ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Unraveling Atomic-Scale Origins of Selective Ionic Transport Pathways and Sodium-Ion Storage Mechanism in Bi2 S3 Anodes

It is a major challenge to achieve a high-performance anode for sodium-ion batteries (SIBs) with high specific capacity, high rate capability, and cycling stability. Bismuth sulfide, which features a high theoretical specific capacity, tailorable morphology, and low cost, has been considered as a promising anode for SIBs. Nevertheless, due to a lack of direct atomistic observation, the detailed understanding of fundamental intercalation behavior and Bi2 S3 's (de)sodiation mechanisms remains unclear. Here, by employing in situ high-resolution transmission electron microscopy, consecutive electron diffraction coupled with theoretical calculations, it is not only for the first time identified that Bi2 S3 exhibits specific ionic transport pathways preferred to diffuse along the (110) direction instead of the (200) plane, but also tracks their real-time phase transformations (de)sodiation involving multi-step crystallographic tuning. The finite-element analysis further disclosed multi-reaction induced deformation and the relevant stress evolution originating from the combined effect of the mechanical and electrochemical interaction. These discoveries not only deepen the understanding of fundamental science about the microscopic reaction mechanism of metal chalcogenide anodes but also provide important implications for performance optimization.
Motion Analysis for Experimental Evaluation of an Event-Driven FES System

In this work, a system for controlling Functional Electrical Stimulation (FES) has been experimentally evaluated. The peculiarity of the system is to use an event-driven approach to modulate stimulation intensity, instead of the typical feature extraction of surface ElectroMyoGraphic (sEMG) signal. To validate our methodology, the system capability to control FES was tested on a population of 17 subjects, reproducing 6 different movements. Limbs trajectories were acquired using a gold standard motion tracking tool. The implemented segmentation algorithm has been detailed, together with the designed experimental protocol. A motion analysis was performed through a multi-parametric evaluation, including the extraction of features such as the trajectory area and the movement velocity. The obtained results show a median cross-correlation coefficient of 0.910 and a median delay of 800 ms, between each couple of voluntary and stimulated exercise, making our system comparable w.r.t. state-of-the-art works. Furthermore, a 97.39% successful rate on movement replication demonstrates the feasibility of the system for rehabilitation purposes.
Robust PRNU estimation from probabilistic raw measurements

Images captured by digital cameras undergo various in-camera processing such as JPEG compression, white balancing, power transforms and other operations to map raw data into nonlinear small gamut image. Due to nonlinear transformation, artifacts or signatures used for camera identification also undergo a significant change. Photo Response Non-Uniformity (PRNU), proved to be useful for uniquely identifying the camera, also undergoes same in-camera operations. Hence estimation of PRNU is affected which often leads to rise in false identification. In this work, we develop a novel algorithm for robust estimation of PRNU from probabilistically obtained raw data. Since not all cameras provide raw data as their output, we compute raw data from the JPEG output using probabilistic color de-rendering procedure. The estimated raw data is modeled as a Poisson process, and Maximum Likelihood Estimation (MLE) is used for PRNU estimation. We then use our estimate of PRNU for identifying the camera using images. We also compare the performance of our algorithm with other state-of-the-art algorithms. Additionally, we demonstrate the robustness of estimate obtained by localizing the forgery in images. The extensive experimental analysis is performed over thousands of patches from various cameras to illustrate the efficiency of proposed approach, which effectively overcomes the state-of-the-art.
Product configuration: a review of the state-of-the-art and future research

Being able to provide companies with a number of advantages in delivering customised products, product configuration has received increasing attention from the academia and lasting interests from industries in the past several decades. While several surveys and reviews have been reported shedding light on specific issues in product configuration, a general overview is missing. By systematically presenting important concepts, definitions and issues underlying product configuration, such a review is of paramount importance to develop practical solutions, which ultimately contributes to efficient design, development and implementation of product configurators in practice. This study, thus, tries to fill this gap by reviewing the state-of-the-art research on product configuration. It touches on the major issues, definitions and concepts in product configuration along with the corresponding studies, such as configuration ontology, system design and development, and configuration solving. Based on the review, future research is highlighted as well.
Analysis of water security and its worldwide context, 2000-2019

The water security (WS) approach must be practical and measurable to address water-related challenges. Since 2000, the term Water Security has been regularly mentioned worldwide. However, nowadays, there is still not an accepted definition either for a country or for a region. This research conducted a meta-analysis in the context of renewable water and global water stress, evaluating 873 scientific papers and 26 definitions of WS, using bibliometric network analysis, geographic information systems, and data mining to analyze the state of the art of WS. The results indicate that the definitions of WS have an anthropocentric character. The term economic in the context of WS definitions is the most relevant, and the term ecosystems are the least relevant. Less than 30 % of the definitions analyzed are operationalized through some measurement instrument. The most researched topics on WS are evaluation, management, and impact. On the frontier of study are the issues of water footprint, operation of springs, and awareness. In the world, countries such as the United States, China, and the United Kingdom, with little renewable water in their respective continents, are the ones that publish the most and try to define the concept of WS. Therefore, it is concluded that the main problem of WS definitions worldwide is that they cannot be operationalized in some local indexes, hindering their implementation. In addition, the environment has not been relevant in WS research and definitions.
Sexual Behaviors and Human Papillomavirus Vaccination in a Heterosexually Active Adult Population at Increased Risk for HIV Infection

Human papillomavirus (HPV) is the most common sexually acquired infection in the US. Vaccination is effective against infection with high-risk HPV strains, yet HPV vaccine coverage is lower in the US than the national target. This study aimed to determine the relationship between sexual behaviors and HPV vaccination in a heterosexually active population at increased risk for HIV infection. Data from 380 participants aged 18-45 years obtained from the National HIV Behavioral Surveillance system increased risk heterosexuals cycle 5 (2019) in Houston, Texas, was analyzed. RDS-Analyst was used to generate population-based descriptive statistics. Modified Poisson regression models clustered on recruitment chain were conducted in SAS 9.4 to assess the relationship between sexual behaviors and HPV vaccination. Only 11.5% of participants had received at least one dose of the HPV vaccine. Regarding behaviors within the past 12 months, 44.8% reported having condomless casual sex, 51.3% reported having concurrent sexual partnerships while in their most recent relationship, 14.5% reported exchanging sex, and participants had an average of 4-5 sex partners. Further, those who exchanged sex had a significantly lower prevalence of HPV vaccine uptake when compared to those who did not exchange sex (adjusted prevalence ratio 0.23; confidence interval 0.10-0.52), while all other measures of sexual behavior were not significantly associated with HPV vaccination. More research is needed to understand the relationship between exchange sex and low prevalence of vaccination, specifically in women who bear the highest burden of poor HPV-related morbidity and mortality.
Wearable Hydrogel-Based Epidermal Sensor with Thermal Compatibility and Long Term Stability for Smart Colorimetric Multi-Signals Monitoring

Hydrogel-based wearable epidermal sensors (HWESs) have attracted widespread attention in health monitoring, especially considering their colorimetric readout capability. However, it remains challenging for HWESs to work at extreme temperatures with long term stability due to the existence of water. Herein, a wearable transparent epidermal sensor with thermal compatibility and long term stability for smart colorimetric multi-signals monitoring is developed, based on an anti-freezing and anti-drying hydrogel with high transparency (over 90% transmittance), high stretchability (up to 1500%) and desirable adhesiveness to various kinds of substrates. The hydrogel consists of polyacrylic acid, polyacrylamide, and tannic acid-coated cellulose nanocrystals in glycerin/water binary solvents. When glycerin readily forms strong hydrogen bonds with water, the hydrogel exhibits outstanding thermal compatibility. Furthermore, the hydrogel maintains excellent adhesion, stretchability, and transparency after long term storage (45 days) or at subzero temperatures (-20 °C). For smart colorimetric multi-signals monitoring, the freestanding smart colorimetric HWESs are utilized for simultaneously monitoring the pH, T and light, where colorimetric signals can be read and stored by artificial intelligence strategies in a real time manner. In summary, the developed wearable transparent epidermal sensor holds great potential for monitoring multi-signals with visible readouts in long term health monitoring.
Identification of Gastrointestinal Digestion Stable Antihypertensive Fish Peptides from Atlantic Mackerel ( Scomber scombrus)

High blood pressure has been recognized as one of the main risk factors of cardiometabolic syndrome by health organizations. Hypertension is medicated by various classes of synthetic drugs; however, adverse effects have repeatedly been reported. Moreover, natural alternatives such as fish peptides have been effective in the treatment and prevention of hypertension. The aim of our study was to fractionate and identify antihypertensive peptides. Fractions were produced using different techniques of solid-phase extraction (SPE), pressure-driven ultrafiltration (UF), and electro04dialysis with UF membrane. According to our results, the hydrophobic fraction of SPE (IC50 5 μg) was the most potent anti-angiotensin converting enzyme (ACE) product. Findings of the study suggest that the separation technique plays an important role in the isolation efficiency of antihypertensive biopeptides. Importantly, the hydrophobic fraction's activity was retained through a static model of an in vitro gastrointestinal digestion system. In conclusion, polarity regardless of charge and size was the most important factor for anti-ACE activity of an Atlantic mackerel biopeptide. In addition, the presence of leucine at either of the extremities (C- or N-terminal) and/or leucine-rich motifs could well explain the hypotensive effect of our active fraction.
General synthesis of 2,1-benzisoxazoles (anthranils) from nitroarenes and benzylic C-H acids in aprotic media promoted by combination of strong bases and silylating agents

Carbanions of phenylacetonitriles, benzyl sulfones, and dialkyl benzylphosphonates add nitroarenes at the ortho-position to the nitro group to form [Formula: see text]-adducts that, upon treatment with trialkylchlorosilane and additional base (t-BuOK or DBU), transform into 3-aryl-2,1-benzisoxazoles in moderate-to-good yields.
Therapies in Autoimmune Peripheral Neuropathies beyond Intravenous Immunoglobulin, Plasma Exchange and Corticosteroids: An Analytical Review

Autoimmune neuropathies are often treatable. First-line immunotherapies include intravenous immunoglobulin (IVIG), plasma exchange and corticosteroids. However, nearly 15-30% of patients are either refractory, partially responsive or chronically dependent on these first-line agents. Lack of full response leads to increased disability in addition to adverse financial implications. Consequently, there is an unmet need for more effective treatments to manage this subset of patients. There has been a remarkable increase in the knowledge about immunopathogenesis, antigenic targets, clinical phenotype correlation, and novel therapeutic agents in the last two decades. These novel agents target specific components of the immune system (humoral, cellular immunity, and complement) and have the potential to improve the management of these disorders. Unfortunately, high-quality evidence from large, controlled studies is scarce considering the relative rarity of these refractory cases, heterogeneity of clinical presentations and ethical concerns limiting the use of a placebo arm. An adaptive clinical trial design in a homogenous cohort with standardized outcomes in multiple centers and the use of historical controls will likely provide valuable scientific evidence about the efficacy and safety of these therapies. In this review, we examine the status of the newer immunotherapies in the treatment of autoimmune neuropathies based on existing data.
Influence of model parameters on 3-D turbulent flow in an electromagnetic stirring system for continuous billet casting

The development of a computational model for a turbulent electromagnetic stirring (EMS) flow system has considerable flexibility in terms of choosing model parameters and system equations. This paper examines the effect of key model parameters on the turbulent flow that is predicted for art industrial scale EMS system. It is shown that the choice of certain parameters can have a profound influence on the predicted flow.
Experience-based knowledge management: a cooperative information systems perspective

Experience-based knowledge management is the art of capitalizing on failures and missed opportunities. Building on a number of interdisciplinary research projects. we study three possible approaches within a cooperative information systems framework, focussing on the facets of pragmatic technology usage, model-based management control, and social work practice and learning, respectively. (C) 2002 Elsevier Science Ltd. All rights reserved.
Robust Real-Time Embedded EMG Recognition Framework Using Temporal Convolutional Networks on a Multicore IoT Processor

Hand movement classification via surface electromyographic (sEMG) signal is a well-established approach for advanced Human-Computer Interaction. However, sEMG movement recognition has to deal with the long-term reliability of sEMG-based control, limited by the variability affecting the sEMG signal. Embedded solutions are affected by a recognition accuracy drop over time that makes them unsuitable for reliable gesture controller design. In this paper, we present a complete wearable-class embedded system for robust sEMG-based gesture recognition, based on Temporal Convolutional Networks (TCNs). Firstly, we developed a novel TCN topology (TEMPONet), and we tested our solution on a benchmark dataset (Ninapro), achieving 49.6% average accuracy, 7.8%, better than current State-Of-the-Art (SoA). Moreover, we designed an energy-efficient embedded platform based on GAP8, a novel 8-core IoT processor. Using our embedded platform, we collected a second 20-sessions dataset to validate the system on a setup which is representative of the final deployment. We obtain 93.7% average accuracy with the TCN, comparable with a SoA SVM approach (91.1%). Finally, we profiled the performance of the network implemented on GAP8 by using an 8-bit quantization strategy to fit the memory constraint of the processor. We reach a 4 x lower memory footprint (460 kB) with a performance degradation of only 3% accuracy. We detailed the execution on the GAP8 platform, showing that the quantized network executes a single classification in 12.84 ms with a power envelope of 0.9 mJ, making it suitable for a long-lifetime wearable deployment.
Radial Bias Alters Perceived Object Orientation

Orientation sensitivity is a fundamental property of the visual system, but not all orientations are created equal. For instance, radially oriented stimuli, aligned with a line intersecting the center of gaze, produce greater activity throughout the visual cortex and are associated with greater perceptual sensitivity compared with other orientations. Here, we discuss a robust visual illusion that is likely related to this preference. Using a continuous response measure, participants (N = 36 adults) indicated the gap position in a peripheral Landolt C placed in one of eight orientations and eight locations along four meridians (vertical, horizontal, 45°, 135°). The error distributions revealed that the perceived gap was attracted toward the radial axis. For instance, the gap in a regular C would often be wrongly perceived as tilted 45° corresponding to the oblique meridian where it was placed. These findings demonstrate an unsuspected early-vision influence on the perceived orientation of an object.
Automated Superpixel-borders-guided Deformation Image Registration for Adaptive Radiotherapy

Registration between the baseline and follow-up lung computed tomography (CT) volumes plays an important role in computer-aided diagnosis and following-up care during adaptive radiotherapy. Diffeomorphic log-Demons as state of the art in Demons implementations is restricted to relatively small deformations, low accuracy, and ignoring some prior structural features. In this paper, an automated superpixel-borders-guided deformation image registration (SBG-DIR) algorithm is proposed. The proposed SBG-DIR method uses the simple linear iterative clustering (SLIC) algorithm to automated superpixels generation. Incorporation of superpixel borders into registration algorithm is implemented by a new similarity criterion based on the binary volume representation of superpixel borders. The binary volume representation enables accurate preserving motion boundaries, contributes to a faster convergence of the objective function and eliminates errors caused by manual interaction. In addition, a subtraction volume is produced by the intensity difference between the first time point CT volume and its warped follow-up CT volume. The subtraction volume can be used for detection of tumor tissue growth or shrinkage, which is an essential part of a CT-based diagnosis. Moreover, to ensure the topology preservation of biological objects, our proposed SBG-DIR method is implemented in the space of diffeomorphisms, in which meaningful biological shapes can be found. Compared with the state-of-the-art Demons, the proposed SBG-DIR method doesn't require any additional optimization, yields a faster convergence and is more accurate and efficient in recovering large deformations. Experimental results indicate that the proposed SBG-DIR method performed better than the state-of-the-art Demons algorithms.
Radiation Exposure of Workers and Volunteers in Shelters and Community Reception Centers in the Aftermath of a Nuclear Detonation

After a nuclear detonation, workers and volunteers providing first aid, decontamination, and population monitoring in public shelters and community reception centers will potentially be exposed to radiation from people they are assisting who may be contaminated with radioactive fallout. A state-of-the-art computer-aided design program and radiation transport modeling software were used to estimate external radiation dose to workers in three different exposure scenarios: performing radiation surveys/decontamination, first aid, and triage duties. Calculated dose rates were highest for workers performing radiation surveys due to the relative proximity to the contaminated individual. Estimated cumulative doses were nontrivial but below the occupational dose limit established for normal operations by the Occupational Safety and Health Administration.
Retinal Vessel Segmentation With Skeletal Prior and Contrastive Loss

The morphology of retinal vessels is closely associated with many kinds of ophthalmic diseases. Although huge progress in retinal vessel segmentation has been achieved with the advancement of deep learning, some challenging issues remain. For example, vessels can be disturbed or covered by other components presented in the retina (such as optic disc or lesions). Moreover, some thin vessels are also easily missed by current methods. In addition, existing fundus image datasets are generally tiny, due to the difficulty of vessel labeling. In this work, a new network called SkelCon is proposed to deal with these problems by introducing skeletal prior and contrastive loss. A skeleton fitting module is developed to preserve the morphology of the vessels and improve the completeness and continuity of thin vessels. A contrastive loss is employed to enhance the discrimination between vessels and background. In addition, a new data augmentation method is proposed to enrich the training samples and improve the robustness of the proposed model. Extensive validations were performed on several popular datasets (DRIVE, STARE, CHASE, and HRF), recently developed datasets (UoA-DR, IOSTAR, and RC-SLO), and some challenging clinical images (from RFMiD and JSIEC39 datasets). In addition, some specially designed metrics for vessel segmentation, including connectivity, overlapping area, consistency of vessel length, revised sensitivity, specificity, and accuracy were used for quantitative evaluation. The experimental results show that, the proposed model achieves state-of-the-art performance and significantly outperforms compared methods when extracting thin vessels in the regions of lesions or optic disc. Source code is available at https://www.github.com/tyb311/SkelCon.