. The source code will be made available (after the review process). |
3,669 | The state of the art of electric and hybrid vehicles | In a world where environment protection and energy conservation are growing concerns, the development of electric vehicles (EV) and hybrid electric vehicles (HEV) has taken on an accelerated pace. The dream of having commercially viable EVs and HEVS is becoming a reality, EVs, and HEVs are gradually available in the market. This paper will provide an overview of the present status of electric and hybrid vehicles worldwide and their state of the art, with emphasis on the engineering philosophy and key technologies. The importance of the integration of technologies of automobile, electric motor drive, electronics, energy storage, and controls and also the importance of the, integration of society strength from government, industry, research institutions, electric power utilities, and transportation authorities are addressed. The challenge of EV commercialization is discussed. |
3,670 | Sparsity-Based Audio Declipping Methods: Selected Overview, New Algorithms, and Large-Scale Evaluation | Recent advances in audio declipping have substantially improved the state of the art. Yet, practitioners need guidelines to choose a method, and while existing benchmarks have been instrumental in advancing the field, larger-scale experiments are needed to guide such choices. First, we show that the clipping levels in existing small-scale benchmarks are moderate and call for benchmarks with more perceptually significant clipping levels. We then propose a general algorithmic framework for declipping that covers existing and new combinations of variants of state-of-the-art techniques exploiting time-frequency sparsity: synthesis vs. analysis sparsity, with plain or structured sparsity. Finally, we systematically compare these combinations and a selection of state-of-the-art methods. Using a large-scale numerical benchmark and a smaller scale formal listening test, we provide guidelines for various clipping levels, both for speech and various musical genres. The code is made publicly available for the purpose of reproducible research and benchmarking. |
3,671 | Probe current determination in analytical TEM/STEM and its application to the characterization of large area EDS detectors | A simple procedure, which enables accurate measurement of transmission electron microscopy (TEM)/STEM probe currents using an energy loss spectrometer drift tube is described. The currents obtained are compared with those measured on the fluorescent screen to enable the losses due to secondary and backscattered electrons to be determined. The current values obtained from the drift tube allow the correction of fluorescent screen current densities to yield true current. They also enable CCD conversion efficiencies to be obtained, which in turn allows images to be calibrated in terms of electron fluence. Using probes of known current in conjunction with a NiO reference specimen enables the X-ray detector solid angle to be determined. The NiO specimen also allows a wide range of other EDS detector parameters to be obtained, including the presence of ice and carbon contamination. A range of performance characteristics are reported for two large area EDS detector systems. Many of the measurements reported herein have been automated via the use of freely available scripts for DigitalMicrograph. |
3,672 | Environmental performance of Cantabrian (Northern Spain) pelagic fisheries: Assessment of purse seine and minor art fleets under a life cycle approach | The perpetuation of fishing activity from an environmentally, socially and economically sustainable approach is essen-tial to guarantee not only the future of coastal populations, but also the supply of high-value seafood for society and the safeguarding of cultural heritage.This article aims to assess the environmental performance associated with fishing fleet operations in Cantabria (north-ern Spain) under a life cycle thinking from a holistic approach. Thus, the Life Cycle Assessment (LCA) methodology was applied under a 'cradle-to-port' approach, setting the functional unit as 1 kg of fresh fish landed. Inventory data on the main inputs and outputs were collected from a sample of 57 vessels covering for the first time the main tech-niques, purse seine and minor art fisheries.The results identified that the vessel use stage was the responsible of most of the impacts. In line with the literature, diesel consumption stood as the chief hotspot in six of the seven impact categories analysed. Purse seiners got a value of 0.25 kg of fuel per kg of fish landed, while the performance of the minor art fleet showed significantly lower consumption (0.07). Regarding impacts on climate change, this study found a quantity of 1.00 and 0.34 kg CO2 eq. per FU, for purse seine and minor arts, respectively. These figures were consistent with the expected results for pelagic fisheries. For the remaining indicators, purse seiners generally performed worse.The LCA methodology provided outcomes that allow the proposal of potential improvements and measures to foster the transition towards a more sustainable smart-fishing sector. Further research efforts should focus on the develop-ment and implementation of renewable energy and low-carbon vessel propulsion technologies. |
3,673 | Potential of paracetamol for reproductive disruption: molecular interaction, dynamics, and MM-PBSA based in-silico assessment | Paracetamol is generally recommended for pain and fever. However, as per experimental and epidemiological data, widespread and irrational or long-term use of paracetamol may be harmful to human endocrine homeostasis, especially during pregnancy. Some researchers suggest that prenatal exposure to paracetamol might alter fetal development and also enhance the risk of reproductive disorders. An imbalance in the levels of these hormones may play a significant role in the emergence of various diseases, including infertility. Therefore, in this study, the interaction mechanism of paracetamol with reproductive hormone receptors was investigated by molecular docking, molecular dynamics (MD) simulations, and Poisson-Boltzmann surface area (MM-PBSA) for assessing paracetamol's potency to disrupt reproductive hormones. The results indicate that paracetamol has the ability to interact with reproductive hormone receptors (estrogen 1XP9; 1QKM with binding energy of -5.61 kcal/mol; -5.77 kcal/mol; androgen 5CJ6 - 5.63 kcal/mol; and progesterone 4OAR -5.60 kcal/mol) by hydrogen bonds as well as hydrophobic and van der Waals interactions to maintain its stability. In addition, the results of the MD simulations and MM-PBSA confirm that paracetamol and reproductive receptor complexes are stable. This research provides a molecular and atomic level understanding of how paracetamols disrupt reproductive hormone synthesis. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), Radius of Gyration and hydrogen bonding exhibited that paracetamol mimic at various attribute to bisphenol and native ligand. |
3,674 | Designing a CHAM Block Cipher on Low-End Microcontrollers for Internet of Things | As the technology of Internet of Things (IoT) evolves, abundant data is generated from sensor nodes and exchanged between them. For this reason, efficient encryption is required to keep data in secret. Since low-end IoT devices have limited computation power, it is difficult to operate expensive ciphers on them. Lightweight block ciphers reduce computation overheads, which are suitable for low-end IoT platforms. In this paper, we implemented the optimized CHAM block cipher in the counter mode of operation, on 8-bit AVR microcontrollers (i.e., representative sensor nodes). There are four new techniques applied. First, the execution time is drastically reduced, by skipping eight rounds through pre-calculation and look-up table access. Second, the encryption with a variable-key scenario is optimized with the on-the-fly table calculation. Third, the encryption in a parallel way makes multiple blocks computed in online for CHAM-64/128 case. Fourth, the state-of-art engineering technique is fully utilized in terms of the instruction level and register level. With these optimization methods, proposed optimized CHAM implementations for counter mode of operation outperformed the state-of-art implementations by 12.8%, 8.9%, and 9.6% for CHAM-64/128, CHAM-128/128, and CHAM-128/256, respectively. |
3,675 | Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost | We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end, we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new metric learning approach for the latter. We also introduce an extension of the NCM classifier to allow for richer class representations. Experiments on the ImageNet 2010 challenge dataset, which contains over 10(6) training images of 1,000 classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-the-art performance. Experimentally, we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art while being orders of magnitude faster. Furthermore, we show how a zero-shot class prior based on the ImageNet hierarchy can improve performance when few training images are available. |
3,676 | Feature selection through binary brain storm optimization | Feature selection stands for the process of finding the most relevant subset of features based on some criterion, which turns out to be an optimization task. In this context, several metaheuristic techniques have been extensively studied achieving results comparable to some state-of-the-art and traditional optimization techniques. This paper introduces a variation of the Brain Storm Optimization (i.e., Binary Brain Storm Optimization) for feature selection purposes, where real-valued solutions are mapped onto a boolean hyper cube using different transfer functions. The proposed Binary Brain Storm Optimization was evaluated under different scenarios and with its results compared to some state-of-the-art techniques. Its overall performance presented suitable results that are comparable to the other techniques, thus showing to be a promising tool to the problem of feature selection. (C) 2018 Elsevier Ltd. All rights reserved. |
3,677 | Photon-Counting Detector CT for Musculoskeletal Imaging: A Clinical Perspective | Photon-counting detector (PCD) CT has emerged as a novel imaging modality that represents a fundamental shift in the way that CT systems detect x-rays. After pre-clinical and clinical investigations showed benefits of PCD CT for a range of imaging tasks, the U.S. FDA in 2021 approved the first commercial PCD CT system for clinical use. The technologic features of PCD CT are particularly well suited for musculo-skeletal imaging applications. Advantages of PCD CT compared with conventional energy-integrating detector (EID) CT include smaller detector pixels and excellent geometric dose efficiency that enable imaging of large joints and central skeletal anatomy at ultrahigh spatial resolution; advanced multienergy spectral postprocessing that allows quantification of gout deposits and generation of virtual noncalcium images for visualization of bone edema; improved metal artifact reduction for imaging of orthopedic implants; and higher CNR and suppression of electronic noise. Given substantially improved cortical and trabecular detail, PCD CT images more clearly depict skeletal abnormalities, including fractures, lytic lesions, and mineralized tumor matrix. The purpose of this article is to review, by use of clinical examples comparing EID CT and PCD CT, the technical features of PCD CT and their associated impact on musculoskeletal imaging applications. |
3,678 | Impact and Detection of GNSS Jammers on Consumer Grade Satellite Navigation Receivers | Jamming is the act of intentionally directing powerful electromagnetic waves toward a victim receiver with the ultimate goal of denying its operations. This paper describes the main types of Global Navigation Satellite System (GNSS) jammers and reviews their impact on GNSS receivers. A survey of state-of-the-art methods for jamming detection is also provided. Different detection approaches are investigated with respect to the receiver stage where they can be implemented. |
3,679 | Current and Emerging Therapies in Pediatric Atopic Dermatitis | Atopic dermatitis (AD) is the most common inflammatory skin disease seen in children. It is a heterogeneous disorder, with a variety of associated manifestations and symptoms. Cases may range from mild to severe. As a result, a spectrum of prescription and nonprescription therapies may be utilized when managing this condition. This article provides an extensive overview of these therapies, with equal consideration provided to current, emerging, and alternative options used in the pediatric population. |
3,680 | Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM | Objective: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a 4-dimensional (4D) deep learning model based on 3D convolution and convolutional long short-term memory (C-LSTM) for hepatocellular carcinoma (HCC) lesion segmentation. Methods: The proposed deep learning model utilizes 4D information on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) images to assist liver tumor segmentation. Specifically, a shallow U-net based 3D CNN module was designed to extract 3D spatial domain features from each DCE phase, followed by a 4-layer C-LSTM network module for time domain information exploitation. The combined information of multi-phase DCE images and the manner by which tissue imaging features change on multi-contrast images allow the network to more effectively learn the characteristics of HCC, resulting in better segmentation performance. Results: The proposed model achieved a Dice score of 0.825 +/- 0.077, a Hausdorff distance of 12.84 +/- 8.14 mm, and a volume similarity of 0.891 +/- 0.080 for liver tumor segmentation, which outperformed the 3D U-net model, RA-UNet model and other models in the ablation study in both internal and external test sets. Moreover, the performance of the proposed model is comparable to the nnU-Net model, which showed state-of-the-art performance in many segmentation tasks, with significantly reduced prediction time. Conclusion: The proposed 3D convolution and C-LSTM based model can achieve accurate segmentation of HCC lesions. |
3,681 | Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning | Lung field segmentation in the posterior-anterior (PA) chest radiograph is important for pulmonary disease diagnosis and hemodialysis treatment. Due to high shape variation and boundary ambiguity, accurate lung field segmentation from chest radiograph is still a challenging task. To tackle these challenges, we propose a joint shape and appearance sparse learning method for robust and accurate lung field segmentation. The main contributions of this paper are: 1) a robust shape initialization method is designed to achieve an initial shape that is close to the lung boundary under segmentation; 2) a set of local sparse shape composition models are built based on local lung shape segments to overcome the high shape variations; 3) a set of local appearance models are similarly adopted by using sparse representation to capture the appearance characteristics in local lung boundary segments, thus effectively dealing with the lung boundary ambiguity; 4) a hierarchical deformable segmentation framework is proposed to integrate the scale-dependent shape and appearance information together for robust and accurate segmentation. Our method is evaluated on 247 PA chest radiographs in a public dataset. The experimental results show that the proposed local shape and appearance models outperform the conventional shape and appearance models. Compared with most of the state-of-the-art lung field segmentation methods under comparison, our method also shows a higher accuracy, which is comparable to the inter-observer annotation variation. |
3,682 | Retinal image enhancement using adaptive histogram equalization tuned with nonsimilar grouping curvelet | Fundus images are broadly used by medical ophthalmologists to detect and assess any customary abnormalities. Fundus imaging sensors capture the eye's rigid portion, which characteristically covers the core, tangential retina, optic disc, and macula. Existing state-of-the-art fundus sensors have the drawback of producing low contrast and noisy information, which makes scientific and algorithmic evaluation very complicated. This article proposes an Adaptive Histogram Equalization-Tuned with Nonsimilar Grouping Curvelet (HET-NOSCU), which works through a joint denoising enhancement approach. The proposed algorithm's main contribution includes (i) use of curvelet features to better preserve edges during denoising. (ii) Adaptive enhancement using the histogram to prevent halo ringing and specular artifacts, which yields superior results than the very recently established state-of -the-art methods, using similar performing parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), and correlation coefficient (CoC). We observe an improvement of 17.66%, 0.93%, and 0.24%, respectively, for the above parameters. |
3,683 | Wetland phosphorus dynamics and phosphorus removal potential | Wetlands are typically defined as inundated areas with hydric soils forming a transitional zone between terrestrial and aquatic systems. Wetlands have numerous ecosystem benefits, one of which is the potential to mitigate or reverse eutrophication of surface water bodies. The physical, chemical, and biological processes governing phosphorus cycling in wetlands are nuanced and complex; understanding these has direct relevance to the restoration of wetlands, particularly for projects aimed at improving water quality in adjacent water bodies. This literature review summarizes these processes and provides recommendations relevant to restoration of permanent and semipermanent flow-through wetlands, such as those in the Upper Klamath Basin of Oregon. It also reviews several wetland restoration studies assessing phosphorus removal. In summary, appropriately designed and managed wetlands can remove 25% to 44% of inflowing total phosphorus. Deposition of particulate matter, adsorption, uptake by biomass, and peat accretion are the primary phosphorus sequestration mechanisms in wetlands, depending on site-specific conditions (e.g., growing season length, vegetation communities, and soil type). In areas with relatively short growing seasons and where wintertime loads are targeted for treatment, as in the Upper Klamath Basin, deposition of particulate matter will be the primary mechanism for phosphorus sequestration in wetlands given that two of the three remaining processes occur during the growing season. Recommendations to maximize phosphorus sequestration in wetlands include the following: designing wetlands for hydraulic residence time of several days to weeks, managing wetlands for rapid establishment of wetland vegetation with limited decomposition potential (e.g., tule [hardstem bulrush] to facilitate peat accretion), and flooding during periods with low water temperatures and initially isolating restored wetlands from adjacent water bodies (both to minimize diffusive flux of phosphorus from wetland sediment to the water column). Relevant to the Upper Klamath Basin, there is also justification to prioritize areas with relatively high particulate phosphorus load given the potential limited capacity for phosphorus treatment associated with other sequestration mechanisms. Finally, a combination of mitigation and restoration strategies is necessary to achieve water quality objectives, meaning that wetland restoration alone may not be sufficient. Monitoring is advised to facilitate application of adaptive management principles. PRACTITIONER POINTS: Appropriately designed and managed wetlands can remove 25% to 44% of inflowing total phosphorus. Deposition of particulate matter, adsorption, uptake by biomass, and peat accretion are the primary phosphorus sequestration mechanisms in wetlands, depending on site-specific conditions (e.g., growing season length, vegetation communities, and soil type). Recommendations to maximize phosphorus sequestration in wetlands include designing wetlands for hydraulic residence time of several days to weeks; managing wetlands for rapid establishment of wetland vegetation with limited decomposition potential (e.g., tule [hardstem bulrush], to facilitate peat accretion); and flooding during periods with low water temperatures and initially isolating restored wetlands from adjacent water bodies (both to minimize diffusive flux of phosphorus from wetland sediment to the water column). A combination of mitigation and restoration strategies are necessary to achieve water quality objectives, meaning that wetland restoration alone may not be sufficient. Monitoring is advised to facilitate application of adaptive management principles. |
3,684 | Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellular manufacturing systems | Cellular manufacturing system (CMS) is regarded as an efficient production strategy for batch type of production. Literature suggests, since the last two decades neural network has been intensively used in cell formation while production factor such as operation time is merely considered. This paper presents a new hybrid neural network approach, Fuzzy ART K-Means Clustering Technique (FAKMCT), to solve the part machine grouping problem in CMS considering operation time. The performance of the proposed technique is tested with problems from open literature and the results are compared to the existing clustering models such as simple K-means algorithm and modified ART1 algorithm as found in the recent literature. The results support the better performance of the proposed algorithm. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts. |
3,685 | Seizure detection based on wearable devices: A review of device, mechanism, and algorithm | With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband-type devices, patch-type devices, and armband-type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure-related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field. |
3,686 | Thiamine supplementation may be associated with improved prognosis in patients with sepsis | Sepsis is a clinical syndrome characterised by a severe disorder of pathophysiology caused by infection of pathogenic micro-organisms. The addition of antioxidant micronutrient therapies such as thiamine to sepsis treatment remains controversial. This study explored the effect of thiamine on the prognosis of patients with sepsis. This study was a retrospective study involving patients with sepsis from the Medical Information Mart for Intensive Care IV. Patients were divided into two groups, the thiamine received group (TR) and the thiamine unreceived group (TUR), according to whether they were supplemented with thiamin via intravenous while in the intensive care unit (ICU). The primary outcome was ICU mortality. The association between thiamine and outcome was analysed using the Cox proportional hazards regression model, propensity score matching (PSM), generalised boosted model-based inverse probability of treatment weighting (IPTW) and doubly robust estimation. A total of 11 553 sepsis patients were enrolled in this study. After controlling for potential confounders using Cox regression models, the TR group had a statistically significantly lower ICU mortality risk than the TUR group. The hazard ratio of ICU mortality for the TR group was 0·80 (95 % CI 0·70, 0·93). We obtained the same results after using PSM, IPTW and doubly robust estimation. Supplementation with thiamine has a beneficial effect on the prognosis of patients with sepsis. More randomised controlled trials are needed to confirm the effectiveness of thiamine supplementation in the treatment of sepsis. |
3,687 | Polycarbonate microplastics induce oxidative stress in anaerobic digestion of waste activated sludge by leaching bisphenol A | Polycarbonate (PC) microplastics are frequently detected in waste activated sludge. However, understanding the potential impact of PC microplastics on biological sludge treatment remains challenging. By tracking the changes in methane production under different concentrations of PC microplastics, a dose-dependent effect of PC microplastics on anaerobic digestion of sludge was observed. PC microplastics at 10-60 particles/g total solids (TS) improved methane production by up to 24.7 ± 0.1 % (at 30 particles/g TS), while 200 particles/g TS PC microplastics reduced methane production by 8.09 ± 0.1 %. Bisphenol A (BPA) leached from 30 particles/g TS PC microplastics (1.26 ± 0.18 mg/L) down-regulated intracellular reactive oxygen species (ROS) production, thereby enhancing enzyme activity, biomass viability, and abundance of methanogenic (Methanobacterium sp. and Methanosarcina sp.), ultimately boosting methane production. Conversely, BPA leached from 200 particles/g TS PC microplastics (4.02 ± 0.15 mg/L) stimulated ROS production, resulting in decreased biomass viability and even apoptosis. Modulation of oxidative stress by leaching monomeric BPA is an underappreciated transformative mechanism for improving the mastery of the potential behavior of microplastics in biological sludge treatment. |
3,688 | Security analysis of indistinguishable obfuscation for internet of medical things applications | As a powerful cryptographic primitive, indistinguishable obfuscation has been widely used to protect data privacy on the Internet of Medical Things (IoMT) systems. Basically, the cryptographic technique protects data privacy using a function to obfuscate medical applications to perform outputs computationally indistinguishable. The state-of-the-art obfuscation technique (GGH13) utilizes a variant of the multilinear map to enhance security. However, in such schemes, it can be observed that noise lies in each element of the matrix, which means the matrix is a full rank matrix with a probability of almost 1 and results that it is unable to establish the relationship between the matrix determinant and rank. In this paper, we propose an attack to break such obfuscator. Specifically, we use approximate eigenvalues to remove the influence of noise on the matrix eigenvalues and build a specific relationship between the determinant and matrix rank. Our analysis shows the structural weakness of the state-of-the-art indistinguishable obfuscation mechanism, and we further discuss the future direction to resolve such privacy issues for IoMT applications. |
3,689 | Parameter-Free Loss for Class-Imbalanced Deep Learning in Image Classification | Current state-of-the-art class-imbalanced loss functions for deep models require exhaustive tuning on hyperparameters for high model performance, resulting in low training efficiency and impracticality for nonexpert users. To tackle this issue, a parameter-free loss (PF-loss) function is proposed, which works for both binary and multiclass-imbalanced deep learning for image classification tasks. PF-loss provides three advantages: 1) training time is significantly reduced due to NO tuning on hyperparameter(s); 2) it dynamically pays more attention on minority classes (rather than outliers compared to the existing loss functions) with NO hyperparameters in the loss function; and 3) higher accuracy can be achieved since it adapts to the changes of data distribution in each mini-batch instead of the fixed hyperparameters in the existing methods during training, especially when the data are highly skewed. Experimental results on some classical image datasets with different imbalance ratios (IR, up to 200) show that PF-loss reduces the training time down to 1/148 of that spent by compared state-of-the-art losses and simultaneously achieves comparable or even higher accuracy in terms of both G-mean and area under receiver operating characteristic (ROC) curve (AUC) metrics, especially when the data are highly skewed. |
3,690 | Dancing Mindfulness: A Phenomenological Investigation of the Emerging Practice | An extensive review of both quantitative and qualitative literature reveals numerous connections between mindfulness practice and psychological well-being. Dancing Mindfulness, as a holistic wellness practice, is a creative approach to mindfulness meditation that draws on dance as the vehicle for engaging in the ancient practice characterized by non-judgment, loving kindness, and present-centered awareness. Of the first participants who learned the Dancing Mindfulness practice in a community-based setting, 10 shared their lived experience with the practice, and these experiences were analyzed using A.P. Giorgi׳s descriptive phenomenological psychological method. As a collective sample, the women described positive experiences with the Dancing Mindfulness practice. Specific themes indicated improvements in emotional and spiritual well-being, increased acceptance, positive changes to the self, and increased application of mindfulness techniques and strategies to real-world living. Another thematic area suggested that dancing and music are the two major components of action within Dancing Mindfulness leading to these benefits. |
3,691 | Time-Ordered Recent Event (TORE) Volumes for Event Cameras | Event cameras are an exciting, new sensor modality enabling high-speed imaging with extremely low-latency and wide dynamic range. Unfortunately, most machine learning architectures are not designed to directly handle sparse data, like that generated from event cameras. Many state-of-the-art algorithms for event cameras rely on interpolated event representations-obscuring crucial timing information, increasing the data volume, and limiting overall network performance. This paper details an event representation called Time-Ordered Recent Event (TORE) volumes. TORE volumes are designed to compactly store raw spike timing information with minimal information loss. This bio-inspired design is memory efficient, computationally fast, avoids time-blocking (i.e., fixed and predefined frame rates), and contains "local memory " from past data. The design is evaluated on a wide range of challenging tasks (e.g., event denoising, image reconstruction, classification, and human pose estimation) and is shown to dramatically improve state-of-the-art performance. TORE volumes are an easy-to-implement replacement for any algorithm currently utilizing event representations. |
3,692 | Semantic Pyramids for Gender and Action Recognition | Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. |
3,693 | Fuzzy ART/RRR-RSS: a two-phase neural network algorithm for part-machine grouping in cellular manufacturing | In this paper an efficient methodology adopting Fuzzy ART neural network is presented to solve the comprehensive part-machine grouping (PMG) problem in cellular manufacturing (CM). Our Fuzzy ART/RRR-RSS (Fuzzy ART/ReaRRangement- ReaSSignment) algorithm can effectively handle the real-world manufacturing factors such as the operation sequences with multiple visits to the same machine, production volumes of parts, and multiple copies of machines. Our approach is based on the non-binary production data-based part-machine incidence matrix (PMIM) where the operation sequences with multiple visits to the same machine, production volumes of parts, and multiple identical machines are incorporated simultaneously. A new measure to evaluate the goodness of the non-binary block diagonal solution is proposed and compared with conventional performance measures. The comparison result shows that our performance measure has more powerful discriminating capability than conventional ones. The Fuzzy ART/RRR-RSS algorithm adopts two phase approach to find the proper block diagonal solution in which all the parts and machines are assigned to their most preferred part families and machine cells for minimisation of inter-cell part moves and maximisation of within-cell machine utilisation. Phase 1 (clustering phase) attempts to find part families and machines cells quickly with Fuzzy ART neural network algorithm which is implemented with an ancillary procedure to enhance the block diagonal solution by rearranging the order of input presentation. Phase 2 (reassignment phase) seeks to find the best proper block diagonal solution by reassigning exceptional parts and machines and duplicating multiple identical machines to cells with the purpose of minimising inter-cell part moves and maximising within-cell machine utilisation. To show the robustness and recoverability of the Fuzzy ART/RRR-RSS algorithm to large-size data sets, a modified procedure of replicated clustering which starts with the near-best solution and rigorous qualifications on the number of cells and duplicated machines has been developed. Experimental results from the modified replicated clustering show that the proposed Fuzzy ART/RRR-RSS algorithm has robustness and recoverability to large-size ill-structured data sets by producing highly independent block diagonal solution close to the near-best one. |
3,694 | Prediction and Description of Near-Future Activities in Video | Most of the existing works on human activity analysis focus on recognition or early recognition of the activity labels from complete or partial observations. Similarly, almost all of the existing video captioning approaches focus on the observed events in videos. Predicting the labels and the captions of future activities where no frames of the predicted activities have been observed is a challenging problem, with important applications that require anticipatory response. In this work, we propose a system that can infer the labels and the captions of a sequence of future activities. Our proposed network for label prediction of a future activity sequence has three branches where the first branch takes visual features from the objects present in the scene, the second branch takes observed sequential activity features, and the third branch captures the last observed activity features. The predicted labels and the observed scene context are then mapped to meaningful captions using a sequence-to-sequence learning-based method. Experiments on four challenging activity analysis datasets and a video description dataset demonstrate that our label prediction approach achieves comparable performance with the state-of-the-arts and our captioning framework outperform the state-of-the-arts. |
3,695 | Using Species Distribution Modeling to contextualize Lower Magdalenian social networks visible through portable art stylistic similarities in the Cantabrian region (Spain) | This research argues for a refocus of the study of prehistoric social networks that involves contextualizing the inter-site links that are often interpreted as indicators of inter-site social interactions. It focuses on the social networks created during the Lower Magdalenian of the Cantabrian region (Spain), and visible through similarities of portable art representations. It uses Species Distribution Modeling and Maximum Classification Likelihood on faunal presence data to reconstruct prehistoric biomes, and contextualize the networks reconstructed through the art analysis. It demonstrates the potential of mapping the recreated networks onto the reconstructed biomes and of identifying the linked sites' foraging and minimal band territories to distinguish between local mobility movement and inter-group social alliances. The results show that, during the Lower Magdalenian, the majority of movements seen through artistic similarities probably represent the seasonal mobility of one or two hunter-gatherer groups, and that only a few intersite links represent social networks used to exchange mates and gather information. (C) 2015 Elsevier Ltd and INQUA. All rights reserved. |
3,696 | Competitive Quantization for Approximate Nearest Neighbor Search | In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity. |
3,697 | Multiscale image quality measures for defect detection in thin films | Manufacturing defects in flat surface products such as thin films, paper, foils, aluminum plates, steel slabs, fabrics, and glass sheets result in degradation of the visual quality of the product image. This leads to less satisfied customers, waste of material, and bad company reputation. This research presents a novel application of image visual quality measures such as the multiscale structural similarity index (MS-SSIM). A novel algorithm has been implemented for fast detection and location of defects in many flat surface products. Comparison of the proposed algorithm with the state-of-the-art approaches indicate promising results. A defect detection accuracy of 99.1 % has been achieved with 98.62 % precision, 97.7 % recall/sensitivity, and 100 % specificity. The discriminant power shows how well the MS-SSIM discriminates very effectively between normal and abnormal surfaces. The MS-SSIM has resulted in much better performance than the single-scale SSI approach but at the cost of relatively lower processing speed. The major advantages of the presented approach are as follows: scale invariance, avoiding the problem of parameter selection in the case of the state-of-the-art Gabor filter banks based approach, the higher detection accuracy, and the quasi real-time processing speed. |
3,698 | Possible effects of dietary advanced glycation end products on maternal and fetal health: a review | Excessive accumulation of advanced glycation end products (AGEs) in the body has been associated with many adverse health conditions. The common point of the pathologies associated at this point is oxidative stress and inflammation. Pregnancy is an important period in which many physiological, psychological, and biological changes are experienced. Along with the physiological changes that occur during this period, the mother maintaining an AGE-rich diet may cause an increase in the body's AGE pool and may increase oxidative stress and inflammation, as seen in healthy individuals. Studies have reported the negative effects of maternal AGE levels on maternal and fetal health during pregnancy. Although gestational diabetes, preeclampsia, endothelial dysfunction, and pelvic diseases constitute maternal complications, a number of pathological conditions such as intrauterine growth retardation, premature birth, neural tube defect, neurobehavioral developmental disorders, fetal death, and neonatal asphyxia constitute fetal complications. It is thought that the mechanisms of these complications have not been confirmed yet and more clinical studies are needed on this subject. The possible effects of dietary AGE levels during pregnancy on maternal and fetal health are examined in this review. |
3,699 | Workplace Aesthetic Appreciation and Exhaustion in a COVID-19 Vaccination Center: The Role of Positive Affects and Interest in Art | Background: Recently, workers employed in vaccination points around the world have been subjected to very high workloads to counter the progress of the COVID-19 epidemic. This workload has a negative effect on their well-being. Environmental psychology studies have shown how the physical characteristics of the workplace environment can influence employees' well-being. Furthermore, studies in the psychology of art show how art can improve the health of individuals. Objectives: The aim of this research was to test a moderated mediation model to verify how appreciation of workplace aesthetics can impact the level of exhaustion of staff working in a vaccination center, the mediating role of positive and negative affects, and the moderating role of interest in art. Methods: Data were collected from a sample of 274 workers (physicians, nurses, reception, and administrative staff) working in the same vaccination center in Italy. Participants answered a self-report questionnaire during a rest break. We used a cross-sectional design. Results: The results show that appreciation of workplace aesthetics impacts employees' level of exhaustion. This relationship is mediated by positive and negative affects, and interest in art moderates the relationship between positive affects and exhaustion. Conclusions: These findings indicate the central role of workplace aesthetics in influencing healthcare workers' well-being, and how interest in art can reduce exhaustion levels. Practical implications of the results are discussed. |
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