Bangladesh University of Textiles

ABSTRACT Hybrid fiber‐reinforced polymer composites combining natural and synthetic reinforcements offer a promising route toward lightweight and sustainable structural materials. In this study, carbon fiber–palm tree fiber reinforced epoxy hybrid composites were fabricated using a hand lay‐up technique with a symmetric sandwich‐type stacking sequence, where carbon fiber layers were placed on the outer surfaces and palm tree fibers formed the core. Mechanical performance was evaluated through tensile, flexural, and impact tests, while microstructural characteristics and chemical interactions were analyzed using scanning electron microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR), respectively. The tensile test results showed tensile strengths ranging from 62.5 to 81.1 MPa, with a maximum Young's modulus of 3.2 GPa and tensile strain values between 3.2% and 3.5%, suggesting possible load sharing between constituents. Flexural testing revealed flexural strengths between 207.3 and 311.0 MPa and flexural moduli ranging from 21.2 to 36.35 GPa, demonstrating the dominant load‐bearing role of the carbon fiber outer layers under bending. Impact testing showed energy absorption values between 1.1 and 2.2 N.m, with average impact energy of 1.47 N.m, which may be associated with the presence of palm fiber layers. SEM analysis revealed mixed‐mode fracture features that are consistent with the observed mechanical behavior. FTIR analysis indicated the presence of characteristic functional groups suggesting chemical compatibility between constituents. Overall, the results demonstrate that carbon fiber–palm tree fiber hybrid composites fabricated via hand lay‐up exhibit mechanically viable performance levels for non‐critical lightweight structural applications, while highlighting the influence of fabrication‐induced variability on structural reliability.

Freight forwarders are crucial to the global supply chain and their efficiency and capacity can improve a country’s logistics performance. This study aims to explore factors influencing the selection of freight forwarders by exporters and importers. An analogy between ocean and air freight forwarders is investigated based on perceived operational efficiency and capacity-related factors. The study also explores choosing international ocean freight forwarders to maintain a competitive advantage over air services. By applying the Best Worst Method (BWM) and Preference ranking organization method for enrichment evaluation (PROMETHEE) I and II methods, this study provides the Multi-Criteria Decision Analysis framework to determine the significance of decision criteria for freight forwarder selection. Results suggest that faster services, commitment and responsiveness are significant operational efficiency-related decision criteria for freight forwarder selection, while competitive rates, financial strength, and tracking and tracing are significant capacity-related decision criteria.

Cardiovascular diseases remain one of the leading causes of mortality worldwide, particularly in low- and middle-income countries. Early and accurate prediction of heart disease is essential for timely intervention and improved patient outcomes. Machine learning techniques offer promising solutions; however, challenges such as class imbalance, lack of interpretability, and limited real-world validation persist. In this study, a machine learning–based heart disease prediction framework was developed using a real-world clinical dataset comprising 5000 patient records collected from healthcare facilities in Bangladesh. Data preprocessing included cleaning, feature encoding, train–test splitting, and class imbalance handling using the Synthetic Minority Oversampling Technique (SMOTE). Multiple machine learning models—Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest—were evaluated using 10-fold stratified cross-validation. Model performance was assessed using accuracy, precision, recall, and F1-score. SHAP (SHapley Additive exPlanations) was employed to enhance model interpretability. The best-performing model was deployed as a web-based decision support system. Among the evaluated models, the Random Forest classifier achieved the best performance, with an accuracy of 98%, recall of 96%, and F1-score of 96%. Ablation studies demonstrated the effectiveness of SMOTE, feature integration, and ensemble modeling. SHAP analysis identified clinically relevant features contributing to heart disease prediction, enhancing transparency and trust in model decisions. The proposed framework provides an accurate, interpretable, and practical solution for heart disease prediction using real-world clinical data. The integration of explainable machine learning and web-based deployment highlights its potential for clinical decision support. Future work will focus on multi-center prospective validation and adaptive model updating to further improve generalizability and real-world applicability. • A large real-world clinical dataset of 5000 patients was used for robust heart disease prediction. • A Random Forest model with SMOTE achieved high performance (98% accuracy, 96% recall). • Comprehensive ablation and model comparison analyses validate methodological robustness. • SHAP-based explainability provides transparent and clinically interpretable predictions. • The proposed system is deployed as a web-based clinical decision support tool.

We present MobiPhysio, a 2D video-based dataset designed to support AI-driven physiotherapy assessment and monitoring. The dataset contains 3686 segmented videos of 9 Active Range of Motion physiotherapy exercises performed by 58 male and female participants. The recordings are done under the variations in lighting, camera angles, occlusion, and jitter in order to mimic real-world conditions. Data collection occurred in two phases: first from non-expert participants at Stamford University Bangladesh, and later from expert participants at the Department of Physiotherapy and Rehabilitation, Jashore University of Science and Technology. The entire process was conducted under the guidance of certified physiotherapists. Each video is further annotated with assessment scores derived from the exercise-specific Exercise Accuracy Assessment Questionnaire (EAAQ), developed under expert guidance. This dataset would enable researchers to build and test AI-powered physiotherapy and rehabilitation systems, examine human motion, and create exercise monitoring solutions using available 2D camera devices like mobile phones without the need of external body-reliant sensors.

Maternal and neonatal mortality in Bangladesh remains high, particularly in rural areas where access to skilled postnatal care (PNC) is limited. This study assessed urban-rural disparities in PNC utilization using data from the 2022 Bangladesh Demographic and Health Survey. A sample of 4,844 ever-married women aged 15-49 with a recent live birth (3,249 from rural areas; 1,595 from urban areas) was analyzed. Outcomes included receiving any PNC, receiving a timely PNC within two days, and receiving a PNC from a trained provider. Socio-demographic, reproductive, and healthcare determinants are analyzed using survey-adjusted weighted logistic regression. Overall, 76.6% of women received PNC, with 70.4% receiving it within 48 hours, and 24.7% from skilled providers. Utilization was consistently higher in urban than rural areas. Education, wealth, antenatal visits, and maternal employment were positively associated with PNC use, with women attending four or more antenatal visits showing nearly twice the odds of receiving timely and skilled PNC. Conversely, higher birth order and regional disparities, particularly in Rangpur, were associated with reduced access to skilled care. Chattogram showed comparatively higher coverage. Persistent inequities in PNC utilization, with rural women lagging in both timeliness and access to skilled providers, indicate targeted interventions addressing education, socio-economic inequality, and service availability are critical to improving maternal and newborn health outcomes in Bangladesh.

ABSTRACT Activated carbon (AC) is a vital porous material with a longstanding history of utilization as an adsorbent. Typically manufactured from fossil precursors such as coal and petroleum coke, its production raises concerns about sustainability and carbon dioxide emissions. In response, biomass has become an appealing, readily accessible renewable resource for AC production, providing a means to support circular economy models by valorizing waste streams. This review provides a comprehensive and critical overview of the progression from biomass to multifunctional activated materials carbon. We carefully analyze integrated production methods, including carbonization and various activation techniques (chemical, physical, physicochemical, and microwave), assessing their advantages and disadvantages, as well as their effects on key parameters such as surface area, pore volume, and yield. Furthermore, we critically evaluate the impact of synthesis parameters like temperature, duration, and impregnation ratio on the final properties of carbons. Moving beyond traditional adsorption, this review highlights cutting‐edge applications of biomass‐derived activated carbon, particularly in sustainable energy storage (e.g., supercapacitors, sodium‐ion batteries, and lithium‐ion batteries) and environmental remediation (removing dyes, heavy metals, pharmaceuticals, and other pollutants from wastewater). This review synthesizes extensive information to guide the design of high‐performance, biomass‐derived AC for specific multifunctional uses.