ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Finite element analysis of electrical machines and transformers State of the art and future trends

Purpose - The purpose of this paper is to discuss the state of the art of finite element analysis of electrical machines and transformers. Electrical machines and transformers are prime examples of multi-physical systems involving electromagnetics, thermal issues, fluid dynamics, structural mechanics as well as acoustic phenomena. An accurate operational performance with different electrical and mechanical load situations is more and more evaluated using various numerical analysis methods including the couplings between the various physical domains. Therefore, numerical analysis methods are increasingly utilized not only for the verification of contractual values of existing machines, but also for the initial design process and for the design optimization of new machines. Design/methodology/approach - The finite element method is the most powerful numerical analysis method for such multi-physical devices. Since optimizations with respect to the overall performance and also the total manufacturing costs will become more important, the utilization of coupled multi-physical analyses is of growing interest. For the fast and powerful application of this numerical analysis method, special attention should be given to the requirements of these electromagnetic devices. Findings - Various methods of coupling the different physical domains of multi-field finite element analyses are described. Thereby, weakly coupled cascade algorithms can be used with most problems in the field of electrical machines and transformers. On the other hand, a prime objective is to derive comprehensive, multi-physical simulation models which are easily incorporated into design tools used by engineering professionals. Research limitations/implications - The development of robust and reliable computer-aided tools for an optimal design of multi-physical devices such electrical machines and transformers has to argue about the best possible coupling of various simulation methods. Special consideration shall be paid more and more to a treatment of uncertainties and tolerances by means of statistical and probabilistic approaches. Originality/value - The paper discusses state of the art of finite element analyses of the mentioned devices. Various optimized methods of modelling and analysis concerning the repetitive structure of electrical machines for electromagnetic analyses are compared with their advantages and drawbacks. Further, various methods of coupling the different domains of multi-field analyses in case of electrical machines and transformers are described.
Ancestry and BMI Influences on Facial Soft Tissue Depths for A Cohort of Chinese and Caucasoid Women in Dunedin, New Zealand

This study measured and assessed facial soft tissue depths (FSTDs) in adult female Chinese and New Zealand (NZ) Europeans (Caucasoids). Ultrasound was used to obtain depths at nine landmarks on 108 healthy subjects (51 Chinese, 57 NZ European), erect positioned, of same age group (18-29 years). Height and weight were also recorded. Statistical analysis focused on comparison of tissue depth between the two ancestry groups and the influence of Body Mass Index (BMI) (kg/m2). Results showed mean depth differences at Supra M2 and Infra M2 landmarks significantly greater for Chinese than Caucasoid women for all three BMI Classes (BMI<20, 20≤BMI<25, 25≤BMI<30), even BMI<20. For both groups BMI positively correlated with FSTD values at all landmarks except Labrale superius. This study enabled ancestry and BMI influence on FSTDs to be observed and compared for two distinct groups. Results add to knowledge about facial tissue depth variation.
Pattern recognition methodologies for pollen grain image classification: a survey

In a large number of scientific areas, such as immunology, forensics, paleoecology, and archeology, the study of pollen, i.e., palynology, plays an important role: from tracking climate changes, studying allergies, to forensic investigations or honey origin analysis. Since the mid-nineties of the last century, the idea for an automated solution to the problem of pollen identification and classification was formulated and since then, several attempts and proposals have been made and presented, based on different technologies, in particular in the field of Computer Vision. However, as of 2021 microscopic analyses are performed mainly manually by highly trained specialists, although the capabilities of artificial intelligence, especially Deep Neural Networks, are steadily increasing. In this work, we analyzed various state-of-the-art research work concerning pollen detection and classification and compared their methods and results. The problems, such as data accessibility, different methods of Machine Learning, and the intended applicability of the proposed solutions are explored. We also identified crucial issues that require further work and research. Our work will provide a thorough view on the current state of the art, its issues, and possibilities for the future.
Multi-label emotion classification in texts using transfer learning

Social media is a widespread platform that provides a massive amount of user-generated content that can be mined to reveal the emotions of social media users. This has many potential benefits, such as getting a sense of people's pulse on various events or news. Emotion classification from social media posts is challenging, especially when it comes to detecting multiple emotions from a short piece of text, as in multi-label classification problem. Most of the previous work on emotion detection has focused on deep neural networks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) such as Long Short -Term Memory (LSTM) Networks. However, none of them has utilized multiple attention mechanisms and Recurrent Neural Networks (i.e., specialized attention networks for each emotion) nor utilized the recently introduced Transformer Networks such as XLNet, DistilBERT, and RoBERTa for the task of classifying emotions with multiple labels. The proposed multiple attention mechanism reveals the contribution of each word on each emotion, which has not been investigated before. In this study, we investigate both the use of LSTMs and the fine-tuning of Transformer Networks through Transfer Learning along with a single-attention network and a multiple-attention network for multi-label emotion classification. The experimental results show that our novel transfer learning models using pre-trained transformers with and without multiple attention mechanisms were able to outperform the current state-of-the-art accuracy (58.8% -Baziotis et al., 2018) in the SemEval-2018 Task-1C dataset. Our best-performing RoBERTa-MA (RoBERTa-Multi-attention) model outperformed the state-of-the-art and achieved 62.4% accuracy (3.6% gain over the state-of-the-art) on the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) dataset for English. Moreover, the XLNet-MA (XLNet-Multi-attention) model outperformed the other proposed models by achieving 45.6% accuracy on the Ren-CECps dataset for Chinese.
Comparative Validation of Single-Shot Optical Techniques for Laparoscopic 3-D Surface Reconstruction

Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper.
A spatial-frequency-temporal 3D convolutional neural network for motor imagery EEG signal classification

Motor imagery (MI) EEG signal classification is a critical issue for brain-computer interface (BCI) systems. In traditional MI EEG machine learning algorithms, feature extraction and classification often have different objective functions, thus resulting in information loss. To solve this problem, a novel spatial-frequency-temporal (SFT) 3D CNN model is proposed. Specifically, the energies of EEG signals located in multiple local SFT ranges are extracted to obtain a novel 3D MI EEG feature representation, and a novel 3D CNN model is designed to simultaneously learn the complex MI EEG features in the entire SFT domains and carry out classification. An extensive experimental study is implemented on two public EEG datasets to evaluate the effectiveness of our method. For BCI Competition III Dataset IVa, the average accuracy rate of five subjects obtained by the proposed method reaches 86.6% and yields 4.1% improvement over the state-of-the-art filter band common spatial pattern (FBCSP) method. For BCI Competition III dataset IIIa, by achieving an average accuracy rate of 91.85%, the proposed method outperforms the state-of-the-art dictionary pair learning (DPL) method by 4.44%.
Particulate matter exposure exacerbates cellular damage by increasing stress granule formation in respiratory syncytial virus-infected human lung organoids

Exposure to atmospheric particulate matter (PM) increases morbidity and mortality in respiratory diseases by causing various adverse health effects; however, the effects of PM exposure on cellular stress under virus-infected conditions remain unclear. The effects of PM under 10 μm (PM10) and diesel PM (DPM) on respiratory syncytial virus (RSV) infection were investigated in human two-dimensional lung epithelial cells and human three-dimensional lung organoids mimicking the lung tissue. We evaluated the formation of stress granules, which are important in cellular adaptation to various stress conditions. Furthermore, we investigated the effects of repeated exposure to PM10 and DPM on DNA damage and cell death during viral infection. PM10 and DPM did not cause stress granule formation in the absence of RSV infection but drastically increased stress granule formation and signal transduction during RSV infection in human lung epithelial cells and human lung organoids. Further, repeated exposure to PM10 and DPM caused cell death by severely damaging DNA under RSV infection conditions. Thus, PM10 and DPM induce severe lung toxicity under stress conditions, such as viral infection, suggesting that the effects of PMs under various stressful conditions should be examined to accurately predict the lung toxicity of PM.
Biomimetic Patch with Wicking-Breathable and Multi-mechanism Adhesion for Bioelectrical Signal Monitoring

Wearable bioelectrical monitoring devices can provide long-term human health information such as electrocardiogram and other physiological signals. It is a crucial part of the remote medical system. These can provide prediction for the diagnosis and treatment of cardiovascular disease and access to timely treatment. However, the patch comfort of the wearable monitoring devices in long-term contact with the skin have been a technical bottleneck of the hardware. In this study, the biomimetic patch with wicking-breathable and multi-mechanism adhesion performance to achieve adaptability and comfortability to human skin has been reported. The patch was designed based on a conical through-hole and hexagonal microgroove to directionally transport sweat from skin to air which gives the patch the breathable performance. The breathable and drainage capability of the biomimetic patch was experimentally verified by analyzing the conical through-hole and hexagonal microgroove with the structural mechanism of wicking. Multi-mechanism adhesion of the Ag/Ni microneedle array and PDMS-t adhesion material ensures the stability of patch signal acquisition. This study provides a new way for enhancing the breathability and adaptability of the patch to realize accurate bioelectrical signal monitoring under sweat conditions on human skin.
Barriers and bridges on water management in rural Mexico: from water-quality monitoring to water management at the community level

Access to sufficient water of suitable quality represents a challenge for achieving several dimensions of sustainable development. Currently, water access is restricted to three of 10 persons globally. In rural areas of Mexico and other low-income countries, coverage could be even less due to the absence of formal supply; thus, rural communities usually perform water management. Surrounding community-based water management, various socio-ecological interactions emerge that determine access to water. Access to water will depend on the obstacles or capacities that arise within the socio-ecological system in which the community is immersed. This work identifies barriers and bridges to water access in a rural environment through mixed methods. The article draws on three case studies in southeastern Mexico by analyzing 90 questionnaires conducted at the household level and three focus groups in parallel with water quality analysis and its relationship with management practices. The barriers and bridges were classified into six water access challenges: (i) access to water in a sufficient quantity, (ii) access to water of adequate quality, (iii) access to water for household crop irrigation, (iv) hygiene and sanitation facilities, (v) collective organization, and (vi) climate variability. The main findings indicate that households' water quantity and quality show deficiencies due to the lack of formal infrastructure and represent a health risk. Water fetching has the highest impact on women and children in poor rural areas, and it is a significant barrier to sustainable development. In contrast, the collective organization proved to be an essential bridge for water access in these communities.
Synthetic-to-Real Domain Adaptation Joint Spatial Feature Transform for Stereo Matching

Most deep learning-based state-of-the-art stereo matching methods significantly depend on large-scale datasets. However, it is implausible to collect sufficient real-world samples with dense and clear ground-truth disparity maps in practice. Although synthetic datasets' appearance has alleviated the demand for extensive real data, there is a domain shift between synthetic and real sets. To tackle this problem, we propose an individually trained synthetic-to-real domain adaptation (SDA) network that maps synthetic images into the real domain. Specifically, our approach translates the data style from synthetic domain to real domain while maintaining the content and the spatial information. First, edge cues are leveraged to guide domain adaptation in preserving the spatial consistency between input and the generated image. Second, we combine the spatial feature transform (SFT) layer to effectively fuse features from the edge map and the source image. Extensive experiments demonstrate that: 1) when only trained on synthetic data and generalized to real data, our model evidently outperforms many state-of-the-art domain adaptation methods; 2) our translated synthetic datasets (TSD) help to improve the generalization capability of any stereo matching CNNs. Codes and data will be available at https://github.com/Archaic-Atom/SDA_network.
Optimal Discriminative Projection for Sparse Representation-Based Classification via Bilevel Optimization

Recently, sparse representation-based classification (SRC) has been widely studied and has produced state-of-the-art results in various classification tasks. Learning useful and computationally convenient representations from complex redundant and highly variable visual data is crucial for the success of SRC. However, how to find the best feature representation to work with SRC remains an open question. In this paper, we present a novel discriminative projection learning approach with the objective of seeking a projection matrix such that the learned low-dimensional representation can fit SRC well and that it has well discriminant ability. More specifically, we formulate the learning algorithm as a bilevel optimization problem, where the optimization includes an l(1)-norm minimization problem in its constraints. Through the bilevel optimization model, the relationship between sparse representation and the desired feature projection can be explicitly exploited during the learning process. Therefore, SRC can achieve a better performance in the transformed subspace. The optimization model can be solved by using a stochastic gradient ascent algorithm, and the desired gradient is computed using implicit differentiation. Furthermore, our method can be easily extended to learn a dictionary. The extensive experimental results on a series of benchmark databases show that our method outperforms many state-of-the-art algorithms.
Randomized Error Removal for Online Spread Estimation in High-Speed Networks

Flow spread measurement provides fundamental statistics that can help network operators better understand flow characteristics and traffic patterns with applications in traffic engineering, cybersecurity and quality of service. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a packet stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs that incur much smaller processing overhead and query overhead than the state of the art, yet achieves significant accuracy improvement in spread estimation. We formally analyze the performance of these new designs. We implement them in both hardware and software, and use real-world data traces to evaluate their performance in comparison with the state of the art. The experimental results show that our best sketch significantly improves over the best existing work in terms of estimation accuracy, packet processing throughput, and online query throughput.
Maximum margin object tracking with weighted circulant feature maps

Support vector machine (SVM) based tracking algorithms training with dense circulant samples have shown favourable performance due to its strong discriminative power and high efficiency. However, the challenges caused by the circulant sampling remain unaddressed. In this study, the authors give each training sample a weight based on their accuracy to reduce the influence of inaccurate samples. Moreover, they reform the SVM model with weighted circulant training samples. Secondly, they advocate an efficient solution by using the property of circulant matrices to solve the learning problem. Thirdly, a model update strategy is introduced to prevent the tracking models polluted by wrong samples. Experimental results on large benchmark datasets with 50 and 100 video sequences demonstrate that the authors' tracking algorithms achieve state-of-art performance in terms of precision and accuracy. In addition, their tracker runs in real time.
Severe Vitamin B12 Deficiency Presenting as Pancytopenia, Hemolytic Anemia, and Paresthesia: Could Your B12 Be Any Lower?

Although severe vitamin B12 deficiency is rare in the United States, recent increases in the adoption of vegan lifestyles have led to a significant rise in the rates of B12 deficiency, along with its hematologic and neurologic sequelae, the latter of which is often irreversible. We describe a case of a 39-year-old male who presented with a several-month history of progressively worsening word-finding difficulties, shortness of breath, and a four-day history of bilateral hand numbness and tingling. Laboratory data revealed pancytopenia with profound anemia. Markers of hemolysis were positive, including elevated indirect bilirubin, disproportionately elevated lactate dehydrogenase (LDH), low haptoglobin, negative direct anticoagulant test, and hypoproliferative reticulocyte index. Blood smear revealed hypersegmented neutrophils and macrocytosis. Vitamin B12 levels were undetectable, and anti-intrinsic factor and parietal cell antibodies were negative. A thorough history revealed a 20-year history of strict veganism without B12 supplementation. He was transfused with packed red blood cells and started on subcutaneous B12 injections with rapid improvement of his symptoms. Early recognition of B12 deficiency causing the constellation of pancytopenia, hemolytic anemia, and neurologic symptoms is vital in preventing irreversible neurologic sequelae. This case also highlights the importance of accurate history taking to aid in early diagnosis of B12 deficiency, especially in the context of rising rates of veganism in the United States.
A systematic review of current status and challenges of vaccinating children against SARS-CoV-2

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has inflicted immense damage to countries, economies and societies worldwide. Authorized COVID-19 vaccines based on different platforms have been widely inoculated in adults, showing up to 100% immunogenicity with significant efficacy in preventing SARS-CoV-2 infections and the occurrence of severe COVID-19. It has also greatly slowed the evolution of SARS-CoV-2 variants, as shown in clinical trials and real-world evidence. However, the total dosage of COVID-19 vaccines for children is much smaller than that for adults due to limitations from parental concern of vaccine safety, presenting a potential obstacle in ending the COVID-19 pandemic. SARS-CoV-2 not only increases the risk of severe multisystem inflammatory syndrome (MIS-C) in children, but also negatively affects children's psychology and academics, indirectly hindering the maintenance and progress of normal social order. Therefore, this article examines the clinical manifestations of children infected with SARS-CoV-2, the status of vaccination against COVID-19 in children, vaccination-related adverse events, and the unique immune mechanisms of children. In particular, the necessity and challenges of vaccinating children against SARS-CoV-2 were highlighted from the perspectives of society and family. In summary, parental hesitancy is unnecessary as adverse events after COVID-19 vaccination have been proven to be infrequent, comprise of mild symptoms, and have a good prognosis.