Prevalence and risk factors of physical and psychological health among readymade garment workers in Bangladesh.

Objective. Poor health outcomes of Bangladeshi readymade garment (RMG) workers tend to be associated with a variety of occupational factors. This study aimed to investigate the prevalence of, and risk factors associated with, the physical and psychological health outcomes of Bangladeshi RMG workers. Methods. Responses to a cross-sectional survey from a convenience sample of 411 adult Bangladeshi RMG workers (mean age = 26.24 years; SD 6.40 years; female = 80%) were analysed using bivariate and multivariate (logistic regression models) analyses. Results. More than half of the participants reported headaches (61.6%) and colds/flu (51.3%), followed by fever (37.2%), diarrhoea (32.8%), bodily pain (29.9%) and respiratory infections (20.9%). For psychological health, stress (69.1%), anxiety (66.2%) and boredom (64.5%) were most prevalent, followed by sleeplessness (51.3%), depression (48.2%) and fear (34.3%). RMG workers from the factories located in Chattogram (a peripheral region compared to Dhaka) reported poorer physical and psychological health outcomes than those working in factories in Dhaka (the capital city of Bangladesh). Overall, compared to males, female RMG workers were more likely to be vulnerable to both physical and psychological health outcomes. Conclusion. Improvement in workplace conditions and safety programmes is needed to safeguard the overall health outcomes of Bangladeshi RMG workers.

Advanced quantum image representation and compression using a DCT-EFRQI approach.

In recent years, quantum image computing draws a lot of attention due to storing and processing image data faster compared to classical computers. A number of approaches have been proposed to represent the quantum image inside a quantum computer. Representing and compressing medium and big-size images inside the quantum computer is still challenging. To address this issue, we have proposed a block-wise DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) approach to represent and compress the gray-scale image efficiently to save computational time and reduce the quantum bits (qubits) for the state preparation. In this work, we have demonstrated the capability of block-wise DCT and DWT transformation inside the quantum domain to investigate their relative performances. The Quirk simulation tool is used to design the corresponding quantum image circuit. In the proposed DCT-EFRQI approach, a total of 17 qubits are used to represent the coefficients, the connection between coefficients and state (i.e., auxiliary), and their position for representing and compressing grayscale images inside a quantum computer. Among those, 8 qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position including one auxiliary qubit. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of rate-distortion performance.

A hybrid approach to enhance the lifespan of WSNs in nuclear power plant monitoring system.

In recent years, the nuclear power plant has received huge attention as it generates vast amounts of power at a lower cost. However, its creation of radioactive wastes is a major environmental concern. Therefore, the nuclear power plant requires a reliable and uninterrupted monitoring system as an essential part of it. Monitoring a nuclear power plant using wireless sensor networks is a convenient and popular practice now. This paper proposes a hybrid approach for monitoring wireless sensor networks in the context of a nuclear power plant in Bangladesh. Our hybrid approach enhances the lifespan of wireless sensor networks reducing power consumption and offering better connectivity of sensors. To do so, it uses both the topology maintenance and topology construction algorithms. We found that the HGETRecRot topology maintenance algorithm enhances the network lifetime compared to other algorithms. This algorithm increases the communication and sensing coverage area but decreases the network performance. We also propose a prediction model, based on linear regression algorithm, that predicts the best combination of topology maintenance and topology construction algorithms.