DFT simulation to study the mechanical, electronic, thermal and superconducting properties of borocarbides materials AXBC where A = Y, La, Th and X = Pd, Pt.

The invention of superconductivity in quaternary borocarbides has been attracted great attraction in the recent time. Here, the elastic, electronic, thermal, superconducting and optical features of borocarbides materials AXBC (A = Y, La, Th and X = Pd, Pt) have been explored through ab-initio method with the help of CASTEP code. To explore the physical features of AXBC (A = Y, La, Th and X = Pd, Pt) we used the well-known density functional simulation (DFT) in addition to generalized gradient approximations (GGA).

Green synthesis of high throughput hydrogen peroxide via sub-atmospheric plasma-water interaction.

Plasma-activated water (PAW), enriched with reactive species such as hydrogen peroxide (HO), presents a sustainable and versatile solution for wastewater treatment, disinfection, agriculture, and biomedical applications. This study investigates the effects of key plasma parameters - treatment time, input power, water purity, and chamber pressure - on HO production. A statistical framework combining response surface methodology (RSM), regression analysis, ANOVA, effect estimation, and marginal means was employed to model and analyze these influences.

Zoonotic Tuberculosis and Dairy Products: Comprehensive Meta-Analysis of Prevalence and Public Health Implications.

Background: Tuberculosis (TB) remains a global health concern, with unpasteurized milk serving as a major source of zoonotic Mycobacterium bovis transmission in endemic, low-resource regions.

Objective: This meta-analysis investigates the prevalence of human TB cases linked to contaminated dairy products, assesses factors influencing transmission dynamics and identifies actionable strategies to reduce risks.

Methods: A systematic meta-analysis of TB cases from contaminated dairy consumption was conducted using PubMed, Web of Science and Scopus (2000-2024).

Brucellosis in Marine Mammals: Meta-Analysis of Prevalence, Infection Patterns, Host Specificity and Zoonotic Potential.

Background: Marine brucellosis, primarily caused by Brucella species, presents significant ecological and public health challenges, impacting marine biodiversity and posing zoonotic risks. Despite ongoing research, data on the disease's prevalence, host specificity and transmission within marine ecosystems remains limited, underscoring the need for comprehensive analysis.

Objective: This review assesses Brucella infection patterns in marine mammals, focusing on prevalence, species susceptibility and implications for conservation and health strategies.

Microplastics occurrence in water, sediment and edible small indigenous fish species in seasonal freshwater wetland ecosystems of Bangladesh.

The widespread presence of microplastics (MP) in all matrices of the freshwater ecosystem is gaining attention globally due to their adverse impacts on the environment, wildlife, and human health. As the occurrence of MP in seasonal wetlands of Bangladesh remains unstudied, this study aimed to investigate the abundance, characteristics, and polymer types of microplastics occurring in water, sediment, and three small indigenous fish species (Puntius sophore, Mystus vittatus, and Channa punctatus, n = 480) in both fresh and dried conditions. The samples were collected from two locations within each of two selected wetlands (Chalan Beel: Boraigram and Gurudaspur, Haor: Mithamain and Astogram) in Bangladesh.

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.

Ecology of pet birds and epidemiology of multidrug resistant Escherichia coli and Staphylococcus spp. and resistant patterns in Budgerigar.

Pet birds are most popular as pets in Bangladesh, with budgerigars being the most common species. However, the emergence of antimicrobial resistance poses a growing threat to both pet birds and their owners. This study aims to investigate the ecology of pet birds and the epidemiology of multidrug resistant (MDR) Escherichia coli and Staphylococcus spp.

Functional Yogurt: Enhancing Color Stability, Functional Quality, and Storage Life With Malabar Spinach ( L.) Fruit.

Malabar spinach ( L.) is a leafy green vegetable rich in betalains, and is found in its fruit. It is also rich in bioactive and antioxidant compounds.

A hybrid deep learning model for sentiment analysis of COVID-19 tweets with class balancing.

The widespread dissemination of misinformation and the diverse public sentiment observed during the COVID-19 pandemic highlight the necessity for accurate sentiment analysis of social media discourse. This study proposes a hybrid deep learning (DL) model that integrates Bidirectional Encoder Representations from Transformers (BERT) for contextual feature extraction with Long Short-Term Memory (LSTM) networks for sequential learning to classify COVID-19-related sentiments. To enhance data quality, advanced text preprocessing techniques, including Unicode normalization, contraction expansion, and emoji conversion, are applied.

Rhizospheric Bacterial Distribution Influencing the Accumulation of Isoflavones, Phenolics, Flavonoids, and Antioxidant Activity in Soybean Roots Within Hydroponic System.

This study investigates how root color in soybeans affects isoflavone composition, rhizosphere bacterial diversity, total phenolics, total flavonoids, and antioxidant activity under a hydroponic cultivation system. Notably, soybean-brown roots (SBRs) accumulated significantly higher contents of isoflavones, exhibiting approximately a 14.9-fold increase in total glycosides (141.

Characterization of Lumpy Skin Disease in Northern Bangladesh: Clinical, Pathological, Biochemical, and Molecular Perspectives.

Lumpy skin disease (LSD) is an economically important disease of cattle, considered as a threat to the livestock industry. This study aimed to assess the clinical symptoms, gross pathology and histopathology, serum biochemical values, and molecular characterization of LSD in cattle. Cattle in Dinajpur, Thakurgaon, Nilphamari, and Rangpur affected by LSD were initially diagnosed through clinical signs and gross lesions.

Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models.

Electronic payment methods are increasingly prevalent worldwide, facilitating both in-person and online transactions. As credit card usage for online payments grows, fraud and payment defaults have also risen, resulting in significant financial losses. Detecting fraudulent transactions is challenging due to the highly imbalanced nature of transaction datasets, where fraudulent activities constitute only a small fraction of the data.

Nuclei segmentation and classification from histopathology images using federated learning for end-edge platform.

Accurate nuclei segmentation and classification in histology images are critical for cancer detection but remain challenging due to color inconsistency, blurry boundaries, and overlapping nuclei. Manual segmentation is time-consuming and labor-intensive, highlighting the need for efficient and scalable automated solutions. This study proposes a deep learning framework that combines segmentation and classification to enhance nuclei evaluation in histopathology images.

Anemia diagnosis from clinical records leveraging tree-driven and XAI-enhanced machine learning.

South Asian countries are highly vulnerable to anemia- a condition affecting numerous children, women, and adolescents. Early detection and effective treatment of anemia could significantly reduce its prevalence. Therefore, in this study, we leveraged clinical records from a Bangladeshi Hospital to develop an adaptive machine learning framework designed to predict anemia by analyzing patients' hematological parameters.

Serum biochemistry, hematology, and reproductive behavior of free-roaming cats in urban and rural habitats.

Objective: A cross-sectional study was conducted to reveal the hematology, serum biochemistry, and reproductive behavior of free-roaming cats in the Sylhet Division of Bangladesh.

Materials And Methods: Overall, 130 free-roaming cats were captured, of which 52 were Tom (male), 69 were Queen (female), and 9 were kittens. Regarding their ages, the cats were divided into three groups: <1 year as kittens, 1 to 2 years as young, and adults over two years.

An effective statistical moment-based feature extraction technique to identify the phosphoglycerylation sites from protein sequences.

A kind of covalent modification known as post-translational modification (PTM) happens following the biosynthesis process, which is important in cell biology research. A reversible PTM called Lysine phosphoglycerylation alters glycolytic enzyme activity and is linked to several disorders, including heart failure, arthritis, and nervous system deterioration. Identification of phosphoglycerylation has been improved using a variety of feature extraction approaches with machine learning technologies.

Freeze-Dried Instant Juice Powders From Guava, Amla, and Jamun: Evaluation of Physicochemical Properties, Bioactive Compounds, and Consumer Acceptance.

The nutritional enhancement and optimum physical properties in formulating instant fruit juice powder are challenging. This study explored the development and evaluation of freeze-dried instant fruit juice powders from three tropical fruits: Guava (), Amla (), and Jamun/black plum (), aimed at offering health-promoting benefits and ensuring year-round supply. The freeze-drying method was chosen so that it helps in retaining the nutritional and sensory qualities of the fruits.

Towards sustainable solutions: Effective waste classification framework via enhanced deep convolutional neural networks.

As industrialization and the development of smart cities progress, effective waste collection, classification, and management have become increasingly vital. Recycling processes depend on accurately identifying and restoring waste materials to their original states, essential for reducing pollution and promoting environmental sustainability. In recent years, deep learning (DL) techniques have been applied strategically to enhance waste management processes, including capturing, classifying, composting, and disposing of waste.

Segmentation-based deep 2D-3D multibranch learning approach for effective hyperspectral image classification.

Deep learning has revolutionized the classification of land cover objects in hyperspectral images (HSIs), particularly by managing the complex 3D cube structure inherent in HSI data. Despite these advances, challenges such as data redundancy, computational costs, insufficient sample sizes, and the curse of dimensionality persist. Traditional 2D Convolutional Neural Networks (CNNs) struggle to fully leverage the interconnections between spectral bands in HSIs, while 3D CNNs, which capture both spatial and spectral features, require more sophisticated design.

Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques.

Background And Aims: Myopia is a prevalent refractive error, particularly among young adults, and is becoming a growing global concern. This study aims to predict myopia among undergraduate students using ensemble machine learning techniques and to identify key risk factors associated with its development.

Methods: A cross-sectional study was conducted in Dinajpur city, collecting 514 samples through a self-structured questionnaire covering demographic information, myopia prevalence and risk factors, knowledge and attitudes, and daily activities.

High-Sensitivity Blood Cell Detection via Terahertz Refractive Index Sensing in Biomedical Applications.

Biosensors are essential tools for detecting and analyzing various elements of human biology. This study introduces an innovative circular-shaped photonic crystal fiber (PCF) with a hexahedron core for the precise detection of blood components. The sensor's performance evaluated using COMSOL Multiphysics software.

A comprehensive image dataset for accurate diagnosis of betel leaf diseases using artificial intelligence in plant pathology.

In South Asian countries, agriculture is a crucial employment field, and a remarkable number of people depend on it for their livelihood. Crop diseases are a significant threat to sustainable development in the agriculture field. Automated efficient crop disease diagnosis techniques developed with comprehensive field image datasets can play a vital role in preventing diseases at an early stage.

Polystyrene Microplastics-Induced Thyroid Dysfunction in Mice: A Study of Gene Expression, Oxidative Stress, and Histopathological Changes.

Background: Polystyrene microplastics (PS-MPs) are pervasive pollutants impacting animals across ecosystems, including livestock and wildlife, through contaminated food, water, and air. MPs may disrupt endocrine function, particularly affecting the thyroid gland, which is essential for metabolism and development.

Objectives: This study investigates the effects of PS-MPs on thyroid function in mice, offering insights relevant to veterinary care by examining changes in gene expression and biochemical markers.

Unraveling Internet Addiction Among Adolescents in Bangladesh and Its Association With the Risk of Eating Disorders, Body Mass Index, and Other Factors: A Cross-Sectional Study.

Background And Aims: Internet-related disorders for example, internet addiction (IA) seem to be frequent among adolescents all over the world. However, there could be a possible link between body mass index (BMI) and the risk of eating disorders (EDs) in connection with IA. This study aimed to determine the relationship between risk of EDs, BMI, and IA among Bangladeshi adolescents, as well as factors associated with IA.

Low latency FPGA implementation of twisted Edward curve cryptography hardware accelerator over prime field.

The performance of any elliptic curve cryptography hardware accelerator significantly relies on the efficiency of the underlying point multiplication (PM) architecture. This article presents a hardware implementation of field-programmable gate array (FPGA) based modular arithmetic, group operation, and point multiplication unit on the twisted Edwards curve (Edwards25519) over the 256-bit prime field. An original hardware architecture of a unified point operation module in projective coordinates that executes point addition and point doubling within a single module has been developed, taking only 646 clock cycles and ensuring a better security level than conventional approaches.