ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Exploring Malaysian Physicians' Intention to Discriminate Against Gay, Bisexual, and Other Men Who Have Sex with Men Patients

Purpose: Gay, bisexual, and other men who have sex with men (MSM) experience high levels of stigma and discrimination. Minimizing the stigma and discrimination is critical to fostering an inclusive environment for care and optimizing health outcomes. This study aimed at exploring the factors related to physicians' intention to discriminate against MSM in Malaysia. Methods: Physicians (N = 542) from two university-affiliated hospitals in Kuala Lumpur, Malaysia, completed an online cross-sectional survey between January and March 2016. Measures included sociodemographic and clinical characteristics, intention to discriminate against MSM, and several stigma-related constructs. Bivariate and multivariable linear regressions were used to evaluate independent correlates of discrimination intent against MSM. Results: Physicians' intention to discriminate against MSM was low (mean [M] = 1.9, standard deviation [SD] = 0.7), but most physicians (70.6%) had a mean score greater than 1.0, indicating that most physicians expressed some degree of intention to discriminate against MSM. A minority of physicians (10.7%), however, had a score of 3.0 or higher, revealing some physicians holding a moderate to high level of discrimination intent toward MSM. The multivariable model demonstrated that physicians who expressed greater prejudice (B = 0.30, p < 0.01), had more MSM-related shame (B = 0.19, p < 0.01), and fear about MSM (B = 0.31, p < 0.01) were more likely to have the intention to discriminate against MSM. Conclusion: Stigma-related constructs including prejudice, MSM-related shame, and fear were independently correlated with increases in a physician's intention to discriminate against MSM. Therefore, implementing interventions to reduce physicians' stigma toward MSM may promote equitable and stigma-free access to health care.
A patent analysis on advanced biohydrogen technology development and commercialisation: Scope and competitiveness

The need of developing renewable energy to reduce the impact on the global environment and climate change of the increasing industrial development has fostered the use of biological processes to produce biofuel from biohydrogen. The present work made a patent analysis of advanced hydrogen production techniques comparing it with similar prior art in China, Japan, the Republic of Korea, the European Union and the United States (U.S.) The aims were to find the scope, competitiveness of prior art, as well as the technology trend on biohydrogen production methods. The patents value was assessed its geographic scope and competitiveness indicators such as green image, low cost, energy efficiency and equipment design. It was found that most of the hydrogen production methods and associated technologies are developed by academic institutions, however their patents are reduced to a local level, and few are patented at international level, which reduces their competitiveness. The China (P.R.C.) is the biggest patent contributor worldwide in terms of hydrogen production methods by academic institutions. Japan is a huge patent contributor, in terms of methods aiming rear-end products application of hydrogen by private companies. The biggest amount of prior art found that the most popular methods of pretreatment and dark fermentation produced coincide with the time of energetic crisis and the green movement to find alternative fuels. Finally, patent analysis of this study can help to discern the current technology trend and to develop the next generation of biohydrogen processes and associated technologies. Copyright (C) 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Intelligent Transportation Systems

The visibility of images of outdoor road scenes will generally become degraded when captured during inclement weather conditions. Drivers often turn on the headlights of their vehicles and streetlights are often activated, resulting in localized light sources in images capturing road scenes in these conditions. Additionally, sandstorms are also weather events that are commonly encountered when driving in some regions. In sandstorms, atmospheric sand has a propensity to irregularly absorb specific portions of a spectrum, thereby causing color-shift problems in the captured image. Traditional state-of-the-art restoration techniques are unable to effectively cope with these hazy road images that feature localized light sources or color-shift problems. In response, we present a novel and effective haze removal approach to remedy problems caused by localized light sources and color shifts, which thereby achieves superior restoration results for single hazy images. The performance of the proposed method has been proven through quantitative and qualitative evaluations. Experimental results demonstrate that the proposed haze removal technique can more effectively recover scene radiance while demanding fewer computational costs than traditional state-of-the-art haze removal techniques.
Locally Structured On-Chip Optofluidic Hollow-Core Light Cages for Single Nanoparticle Tracking

Nanoparticle tracking analysis (NTA) is a widely used methodology to investigate nanoscale systems at the single species level. Here, we introduce the locally structured on-chip optofluidic hollow-core light cage, as a novel platform for waveguide-assisted NTA. This hollow waveguide guides light by the antiresonant effect in a sparse array of dielectric strands and includes a local modification to realize aberration-free tracking of individual nano-objects, defining a novel on-chip solution with properties specifically tailored for NTA. The key features of our system are (i) well-controlled nano-object illumination through the waveguide mode, (ii) diffraction-limited and aberration-free imaging at the observation site, and (iii) a high level of integration, achieved by on-chip interfacing to fibers. The present study covers all aspects relevant for NTA including design, simulation, implementation via 3D nanoprinting, and optical characterization. The capabilities of the approach to precisely characterize practically relevant nanosystems have been demonstrated by measuring the solvency-induced collapse of a nanoparticle system which includes polymer brush-based shells that react to changes in the liquid environment. Our study unlocks the advantages of the light cage approach in the context of NTA, suggesting its application in various areas such as bioanalytics, life science, environmental science, or nanoscale material science in general.
More Robust and Reliable Optimized Energy Conversion Facilitated through Electric Machines, Power Electronics and Drives, and Their Control: State-of-the-Art and Trends

According to the special section entitled 'Robust design and analysis of electric machines and drives', to be published in IEEE Transactions on Energy Conversion, the authors present an introduction to tolerance analysis, robust optimization, and measures to improve the reliability of electric machines, power electronics and drives, and their robust control in general. A comprehensive review of modeling uncertainties and evaluating robustness and reliability based measures is presented. In addition, techniques facilitating solving dedicated optimization scenarios are introduced. The most recent research activities will be illustrated. The article thus enables to easily catch up with the state-of-the-art in these fields and to take notice of ongoing and future work.
Investigating Generalized Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection

Recently proposed methods in intrusion detection are iterating on machine learning methods as a potential solution. These novel methods are validated on one or more datasets from a sparse collection of academic intrusion detection datasets. Their recognition as improvements to the state-of-the-art is largely dependent on whether they can demonstrate a reliable increase in classification metrics compared to similar works validated on the same datasets. Whether these increases are meaningful outside of the training/testing datasets is rarely asked and never investigated. This work aims to demonstrate that strong general performance does not typically follow from strong classification on the current intrusion detection datasets. Binary classification models from a range of algorithmic families are trained on the attack classes of CSE-CIC-IDS2018, a state-of-the-art intrusion detection dataset. After establishing baselines for each class at various points of data access, the same trained models are tasked with classifying samples from the corresponding attack classes in CIC-IDS2017, CIC-DoS2017 and CIC-DDoS2019. Contrary to what the baseline results would suggest, the models have rarely learned a generally applicable representation of their attack class. Stability and predictability of generalized model performance are central issues for all methods on all attack classes. Focusing only on the three best-in-class models in terms of interdataset generalization, reveals that for network-centric attack classes (brute force, denial of service and distributed denial of service), general representations can be learned with flat losses in classification performance (precision and recall) below 5%. Other attack classes vary in generalized performance from stark losses in recall (-35%) with intact precision (98+%) for botnets to total degradation of precision and moderate recall loss for Web attack and infiltration models. The core conclusion of this article is a warning to researchers in the field. Expecting results of proposed methods on the test sets of state-of-the-art intrusion detection datasets to translate to generalized performance is likely a serious overestimation. Four proposals to reduce this overestimation are set out as future work directions.
Comparative studies of soluble and immobilized Fe(III) heme-peptide complexes as alternative heterogeneous biocatalysts

Fe(III) heme is known to possess low catalytic activity when exposed to hydrogen peroxide and a reducing substrate. Efficient non-covalently linked Fe(III) heme-peptide complexes may represent suitable alternatives as a new group of green catalysts. Here, we evaluated a set of heme-peptide complexes by determination of their peroxidase-like activity and the kinetics of the catalytic conversion in both, the soluble and the immobilized state. We show the impact of peptide length on binding of the peptides to Fe(III) heme and the catalytic activity. Immobilization of the peptide onto a polymer support maintains the catalytic performance of the Fe(III) heme-peptide complex. This study thus opens up a new perspective with regard to the development of heterogeneous biocatalysts with a peroxidase-like activity.
Crude oil associated heavy metals (HMs) contamination in agricultural land: Understanding risk factors and changes in soil biological properties

Ecological and human risks of crude oil associated heavy metals (HMs) in the contaminated agricultural lands were evaluated employing different indices. The indices that were employed includes enrichment factor (EF), contamination factor (Cf),pollution load index (PLI), geo-accumulation index (Igeo), ecological risk index (ERI), contamination degree (Cd), Nemerow's pollution index (PN), exposure factor (ExF), hazard quotient (HQ) and hazard index (HI). Besides, the adverse effects of crude oil associated HMs on the soil biological properties were also analyzed. The results of Cf and EF were found consistent with each other showing the HMs in the decreasing order of contamination as Mn > Zn > Cr > Ni > Cu. The Igeo and ERI fall in the grade (Igeo>5) and (ERI ≥40) respectively. The results of PLI, Cd, PN and ExF values clearly indicate a high environmental risk of crude oil-associated HMs. The results of the human health risks assessment revealed the maximum level of HMs enters the body via ingestion. There were significant(p < 0.05) decreases (5.7-15.5 folds) in the activities of cellulase (0.194 ± 0.02-0.998 ± 0.1), phosphatase (0.173 ± 0.3-0.612 ± 1.5), catalase (0.328 ± 0.3-2.036 ± 1.5), urease (0.44 ± 0.3-1.80 ± 1.2), dehydrogenase (0.321 ± 0.2-0.776 ± 0.7),polyphenol oxidase (0.21 ± 0.5-0.89 ± 2.5)and peroxidase (0.13 ± 0.4-0.53 ± 1.03)in crude oil-contaminated soil. The Pearson's correlation confirmed the significant negative impact of HMs on the soil's biological properties.
Complete Neurologic Recovery of Cerebral Fat Embolism Syndrome in Sickle Cell Disease

Sickle cell disease is one of the most common inherited hemoglobinopathies diagnosed in the United States. Patients often present with severe anemia, pain crises, infections, and vaso-occlusive phenomena. Complications of these disorders can lead to significant debilitating morbidity and mortality. Fat embolism syndrome (FES) is a rare and devastating complication of sickle cell disease. It usually presents with a rapidly deteriorating clinical course, and the prognosis is dismal. We report a case of FES in a 19-year-old African American male with a history of sickle cell disease who presented with tonic-clonic seizures and was found to have multi-organ failure. FES was diagnosed 20 days from a presentation based on blood cytopenias and magnetic resonance imaging findings that were obscured at the initial presentation. We describe in this report, the patient's course from presentation until diagnosis and resolution. Our case is peculiar as the patient had a very good outcome without the need for red blood cell (RBC) exchange; instead, supportive treatment and simple RBC transfusions were enough to change the clinical course of this almost fatal syndrome.
Mesenteric Ischemia in Patients with Coronavirus 2019: A Scoping Review

Background: Coronavirus 2019 (COVID-19) is a systemic disease associated with severe gastrointestinal complications including life-threatening mesenteric ischemia. We sought to review and summarize the currently available literature on the presentation, management, and outcomes of mesenteric ischemia in patients with COVID-19. Patients and Methods: The PubMed database was searched to identify studies published between January 2020 and January 2021 that reported one or more adult (≥18 years) patients with COVID-19 who developed mesenteric ischemia during hospitalization. The demographic characteristics, clinical and imaging findings, management, and outcomes of patients from each study were extracted and summarized. Results: A total of 35 articles reporting on 61 patients with COVID-19 with mesenteric ischemia met the eligibility and were included in our study. The mean age was 60 (±15.9) years, and 53% of patients were male. Imaging findings of these patients included mesenteric arterial or venous thromboembolism, followed by signs of mesenteric ischemia. Sixty-seven percent of patients were taken to the operating room for an exploratory laparotomy and bowel resection and 21% were managed conservatively. The terminal ileum was the most commonly involved area of necrosis (26%). The mortality rate of patients with COVID-19 with mesenteric ischemia was 33%, and the most common cause of death was multiorgan failure or refractory septic shock. Twenty-seven percent of patients managed operatively died during the post-operative period. Conclusions: Mesenteric ischemia in patients with COVID-19 is a devastating complication associated with a high rate of morbidity and mortality. Further efforts should focus on developing strategies for early recognition and management.
Antimicrobial Shape Memory Polymer Hydrogels for Chronic Wound Dressings

Chronic wounds can remain open for several months and have high risks of amputation due to infection. Dressing materials to treat chronic wounds should be conformable for irregular wound geometries, maintain a moist wound bed, and reduce infection risks. To that end, we developed cytocompatible shape memory polyurethane-based poly(ethylene glycol) (PEG) hydrogels that allow facile delivery to the wound site. Plant-based phenolic acids were physically incorporated onto the hydrogel scaffolds to provide antimicrobial properties. These materials were tested to confirm their shape memory properties, cytocompatibility, and antibacterial properties. The incorporation of phenolic acids provides a new mechanism for tuning intermolecular bonding in the hydrogels and corollary mechanical and shape memory properties. Phenolic acid-containing hydrogels demonstrated an increased shape recovery ratio (1.35× higher than the control formulation), and materials with cytocompatibility >90% were identified. Antimicrobial properties were retained over 20 days in hydrogels with higher phenolic acid content. Phenolic acid retention and antimicrobial efficacy were dependent upon phenolic acid structures and interactions with the polymer backbone. This novel hydrogel system provides a platform for future development as a chronic wound dressing material that is easy to implant and reduces infection risks.
Efficient Implementation of Adaptive Interpolation Filters for Low Complexity Video Coding

This paper presents novel methods for implementing adaptive interpolation filtering techniques for video coding on 16-bit integer architectures. The proposed methods offer significant complexity reduction over the state-of-the-art, making, them especially valuable for resource constrained mobile multimedia devices, with high coding efficiency.(1)
Prehistoric charcoal drawings in the caves in the Slovak Republic, Central Europe: Successful radiocarbon dating by a micro-sample C-14 AMS

In Central Europe, only a few caves with ancient drawings on the walls are known. During the past years, simple lines and sketches made of charcoal or smearing traces from torches are found mainly in less accessible locations in some caves of the Slovak Karst. Previous attempts to date these findings were unsuccessful since the painted layers were too thin to allow sampling and enable routine AMS dating. Now the application of the small mass radiocarbon accelerator mass-spectrometry (AMS) technique developed at the Australian Nuclear Science and Technology Organisation (ANSTO) made possible successful C-14 determinations for a set of cave drawings and markings from the Slovak Karst. This research confirmed the prehistoric/protohistoric nature of the drawings/sketches in Uikova Diera, Silicka Ladnica, Ardovska and Domica Caves. Moreover, this research widens the scope for prehistoric rock art dating, one of the major constrains in rock art studies.
Bangla-BERT: Transformer-Based Efficient Model for Transfer Learning and Language Understanding

The advent of pre-trained language models has directed a new era of Natural Language Processing (NLP), enabling us to create powerful language models. Among these models, Transformer-based models like BERT have grown in popularity due to their cutting-edge effectiveness. However, these models heavily rely on resource-intensive languages, forcing other languages into multilingual models(mBERT). The two fundamental challenges with mBERT become significantly more challenging in a resource-constrained language like Bangla. It was trained on a limited and organized dataset and contained weights for all other languages. Besides, current research on other languages suggests that a language-specific BERT model will exceed multilingual ones. This paper introduces Bangla-BERT,a a monolingual BERT model for the Bangla language. Despite the limited data available for NLP tasks in Bangla, we perform pre-training on the largest Bangla language model dataset, BanglaLM, which we constructed using 40 GB of text data. Bangla-BERT achieves the highest results in all datasets and vastly improves the state-of-the-art performance in binary linguistic classification, multilabel extraction, and named entity recognition, outperforming multilingual BERT and other previous research. The pre-trained model is assessed against several non-contextual models such as Bangla fasttext and word2vec the downstream tasks. Finally, this model is evaluated by transfer learning based on hybrid deep learning models such as LSTM, CNN, and CRF in NER, and it is observed that Bangla-BERT outperforms state-of-the-art methods. The proposed Bangla-BERT model is assessed by using benchmark datasets, including Banfakenews, Sentiment Analysis on Bengali News Comments, and Cross-lingual Sentiment Analysis in Bengali. Finally, it is concluded that Bangla-BERT surpasses all prior state-of-the-art results by 3.52%, 2.2%, and 5.3%.
A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation

White matter (WM) lesions are thought to play an important role in multiple sclerosis (MS) disease burden. Recent work in the automated segmentation of white matter lesions from magnetic resonance imaging has utilized a model in which lesions are outliers in the distribution of tissue signal intensities across the entire brain of each patient. However, the sensitivity and specificity of lesion detection and segmentation with these approaches have been inadequate. In our analysis, we determined this is due to the substantial overlap between the whole brain signal intensity distribution of lesions and normal tissue. Inspired by the ability of experts to detect lesions based on their local signal intensity characteristics, we propose a new algorithm that achieves lesion and brain tissue segmentation through simultaneous estimation of a spatially global within-the-subject intensity distribution and a spatially local intensity distribution derived from a healthy reference population. We demonstrate that MS lesions can be segmented as outliers from this intensity model of population and subject. We carried out extensive experiments with both synthetic and clinical data, and compared the performance of our new algorithm to those of state-of-the art techniques. We found this new approach leads to a substantial improvement in the sensitivity and specificity of lesion detection and segmentation.
Evaluating Snow Bidirectional Reflectance of Models Using Multiangle Remote Sensing Data and Field Measurements

Because of the anisotropy of snow surface reflectance, it is essential to select a proper snow bidirectional reflectance model for extracting snow cover and inverting snow properties from remote sensing image, especially during the period of snow rapidly changed. In this letter, the ability of reproducing snow bidirectional reflectance by three semiempirical bidirectional reflectance distribution function (BRDF) models (Ross-Li, Roujean, and Raman-Pinty-Verstraete) and the asymptotic radiative transfer theory (ART) model was evaluated using the polarization and directionality of the earth reflectance (POLDER) data. In addition, the ART model was compared with the bicontinuous geometric optics (bic-GORT) model based on field measurements. The results indicated that the root mean square errors (RMSEs) are small and similar for all models during the stable-snow period. The physical models perform better than the semiempirical models in capturing the bidirectional signatures of snow during the periods of snow rapidly changed. The bic-GORT model achieves higher accuracy, while the ART model holds the advantages of simple and efficient to be used.