A comprehensive dataset of rice leaf images for disease detection using machine learning.

This manuscript presents a comprehensive, expert-annotated dataset comprising 19,000 rice leaf images, including 2,753 original images and 16,247 augmented images, sourced from the Bangladesh Rice Research Institute (BRRI). The dataset includes seven disease classes: Healthy (603 original images), Rice Blast (696 original images), Scald (421 original images), Leaf-folder Injury (247 original images), Insect Infestation (281 original images), Rice Stripes (266 original images), and Tungro Disease (239 original images). These images, captured under varying environmental conditions using smartphone cameras, accurately reflect real-world conditions.

Theoretical Insights into Enhanced Electrocatalytic Arsenic Oxidation on Au-Decorated Pt Surfaces in Neutral Medium.

In this study, we present a comprehensive theoretical and experimental investigation of the electrocatalytic oxidation of arsenite on Au immobilized Pt surfaces in a neutral medium. Theoratically, density functional theory (DFT) calculations revealed that thePt-Au bimetallic system exhibits superior adsorption energy (E = -2.045 eV) compared to bare Au (-0.

Substituted D-Galactose-Conjugated Thiazole-Thioureas Derivatives as Promising Antidiabetic Agents: Synthesis, In Vitro Inhibition, and Molecular Simulation for α-Amylase, α-Glucosidase, Protein Glycation, and Oxidative Stress.

A thiourea series (7a-l) containing 4-arylthiazoles and the d-galactose moiety were synthesized and explored for their multi-target inhibition against α-amylase from porcine pancreas and α-glucosidase from Saccharomyces cerevisiae. Among these thioureas, 7h was the most potent inhibitor for α-amylase (IC of 7.84 ± 0.

SPAS-Dataset-BD: Dataset for smart precision agriculture system in Bangladesh.

Precision agriculture harnesses data-driven techniques to optimize crop production, resource use, and sustainability. However, low-income countries like Bangladesh face a shortage of localized, high-quality datasets that reflect regional agroclimatic conditions and cropping practices. To address this gap, we present SPAS-Dataset-BD, a robust dataset compiled through a hybrid approach: secondary extraction from the Bangladesh Bureau of Statistics (BBS) 2022 Yearbook and primary on-field surveys of 223 farmers across ten diverse districts.

Integrating Genetic Insights and Artificial Intelligence for Enhanced Oral and Maxillofacial Cancer Care.

The integration of artificial intelligence (AI) with genetic insights presents promising avenues for revolutionizing the landscape of oral and maxillofacial cancer (OMC) care. This proposed chapter, titled, aims to explore the synergistic potential of AI and genetic understanding in advancing OMC diagnosis, treatment, and prognosis. The chapter will delve into the genetic changes, including mutations and polymorphisms, contributing to the pathogenesis of oral squamous cell carcinomas (OSCCs) and other malignancies in the oral and maxillofacial region.

Advancing breast cancer prediction: Comparative analysis of ML models and deep learning-based multi-model ensembles on original and synthetic datasets.

Breast cancer is a significant global health concern with rising incidence and mortality rates. Current diagnostic methods face challenges, necessitating improved approaches. This study employs various machine learning (ML) algorithms, including KNN, SVM, ANN, RF, XGBoost, ensemble models, AutoML, and deep learning (DL) techniques, to enhance breast cancer diagnosis.

d‑Glucose-Conjugated Benzo[]thiazole-thiazolidin-4-one Hybrids: Design and Synthesis as Multitarget Anti-Alzheimer Compounds.

A new series of d-glucose-conjugated 2,3-thiazoline-benzo-[]-thiazole hybrid compounds - were synthesized from reaction of the corresponding thioureas - and ethyl bromoacetate in the presence of sodium acetate in chloroform. The evaluations for anti-Alzheimer activity showed that some compounds exhibited remarkable inhibitory activity against AChE and BChE as well as against MAO-A and MAO-B enzymes. The orders of inhibition for each enzyme of these compounds were as follows: > (against AChE), > (against BChE), > > (against MAO-A), and > (against MAO-B).

BanglaHealth: A Bengali paraphrase dataset on health domain.

In the landscape of natural language processing (NLP) research, the availability of comprehensive datasets plays a pivotal role in advancing various tasks, including paraphrasing. However, for languages such as Bengali, the availability of such datasets remains limited, particularly in specialized domains like health. Recognizing this gap, this study endeavours to address the scarcity of resources by presenting a novel Bengali paraphrasing dataset specifically tailored to the health domain.

ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an increasing mortality rate in recent years. Regular monitoring is essential for effective management, as early detection and timely treatment greatly improve survival outcomes. The electrocardiogram (ECG) remains the standard method for detecting arrhythmias, traditionally analyzed by cardiolo- gists and clinical experts.

Computational study of thermofluidic characteristics of AlO-Cu hybrid nanofluids in backward facing step channel with varying step angles.

Backward-Facing Step (BFS) channels are frequently utilized in technical implementation due to their ability to model intricate fluid flow patterns, such as recirculation zones, which have significant impacts on thermal exchange rates. This research investigates the thermal and heating efficiency of AlO-Cu hybrid nanofluids in a BFS channel, focusing on the effects of varying step angles and Reynolds numbers (Re) on thermal efficiency. ANSYS Fluent (2021 R2) was used to analyze AlO-Cu nanofluids across Reynolds numbers (500-800), step angles (70°, 75°, 80°, and 85°), and particle volumes (1%-4%) applying the finite volume method.

Identifying key indicators to develop a novel mobile application for early screening of postpartum depression in developing countries.

Postpartum depression (PPD) poses significant risks to both maternal and child health, yet it remains underdiagnosed and undertreated, particularly in developing countries like Bangladesh. Factors such as hormonal changes, lack of communal support and socioeconomic stressors primarily contribute to its prevalence. Despite advancements in research, technological solutions for PPD support are lacking in regions where mental health is deemed negligible.

Influence of shape memory alloy in seismic response of coupled shear wall of concrete structures.

Super-elastic Shape Memory Alloys (SMAs) can undergo reversible phase transitions, experience considerable inelastic deformation, and restore their original shape after heating or stress reduction. These features of SMAs make them suitable candidates as the reinforcement of concrete shear walls particularly in seismic events. A ten-story reinforced concrete building with a SMA reinforced coupled shear wall in a high seismic zone of Bangladesh is investigated using to better understand the dynamic response of SMAs in the walls.

A comprehensive dataset of rice field weed detection from Bangladesh.

In agricultural research, particularly concerning rice cultivation, the presence of weeds within rice fields is acknowledged as a significant contributor to both diminished crop quality and increased production costs. Rice fields, due to their inherently moist environment, offer ideal conditions for weed proliferation. Traditionally, the control of these weeds has been managed through labor-intensive manual methods.

Digitize-HCD: A dataset for digitization of handwritten circuit diagrams.

Handwritten and legacy engineering diagrams are still commonly used in many industries and academic settings, but the lack of digitization limits their utility in modern workflows. While significant effort has been made to digitize handwritten content in other engineering domains, the digitization of handwritten circuit diagrams remains underexplored. Automating this process would enable the development of tools capable of converting handwritten circuit diagrams into machine-readable formats that can be instantly interpreted by circuit simulation software.

Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis.

LipBengal: Pioneering Bengali lip-reading dataset for pronunciation mapping through lip gestures.

The dataset represents a significant advancement in Bengali lip-reading and visual speech recognition research, poised to drive future applications and technological progress. Despite Bengali's global status as the seventh most spoken language with approximately 265 million speakers, linguistically rich and widely spoken languages like Bengali have been largely overlooked by the research community. fills this gap by offering a pioneering dataset tailored for Bengali lip-reading, comprising visual data from 150 speakers across 54 classes, encompassing Bengali phonemes, alphabets, and symbols.

FocusUNet: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.

The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve survival rates by up to 90%. Automated polyp segmentation in colonoscopy images expedites diagnosis and aids in the precise identification of adenomatous polyps, thus mitigating the burden of manual image analysis. This study introduces FocusUNet, an innovative bi-level nested U-structure integrated with a dual-attention mechanism.

ECCDN-Net: A deep learning-based technique for efficient organic and recyclable waste classification.

Efficient waste management is essential to minimizing environmental harm as well as encouraging sustainable progress. The escalating volume and sophistication of waste present significant challenges, prompting innovative methods for effective waste categorization and management. Deep learning models have become highly intriguing tools for automating trash categorization activities, providing effective ways to optimize processes for handling waste.

Bond behavior of confined rebar in recycled brick aggregate concrete using bending pullout test.

The pertinence of recycled brick aggregate (RBA) in concrete structures largely depends on their bonding ability with the rebars. Considering the practical detailing of concrete structures where main rebars are usually confined by stirrups, this study conducted bending pullout tests on 12 beam specimens to study the influence of concrete confinement on the bond behavior of rebar in RBA concrete. The investigated variables include RBA%, rebar diameter (), embedment length ( ), and the ratio of concrete cover to rebar diameter ().

A multiple biomolecules-based rapid life detection protocol embedded in a rover scientific subsystem for soil sample analysis.

The study of whether life exists, is extinct, or not depends on various sophisticated experimental studies, as many different signatures of life can be used. The experimental procedures that can be performed to identify life can be further restricted by time, resources, and mobility constraints. Therefore, any research analyzing the presence of extraterrestrial life must be precise and unambiguous.

A Synergistic Overview between Microfluidics and Numerical Research for Vascular Flow and Pathological Investigations.

Vascular diseases are widespread, and sometimes such life-threatening medical disorders cause abnormal blood flow, blood particle damage, changes to flow dynamics, restricted blood flow, and other adverse effects. The study of vascular flow is crucial in clinical practice because it can shed light on the causes of stenosis, aneurysm, blood cancer, and many other such diseases, and guide the development of novel treatments and interventions. Microfluidics and computational fluid dynamics (CFDs) are two of the most promising new tools for investigating these phenomena.

Precise surface engineering: Leveraging chemical vapor deposition for enhanced biocompatibility and durability in biomedical implants.

Biomedical implants have revolutionized modern medicine, providing diverse treatment options for various medical conditions. Ensuring the long-term success of certain materials used in various applications requires careful consideration of their ability to interact with biological systems and withstand harsh biological conditions. Optimizing surface properties is crucial for successfully integrating biomedical implants into the human body, ensuring biocompatibility, durability, and functionality.

A comprehensive overview of recent progress in MXene-based polymer composites: Their fabrication processes, advanced applications, and prospects.

MXenes are a group of 2D transition metal carbonitrides, nitrides and carbides that have become widely recognized as useful materials since they were first discovered in 2011. MXenes, with their exceptional layered structures and splendid external chemistries, have excellent electrical, optical, and thermal properties, making them suitable for catalysis, biomedical uses, environmental remediation, energy storage, and EMI shielding. Over forty MXene compounds with surface terminations like hydroxyl, oxygen, or fluorine are hydrophilic and easily integrated into various applications.