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.

Electric field-induced modulation of mechanical behavior in polyelectrolyte materials: A multiscale molecular dynamics study.

Polymers with multifunctional capabilities are increasingly important for emerging technologies, particularly in applications requiring electro-responsive behavior. Polyelectrolytes, which are charged polymers, are promising candidates for electrically triggered actuators, artificial muscles, biomedicine, and flexible electronics, where modulation of mechanical properties is crucial for maintaining structural integrity and performance. This study employs molecular dynamics simulations to explore how electric fields influence the mechanical behavior of polyelectrolytes.

Platinum/nitrogen-co-doped TiO as photocatalyst and light-free catalytic adsorbent for gaseous formaldehyde.

Platinum and nitrogen co-doped titanium dioxide (Pt/N-TiO, with 1 wt% Pt and an N/Ti molar ratio of 1) has been synthesized. This Pt/N co-doping strategy creates Schottky junctions, reduces the bandgap energy (3.25 to 2.

Emerging two-dimensional nanomaterial and its modifications for enhanced antiviral applications: a review.

Highly resilient pathogens, especially viruses and antibiotic-resistant bacteria, present formidable challenges to public health due to their ability to evade conventional treatments. Traditional microbial disinfection methods, such as chemical deactivation and physical filtration, often fail to effectively neutralize viruses, thus leading to harmful by-products. In light of these limitations, there is a growing need for innovative solutions to address viral disinfection.

Facile preparation of cobalt-oxide nanostructures with enriched cobalt(ii) ion surface using for energy application.

The increasing demand for efficient energy conversion and storage systems necessitates the development of high-performance, cost-effective electrode materials. To address this challenge, we employed rotten (eggplant) juice as a precursor for the fabrication of low-cost, earth-abundant, and active electrode materials based on cobalt oxide (CoO) nanostructures. Different volumes of rotten juice (5 mL, 10 mL, 15 mL, and 20 mL) were utilized during the precipitation process to synthesize CoO nanostructures.

Synthetic Orthopantomography Image Generation Using Generative Adversarial Networks for Data Augmentation.

Objective: To overcome the scarcity of annotated dental X-ray datasets, this study presents a novel pipeline for generating high-resolution synthetic orthopantomography (OPG) images using customized generative adversarial networks (GANs).

Methods: A total of 4777 real OPG images were collected from clinical centres in Pakistan, Thailand, and the U.S.

Awareness, knowledge, and attitude towards basic life support among healthcare professional students in Bangladesh.

A comprehensive understanding of emergency care is an essential skill for all medical students to effectively manage emergency situations. The aim of this study was to evaluate healthcare professional students' understanding of basic life support (BLS) and to identify the factors that influence this knowledge. A Multi-center cross-sectional study was conducted across eight educational institutions in the Dhaka, Jashore, and Sylhet divisions of Bangladesh from January to June 2024, which included final-year medicine, physiotherapy, and nursing students.

Impact of genotypes, environmental stresses, and genotype by environment interactions on growth and yield of quinoa at flowering stage.

Flowering is a critical growth stage of quinoa (Chenopodium quinoa Willd.), with a strong influence on growth and grain yield. To understand factors affecting such flowering stage effects, we measure the differential effects of genotype (G), environmental stress (E), and genotype by environment interaction (G × E) on quinoa growth and yield-related traits during the flowering stage.

Leveraging Weighted Quartet Distributions for Enhanced Species Tree Inference from Genome-Wide Data.

Species tree estimation from genes sampled from throughout the whole genome is challenging because of gene tree discordance, often caused by incomplete lineage sorting (ILS). Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and theoretical guarantees of robustness to arbitrarily high amounts of ILS. ASTRAL, the most widely used quartet-based method, aims to infer species trees by maximizing the number of quartets in the gene trees consistent with the species tree.

Improving lung cancer detection with enhanced convolutional sequential networks.

Lung cancer is the most common cause of cancer-related deaths worldwide, and early detection is extremely important for improving survival. According to the National Institute of Health Sciences, lung cancer has the highest rate of cancer mortality, according to the National Institute of Health Sciences. Medical professionals are usually based on clinical imaging methods such as MRI, X-ray, biopsy, ultrasound, and CT scans.

Machine learning-based prediction of unconfined compressive strength of organic-rich clay shales using hybrid destructive and non-destructive inputs.

The unconfined compressive strength of organic-rich clay shale is a fundamental parameter in geotechnical and energy applications, influencing drilling efficiency, wellbore stability, and excavation design. This study presents machine learning-based predictive models for unconfined compressive strength estimation, trained on a comprehensive dataset of 1217 samples that integrate non-destructive indicators such as ultrasonic pulse velocity, shale fabric metrics, wettability potential and destructive field-derived parameters. A dual-model framework was implemented using Support Vector Machine, Decision Tree, K-Nearest Neighbor, and Extreme Gradient Boosting (XGBoost) algorithms.

The Potential of Using Phosphate-Solubilizing Bacteria as a Controlled-Release Biofertilizer Under Salt Stress Conditions.

Phosphorus deficiency in saline soils, despite high total phosphorus content, limits crop productivity due to low bioavailability. This study aimed to evaluate the phosphate-solubilizing potential and salt stress responses of Priestia megaterium PN18, a bacterium isolated from saline-affected soil, and to assess its suitability as a biofertilizer through cell encapsulation. PN18 was examined for biofilm formation, exopolysaccharide (EPS) production, and sodium uptake under NaCl concentrations ranging from 0.

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.

A cost effective real time rail track monitoring system leveraging multi sensor fusion and multi objective optimization.

Derailments, common in developing nations, often result from dislodged or defective rail blocks, leading to substantial property damage and loss of life. Developing an automated real-time wireless sensing system for preventing derailments is a complex challenge, particularly in resource-constrained regions with limited network infrastructure. Existing research has yet to provide a practical solution that effectively addresses the need for long-distance sensing and optimized sensor deployment.

Tribology of EDM Recast Layers Vis-À-Vis TIG Cladding Coatings: An Experimental Investigation.

Tribological performance is critical for the longevity and efficiency of machined components in industries such as aerospace, automotive, and biomedical. This study investigates whether electrical discharge machining recast layers can serve as a cost-effective and time-efficient alternative to conventional tungsten inert gas cladding coatings for enhancing surface properties. The samples were prepared using electrical discharge machining and tungsten inert gas cladding.

LOCAS: multilabel mRNA localization with supervised contrastive learning.

The subcellular localization of messenger RNAs (mRNAs) plays a crucial role in gene regulation, ensuring precise spatial and temporal control of protein synthesis. Traditional computational approaches for mRNA localization have primarily relied on single-label classification models, which fail to capture the inherent multi-label nature of mRNA localization. Recent advancements have introduced deep learning-based multi-label prediction frameworks; however, existing methods often lack an effective way to model the relationships between multiple localizations.

UnCOT-AD: Unpaired Cross-Omics Translation Enables Multi-Omics Integration for Alzheimer's Disease Prediction.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder, posing a growing public health challenge. Traditional machine learning models for AD prediction have relied on single omics data or phenotypic assessments, limiting their ability to capture the disease's molecular complexity and resulting in poor performance. Recent advances in high-throughput multi-omics have provided deeper biological insights.

Comprehensive review on fog collectors for drinking water supply in remote areas.

Fog water harvesting has emerged as a promising and cost-effective solution to address water scarcity, particularly in remote and arid regions where conventional water supply systems are often unfeasible. This review highlights the potential of fog collectors for providing drinking water, with a focus on successful large fog collector projects worldwide. Despite their potential, sustainability challenges persist due to maintenance issues influenced by environmental conditions and social factors.

Bioaccumulation of elements in the edible part of vegetables from the agricultural soil: Source apportionment and potential risks.

Heavy metal (HM) pollution analysis is critical for understanding environmental contamination pathways, safeguarding food safety, and protecting human health. This study evaluates HM contamination in soils and vegetables from Raozan and Rangunia upazilas, Chattogram, Bangladesh, to identify contamination sources, assess ecological risks, and evaluate potential human health impacts. Seventeen elements (Na, Al, K, Sc, Cr, Mn, Fe, Co, Zn, As, Rb, Sb, Cs, La, Sm, Th, U) were analyzed using neutron activation analysis.

High performance grid structured MIMO antenna with regression machine learning for high-speed sub THz and THz 6G IoT applications.

This study introduces an innovative approach that leverages machine learning techniques to optimize antenna gain for next-generation wireless communication and Internet of Things (IoT) systems operating in the Terahertz (THz) frequency spectrum. Designed on a 160 × 160 μm² polyimide substrate, the antenna is analyzed using CST-2018 simulations and RLC circuit modeling. The proposed antenna demonstrates outstanding performance by achieving high peak gains of 11.

Enhancing the early detection of Alzheimer's disease using an integrated CNN-LSTM framework: A robust approach for fMRI-based multi-stage classification.

Alzheimer's Disease poses a significant challenge as a progressive and irreversible neurological condition striking the elderly population. Its incurable nature correlates with a significant rise in death rates. However, early detection can slow its progression and facilitate prompt intervention, thereby mitigating mortality risks.

Tailoring electronic and optoelectronic properties of 2D-SiC defects and doping: a first-principles study toward efficient white light-emitting diodes.

The advent of graphene catalyzed extensive exploration into two-dimensional (2D) materials, owing to their extraordinary electronic, mechanical, and optical properties. Among these, two-dimensional silicon carbide (2D-SiC) has emerged as a compelling candidate for next-generation optoelectronic devices due to its inherent planar structure, robust mechanical strength, high exciton binding energy, high thermal stability, and wide band gap. In this work, we present a comprehensive first-principles investigation into the effects of intrinsic point defects including vacancies and antisites as well as substitutional doping with various single foreign atom (, As, Bi, Ga, Ge, In, P, Pb, Sb, Sn, Te, Ca, K, Mg) on the electronic and optical properties of 2D-SiC.

Synergistic effects of hydroxylation and structural defects in hexagonal boron nitride for dye removal from wastewater.

Hexagonal boron nitride (h-BN) was structurally modified using a simple and efficient chemical weathering method and assessed for its ability to remove anionic Congo Red (CR) and cationic Neutral Red (NR) dyes from wastewater. Batch sorption experiments explored variables including solution pH (2-12), modified h-BN dosage (0.25-1.