Bangladesh University of Professionals

ABSTRACT Coupling the ecological modernization theory with the natural resource‐based view of firms, we investigate the impact of Industry 4.0 (I4.0) technologies and proactive environmental strategies on firms' environmental performance. Additionally, we assess the mediating effects of circular economy practices and green innovation on the relationship between Industry 4.0 technologies, proactive environmental strategies, and environmental performance. Using a cross‐sectional design, we collected data from the managers and senior executives of 282 apparel manufacturing firms in Bangladesh. Our SEM findings showed that I4.0 technologies and proactive environmental strategies have a positive impact on environmental performance and that circular economy practices and green innovation mediate these relationships. Complementing these findings, our fsQCA results reveal multiple alternative configurations of I4.0 technologies, proactive environmental strategies, circular economy practices, and green innovation that are sufficient for achieving high environmental performance, highlighting causal asymmetry and equifinality. Our findings refine ecological modernization theory and the natural resource‐based view by showing that environmental performance is not the result of isolated drivers, but rather of specific combinations of technological, strategic, and innovation‐oriented capabilities.

Diabetes mellitus is a chronic metabolic disorder characterized by persistent high blood sugar levels due to insulin deficiencies, leading to complications such as diabetic foot ulcers (DFUs). DFUs are associated with high morbidity and a significant risk of amputation, necessitating precise monitoring and management. Manual measurement of ulcer areas is labor-intensive and error-prone, prompting the need for automated, computer-aided methods. Deep learning (DL) techniques have shown promise in this domain, enhancing the accuracy and efficiency of ulcer detection and segmentation. This study investigates various methodologies for DFU segmentation, focusing on advanced DL models. We propose FootSegONN, an EfficientNet-based encoder and Self-organized Operational Neural Network (Self-ONN) and Feature Pyramid Network-based decoder for foot ulcer segmentation, evaluated on a publicly available chronic wound dataset consisting of 1010 diabetic foot images. Self-ONNs address the limitations of conventional Convolutional neural networks by achieving ultimate heterogeneity and boosting network diversity while maintaining computational efficiency. To ensure robust validation, fivefold cross-validation was applied, and more than 10 different segmentation models were utilized. The STAPLE algorithm is employed to combine mask predictions from top-performing models, and a post-processing approach is investigated to enhance performance further. The proposed method achieved a state-of-the-art Dice score of 91.55%. Furthermore, the combined FootSegONN model achieved a Dice score of 88.97% on the external validation dataset. Gradient-weighted class activation map (Grad-CAM) visualization was utilized to assess the model’s interpretability.

The Objective of the current study is to see the influence of three dimensions role stressors (role ambiguity, role conflict and role overload) on Work-Family Conflict (WFC) of female employees working different private comitial banks (PCBs) in banking industry in Bangladesh. The study also examines the moderating effect of job insecurity between three dimensions of role stressors and job insecurity. The study has collected 363 data purposively and analyzed by partial least square (PLS 4.0.9). Findings of the analysis showed significant positive impact of all three dimensions of role stressors on WFC. On the other hand, among the three moderating effect job insecurity only significantly moderate between role conflict and WFC. Based on the findings it can be postulated that organization should be concerned about different role stressors that may help the organization to reduce WFC.

Accidental falls have emerged as a major public health concern, especially among individuals aged 65 and older, due to their high incidence and severe consequences. Without timely intervention, such falls can result in fractures, traumatic brain injuries, and long-term complications. As a result, considerable research has focused on developing automated fall detection systems that integrate intelligent algorithms with sensor-based data acquisition to enable rapid response and medical assistance. This study follows the PRISMA framework to conduct a systematic literature review. A comprehensive search was performed across major databases including PubMed, Google Scholar, Scopus, and IEEE Xplore using fall detection–related keywords. From an initial pool of 596 articles, duplicates were removed and strict inclusion/exclusion criteria were applied, resulting in 182 relevant articles for in-depth analysis. This review examines a wide range of Artificial Intelligence (AI) and Machine Learning (ML) approaches applied to fall detection using diverse sensor modalities, including wearable, vision-based, ambient, and multimodal systems. Additionally, it summarizes the publicly available datasets and sensor configurations used in existing studies. This broad perspective is essential to compare trade-offs across sensing modalities and support effective system development for real-world deployment. This review provides a comparative overview of AI/ML-based fall detection approaches, highlighting differences in accuracy, sensitivity, and dataset usage as reported in existing literature. In the end, many open research challenges in using AI and ML to detect falls are outlined, along with potential future perspectives.

Abstract Introduction: Drowning is the leading cause of death among children aged 1–17 years in rural Bangladesh. Despite substantial progress in reducing infectious disease mortality–from 54 per 100,000 in 2011 to 23.62 per 100,000 in 2021–child drowning remains overlooked. This study aims to identify risk factors associated with childhood drowning deaths in two geographically distinct regions: the northern mainland and Sandwip Island. Methods: A cross-sectional study was conducted between January and March 2024. A total of 322 households were selected using purposive sampling. Face-to-face interviews were conducted, and data were analyzed using logistic regression models to examine sociodemographic, environmental, and caregiving risk factors associated with childhood drowning deaths. Results: Children in Sandwip Island had a higher risk of drowning death compared to those in the northern mainland (odds ratio [OR] = 2.10). Among drowning victims, 75.16% were under 4 years old. Children under grandparental care had 2.12 times higher odds of drowning death. Boys were more likely to experience drowning incidents, yet mortality was higher among girls (66.10% vs. 60.78%). Higher parental education levels were associated with lower drowning mortality. Households using open water as a usual showering place had lower drowning deaths than those using tubewells or indoor washrooms (OR = 0.50). Conclusion: Findings highlight the need for targeted interventions, including improved supervision in high-risk areas, enhanced water safety measures, and caregiver education programs.

The rapid rate of creating malware variants requires advanced detection techniques beyond traditional signature-based techniques. This paper discusses an advanced Behavioral DNA Fingerprinting technique that combines deep learning with cryptographic fingerprinting to detect and re-identify malware. Our technique extracts deep behavioral features from Windows Portable Executable (PE) files, generates unique cryptographic fingerprints, and maintains an up-to-date database for efficient threat detection. Experimental evaluation using the EMBER dataset achieves excellent performance with 91.43% accuracy, 91.66% precision, and 91.16% recall. The fingerprint repository achieves perfect matching accuracy, facilitating efficient re-identification of duplicate malware samples without sacrificing a balanced threat signature coverage.

Background: Nurses working in maternity wards in Bangladesh face complex clinical and emotional challenges due to maternal complications, high patient loads, and limited resources. Resilience has been recognized as a critical factor in helping nurses cope with stress, adapt to demanding environments, and sustain compassionate care. Objective: This study aimed to explore and describe the experiences of resilience among nurses working in maternity wards in Bangladesh. Methods: A qualitative interpretive descriptive design was employed, guided by Thorne’s framework. Purposive sampling recruited eight registered nurses from maternity units, including labor and delivery, postnatal, and maternal high-dependency wards, in tertiary hospitals. Semi-structured individual interviews were conducted between October 2020 and January 2021, lasting 30–90 minutes, and were audio recorded. Data were analyzed inductively to identify themes reflecting resilience experiences. Trustworthiness was ensured using COREQ guidelines. Results: Three overarching themes emerged: (1) The transition period, reflecting anxiety and lack of preparedness during the early stages of maternity nursing; (2) Gaining trust of mothers, families, and colleagues, highlighting the challenges of acceptance, communication, and professional recognition; and (3) Having a positive mindset, emphasizing psychological resilience, optimism, and self-care practices that enabled nurses to cope with workplace stress. Conclusion: Resilience among maternity nurses in Bangladesh is developed through a dynamic interplay of personal adaptation, social support, and psychological coping strategies. Strengthening structured mentorship, fostering supportive workplace cultures, and integrating resilience training into professional development are crucial to enhance maternal care quality and reduce burnout among maternity nurses.