Utilizing machine learning algorithms for personalized workout recommendations and monitoring: A systematic review on smartwatch-assisted exercise prescription.

Background: Smartwatches, equipped with advanced sensors, have become increasingly prominent in health and fitness domains. Their integration with machine learning (ML) algorithms presents novel opportunities for personalized exercise prescription and physiological monitoring.

Objective: This systematic review aimed to evaluate the effectiveness, limitations, and practical applications of smartwatch-ML systems in delivering tailored fitness interventions and health tracking.

Current status and challenges in electroencephalography (EEG)-based driver fatigue detection: a comprehensive survey.

Driver fatigue is a major contributor to traffic accidents, leading to increased fatality rates and severe damage compared to incidents involving alert drivers. Electroencephalography (EEG) has emerged as a widely used method for detecting driver fatigue due to its ability to capture brain activity patterns. This survey provides a thorough analysis of devices that detect driver fatigue using EEG, analyzing existing methodologies, challenges, and future research directions.

Impact of Nanonutrient Complex on Growth Performance, Feed Efficiency, and Hematobiochemical Profiles in Asian Catfish, .

Understanding how nanonutrients influence the growth and physiological processes of cultivable fish can boost fish production efficiency with less management, advancing aquaculture toward global food security. In this study, a 60-day feeding trial was conducted to determine the effects of a nanonutrient complex (NNC) on the growth performances and physiology of Asian catfish, . Nanoparticles (NPs; Zn, Cu, and Fe) were synthesized from their metallic salts using an established acoustic method and characterized via scanning electron microscopy.

Genome-wide identification and functional characterization of magnesium transporter (MGT) gene family in soybean (Glycine max L.) and their expression profiles in response to aphid infestation, dehydration, and salt stresses.

The divalent cation, Magnesium (Mg2+), is an essential mineral element for plant growth and development. Magnesium transporter (MGT) plays a vital role in maintaining Mg2 + homeostasis within plant cells. Although extensive research has been conducted in several crop species, no comprehensive study has yet been carried out on the MGT gene family in soybean (Glycine max L.

Dual graph attention network for robust fault diagnosis in photovoltaic inverters.

This paper presents a novel deep learning framework based on a Dual Graph Attention Network (DualGAT) to enhance the accuracy and robustness of fault diagnosis in photovoltaic (PV) inverters operating under diverse environmental and operational conditions. Given the critical role of PV inverters in ensuring stable energy conversion, early and reliable detection of open-circuit faults is essential to prevent performance degradation and equipment failure. To address this, a detailed simulation model of a grid-connected PV inverter was developed in MATLAB/Simulink, incorporating variations in irradiance and temperature to generate realistic fault scenarios.

How consistent is the interpretation of renal scarring in pediatric patients using technetium-99m dimercaptosuccinic acid scintigraphy.

Background: Technetium-99m dimercaptosuccinic acid (DMSA) scintigraphy plays a critical role in pediatric imaging for detecting renal cortical scarring, which is essential for diagnosing and managing kidney damage in children. However, variability in observer interpretation poses challenges, potentially impacting clinical decision-making and outcomes.

Objective: This study aims to assess intra- and inter-observer agreement in interpreting DMSA scans for detecting renal cortical scarring in pediatric patients, focusing on the presence, location, and percentage of kidney involvement.

Inductive and Transfer Learning-Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases.

Purpose: This study presents NeuroDL, a novel deep learning-based diagnostic framework designed for the automated detection of brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI). The objective is to enhance diagnostic precision and efficiency in neurology through advanced computer-aided decision support.

Methods: NeuroDL utilizes convolutional neural networks (CNNs) trained on two publicly available, annotated MRI datasets.

Comprehensive Fault Diagnosis of Three-Phase Induction Motors Using Synchronized Multi-Sensor Data Collection.

Induction motors are critical to industrial operations but are prone to mechanical and electrical faults. This paper introduces a new dataset for comprehensive fault diagnosis of three-phase induction motors, featuring synchronized multi-sensor data collection. Real-time measurements of vibration, voltage, and current were captured from a 0.

Prevalence of Diabetes Mellitus and Hypertension Among COVID-19 Patients: A Cross-Sectional Study Exploring Associations With Sociodemographic and Biological Factors in Rajshahi Division, Bangladesh.

Background And Aims: Diabetes mellitus (DM) and hypertension (HT) are major global health concerns, with a rising prevalence worsened by the COVID-19 pandemic. The interplay between these chronic conditions and COVID-19 presents a unique public health challenge, particularly in low- and middle-income countries such as Bangladesh. This study aimed to (1) determine the prevalence of DM and HT among individuals affected by COVID-19 pandemic in Rajshahi Division, Bangladesh, and (2) explore associations with sociodemographic and biological factors.

The energy-health-environment nexus: assessing the transboundary impacts of coal-fired power plants.

Point source emissions from large coal-fired power plants are pivotal in the energy-health-environment nexus, impacting energy security, air quality, and public health outcomes. Despite this, there is a lack of interdisciplinary prospective studies focusing on the effects of power plant emissions on the population residing downwind. To address this gap, a comprehensive, multicenter, interdisciplinary study on a transboundary scale (India and Bangladesh) has been launched, which includes modeling power plant emissions, seasonal collection of particulate matter and its chemical analysis, socioeconomic surveys of the case (downwind) and control (upwind) populations, lung health assessments, and transcriptomic analyses of blood samples.

Research experience and career factors in relation to mental health problems: Prevalence, risk factors, and machine learning-based predictive estimates.

Background: Mental health issues, including depression, anxiety, and insomnia, are increasingly prevalent among university students and graduates, especially those involved in academic research. The impact of research-related characteristics on mental health remains underexplored.

Aim: We examined this relationship using machine learning alongside traditional statistical analyses and GIS mapping.

Identification of chlorpyrifos and carbofuran residues in Kumari Beel, Bangladesh, and their coexposure effect on the cartilage development during zebrafish embryogenesis.

Chlorpyrifos (organophosphorus) and carbofuran (carbamate) are widely used pesticides in Bangladesh, raising concerns about their environmental and ecotoxicological impacts, particularly on aquatic life. In this study, pesticide concentrations in water, soil sediment, and fish samples collected from Kumari Beel, Rajshahi, Bangladesh, were measured using HPLC analysis. Concentrations were highest in fish, ranging from 0.

Biocompatible methyl cellulose/polyvinyl pyrrolidone biocomposite hydrogels for sustained release of quercetin drug.

Hydrogels exhibit potential for controlled drug delivery; however, their clinical application is constrained by a low drug loading capacity, inadequate mechanical toughness, and frequently result in insufficient drug release performance. This research investigates biocomposite hydrogels composed of methyl cellulose (MC) and polyvinyl pyrrolidone (PVP) for the delivery of quercetin (QC). The synthesized hydrogels were assessed for swelling, water vapor transmission rate, porosity, and rheological properties.

Cancer-Associated Fibroblasts: Immunosuppressive Crosstalk with Tumor-Infiltrating Immune Cells and Implications for Therapeutic Resistance.

Cancer is no longer considered as an isolated event. Rather, it occurs because of a complex biological drive orchestrating different cell types, growth factors, cytokines, and signaling pathways within the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are the most populous stromal cells within the complex ecosystem of TME, with significant heterogeneity and plasticity in origin and functional phenotypes.

The effects of household water, sanitation, and hygiene on early childhood education enrollment: evidence from the 2022 Bangladesh demographic and health survey.

Background: Effective early childhood education (ECE) programs, including elementary schools, kindergartens, and daycare facilities, are instrumental in fostering cognitive, social, emotional, and motor development. Access to water, sanitation, and hygiene (WASH) facilities, as mandated by Sustainable Development Goals (SDGs) 6, is integral in bolstering health and enhancing educational engagement globally. This study examines the impact of WASH facilities and sociodemographic factors on ECE enrollment in Bangladesh.

MangoImageBD: An extensive mango image dataset for identification and classification of various mango varieties in Bangladesh.

The mango image dataset presented in this article contains clear and detailed images of the fifteen most common and popular mango () varieties in Bangladesh: Amrapali, Ashshina Classic, Ashshina Zhinuk, Banana Mango, Bari-4, Bari-11, Fazli Classic, Fazli Shurmai, Gourmoti, Harivanga, Himsagor, Katimon, Langra, Rupali, and Shada. The mango specimens were sourced from various fruit markets across six districts of Bangladesh, namely Rajshahi, Chapai Nawabganj, Satkhira, Panchagarh, Rangpur, and Dhaka, which are famous for popular mango cultivation and availability to ensure a wide geographic representation. To maintain the quality and uniformity of images across the dataset, the images were captured using a high-definition smartphone camera under a standardized and controlled environment.

Hybrid Neural Networks for Precise Hydronephrosis Classification Using Deep Learning.

Objective: To develop and evaluate a deep learning framework for automatic kidney and fluid segmentation in renal ultrasound images, aiming to enhance diagnostic accuracy and reduce variability in hydronephrosis assessment.

Methods: A dataset of 1731 renal ultrasound images, annotated by four experienced urologists, was used for model training and evaluation. The proposed framework integrates a DenseNet201 backbone, Feature Pyramid Network (FPN), and Self-Organizing Neural Network (SelfONN) layers to enable multi-scale feature extraction and improve spatial precision.

First-principles study of MAlC (M = Ti, Zr) MAX phases under hydrostatic pressure: material design for industrial applications.

MAX phase compounds, combining metallic and ceramic properties, are ideal for high-pressure environments due to their excellent electrical and thermal conductivity, corrosion and oxidation resistance, and damage tolerance. This study investigates the structural, mechanical, electronic, thermal, and optical properties of MAlC (M = Ti, Zr) under hydrostatic pressure. Negative formation energies and positive phonon dispersion confirm thermodynamic and dynamic stability, while mechanical stability aligns with Born's criteria.

Novel Co-rendering Process for Enhancing Lard Quality-focusing on Oxidative Stability and Fatty Acid Composition of the Co-rendered Oils.

This study developed co-rendered oils by extracting sesame seed oil (SSO) using lard as a green extraction solvent during the co-rendering of pork fat and ground sesame seeds at 170°C in an air fryer, with varying fat-to-seed ratios (80:20 to 40:60). Compared to lard, which is high in saturated fatty acids (SFA), and SSO, which is rich in polyunsaturated fatty acids (PUFA), the co-rendered oils demonstrated significantly improved fatty acid composition. As the proportion of sesame seeds increased, the oils showed a marked reduction in atherogenic index (AI) and thrombogenic index (TI), decreasing from 0.

Oxidative Stress in DNA Damage and Neurodegenerative Diseases: Unveiling The Mechanisms and Therapeutic Opportunities.

Oxidative stress is marked by disproportionate levels of reactive oxygen species (ROS) and antioxidant defenses and is a key factor in initiating DNA damage and neurodegenerative diseases. Increased reactive oxygen species (ROS) levels can lead to oxidative DNA lesions, disrupting cellular function and contributing to genomic instability. Oxidative stress is linked to neuronal degeneration, particularly in conditions such as Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD), where DNA damage accelerates the progression of these disorders.

Multisite study using a customised NLP model to predict disposition in the emergency department: protocol paper.

Introduction: To address timely care in emergency departments, artificial neural networks (ANNs) with natural language processing will be applied to triage notes to predict patient disposition. This study will develop a predictive model that predicts disposition and type of admission.

Methods And Analysis: This will include data preprocessing and quality enhancement, masked language modelling, ANN-based fusion network for prediction.

Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning.

Parkinson's disease (PD) is a progressive neurological disorder that impairs movement control, leading to symptoms such as tremors, stiffness, and bradykinesia. Early and accurate PD detection is essential for effective management and improving patient outcomes. Many researchers analyzing handwriting data for PD detection typically rely on computing statistical features over the entirety of the handwriting task.

Genetically Modified Lactic Acid Bacteria: a Promising Mucosal Delivery Vector for Vaccines.

The advent of mucosal vaccines that target the primary entry points of many pathogens has revolutionized the field of immunology. Genetically modified lactic acid bacteria (gmLAB), which include genera such as Lactobacillus and Bifidobacterium, have emerged as promising vectors for delivering antigens to mucosal surfaces. These gram-positive, non-pathogenic microorganisms exhibit inherent probiotic properties, can survive through the gastrointestinal tract, and efficiently interact with the host immune system.

Sustainable management of end-of-life of solar photovoltaic panels: A strength, weakness, opportunity and threat analysis of recycling.

Recycling end-of-life (EoL) solar panels is recognized as a sustainable option to keep their EoL clean and also to ensure material circularity. However, understanding the concerned stakeholders' viewpoints and analysing the strengths, weaknesses, opportunities and threats of recycling in the target country context is very important. This study aims to achieve these objectives in the context of Bangladesh.

Single nucleus RNA sequencing profile analysis to reveal cell type specific common molecular drivers of Parkinson's disease and therapeutic agents.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, characterized by progressive motor and cognitive decline, leading to long-term disability and significantly impacting quality of life. While PD research has traditionally focused on dopaminergic neurons in the substantia nigra (SN), emerging evidence also suggests glial involvement in disease progression. So, this study explored PD-associated key genes from neuronal and glial cell types to uncover pathogenetic mechanisms and potential therapeutics by employing single-nucleus RNA sequencing (snRNA-seq) data from the accession number GSE184950.