Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis.

Ovarian cancer, a deadly female reproductive system disease, is a significant challenge in medical research due to its notorious lethality. Addressing ovarian cancer in the current medical landscape has become more complex than ever. This research explores the complex field of Ovarian Cancer Subtype Classification and the crucial task of Outlier Detection, driven by a progressive automated system, as the need to fight this unforgiving illness becomes critical.

GalliformeSpectra: A hen breed dataset.

This article presents a comprehensive dataset featuring ten distinct hen breeds, sourced from various regions, capturing the unique characteristics and traits of each breed. The dataset encompasses Bielefeld, Blackorpington, Brahma, Buckeye, Fayoumi, Leghorn, Newhampshire, Plymouthrock, Sussex, and Turken breeds, offering a diverse representation of poultry commonly bred worldwide. A total of 1010 original JPG images were meticulously collected, showcasing the physical attributes, feather patterns, and distinctive features of each hen breed.

Effect of chromium doping on the band gap tuning of titanium dioxide thin films for solar cell applications.

A simple and inexpensive spray pyrolysis deposition (SPD) approach was used to produce TiO and Cr (2-8) at.%-doped TiO thin films. To explore the morphological features of the films, FE-SEM micrographs were used and found that 6 and 8 at.

Heat generation/absorption effect on natural convective heat transfer in a wavy triangular cavity filled with nanofluid.

This study is numerically executed to investigate the influence of heat generation or absorption on free convective flow and temperature transport within a wavy triangular enclosure filled by the nanofluid taking the Brownian effect of nanoparticles. The water (HO) is employed as base fluid and copper (Cu) as nanoparticles for making effective Cu-HO nanofluids. The perpendicular sinusoidally wavy wall is cooled at low temperature while the horizontal bottom sidewall is heated non-uniformly (sinusoidal).

Green synthesis of silver nanoparticles by using Allium sativum extract and evaluation of their electrical activities in bio-electrochemical cell.

An electrical application of green synthesized silver nanoparticles (Ag NPs) by developing a unique bio-electrochemical cell (BEC) has been addressed in the report. Here, garlic extract (GE) has been used as a reducing agent to synthesize Ag NPs, and as a bio-electrolyte solution of BEC. Ag NPs successfully formed into face-centered cubic structures with average crystallite and particle sizes of 8.

Bakuchiol, a natural constituent and its pharmacological benefits.

Background And Aims: Natural compounds extracted from medicinal plants have recently gained attention in therapeutics as they are considered to have lower Toxicity and higher tolerability relative to chemically synthesized compounds. Bakuchiol from L. is one such compound; it is a type of meroterpene derived from the leaves and seeds of plants.

Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography.

Nowadays, wireless sensor networks (WSNs) have a significant and long-lasting impact on numerous fields that affect all facets of our lives, including governmental, civil, and military applications. WSNs contain sensor nodes linked together via wireless communication links that need to relay data instantly or subsequently. In this paper, we focus on unmanned aerial vehicle (UAV)-aided data collection in wireless sensor networks (WSNs), where multiple UAVs collect data from a group of sensors.

QAmplifyNet: pushing the boundaries of supply chain backorder prediction using interpretable hybrid quantum-classical neural network.

Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy.

DFT Studies of the Photocatalytic Properties of MoS-Doped Boron Nitride Nanotubes for Hydrogen Production.

This study investigated the photocatalytic properties of MoS-doped boron nitride nanotubes (BNNTs) for overall water splitting using popular density functional theory (DFT). Calculations of the structural, mechanical, electronic, and optical properties of the investigated systems were performed using both the generalized gradient approximation and the GW quasi-particle correction methods. In our calculations, it was observed that only (10, 10) and (12, 12) single-walled BNNTs (SWBNNTs) turned out to be stable toward MoS doping.

Assessing the impact of blockchain technology on the overall performance of sustainable supply chains: an analytical perspective.

Supply chain control and sustainability can be significantly improved using distributed ledger technologies such as blockchain. The blockchain has the potential to facilitate responsible sourcing appropriately, compliance with weather requirements, and sustainable delivery chains. The purpose of this study is to address the hassle of managing conservatism when approaching era adoption and to explore the performance enhancements in blockchain-generated implementations.

An automated materials and processes identification tool for material informatics using deep learning approach.

This article reports a tool that enables Materials Informatics, termed as MatRec, via a deep learning approach. The tool captures data, makes appropriate domain suggestions, extracts various entities such as materials and processes, and helps to establish entity-value relationships. This tool uses keyword extraction, a document similarity index to suggest relevant documents, and a deep learning approach employing Bi-LSTM for entity extraction.

Circular value creation: business models and supply chain strategies.

This paper aims to advance research on the circular economy, sustainable innovation through adopting a circular business model (CBM), and circular supply chain management (CSCM). The circular economy is gradually acknowledged as promising to attain ecological growth by minimising resource input, waste, emissions and energy loss. This article investigates the environmental efficacy of circular value creation and its implications for business models and supply chain strategies.

Depression, anxiety, stress, and fear of COVID-19 among Bangladeshi medical students during the first wave of the pandemic: a mixed-methods study.

Aim: This study aims to investigate depression, anxiety, stress, and fear of the COVID-19 pandemic and the associated risk factors among Bangladeshi medical students. It also explored qualitative insights on mental health from medical students during the first wave of the pandemic.

Methods: This mixed-methods study was conducted online in Bangladesh from June 2020 to September 2020.

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel.

Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation.

The potentials of boron-doped (nitrogen deficient) and nitrogen-doped (boron deficient) BNNT photocatalysts for decontamination of pollutants from water bodies.

This work investigates the structural, elastic, electronic, and photoabsorption properties of boron- (N-deficient) and nitrogen- (B-deficient) doped single-walled boron nitride nanotube (SWBNNT) for photocatalytic applications for the first time. All calculations of the optimized systems were performed with DFT quantum simulation codes. The results of the structural analysis showed that SWBNNT is stable to both B and N dopants.

Changes in the prevalence of dementia in Australia and its association with geographic remoteness.

Background: The exact prevalence of dementia in Australia is ambiguous. Australia is a vast continent with a small population, and 80% of the population live in five cities. This study explores recent changes in the prevalence of dementia.

An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors.

Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict falls before they happen, provide safeguards during the fall, and issue remote notifications after the fall is essential to improving the level of care for the elderly.

HQA-Data: A historical question answer generation dataset from previous multi perspective conversation.

This data article contains a quality assurance dataset for training the chatbot and chat analysis model. This dataset focuses on NLP tasks, as a model that serves and delivers a satisfactory response to a user's query. We obtained data from a well- known dataset known as "The Ubuntu Dialogue Corpus" for the purpose of constructing our dataset.

A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron.

Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset.

Study of kaempferol in the treatment of COVID-19 combined with Chikungunya co-infection by network pharmacology and molecular docking technology.

Chikungunya (CHIK) patients may be vulnerable to coronavirus disease (COVID-19). However, presently there are no anti-COVID-19/CHIK therapeutic alternatives available. The purpose of this research was to determine the pharmacological mechanism through which kaempferol functions in the treatment of COVID-19-associated CHIK co-infection.

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.

With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health monitoring remedy. IoT-based systems can gather and analyze a wide range of physiological data, including blood oxygen levels, heart rates, body temperatures, and ECG signals, and then provide real-time feedback to medical professionals so they may take appropriate action.

Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms.

Diabetes is one of the most serious chronic diseases that result in high blood sugar levels. Early prediction can significantly diminish the potential jeopardy and severity of diabetes. In this study, different machine learning (ML) algorithms were applied to predict whether an unknown sample had diabetes or not.

The use of chatbots in university EFL settings: Research trends and pedagogical implications.

This mini-review aims to identify major research trends, models, and theories and provide specific pedagogical implications for teaching when using chatbots in EFL classes. This study follows the guidelines of the PRISMA methodology and searches for open-access empirical studies in two reputable databases, Web of Science and Scopus. The results of this mini-review confirm the findings of other research studies, which show that the present research on the use of chatbots in university EFL settings focuses on their effectiveness, motivation, satisfaction, exposure, and assessment.

Non-communicable diseases risk factors among the forcefully displaced Rohingya population in Bangladesh.

Rohingya refugees of Ukhiya, Cox's bazar are an unaccounted group of people who form the largest cluster of refugees worldwide. Non-communicable disease (NCD) alone causes 70% of worldwide deaths every year therefore, the trend of NCD among Rohingya refugees demands proper evaluation and attention. The objective of this study was to measure the NCD risk factors among a convenient sample of Rohingya refugees.

The Covid-19 and online learning process in Bangladesh.

This study investigates the augmentation of students' engagement in the online learning process using Zoom platform. To engage students more in the online classes we have conducted a survey on four universities students in the four dimensions. To investigate effective online class, we have gone through descriptive statistics followed by principal component analysis (PCA) and factor regression model to identify predicted factors that engage students more in the Zoom online classes.