ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals

Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as powerful tools in this context. We present ResEMGNet, designed to identify ALS and Myopathy directly from raw electromyography (EMG) signals. Unlike traditional methods that require intricate handcrafted feature extraction, ResEMGNet takes raw EMG data as input, reducing computational complexity and enhancing practicality. Our approach was rigorously evaluated using various metrics in comparison to existing methods. ResEMGNet exhibited exceptional subject-independent performance, achieving an impressive overall three-class accuracy of 94.43\%

A Multi Constrained Transformer-BiLSTM Guided Network for Automated Sleep Stage Classification from Single-Channel EEG

Sleep stage classification from electroencephalogram (EEG) is significant for the rapid evaluation of sleeping patterns and quality. A novel deep learning architecture, ``DenseRTSleep-II'', is proposed for automatic sleep scoring from single-channel EEG signals. The architecture utilizes the advantages of Convolutional Neural Network (CNN), transformer network, and Bidirectional Long Short Term Memory (BiLSTM) for effective sleep scoring. Moreover, with the addition of a weighted multi-loss scheme, this model is trained more implicitly for vigorous decision-making tasks. Thus, the model generates the most efficient result in the SleepEDFx dataset and outperforms different state-of-the-art (IIT-Net, DeepSleepNet) techniques by a large margin in terms of accuracy, precision, and F1-score

Comparative Analysis of Production Processes and Quality Parameters of Two Different Semi-Combed Yarns

Semi-combed yarn represents a type of ring-spun yarn produced by modifying the typical carded and combed yarn manufacturing process. Carded yarn is inferior in quality, while combed yarns are not cost efficient. The semi-combed yarn has therefore emerged as an alternative to fully combed yarn to facilitate a reasonable quality-cost compromise. This paper reports two manufacturing techniques and associated features of the cotton ring-spun semi-combed yarn of the same count. One process involves sliver mixing in draw frame, and the other by reducing the noil extraction percentage in comber. The aim of this study is to run a comparative analysis of both the end products against themselves and their carded and combed counterparts to establish their acceptance in the industrial scale. Important quality parameters such as unevenness (U%), coefficient of variation (CVm%), thick place(+50%)/km, thin place(−50%)/km, neps(+200%)/km, hairiness (H), strength, elongation, CSP, and cost have been evaluated, analyzed, and compared among these products. In several cases, the quality of semi-combed yarn was comparable to fully combed yarns and better than carded yarns. This offers a cost-effective and sustainable alternative to combed yarn. Comparison shows that the noil extraction process offers less hairiness, and more sustainability and involves no extra operation to develop the yarn

SocialTrove: A Self-summarizing Storage Service for Social Sensing

The increasing availability of smartphones, cameras, and wearables with instant data sharing capabilities, and the exploitation of social networks for information broadcast, heralds a future of real-time information overload. With the growing excess of worldwide streaming data, such as images, geotags, text annotations, and sensory measurements, an increasingly common service will become one of data summarization. The objective of such a service will be to obtain a representative sampling of large data streams at a configurable granularity, in real-time, for subsequent consumption by a range of data-centric applications. This paper describes a general-purpose self-summarizing storage service, called SocialTrove, for social sensing applications. The service summarizes data streams from human sources, or sensors in their possession, by hierarchically clustering received information in accordance with an application-specific distance metric. It then serves a sampling of produced clusters at a configurable granularity in response to application queries. While SocialTrove is a general service, we illustrate its functionality and evaluate it in the specific context of workloads collected from Twitter. Results show that SocialTrove supports a high query throughput, while maintaining a low access latency to the produced real-time application-specific data summaries. As a specific application case-study, we implement a fact-finding service on top of SocialTrove.Army Research Laboratory, Cooperative Agreement W911NF-09-2-0053DTRA grant HDTRA1-10-1-0120NSF grants CNS 13-29886, CNS 09-58314, CNS 10-35736Ope

Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey

The purpose of this study was to investigate the prevalence of depression and anxiety among Bangladeshi university students during the COVID-19 pandemic. It also aimed at identifying the determinants of depression and anxiety. A total of 476 university students living in Bangladesh participated in this cross-sectional web-based survey. A standardized e-questionnaire was generated using the Google Form, and the link was shared through social media—Facebook. The information was analyzed in three consecutive levels, such as univariate, bivariate, and multivariate analysis. Students were experiencing heightened depression and anxiety. Around 15% of the students reportedly had moderately severe depression, whereas 18.1% were severely suffering from anxiety. The binary logistic regression suggests that older students have greater depression (OR = 2.886, 95% CI = 0.961–8.669). It is also evident that students who provided private tuition in the pre-pandemic period had depression (OR = 1.199, 95% CI = 0.736–1.952). It is expected that both the government and universities could work together to fix the academic delays and financial problems to reduce depression and anxiety among university students

Dragon Fruit (<i>Hylocereus polyrhizus</i>): A Green Colorant for Cotton Fabric

The textile industry has been exploring sustainable chemicals and natural alternatives to replace harmful and carcinogenic substances used in different stages of textile production for dyeing textiles. Natural dyes are gaining popularity, as they are environmentally friendly and less harmful. Betacyanin, a type of pigment obtained from red pitahaya, commonly known as red dragon fruit (Hylocereus polyrhizus), has peels that are available as agricultural waste and can be used to meet the demand for natural dye production. This study aimed to explore and utilize dragon fruit’s peel as a natural colorant for dyeing 100% cotton knit fabric (scoured and bleached single jersey plain knit) of 170 g/m2, which could transform a low-value material into a valuable product. However, cotton’s phenolic nature and oxidation process result in negative charges on its surface, making natural dyeing challenging. Cationization with cationic agents (ForCat NCH, a mixture of cationic polyamine and 1,3,dichlori-2-propanol) and mordanting (potassium alum or potassium aluminum sulfate) were carried to improve dye exhaustion and enhance colorfastness properties. Spectrophotometer 800 was used to measure color strength (K/S), and several fastness tests, including wash, perspiration, and rubbing were conducted to assess the final product’s performance. The process parameters, such as temperatures, times, pH levels, and dye concentrations were varied to understand better the optimum conditions

Prescription patterns in an intensive care unit of COVID‐19 patients in Bangladesh: A cross‐sectional study

Abstract Background and Aims To reduce death rates for critical patients hospitalized in intensive care units (ICUs), coronavirus (COVID‐19) lacks proven and efficient treatment methods. This cross‐sectional study aims to evaluate how physicians treat severe and suspected COVID‐19 patients in the ICU department in the absence of an established approach, as well as assess the rational use of the medication in the ICU department. Methods Between June 16, 2021, and December 10, 2022, a total of 428 prescriptions were randomly gathered, including both suspected (yellow zone) and confirmed (red zone) COVID‐19 patients. For data management, Microsoft Excel 2021 was utilized, while STATA 17 provided statistical analysis. To find associations between patients' admission status and demographic details, exploratory and bivariate analyses were conducted. Results Of the 428 patients admitted to the ICU, 228 (53.27%) were in the yellow zone and 200 (46.73%) were in the verified COVID‐19 red zone. The majority of patients were male (54.44%), and the age range from 41 to 60 was the most common (41.82%). No significant deviation was detected to the yellow and red groups' prescription patterns. A total of 4001 medicines (mean 9.35/patient) were prescribed. Antiulcerants, antibiotics, respiratory, analgesics, anticoagulants, vitamins and minerals, steroids, cardiovascular, antidiabetic drugs, antivirals, antihistamines, muscle relaxants, and antifungal treatments were widely prescribed drugs. Enoxaparin (67.06%) appeared as the most prescribed medicine, followed by montelukast (60.51%), paracetamol (58.41%), and dexamethasone (51.64%). Conclusion The prescription patterns for the yellow and red groups were comparable and mostly included symptomatic treatment. Respiratory drugs constituted the most frequent therapeutic class. Polypharmacy should be taken under considerations. In ICU settings, the outcomes emphasize the need of correct diagnosis, cautious antibiotic usage, suitable therapy, and attentive monitoring

Advanced Cybercrime Detection: A Comprehensive Study on Supervised and Unsupervised Machine Learning Approaches Using Real-world Datasets

In the ever-evolving field of cybersecurity, sophisticated methods—which combine supervised and unsupervised approaches—are used to tackle cybercrime. Strong supervised tools include Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), while well-known unsupervised methods include the K-means clustering model. These techniques are used on the publicly available StatLine dataset from CBS, which is a large dataset that includes the individual attributes of one thousand crime victims. Performance analysis shows the remarkable 91% accuracy of SVM in supervised classification by examining the differences between training and testing data. K-Nearest Neighbors (KNN) models are quite good in the unsupervised arena; their accuracy in detecting criminal activity is impressive, at 79.56%. Strong assessment metrics, such as False Positive (FP), True Negative (TN), False Negative (FN), False Positive (TP), and False Alarm Rate (FAR), Detection Rate (DR), Accuracy (ACC), Recall, Precision, Specificity, Sensitivity, and Fowlkes–Mallow's scores, provide a comprehensive assessment

Beta, Delta, and Omicron, Deadliest Among SARS-CoV-2 Variants: A Computational Repurposing Approach

SARS-CoV-2 has been highly susceptible to mutations since its emergence in Wuhan, China, and its subsequent propagation due to containing an RNA as its genome. The emergence of variants with improved transmissibility still poses a grave threat to global health. The spike protein mutation is mainly responsible for higher transmissibility and risk severity. This study retrieved SARS-CoV-2 variants structural and nonstructural proteins (NSPs) sequences from several geographic locations, including Africa, Asia, Europe, Oceania, and North and South America. First, multiple sequence alignments with BioEdit and protein homology modeling were performed using the SWISS Model. Then the structure visualization and structural analysis were performed by superimposing against the Wuhan sequence by Pymol to retrieve the RMSD values. Sequence alignment revealed familiar, uncommon regional among variants and, interestingly, a few unique mutations in Beta, Delta, and Omicron. Structural analysis of such unique mutations revealed that they caused structural deviations in Beta, Delta, and Omicron spike proteins. In addition, these variants were more severe in terms of hospitalization, sickness, and higher mortality, which have a substantial relationship with the structural deviations because of those unique mutations. Such evidence provides insight into the SARS-CoV-2 spike protein vulnerability toward mutation and their structural and functional deviations, particularly in Beta, Delta, and Omicron, which might be the cause of their broader coverage. This knowledge can help us with regional vaccine strain selection, virus pathogenicity testing, diagnosis, and treatment with more specific vaccines

Factors associated with moderate wasting among marginalized 6 to 23-month aged children in Bangladesh: Findings of the Suchana program baseline survey data

Suchana—a large-scale, 7-year nutrition program that started in 2015—is being implemented in 250,000 households in the marginalized segment in north-east Bangladesh, with the aim of improving childhood nutrition status. Untreated childhood moderate wasting may develop to severe wasting, which is associated with a 10-fold higher risk of mortality compared to children of normal weight relative to height/length. Identifying the diverse, age-specific risk factors for moderate wasting may help such programs to formulate tailored interventions to prevent and treat childhood malnutrition in rural communities. The objective of this study was to identify the age-specific factors associated with moderate wasting among 6–23-month-old children in beneficiary households. Cross-sectional data on 4,400 children was collected through systematic sampling between November 2016 and February 2017 using the Suchana beneficiary list. In total, 8.1% of 6–11 month-olds and 10.3% of 12– 23 month-olds suffered moderate wasting; 12–23-month-olds had a 1.3-fold higher risk of moderate wasting than 6–11-month-olds. Our results of logistic regression models suggest that larger household size, higher maternal body mass index (BMI), and maternal food consumption status more than usual during the recent pregnancy were associated with a reduced risk of moderate wasting among 6–11-month-olds. Higher maternal BMI, normal maternal food consumption status during last pregnancy, being female and maternal knowledge on diarrheal management, were associated with a reduced risk of moderate wasting among 12–23-month-olds. In conclusion, beyond maternal BMI and maternal food consumption status during the last pregnancy, the factors associated with moderate wasting among 6–23-month-olds in the poorest households in Bangladesh are age-specific