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.

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.

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.

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.

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.

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.

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.

Optimizing electroporation via pulse modulation: a molecular dynamics study.

Efficient molecular transport via reversible electroporation requires sustained existence of the pore without causing irreversible cellular damage. In this study, we used molecular dynamics simulations to investigate pore formation during electroporation, and we characterized the transition to hydrophilic pores. Our simulations reveal that during the hydrophilic state, the reapplication of an electric field, even at reduced magnitudes, extends the pore duration while maintaining structural integrity.

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.

Tuning the electrochemical performance of a hierarchical MoO/CdO binary heterostructure for supercapacitor applications.

Cadmium oxide (CdO)-incorporating molybdenum trioxide (MoO) nanocomposites were synthesized using a facile hydrothermal method by varying the CdO content (1%, 3%, and 5%) to comprehend the influence of CdO concentration on the electrochemical performance of MoO. The structural and morphological properties of the synthesized nanomaterials were characterized using X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM). XRD showed that MoO has an orthorhombic structure, and FE-SEM showed that it has a nanobelt shape (0.

Enhancing B-mode-based breast cancer diagnosis via cross-attention fusion of H-scan and Nakagami imaging with multi-CAM-QUS-Driven XAI.

B-mode ultrasound is widely employed for breast lesion diagnosis due to its affordability, widespread availability, and effectiveness, particularly in cases of dense breast tissue where mammography may be less sensitive. However, it disregards critical tissue information embedded in raw radiofrequency (RF) data. While both modalities have demonstrated promise in computer-aided diagnosis (CAD), their combined potential remains largely unexplored.

Predicting disinfection by-products (DBPs) in supply water within a real water distribution network using an artificial neural network.

This study develops an artificial neural network (ANN) model using advanced learning algorithm techniques to predict disinfection by-products (DBPs) in supply water. The model is based on real-scale data from the Chattogram water distribution network (WDN), a coastal city in Bangladesh. The research revealed a significant presence of free halide components in both the source and distributed water, indicating the potential formation of bromo- and iodo-DBPs.

Microwave assisted starch stabilized green synthesis of zinc oxide nanoparticles for antibacterial and photocatalytic applications.

Nanostructured particles offer outstanding diversities of applications in the fields of nanotechnology, nano-engineering, nano-biotechnology, etc. Morphological structure, size distribution, electronic behavior including intrinsic characteristics of nanoparticles depend on the source and synthesis methods. Here, an eco-friendly approach using microwave irradiation for the synthesis of zinc oxide (ZnO) nanoparticles has been reported.

Memsensing by surface ion migration within Debye length.

Integration between electronics and biology is often facilitated by iontronics, where ion migration in aqueous media governs sensing and memory. However, the Debye screening effect limits electric fields to the Debye length, the distance over which mobile ions screen electrostatic interactions, necessitating external voltages that constrain the operation speed and device design. Here we report a high-speed in-memory sensor based on vanadium dioxide (VO) that operates without an external voltage by leveraging built-in electric fields within the Debye length.

Future changes in hydro-climatic extremes of a large transboundary river basin using multi-model bias-corrected CMIP6 projections.

The Upper Meghna Basin (UMB), a critical large transboundary highly responsive catchment shared between Bangladesh and India, is facing escalating hydro-climatic extremes under climate change, impacting millions of livelihoods. This study innovatively assesses the future changes with monthly, seasonal, and annual matrices of near and far future rainfall, temperature, high flows, and low flows including flood frequencies, shifting patterns, and probability of exceedances using the Soil and Water Assessment Tool (SWAT), for the very first time, forced by bias-corrected CMIP6 projections of two unique SSP2-4.5 and SSP3-7.

Indigenous wood species classification using a multi-stage deep learning with grad-CAM explainability and an ensemble technique for Northern Bangladesh.

Wood species recognition has recently emerged as a vital field in the realm of forestry and ecological conservation. Early studies in this domain have offered various methods for classifying distinct wood species found worldwide using data collected from a particular region. An image dataset has been developed for wood species classification of Bangladeshi forest.

Towards the Integration of an Anti-Contractile Compound Within Drug-Coated Balloon Therapy.

Purpose: Drug-coated balloon (DCB) therapy is a promising approach to treat peripheral artery disease (PAD), wherein lesion site preparation, balloon inflation, and the local delivery of anti-proliferative drugs such as paclitaxel (PTX) restores and retains lumen patency. Although largely successful in PAD applications, broader clinical deployment is in part limited by the occurrence of late lumen loss due to inward vessel remodeling at the treatment site, a maladaptive chronic response that has been clinically-observed to coincide with elevations in resident vascular smooth muscle cell (vSMC) tone. This study aims to explore a novel strategy to improve DCB efficacy via drug-based attenuation of vSMC tone at the treatment site.

Mathematical bridge between epidemiological and molecular data on cancer and beyond.

Background: At least six different mathematical models of cancer and their countless variations and combinations have been published to date in the scientific literature that reasonably explain epidemiological prediction of multi-step carcinogenesis.] Background: At least six different mathematical models of cancer and their countless variations and combinations have been published to date in the scientific literature that reasonably explain epidemiological prediction of multi-step carcinogenesis. Each one deals with a particular set of problems at a given organizational level ranging from populations to genes.

Effects of different types of modifiers on structural variation of nano-hydroxyapatite for efficient application.

Hydroxyapatite (HAp) has emerged as a biomaterial of significant interest due to its intrinsic biocompatibility and structural similarity to natural bone minerals. While HAp is traditionally derived from natural sources, chemical synthesis conventional methods, such as wet chemical precipitation and sol-gel processing, and newer techniques like microwave-assisted synthesis and hydrothermal methods have enabled greater control over its physicochemical properties. With the expansion of applications beyond conventional biomedical uses, recent research has concentrated on engineering nanohydroxyapatite with precisely tailored morphologies and structures.

Doping-induced performance optimization in monolayer WS memristor: reduced variability and contact resistance.

The memristor is a cornerstone for developing novel non-volatile memory devices that enable brain-like efficient processing and storage capabilities. Two-dimensional transition metal dichalcogenide (TMDC)-based memristors are gaining increasing attention due to the advantages they present over their bulk counterparts. In this work, we employed first-principles calculations to demonstrate that dopants play a significant role in reducing the cycle-to-cycle variability and in lowering the contact resistance in monolayer WS-based memristor.

Multi-fidelity neural network-based prediction of tensile strength of high-entropy alloy (FeNiCoCrCu) using molecular dynamics data.

Context: High-entropy alloys (HEAs) represent a class of advanced materials with superior mechanical, thermal, and chemical properties. FeNiCoCrCu HEA has been of particular interest due to its excellent tensile strength, corrosion resistance, and thermal stability. However, it is a significant challenge to understand and optimize the mechanical properties of such alloys due to the complex structure.