Asian University of Bangladesh

Hakaluki Haor, the largest freshwater wetland in Bangladesh, plays a crucial ecological and socio-economic role by supporting inland capture fisheries and sustaining local livelihoods; however, its morphology and hydrological dynamics have undergone significant transformations over recent decades. This study integrated remote sensing and geographic information systems techniques with time-series statistical analyses to quantify water area changes from 1990 to 2020 and examine their implications for fish production and community livelihoods. Landsat imagery and the Modified Normalized Difference Water Index were applied to assess waterbody dynamics, while Mann–Kendall trend tests, Sen’s slope estimator, Pettitt’s change-point detection, and Spearman correlation were used to quantify long-term trends and structural shifts. Results revealed significant water area changes in Hakaluki Haor between 1990 and 2020, mainly due to erratic rainfall and discharge from transboundary rivers. Fish catch showed a statistically significant decreasing trend (Sen’s slope = −83.68 MT/year, p < .001), while water area exhibited a significant increasing trend (Sen’s slope = 19.55 ha/year, p = .001). Fish catch and abundance in the haor were influenced by a combination of climatic factors and anthropogenic pressure, with a notable decline between 1990 and 2015 associated with erratic rainfall, temperature fluctuation, beel siltation, and excessive use of destructive fishing gears. Change-point analysis indicated significant breaks in the fish production around 2004 to 2005 and near 2015, coinciding with extreme rainfall events ( z -score = 1.25 in 2015), which adversely affected fisher livelihoods, reducing fishing participation, prompting occupational diversification, seasonal migration, and gradual socio-economic transitions. Post-2015, fish catch improved following government-led re-excavation of beels and canals, undertaken in collaboration with non-governmental organizations. Sustainable management of haor wetlands requires restoring hydrological connectivity, mitigating destructive fishing, and strengthening climate-resilient fisheries governance.

This is an empirical study that explores the socio-economic conditions of fishermen living along the Turag River which is a peri-urban area of Dhaka in Bangladesh. By using a mixed-methods approach, this study combines a household survey of 150 fisher households with in-depth interviews, focus group discussions, case studies, and key informant interviews conducted in the villages of Rostompur, Paragram, and Noapara. The analysis is guided by the Sustainable Livelihoods Framework (SLF), which enables a comprehensive understanding of how environmental, economic, and institutional factors interact to shape livelihood outcomes. The finding shows that it is a multidimensional crisis. The intensive and destructive environmental degradation caused by industrial pollution has significantly exhausted the stock of fish, which is the main natural capital of the community. Households are highly vulnerable socio-economically with low literacy (88.7% primary school education and less), precarious incomes (60% earn 600 BDT or less/day), poor housing, and limited access to formal credit, resulting in their having to rely on exploitative middlemen. Diversification of livelihoods into the daily labor and informal labor is more rampant but is a sign of coping and not of an upward mobility. There is a strong change in intergenerational shift where 85% of parents are not encouraging their children to become fishers, but again lack of funds and education is creating a barrier towards other dreams. This study adds a SLF-enlightened holistic perspective of how interconnected environmental, economic and institutional failures entrap and leave peri-urban fishing communities in a vicious cycle. It finds that this cycle cannot be broken without combined policy action targets on ecological recovery of Turag River, greater education and professional training, better access to credit, and greater government to rein in pollution and market abuse. This study highlights the necessity of the sustainable development approaches that would balance between economic development, social welfare and environmental health in rapidly urbanizing contexts.

This bibliometric analysis investigates the integration of big data, chatbots, and data mining in academic libraries, aiming to understand current trends, regional disparities, and future implications. Using comprehensive bibliometric methods, the study analyzes publications and citations from 2014 to the present, highlighting the significant growth in research and application of these technologies. The findings reveal a substantial increase in research output, with China leading in publication volume, followed by the United States and other Asian countries. The study identifies the critical role of big data in enhancing operational efficiencies, chatbots in improving user interactions, and data mining in uncovering hidden trends. However, challenges such as the digital divide and data privacy concerns are prominent. The study concludes that while these technologies offer transformative potential, addressing infrastructural disparities and implementing robust data governance frameworks are essential. The implications for researchers, practitioners, policymakers, and academic institutions include promoting interdisciplinary research, enhancing digital literacy, and fostering international collaboration. Embracing these technologies can significantly improve the role of academic libraries as dynamic, user-centric centers of knowledge and learning, effectively meeting the evolving needs of their users in the digital age.

A simulation-based study has been done on the basis of surface plasmon resonance (SPR) for photonic crystal fiber (PCF)-based sensor for detecting various cancerous cells. The sensor is coated with titanium dioxide (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{TiO}_{2}$</tex>) and gold (Au) for the stability of the sensor. The simulation was run in COMSOL Multiphysics using the finite element technique and the PML boundary condition. The proposed sensor facilitates the identification of multiple cancer cells by detecting the shift in resonance wavelength of malignant cells in comparison to normal cells. The maximum wavelength sensitivity for several malignant cells, including MDA-MB-231, MCF-7, PC12, and Jurkat, was recorded at 2142.85, 1428.57, and 5714.285 nmRIU<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, respectively. The maximum amplitude sensitivity varies by cancer cell type and ranges from -141 to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$-213 \text{RIU}^{-1}$</tex>. The sensor possesses a maximum detection limit of 0.014 and a significant resolution of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.75 \times 10^{-5} \text{RIU}$</tex>, indicating its utility in identifying malignant cells.

Groundwater poisoning by arsenic is a major public health concern, especially in areas like Bangladesh where longterm exposure causes chronic illnesses like cancer and skin disorders. This study introduces a novel deep learning framework, PT-TwinDCNN, designed for the automated detection of skin diseases caused by arsenic exposure. The proposed model integrates DenseNet121 and ResNet50V2 in a parallel architecture to extract diverse and complementary feature representations. A real-world dataset comprising 1,482 images-captured from both arsenic-affected and unaffected individuals-is used for model training and evaluation. Comprehensive preprocessing techniques, including color inversion, data augmentation, and group normalization, were employed to enhance model robustness and generalizability. The system achieved outstanding performance with an accuracy of 97.23%, precision of 97.31%, and recall of 96.8 %. Additionally, explainable AI techniques were integrated to provide interpretability and increase clinician trust. The proposed solution demonstrates significant potential for realtime, accessible screening in resource-constrained environments, offering an impactful tool for early diagnosis and public health intervention.

This study indicates that the association between human resource (HR) compliances and employee pro-organizational behavior (EPB) has been comparatively less researched in academia. This empirical paper lines up with the correlation between HR compliance and EPB, considering the mediating effect of corporate social responsibility (CSR). The independent factor, HR compliance, was further divided into four components: “work environment”, “health and safety”, “flexibility of work,” ‘compensation’. For organizations wishing to increase their workforce’s pro-organizational behavior, the study offers useful insights. Partial least square structural equation modeling is employed in this research to assess the perceptions of front-line banking industry workers in Bangladesh. The findings underscore that effective management of compensation, health and safety measures, and a positive work environment significantly enhance CSR practices, which in turn positively impact EPB. While work flexibility shows some influence on CSR, its direct impact on EPB is negligible. We expect the outcomes to be contributory to the upcoming research in investigating the role of HR compliance in creating positive output from employee EPB through the sense of CSR in the industry sector. Further, such a research effort can aid the organization’s policymakers motivate employees for their positive pro-organizational behavior.

The extent to which menopause status contributes to depressive symptoms remains controversial. This study aimed to examine associations between menopausal status and depressive symptoms and the heterogeneity in these associations. We conducted three consecutive national surveys of community-dwelling Australian women aged 18-79 years between October 2013 and July 2017. Depressive symptoms were measured by Beck Depression Inventory II (BDI-II) score (range 0-63). Symptoms were classified into a binary outcome of minimal to mild (BDI-II score <20) and moderate to severe (MS) (BDI-II score ≥20). The average effect (average treatment effect [ATE]) and conditional average effect of the menopausal status on depressive symptoms were investigated by causal forest method. The prevalence of MS depressive symptoms in 10,351 participants was 23.7% (95% CI: 22.9-24.5). Compared with premenopause, the BDI-II score was higher for the menopause transition (ATE = 2.43 units, 95% CI: 1.20-3.65) and postmenopause (ATE = 3.03 units, 95% CI: 1.25-4.81). Compared with premenopause, the menopause transition was associated with an average increase in the prevalence of MS depressive symptoms by 6 percentage points (ATE = 0.06, 95% CI: 0.01-0.12). There were no differences in depressive symptoms between the menopause transition and postmenopause.In menopause transition, the BDI-II score was -1.22 units lower and the prevalence of MS depressive symptoms was 5 percentage points lower among employed women compared with unemployed women. In postmenopause, the BDI-II score was 1.75 units higher for unpartnered women compared with partnered women. The menopause transition and postmenopause are associated with a higher BDI-II score and an increased prevalence of MS depressive symptoms compared with premenopause. These associations are favorably modified by paid employment in the menopause transition and unfavorably by being unpartnered postmenopause.

Industrialization boosts economic growth but causes pollution, harming public health and raising healthcare costs. Inflation compounds this challenge, as rising prices make healthcare services, both preventive and curative, less accessible and affordable for the public. This research aims to identify the impact of industrialization and environmental pollution on health expenditure in Bangladesh. The study used time series data collected from 1997–2023. The study uses Johansen’s co-integration test, which identifies one co-integrated equation, and applies the Vector Error Correction Model (VECM) for estimation. The VECM results show a strong long-term connection. The error correction coefficient for health expenditure (which is the dependent variable) is negative and significant at 1%, showing a stable long-term connection between environmental pollution, industrialization, inflation, and health expenditure. Furthermore, all independent variables—industrialization, pollution and inflation—have a positive and significant impact on health expenditure. There is also evidence of short-term causality among these variables. To address these issues, the study recommends that the Bangladeshi government adopt targeted policies, including promoting sustainable agriculture practices, expanding renewable energy sources, and restoring ecosystems. Enhancing urban green spaces, regulating industrial waste, and investing in healthcare infrastructure are crucial steps to mitigate pollution's health impacts and support sustainable economic growth. Community engagement in environmental practices would support these efforts, fostering sustainable development and economic resilience.

Microplastics (MPs) are an emerging environmental issue that might endanger the health of agricultural soil. Even though several research on the particular toxicity of MPs to species have been carried out, there is little information on MPs' impacts on soil physicochemical properties and heavy metals (HMs) availability of HMs contaminated and without contaminated soils. This study examined the changes in soil characteristics for both HMs contaminated and without contaminated soils by five distinct MPs, including Polyethylene (PE), Polyethylene terephthalate (PET), Polystyrene Foam (PS), Polyamide (PA), and a combination of these four types of MPs (Mixed MPs), at two different concentrations (0.2% and 1%; w/w), where soil incubation experiments were setup for this studies and the standard analytical techniques employed to measure soil characteristics and toxic metal availability. After the ending of soil incubation studies (90 days), significant changes have been observed for physicochemical properties [bulk density, porosity, water holding capacity, pH, electrical conductivity (EC), organic carbon (OC), and organic matter (OM)]. The soil nutrients change in descending order was found as NH4+ -N> PO43+ > Na > Ca > NO3- > Mg for lower concentrations of MPs compared to higher concentrations. The HMs availability is reducing with increasing MPs concentration and the descending order for metal availability was as follows Pb > Zn > Cd > Cr > Cu > Ni. Based on MP type, the following descending order of MPs PS > Mix (MPs) > PA > PET > PE, respectively act as a soil properties influencer. Usually, effects were reliant on MPs' category and concentrations. Finally, this study concludes that MPs may modify metal movements, and soil quality; consequently, a possible threat will be created for soil health.

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