Jahangirnagar University (abbreviated as JU) is a public research university located in Savar, Dhaka, Bangladesh]. It is the only fully residential university of Bangladesh. It operated as a project until 1973, when the 'Jahangirnagar Muslim University Act' was amended as the 'Jahangirnagar University Act'.In 2019, the university had 17,212 students, 836 teachers, and 2,018 other employees. It has been ranked third several times in national public university rankings. JU is the fourth oldest generalized public university in Bangladesh. Also, it has fourth largest university campus in Bangladesh.
Mesalazine (MES) is a first-line therapy for inflammatory bowel disease (IBD); however, its clinical use is limited by variable patient response, intolerance, and reduced efficacy in severe cases. To address these challenges, we have designed and evaluated seventeen MES derivatives (M1–M17) using a comprehensive in silico strategy. Density functional theory with the B3LYP/6-311++G(d,p) basis set was employed to optimize geometries and explore electronic structure, stability, and reactivity. Structural characterization was further supported by FTIR and UV–visible spectral analysis. Molecular docking against five key IBD-associated targets (TNF, NOD2, ATG16L1, IL23R, and IL6) reveled that M1, M5–M8, M10, M11, M14, M16, and M17 showed stronger binding affinities than MES, where the M6 and M8 showed the strongest binding affinity. MES was more toxic towards the human body, although some of its derivatives were less toxic compared to MES, while M8 demonstrated an overall improved ADMET profile as an IBD medication. Molecular dynamics simulations of both M8–NOD2 and MES–NOD2 complexes revealed stable behavior in each system; however, M8 reduced structural fluctuations, flexibility, and solvent exposure, whereas MES promoted a more compact and conformationally restricted state, indicating distinct stabilization mechanisms, with M8 demonstrating superior dynamic control. Collectively, these findings identify M8 as a promising drug candidate for IBD, warranting further experimental validation through in vitro and in vivo studies.
Soil load-bearing capacity (SLBC) is a critical determinant of sustainable soil management and the efficient operation of agricultural machinery, yet continuous field-based assessment is constrained by high costs and labor demands. This study presents a novel, spatially explicit remote sensing–machine learning (RS–ML) framework for mapping SLBC across croplands in northeastern Bangladesh, particularly in haor regions. Field observations from 127 agricultural sites were collected using a Dynamic Cone Penetrometer (DCP), with penetration-per-blow values converted to DCP indices and subsequently transformed into California Bearing Ratio (CBR) and SLBC. A predictor set, including soil properties, topographic derivatives, and spectral indices, was preprocessed in Google Earth Engine and harmonized into raster layers at 250 m resolution. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) models were trained using hyperparameter tuning, 10-fold cross-validation, and a 70/30 train–test split. During 10-fold cross-validation, XGBoost (MSE 3.03–43.57, RMSE 1.74–6.60, MAE 1.49–3.92, R² 0.567–0.955) and GBM (MSE 2.81–46.60, RMSE 1.68–6.83, MAE 1.32–3.75, R² 0.531–0.958) showed better predictive performance for SLBC than RF (MSE 6.59–60.91, RMSE 2.57–7.80, MAE 2.14–4.44,R² 0.230–0.901). However, RF achieved lower error on the test dataset, while the Wilcoxon signed-rank test showed no statistically significant differences among the models. Bulk Density was identified as the most influential factor shaping spatial variability in soil load-bearing capacity (SLBC). Higher SLBC in Sylhet Sadar and lower values in Tahirpur and Bishwambarpur were observed. Limitations include relatively homogeneous soil conditions, limited field coverage, lack of seasonal variability, and restricted model extrapolation beyond training data. Despite these constraints, the proposed ML–RS framework provides spatially explicit SLBC predictions that can inform data-driven decision-making and support mechanized farming in wetland-prone croplands of northeastern Bangladesh and similar agroecosystems. This graphical abstract presents a spatially explicit, multi-feature-based machine learning–remote sensing (ML–RS) framework for mapping Soil Load Bearing Capacity (SLBC) across persistent croplands in northeastern Bangladesh. Field-based SLBC measurements were collected from 127 locations using a Dynamic Cone Penetrometer (DCP). Penetration-per-blow values were converted into DCP indices and subsequently transformed into California Bearing Ratio (CBR) and SLBC using established empirical relationships, which served as the model target. A comprehensive suite of multi-source remote sensing predictors was assembled, including soil intrinsic properties (bulk density, sand, silt, and clay), hydro-thermal variables (soil moisture and soil temperature), topographic derivatives (slope, aspect, curvature, and topographic wetness index), and vegetation- and water-related spectral indices (NDVI and NDWI). All predictor layers were harmonized to 250 m spatial resolution across croplands using integrated workflows in Google Earth Engine, ArcGIS, and Python. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) models were trained with hyperparameter tuning, 10-fold cross-validation, and a 70/30 train–test split. XGBoost and GBM showed stronger and faster-converging performance during cross-validation and learning-curve analysis than RF. However, RF achieved lower error on the independent test dataset, while the Wilcoxon signed-rank test indicated no statistically significant differences among the models. Bulk Density was the most influential factor shaping SLBC, with higher values in Sylhet Sadar and lower values in Tahirpur and Bishwambarpur. With larger data coverage, incorporation of seasonal data, machinery interaction, and hybrid models, the ML–RS framework could become more scalable and provide more rigorous SLBC predictions. A spatial, multi-feature ML–RS framework mapped SLBC across northeastern Bangladesh croplands. Model performance varied between cross-validation and test evaluation, but differences were not statistically significant. SLBC was higher in Sylhet Sadar, comparatively lower in Bishwambarpur, and Tahirpur. Bulk density was the dominant positive driver of SLBC. Larger datasets, seasonal effects, machinery interaction, and hybrid models may improve predictions.
This study presents an integrated assessment of natural radioactivity and associated radiological hazards in coastal sediments and marine biogenic materials from St. Martin's Island, the only coral island in Bangladesh. Activity concentrations of 226Ra, 232Th, and 40K (Bq/kg) showed clear matrix-dependent variations, with mean values of 19 +/- 1, 31 +/- 3, and 320 +/- 22 in sands; 20 +/- 1, 26 +/- 2, and 273 +/- 17 in rocks; 10 +/- 1, 12 +/- 1, and 150 +/- 10 in coral skeletons; and 10 +/- 1, 9 +/- 1, and 110 +/- 8 in seashells, respectively. Some of the sediment samples exhibited activity concentrations exceeding global average values, whereas all coral and seashell samples remained well below these reference levels. All evaluated radiological hazard indices were significantly lower than internationally recommended limits, indicating negligible radiological risk for residents, visitors, and associated personnel. This study provides the most comprehensive radiological baseline to date for St. Martin's Island by offering the first integrated comparison of abiotic (sand and rock) and biogenic (coral skeletons and seashells) marine matrices in the coastal environment. These findings establish a comprehensive radiological baseline that will support future environmental monitoring programs and radiological safety assessments in Bangladesh, particularly for coastal regions where natural and anthropogenic influences may evolve over time.
Rapid urban growth in tropical megacities is putting serious pressure on critical ecosystem services, thereby complicating the implementation of sustainable urban planning frameworks. To better understand and address these challenges, we used artificial intelligence (AI) and satellite data to map and predict land-use changes in Chittagong City Corporation (CCC), Bangladesh, from 2000 to 2048. We combined Random Forest (RF) classification with a Cellular Automata–Artificial Neural Network (CA–ANN) model. The Random Forest method classified four land-cover types: vegetation, built-up areas, barren land, and open water bodies, with over 97
A five-component collisionless unmagnetized dusty plasma comprising inertial warm adiabatic ions, Boltzmann distributed hot electrons and hot protons, nonextensive cold electrons with arbitrarily charged immobile dust grains is considered to investigate the linear and nonlinear behavior of dust-ion-acoustic solitary waves. The reductive perturbation method is employed to derive the Kadomtsev–Petviashvili equation, and the pseudo-potential approach provides the solution for nonlinear structures. This system supports only positive potential solitary excitations. The phase speed, amplitude, width, and other characteristics of the solitons are strongly influenced by various plasma parameters such as dust polarity, ion temperature, nonextensivity, adiabaticity, and others. These results offer valuable insights into the properties of solitary excitations in multi-component laboratory and space plasmas where warm ion pressure plays a significant role.
