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Noise pollution is a major challenge in urban contexts all around the world. The study was designed to assess road traffic noise pollution with possible health effects on those living in the study region. The IDW spatial interpolation approach and an ArcGIS-based evaluation were used to map the recorded noise levels in the research region. The noise descriptors including Noise Climate (NC), Traffic Noise Index (TNI), Equivalent Noise Level (Leq), and Noise Pollution Level (NPL) were computed. The required information has been collected through a questionnaire survey and previously published documents. The study reveals that the current noise level is higher than the recommended national threshold at every location. According to the study, the Nathullabad region had the highest level of noise pollution (86.5 dBA), while the Kaunia Abasik area had the lowest level (67.8 dBA). Study findings also show that in the area context, the highest levels of noise pollution are found in commercial areas (82 dBA), followed by industrial areas (80.4 dBA),mixed areas (81.3 dBA), and residential areas (72.7 dBA). The lowest level is found in sensitive areas (72.5 dBA). Statistical analyses, including one-way ANOVA, Tukey HSD post-hoc and LSD post-hoc test results, showed that there was no statistically significant difference (p > 0.05) between the noise pollution levels (NPL) in the morning, noon, and evening shifts. The results showed that 32 % of respondents stated they felt disturbed while working, and 27% of respondents said it was somewhat sensitive for them. As the last step in minimizing noise pollution in the research area, 37 % of respondents reported enforcing the regulations, 31% suggested making hydraulic horns illegally, and 21 % suggested raising public awareness. This study may contribute to academic knowledge and assist decision-makers of government officials in formulating appropriate local strategies to deal with this grave environmental problem.

In recent years, recognizing the visual focus of attention (VFoA) has attracted much attention among computer vision experts due to its various Human–Computer Interaction (HCI) or Human-Robot Interaction (HRI) applications. Although eye gaze is a potential cue to determine someone’s focus of attention (FOA), it is challenging to determine FOA alone when the interacting partners are far away or the camera cannot capture high-resolution images from long-distance. Therefore, the head pose can be used as an approximation to recognize the focus of someone’s attention. This paper proposes a vision-based framework to detect the FOA of humans using nine head poses consisting of four main modules: face detection and facial key-point selection (FDKPSM), head pose classification (HPCM), object localization and classification (OLCM), and focus of attention estimation (FoAEM). The FDKPSM uses the Multi-task Cascaded Neural Network (MTCNN) framework to detect head poses, and the HPCM classifies them into nine classes using the ResNet18. To estimate the FoA, the FoAEM uses a mapping Algorithm (EFoA) which integrates head poses on the focused object. Experimental results show that the proposed model outperformed other deep learning models by achieving the highest accuracy on three datasets: BIWI-M (96.97%), Pointing’04-M (96.04%) and HPoD 9 (98.99%). The visual focus of the attention model gained an accuracy of 94.12% in the multi-object scenario.

Sundarban, the world biggest mangrove forest, was announced as a World Heritage in 1997 and Ramsar Site in 2007. It's 62% lies in Bangladesh (Khulna, Bagerhat, Satkhira, Pirojpur, and Barguna Regions) and 38% in India, which become a vanguard for coastal communities. This ecosystem's floral and faunal structure incorporates 334 floral, 300 birds, 120 fish, 35 reptiles, and 32 mammals species. It contributes 41% of the total forest income of Bangladesh. This paper is intended to recognize the significant reasons for biodiversity calamity and its related effects on lives and degradation of the environment. A total of 105 published articles and reports were identified by searching four keywords: degradation, biodiversity, Sundarban, and Bangladesh. By considering the scope, 28 articles and 17 reports were incorporated to satisfy the objectives. The study revealed that the prominent causes of biodiversity loss of Sundarban might be due to intensified shrimp cultivation, increased dependency on forest and illicit felling, changing land use pattern, oil spillage, and pollution, forest fire, improper management practices, plant diseases, tourism activities, upstream withdrawal of river water, salinity intrusion, climate change, and some natural causes. The impacts were the loss of diversity of floral and faunal species subjected to regionally extinct, critically endangered, endangered, vulnerable, near threatened, and least concern condition. Finally, the study recommended the formulation of a special mangrove forest policy and proper management practices of Sundarban with several environmental protection strategies that should be adopted to conserve this unique ecosystem