Begum Rokeya University, Rangpur (BRUR), formerly Rangpur University, is a government-financed public university in Bangladesh. It is the only general categorised university in Rangpur Division and the second public university in the region. It was named after the feminist writer and social worker Begum Rokeya. It is the 30th public university of Bangladesh. It was established on 12 October 2008 under Begum Rokeya University Act, 2009 (2009–29)..
This study examines the determinants of green purchase behavior (GPB) among young consumers in a digital context by integrating drive for environmental responsibility (DER), social media attachment (SMA), green product knowledge (GPK), and green consumption values (GCV) within the Theory of Reciprocal Determinism (TRD). In addition, it investigates the moderating role of SMA in the relationship between DER and GPB, as well as the role of attitude in the link between GCV and GPB. Survey data from 285 participants aged 18 to 30 collected using a non-probability sampling method. Partial Least Squares Structural Equation Modeling (PLS-SEM) were utilized in the study. The findings reveal that DER, SMA, GPK, and GCV positively influence GPB. However, the moderating effects of SMA and attitude are not supported, indicating that these factors act as independent predictors rather than conditional influencers of GPB. These results provide theoretical insights into the stability of behavioral drivers and reinforce the importance of cognitive and environmental determinants within the TRD framework, even without significant interaction effects. The findings emphasize the significance of individual DER, digital engagement, GPK, and values among urbanized and digitally connected youth, promoting GPB in the digital era. The research offers an original contribution by integrating DER and SMA within the TRD framework in the context of Malaysian digital sustainability. It extends green consumption beyond the Western perspective and provides novel empirical evidence from an emerging economy. The study highlights the theoretical, practical, and policy implications of these findings.
This study numerically explores the performance of (BA)2GeI4-based two-dimensional perovskite solar cells using the SCAPS-1D simulation platform. A total of 108 device structures were analyzed through different combinations of electron and hole transport layers, where the ITO/IGTO/(BA)2GeI4/MoO3/Ni configuration achieved the highest efficiency of approximately 30.26
Photovoltaic solar technology’s primary challenges include toxicity, long-term stability, and elevated production costs. To address these issues, our research investigates the potential of lead-free noble metal halide perovskites, particularly Cs2TiI4Br2, which, due to the narrow bandgap, shows potential as an absorber material, excellent light absorption capabilities, and cost efficiency. We utilize SCAPS-1D simulations to analyze solar cell (SC) architectures that integrate Cs2TiI4Br2 with PCBM as the electron transport layer (ETL). Our goal is to determine the optimal photovoltaic parameters by examining how absorber thickness, temperature, defect, and doping concentrations influence device performance. Additionally, we explore various back and front contact materials to find the best electrode for optimized solar cells. Considering the configurations that were investigated, the most effective design was identified as Au (metal contact)/Cs2TiI4Br2 (absorber)/PCBM (ETL)/ITO, obtaining a power conversion efficiency (PCE) of 28.77
Groundwater depletion threatens sustainable urban water supply in northern Bangladesh, where overextraction, land-use change, and climate variability accelerate groundwater level decline. This study proposes a hybrid ensemble machine learning framework to improve short-term groundwater-level prediction by incorporating hydro-meteorological and anthropogenic variables. Ten individual algorithms and six hybrid ensemble models were evaluated across eight observation wells in Rangpur and Dinajpur under contrasting hydrological conditions. Model performance was assessed using mean absolute error, root mean square error, coefficient of determination, and correlation metrics. The best-performing hybrid ensembles reliably offered the highest predictive accuracy and stability across all wells. In the test stage, the hybrid models achieved root mean square errors of 0.01-0.24 m and mean absolute errors of 0.01-0.21 m, with coefficients of determination ranging from 0.99 to 1.00. The proposed framework provides a robust data-driven tool for groundwater forecasting and supports sustainable urban water resource planning in data-scarce regions.
This study investigates the spatiotemporal variability of extreme precipitation indices (EPIs) and their linkages with large-scale climate drivers in Bangladesh. Daily precipitation data from 20 meteorological stations for the period 1980-2017 were analysed using the Mann-Kendall trend test and Sen's slope estimator to detect trends and abrupt change points, and complemented by Mantel tests, Geo-Detector modelling, and partial wavelet coherence (PWC) analysis to examine climatic influences. Among the eight EPIs considered, consecutive dry days (CDD), annual maximum 5-day precipitation (RX5DAY), and the frequency of heavy rainfall days (R10 and R30) exhibited increasing trends, whereas consecutive wet days (CWD), the simple daily intensity index (SDII), annual total wet-day precipitation (PRCPTOT), and maximum 1-day precipitation (RX1DAY) showed declining tendencies. Mutation analyses indicated turning points in 1997 for CDD and in 2004 for R10 and R30, marking intensified dry spells and shifts in heavy rainfall occurrence, while declines in CWD, SDII, and RX1DAY originated around 1988. Spatially, precipitation frequency and intensity are highest in the northeastern and southeastern regions, particularly along the coastal belt. Mantel and Geo-Detector results identified the Indian Ocean Dipole (IOD), El NI & Ntilde;O-Southern Oscillation (ENSO), Arctic Oscillation (AO), and SUNSPOT as key climatic drivers influencing monsoon dynamics and moisture transport from the Bay of Bengal. PWC analysis further revealed positive influences of ENSO on rainfall frequency and negative associations of the AO and SUNSPOT with precipitation intensity. These findings enhance understanding of hydroclimatic extremes in Bangladesh and provide a scientific basis for adaptive water resources management and flood risk mitigation.
