Spiritual assessment in palliative care: multicentre study.

Objectives: This study aims to provide an in-depth exploration of everyday spiritual concerns of patients with advanced cancer seeking palliative care in Bangladesh, and assess their spiritual well-being (SWB).

Methods: This study was conducted among 163 patients with advanced cancer from three tertiary care hospitals in Bangladesh. It was divided into two parts: a quantitative segment that assessed the SWB of the participants using the EORTC QLQ SWB32, and a qualitative segment that explored their spiritual history.

tRF-BERT: A transformative approach to aspect-based sentiment analysis in the bengali language.

In recent years, the surge in reviews and comments on newspapers and social media has made sentiment analysis a focal point of interest for researchers. Sentiment analysis is also gaining popularity in the Bengali language. However, Aspect-Based Sentiment Analysis is considered a difficult task in the Bengali language due to the shortage of perfectly labeled datasets and the complex variations in the Bengali language.

Predictive landslide susceptibility modeling in the southeastern hilly region of Bangladesh: application of machine learning algorithms in Khagrachari district.

Landslides pose a severe threat to people, buildings, and infrastructure. The rugged terrain of the Chattogram Hill Tract region in southeastern Bangladesh frequently experiences landslides, particularly during rainy seasons. This study provides a comparative analysis of innovative machine learning (ML) algorithms used for the purpose of landslide susceptibility (LS) mapping for the Khagrachari district of Bangladesh.

Sensitivity study on convective heat transfer in a driven cavity with star-shaped obstacle and hybrid nanofluid using response surface methodology.

Sensitivity analysis is significant for understanding and measuring the impact of various parameters and input variables on heat transfer phenomena. The main objective of the current work is to examine the sensitivity of a numerical analysis of mixed convection in a lid-driven square cavity with a magnetic field. The cavity also contains a heated, star-shaped obstacle and is filled with a hybrid nanofluid.

Ultra-dense Green InGaN/GaN Nanoscale Pixels with High Luminescence Stability and Uniformity for Near-Eye Displays.

Ultra-dense (>4,000 pixels per inch) and highly stable full-color III-nitride nanoscale pixels are crucial for near-eye display technologies like virtual and augmented-reality glasses. In this context, InGaN-based long wavelength green microscale light-emitting diodes face major bottlenecks, such as low efficiency and inadequate wavelength stability. These challenges are associated with the presence of both nonradiative surface defects and the strain induced quantum-confined Stark effect.

Factors Associated with Psychological Distress, Fear, and Coping Strategies during the COVID-19 Pandemic in Thailand: A Cross-Sectional Study.

Introduction: Despite being initially perceived as a local infectious disease, COVID-19 has emerged as one of the most perilous global health threats, significantly impacting the psychological well-being of individuals worldwide.

Objective: This cross-sectional descriptive study investigates the psychological distress, fear, and coping mechanisms among Thai people residing in Thailand during the COVID-19 pandemic.

Methods: This online cross-sectional survey targeted Thai individuals during the first and third waves of the COVID-19 pandemic.

SbS/AlGaAs-based reconfigurable metasurface for dynamic polarization and directionality control of quantum emitter emission.

In this article, we report, to the best of our knowledge, for the first time, phase change material (PCM)-based reconfigurable metasurfaces for tailoring different degrees of freedom (DoF) of the quantum emitter (QE) emission, namely polarization and directionality, two key controlling factors in applications such as quantum computing, communication, and chiral optics. We have used a hybrid plasmon-QE coupled bullseye grating system utilizing the unexplored concept of composite nano-antennas in quantum photonics as the basic building block of the structures. Carefully engineered azimuthal width profile of the SbS/AlGaAs composite ridge and selectively controlled transition of PCM (SbS) states provide dynamic control over the amplitude and phase of the scattered radiation.

Adsorptive removal of toxic heavy metals from wastewater using water hyacinth and its biochar: A review.

Heavy metal contamination in aquatic ecosystems worsens due to rapid industrial expansion. Biochar, an efficient and economical adsorbent, has attracted much interest in environmental science, particularly in removing heavy metals (HMs). The paper covers basic details on biochar, its preparation, and potential chemical and inorganic modifications.

Artificial Neural Network analysis on the effect of mixed convection in triangular-shaped geometry using water-based Al2O3 nanofluid.

The objective of the study is to investigate the fluid flow and heat transfer characteristics applying Artificial Neural Networks (ANN) analysis in triangular-shaped cavities for the analysis of magnetohydrodynamics (MHD) mixed convection with varying fluid velocity of water/Al2O3 nanofluid. No study has yet been conducted on this geometric configuration incorporating ANN analysis. Therefore, this study analyzes and predicts the complex interactions among fluid flow, heat transfer, and various influencing factors using ANN analysis.

Human sleeping pose estimation from IR images for in-bed patient monitoring using image processing and deep learning techniques.

Human Pose Estimation (HPE) is a crucial step towards understanding people in images and videos. HPE provides geometric and motion information of the human body, which has been applied to a wide range of applications (e.g.

Tracing fluorescent dissolved organic matter (fDOM) characteristics and water quality parameters: Insights from an urban industrial river to marine zone.

This study aims to identify continuous water quality changes and identify fluorescence properties from urban rivers to marine zones. Various types of natural and anthropogenic sources derived dissolved organic matter (DOM) have been identified in this study. These include soil-derived DOM, plant remnants, and soluble particles produced when organic material partially decomposes and is released by microorganisms, such as bacteria, algae, and plants.

Hybrid noise reduction-based data-driven modeling of relative humidity in Khulna, Bangladesh.

In this study, a hybrid Machine Learning (ML) approach is proposed for Relative Humidity (RH) prediction with a combination of Empirical Mode Decomposition (EMD) to improve the prediction accuracy over the traditional prediction technique using a Machine Learning (ML) algorithm called Support Vector Machine (SVM). The main objective of proposing this hybrid technique is to deal with the extremely nonlinear and noisy humidity pattern in Khulna, Bangladesh, which is experiencing rapid urbanization and environmental change. To develop the model, data on temperature, relative humidity, rainfall, and wind speed were collected from the Bangladesh Meteorological Department (BMD), and the data was divided into three phases: 70 % of the historical dataset as training data for the model, 15 % of the data set as the validation phase, and the remaining 15 % of the data set as the test phase of the model.

Human inhalation exposure assessment of the airborne microplastics from indoor deposited dusts during winter in Dhaka, Bangladesh.

Microplastic (MP) contamination has become a concern due to its ubiquitous presence. Recent studies have found MPs to be present in multiple human organs. This study was carried out to evaluate the presence and characterize MPs in indoor dust deposition.

Innovation in public health surveillance for social distancing during the COVID-19 pandemic: A deep learning and object detection based novel approach.

The Corona Virus Disease (COVID-19) has a huge impact on all of humanity, and people's disregard for COVID-19 regulations has sped up the disease's spread. Our study uses a state-of-the-art object detection model like YOLOv4 (You Only Look Once, version 4), a very effective tool, on real-time 25fps, 1920 X 1080 video data streamed live by a camera-mounted Unmanned Aerial Vehicle (UAV) quad-copter to observe proper maintenance of social distance in an area of 35m range in this study. The model has demonstrated remarkable efficacy in identifying and quantifying instances of social distancing, with an accuracy of 82% and little latency.

The urban air quality nexus: Assessing the interplay of land cover change and air pollution in emerging South Asian cities.

Air quality degradation presents a significant public health challenge, particularly in rapidly urbanizing regions where changes in land use/land cover (LULC) can dramatically influence pollution levels. This study investigates the association between LULC changes and air pollution (AP) in the five fastest-growing cities of Bangladesh from 1998 to 2021. Leveraging satellite data from Landsat and Sentinel-5P, the analysis reveals a substantial increase in urban areas and sparse vegetation, with declines in dense vegetation and water bodies over this period.

Two-year epidemiology of post-COVID-19 conditions in Bangladesh: a cohort study of post-COVID-19 from 12,925 SARS-CoV-2 cases between July and December 2021-2023 in Bangladesh.

Background: Post-COVID-19 conditions (PCCs), also known as long COVID, is persistently debilitating disorders that need investigation on their incidence, morbidity, and case-fatality rate.

Purpose: The objectives of this cohort study were to determine the incidence, characteristics, case-fatality, morbidity, and recovery of post-COVID-19 symptoms throughout a two-year period of observation.

Methods: This was a population-based cohort study of post-COVID-19 cases among 12,925 SARS-CoV-2 positive individuals in eight administrative districts of Bangladesh between July and December 2021-2023.

Knowledge attitude and practice of antibiotic use among medical students in Bangladesh: A cross-sectional study.

Background And Aims: Antibiotic misuse represent a significant global health challenge, with medical students positioned as key figures in promoting responsible antibiotic usage. This study investigates the knowledge, attitudes, and practices (KAP) regarding antibiotic use among medical students in Bangladesh, aiming to identify areas for targeted educational and policy interventions.

Methods: This cross-sectional survey was conducted among 501 medical students across various years of study in Bangladesh, collecting data over a 3-month period.

Evaluating the sources of microplastic contamination and quantifying its abundance in the Balu River, Dhaka, Bangladesh.

Microplastics (MPs) are prevalent environmental pollutants due to their durable composition, extensive use, and improper disposal. Despite their widespread presence, rivers have received less attention in microplastic research than other water bodies. This study focused on investigating the origins, prevalence, spatial distribution, and physicochemical characteristics of microplastics in the surface waters of the Balu River, located in Dhaka, Bangladesh.

Impact of climate change on vector-borne diseases: Exploring hotspots, recent trends and future outlooks in Bangladesh.

Climate change is a significant risk multiplier and profoundly influences the transmission dynamics, geographical distribution, and resurgence of vector-borne diseases (VBDs). Bangladesh has a noticeable rise in VBDs attributed to climate change. Despite the severity of this issue, the interconnections between climate change and VBDs in Bangladesh have yet to be thoroughly explored.

Mobile game addiction and its association with musculoskeletal pain among students: A cross-sectional study.

Background: The purpose of this study was to ascertain whether there is a difference in musculoskeletal pain between those who are addicted to mobile games and those who are not, to ascertain the association between mobile game addiction and socio-demographic variables, and to ascertain the pain predictor for mobile game addiction on different musculoskeletal regions.

Methods: There were 840 students in all, both males and females, in this cross-sectional survey from three distinct Bangladeshi institutions. The Nordic Musculoskeletal Discomfort Questionnaire, the Gaming Addiction Scale, and the demographic data form were distributed to the participants.

Predicting the mechanical properties of pristine and defective carbon nanotubes using a random forest model.

Data-driven models have lately emerged as a faster and less time-consuming method for computing material properties than computationally expensive conventional molecular dynamics and density functional theory-based simulations. Here, we developed a random forest (RF) model for comprehensively predicting mechanical properties such as stress and Poisson's ratio under varying strain and ultimate tensile strain of pristine and defective carbon nanotubes (CNTs). The variations in stress and Poisson's ratio with the strain of CNTs with a 0.