Due to the rising rates of injury, morbidity, and fatalities in motorcycle crashes, drivers in Bangladesh are now very concerned about their safety. On the other hand, empirical data regarding the severity of motorcycle crashes is lacking. This study aims to investigate the relationship between the motor cycle crash risk and unsafe riding behavior in Dhaka city. A structural equation model (SEM) has been developed with 318 normally distributed data to identify the motor cycle crash risk and unsafe riding behavior. The developed SE model is further validated by evaluating the responses by means of relative importance index (RII) approach. From the study, “Behavioral impact” latent variable has the most influence on motor cycle crashes. Under the “Behavioral impact” latent variable, “Aggressive riding”, “Violating traffic rules”, and “Breaking traffic rules” have the most significance impact on motor cycle crash risk and unsafe riding behavior. These findings can help the policy makers and stakeholders to minimize the motor cycle crashes and ensure roadway safety in Dhaka city.
Automated voice disorder classification is useful in clinical assessments. It helps identify voice disorders quickly and supports faster and better management. In this article, the author describes a strong mechanism of voice disorder classification, which is an amalgamation of the deep learning and traditional machine learning approaches. This study utilizes five pre-trained Convolutional Neural Networks (CNNs) to capture high-level characteristics from endoscopic images. Five different classifiers, namely, Support Vector Machine (SVM), Random Forest, k-Nearest Neighbors (KNN), Decision Tree, and Gradient Boosting (XGB) are then used to improve the CNN-based feature extraction model to predict 14 types of voice pathology. This study uses a series of preprocessing techniques on the images so that to enhance the classification accuracy. Contrast enhancement technique is then used to enhance the contrast of the images to allow CNNs to detect important features more effectively. In addition, principal component analysis reduces the number of dimensions by keeping significant features and reducing the complexity of the computations. The study supports these results with comprehensive cross-validation procedure, which is guaranteed to provide the model to work well in generalizing to different data subsets. This two-step system based on CNNs to extract features and the traditional classifiers to perform the final classification has an impressive accuracy in the test of 95.77
In the context of railway travel, fear of crime has a significant impact on safety perceptions arising from personal safety concerns and other negative experiences. Drawing upon the Broken Windows and Routine Activity theories, this study investigates factors influencing railway passengers’ fear of crime in Bangladesh. Using a quantitative research design and convenience sampling, data were collected from 140 regular railway passengers through structured questionnaires in 2025. The primary data was collected from the passengers who frequented Dhaka, Joydebpur, or Tangail railway stations. Exploratory factor analysis reveals six key items under two major factors: environmental factors (traveling at night, presence of homeless people, drug abusers, and youth gangs) and experiential factors (sexual harassment and luggage theft). Additionally, the findings reveal that female passengers experience significantly higher levels of fear than men when traveling alone. The findings highlight the importance of enhancing the public transit environment to reduce fear of crime in railways and provide a foundation for future studies. Finally, future studies are encouraged to evaluate Crime Prevention through Environmental Design (CPTED) strategies and the role of media in shaping safety perceptions.
Self-employment is emerging as a dynamic alternative to traditional wage and unpaid labour, offering opportunities for individuals in the workforce—such as contributing family workers—to move from unpaid work to more flexible and autonomous economic activities. It significantly drives economic growth by creating new earning opportunities and reducing unemployment rates in developing countries. This study explores the dynamics of self-employment in Bangladesh, focusing on the key determinants driving the likelihood of being self-employed compared to unpaid worker. Using data from the 2022 Quarterly Labour Force Survey, this study employs a Multinomial Logit Model (MNL) for the analysis. The findings highlight that factors such as age, education, household wealth, household headship, and urban residence positively influence the choice of self-employment over unpaid labour. In contrast, factors such as being female, having disability, larger household size, greater land holding, and residing in Rajshahi, Rangpur, and Sylhet divisions reduce this likelihood. The findings suggest that education and wealth are particularly crucial for individuals engaged in self-employment, while gender disparities persist, with women facing considerable challenges in being engaged in self-employment vis-à-vis unpaid labour. Urban residence provides better opportunities for economic independence compared to rural regions.
Industry 5.0 aspires to enhance human–machine collaboration in the clothing industry by integrating human intelligence with advanced technologies. This review discusses the transition from Industry 4.0, which focused on automation and ethical supply chain management, to Industry 5.0, which emphasizes robotics, the Internet of Things (IoT), and Artificial Intelligence (AI) to enable customized manufacturing, sustainability, and improved worker roles. It also aims to provide a comprehensive overview of Industry 5.0’s impact on additive manufacturing in the clothing sector. It figures out the framework of this transition, explores emerging opportunities, and identifies key challenges that can arise. It summarizes the significant opportunities of Industry 5.0 in promoting innovation and productivity within the clothing industry. Observations from successful case studies indicate that clothing bands that embrace Industry 5.0 in additive manufacturing are enjoying the ultimate benefits. Key benefits include mass customization, intelligent resource management for reduced environmental impact, and the production of high-value employments. However, major challenges persist, including the requirements of substantial technological investments, concerns over job displacement, and data security risks. Addressing these challenges requires strategic framework, strong technological integration, and workforce reskilling programs.
