Search Results (5,758)

Power transformers play a vital role in adjusting voltage levels during transmission. This study focuses on optimizing the structural design of power transformer tanks, particularly high-voltage (HV) tank walls, to enhance their mechanical robustness, performance, and operational reliability. This research investigates various stiffener designs and their impact on stress distribution and deformation through finite element analysis (FEA). Ten different configurations of stiffeners, including thickness, width, type, and position variations, were evaluated to identify the optimal design that minimizes stress and deflection while considering weight constraints. The results indicate that specific configurations, particularly those incorporating 16 mm thick H beams, significantly enhance structural integrity. Experimental validation through pressure testing corroborated the simulation findings, ensuring the practical applicability of the optimized designs. This study’s findings have implications for enhancing the longevity and reliability of power transformers, ultimately contributing to more efficient and resilient power transmission systems. Full article

This research aims to comprehensively analyze the influential challenges within the healthcare system of Bangladesh using the DEMATEL–ISM-based Multi-Criteria Decision Analysis (MCDA) approach. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology will be used to identify, evaluate, and prioritize challenges that have a notable impact on the efficiency and effectiveness of healthcare delivery in Bangladesh. The ISM methodology will help create hierarchical structures of challenges and explore the interdependencies among different factors. DEMATEL, a robust tool in decision science, will assist in understanding complex relationships among various criteria. Through this approach, this study intends to reveal the interconnections and causal relationships among different challenges, providing a systematic understanding of their impact on the healthcare system. Population growth was found to be the most challenging factor in the healthcare system of Bangladesh. The outcomes of this research are expected to contribute valuable insights to policymakers, healthcare practitioners, and stakeholders involved in the enhancement of the healthcare system in Bangladesh. Full article

In contrast from traditional unmanned aerial vehicle communication via unlicensed spectrum, connecting unmanned aerial vehicles with cellular networks can extend their communication coverage and improve the quality of their service. In addition, the emerging dual-functional radar communication paradigm in cellular systems can better meet the requirements of location-sensitive tasks such as reconnaissance and cargo delivery. Based on the above considerations, in this paper, we study the simultaneous communication and target sensing issue in cellular-connected unmanned aerial vehicle systems. Specifically, we consider a two-cell coordinated system with two base stations, cellular unmanned aerial vehicles, and potential aerial targets. In such systems, the communication security issue of cellular unmanned aerial vehicles regarding eavesdropping on their target is inevitable since the main beam of the transmit waveform needs to point to the direction of the target for achieving a sufficient detection performance. Aiming at protecting the privacy of cellular transmission as well as performing target sensing, we exploit the physical layer security technique with the aid of constructive interference-based precoding. A transmit power minimization problem is formulated with constraints on secure and reliable cellular transmission and a sufficient radar signal-to-interference-plus-noise ratio. By specially designing the transmit beamforming vectors at the base stations, the received signals at the cellular users are located in the decision regions of the transmitted symbols while the targets can only receive wrong symbols. We also compare the performance of the proposed scheme with that of the traditional one without constructive interference. The simulation results show that the proposed constructive interference-based strategy can meet the requirements of simultaneous target sensing and secure communication, and also save transmit power compared with the traditional scheme. Full article

Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, considering seven factors influencing groundwater recharge: rainfall, soil type, geology, slope, lineament density, land use/land cover, and drainage density. The AHP method was employed to assess the variability of groundwater recharge potential within the 7586 km² study area. Thematic maps of relevant factors were processed using ArcGIS software. Results indicate that 9.23% (700.22 km²), 47.68% (3617.13 km²), 37.12% (2816.13 km²), and 5.97% (452.70 km²) of the study area exhibit poor, moderate, good, and very good recharge potential, respectively. The annual recharge volume is estimated at 2554 × 10⁶ m³/year, constituting 22.7% of the total precipitation volume (11,227 × 10⁶ m³/year). Analysis of individual factors revealed that geology has the highest influence (33.57%) on recharge potential, followed by land use/land cover (17.74%), soil type (17.25%), and rainfall (12.25%). The consistency ratio of the pairwise comparison matrix was 0.0904, indicating acceptable reliability of the AHP results. The spatial distribution of recharge zones shows a concentration of poor recharge potential in areas with low rainfall (1200–1400 mm/year) and high slope (6–40%). Conversely, very good recharge potential is associated with high rainfall zones (1800–2200 mm/year) and areas with favorable geology (sedimentary deposits). This study provides a quantitative framework for assessing groundwater recharge potential in the Barind tract. The resulting maps and data offer valuable insights for policymakers and water resource managers to develop targeted groundwater management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to agricultural water demand and climate change adaptation. Full article

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model’s performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality. Full article

Aflatoxin B1 is a toxic substance in almonds, other nuts, and grains that poses potential serious health risks to humans and animals, particularly in warm, humid climates. Therefore, it is necessary to remove aflatoxin B1 before almonds enter the supply chain to ensure food safety. Hyperspectral imaging (HSI) is a rapid, non-destructive method for detecting aflatoxin B1 by analyzing specific spectral data. However, HSI increases data dimensionality and often includes irrelevant information, complicating the analysis process. These challenges make classification models for detecting aflatoxin B1 complex and less reliable, especially for real-time, in-line applications. This study proposed a novel hybrid spectral band selection algorithm to detect aflatoxin B1 in almonds based on multilayer perceptron (MLP) network weights and spectral refinement (W-SR). In the proposed process, the hyperspectral imaging (HSI) spectral rank was firstly generated based on MLP network weights. The rank was further updated using a spectral confidence matrix. Then, a spectral refinement process identified more important spectra from the lower-ranked ones through iterative processes. An exhaustive search was performed to select an optimal spectral subset, consisting of only the most significant spectral bands, to make the entire process suitable for real-time, in-line aflatoxin B1 detection in industrial environments. The experimental results using the artificially contaminated almonds dataset achieved a cross-validation accuracy of 98.67% with an F1-score of 0.982 for the standard normal variate (SNV) processed data with only four spectral bands. Comparative experiment results showed that the proposed MLPW-SR spectral band selection algorithm outperforms baseline methods. Full article

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply. Full article

Background: Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches to predict the risk of EDS. Methods: A cross-sectional study was conducted among 1496 adolescents using a structured questionnaire. Data were collected through a two-stage stratified cluster sampling method. Chi-square tests and logistic regression analyses were performed using SPSS. Machine learning models, including Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM), were employed to identify and predict EDS risk factors using Python and Google Colab. Results: The prevalence of EDS in the cohort was 11.6%. SHAP values from the CatBoost model identified self-rated health status, gender, and depression as the most significant predictors of EDS. Among the models, GBM achieved the highest accuracy (90.15%) and precision (88.81%), while CatBoost had comparable accuracy (89.48%) and the lowest log loss (0.25). ROC-AUC analysis showed that CatBoost and GBM performed robustly in distinguishing between EDS and non-EDS cases, with AUC scores of 0.86. Both models demonstrated the superior predictive performance for EDS compared to others. Conclusions: The study emphasizes the role of health and demographic factors in predicting EDS among adolescents in Bangladesh. Machine learning techniques offer valuable insights into the relative contribution of these factors, and can guide targeted interventions. Future research should include longitudinal and interventional studies in diverse settings to improve generalizability and develop effective strategies for managing EDS among adolescents. Full article

Heparin, a naturally occurring polysaccharide, has fascinated researchers and clinicians for nearly a century due to its versatile biological properties and has been used for various therapeutic purposes. Discovered in the early 20th century, heparin has been a key therapeutic anticoagulant ever since, and its use is now implemented as a life-saving pharmacological intervention in the management of thrombotic disorders and beyond. In addition to its known anticoagulant properties, heparin has been found to exhibit anti-inflammatory, antiviral, and anti-tumorigenic activities, which may lead to its widespread use in the future as an essential drug against infectious diseases such as COVID-19 and in various medical treatments. Furthermore, recent advancements in nanotechnology, including nano-drug delivery systems and nanomaterials, have significantly enhanced the intrinsic biofunctionalities of heparin. These breakthroughs have paved the way for innovative applications in medicine and therapy, expanding the potential of heparin research. Therefore, this review aims to provide a creation profile of heparin, space for its utilities in therapeutic complications, and future characteristics such as bioengineering and nanotechnology. It also discusses the challenges and opportunities in realizing the full potential of heparin to improve patient outcomes and elevate therapeutic interventions. Full article

The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional IDSs have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks to address the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90% accuracies across various metrics, including precision, recall, and F1 score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyberattacks, including Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), data injection, ransomware, and several others, showcasing its robustness. This research makes a great advancement to the IDSs by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models. Full article

This study describes the experiences of women in rural Bangladesh, and the cultural practices related to how they take care of themselves and their babies, in the early postnatal period. Data were gathered through immersion in the field for six months, participant observation, document collection and in-depth interview techniques to gain a deep understanding of women’s everyday lives and practices during the postnatal period. In-depth interviews were undertaken with 28 mothers who had had at least one live child within the five years before the date of data collection, in Purba Sharifabad village of the Barisal Division in Bangladesh. The key findings of this study highlight the role of culture in the experiences of women during the postnatal period, and reveal that cultural practices, beliefs and traditions are deeply embedded in the lives of rural women. This study focuses on cultural practices which have an effect on the choices of women regarding care and support from healthcare facilities. Culture, together with poverty and low levels of education, leads them to resist healthcare from a skilled birth attendant and encourages them to believe in and trust traditional care practices during the postnatal period. Therefore, the findings of this study may contribute to future policies, planning, programs and research in Bangladesh by providing an understanding of the importance of culturally and socially rooted traditional beliefs and cultural practices. These need to be addressed for maternal and newborn healthcare initiatives to be effective, particularly in rural Bangladesh. Full article

Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN. Full article

The purpose of this study is to understand Green Human Resource Management (GHRM) practices, considerations that facilitate Green Organizational Culture (GOC) and Organizational Agility (OA), and the connection between these factors and the social performance (SP) and environmental performance (EP) of businesses. The target population of the current study comprises a Bangladeshi organization (manufacturing, university and service). In this quantitative study, data were collected using structured questionnaires. The final sample consisted of 445 respondents, and the data were analyzed using the Structural Equation Modeling (SEM) technique. Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted to ensure the validity and reliability of the measurement model. The results show that social GHRM has a beneficial effect on social and environmental performance factors. This research also discovered employee’s ideas about GRRM, GOC OA, and improving an organization’s social and environmental performance for long-term growth. Our results show that hiring, teaching, evaluating, and rewarding employees in ways that avoid harming the natural world are all examples of HR management practices that help create a green workplace culture. This work adds to the resource-based view (RBV) and transitive leadership (TL) theory by showing the things that contribute to an organization’s green culture and agility help the link between social and environmental performance and green HRM practices. Organizations in Bangladesh can benefit greatly from the GHRM model that this study suggests, as OC and OA have a good effect on both society and the environment. Full article

Background/Objectives: An Android platform-based customised app and web-linked system was developed to aid in implementing selected nutrition interventions by community health workers (CHWs) in a community-based cluster randomised trial (c-RCT) in rural Bangladesh. Methods: Here, we describe the architecture of the intervention delivery system, and explore feasibility of employing mHealth as CHWs’ job aid, employing a mixed-method study design covering 17 visits per mother-child dyad. We analysed CHWs’ real-time visit information from monitoring and documentation data, and CHWs’ qualitative interviews to explore the advantages and barriers of using mHealth as a job aid. Results: Intervention coverage was high across the arms (>90%), except around the narrow perinatal period (51%) due to mothers’ cultural practice of moving to their parents’ homes and/or hospitals for childbirth. CHWs mentioned technical and functional advantages of the job aid including device portability, easy navigability of content, pictorial demonstration that improved communication, easy information entry, and automated daily scheduling of tasks. Technical challenges included charging tablets, especially in power cut-prone areas, deteriorated battery capacity over continuous device usage, unstable internet network in unsupportive weather conditions, and device safety. Nevertheless, onsite supervision and monitoring by expert supervisors remained important to ensure intervention quality. Conclusions: With appropriate training and supervision, CHWs utilised the tablet-based app proficiently, attaining high coverage of long-term visits. mHealth was thus useful for designing, planning, scheduling, and delivering nutrition interventions through CHWs, and for monitoring and supervision by supervisors. Therefore, this application and job aid can be adopted or replicated into the currently developing national health systems platform for improving coverage and quality of preventive maternal and child nutrition services. In addition, continuous supportive supervision by skilled supervisors must be accompanied to ensure CHWs’ task quality. Finally, future studies should rigorously assess undesirable health and environmental effects of mHealth before and after mainstreaming, effective interventions addressing device-induced health hazards should be designed and scaled up, and effective e-waste management must be ensured. Full article

Artificial intelligence (AI) is fundamentally transforming the marketing landscape, enabling significant progress in customer engagement, personalization, and operational efficiency. The retail sector has been at the forefront of the AI revolution, adopting AI technologies extensively to transform consumer interactions, supply chain management, and business performance. Given its early adoption of AI, the retail industry serves as an essential case context for investigating the broader implications of AI for consumer behavior. Drawing on 404 articles published between 2000 and 2023, this study presents a comprehensive bibliometric and content analysis of AI applications in retail marketing. The analysis used VOSviewer (1.6.20.0 version) and Bibliometrix (version 4.3.1) to identify important contributors, top institutions, and key publication sources. Co-occurrence keyword and co-citation analyses were used to map intellectual networks and highlight emerging themes. Additionally, a focused content analysis of 50 recent articles was selected based on their relevance, timeliness, and citation influence. It revealed six primary research streams: (1) consumer behavior, (2) AI in retail marketing, (3) business performance, (4) sustainability, (5) supply chain management, and (6) trust. These streams were categorized through thematic relevance and theoretical significance, emphasizing AI’s impact on the retail sector. The contributions of this study are twofold. Theoretically, it integrates existing research on AI in retail marketing and outlines future research in areas such as AI’s role in the domain of consumer behavior. From an empirical standpoint, the study highlights how AI can be applied to enhance customer experiences and improve business operations. Full article