State University of Bangladesh (SUB) is a private university in Dhanmondi, Dhaka, Bangladesh. It was established in 2002 under the Private University Act 1992.
The multidrug-resistant (MDR) Escherichia coli, particularly β-lactamase producing strains, has become a global health challenge, where wastewater systems, specially from hospitals, serve as critical reservoirs for the dissemination of resistance genes. The objectives of the study were to investigate the whole genome sequence diversity and genetic variations focusing on their evolutionary relationships, genetic similarity, and single nucleotide polymorphisms (SNPs) of pathogenic β-lactamase producing E. coli strains. A total of four β-lactamase producing E. coli strains, from differently located tertiary care hospitals, were included in this study. A heatmap of genetic similarity revealed near-identical genetic makeup among the strains. A number of genes including AcrAB-TolC, β-lactamases, and resistance determinants such as glpT, gyrA, msbA, and tet(M) were identified in these strains. However, the presence of virulence genes of the aerobactin synthesis gene (iucA, iutA) and type III secretion systems (espX1, espX4, espX5) in the strain has the potential for pathogenicity. These resistance genes were identified at the genomic level; however, their transcriptional expression was not evaluated and the detection of virulence-associated genes suggests that the isolates have the potential to cause disease and pathogenicity. These findings provide insights into the whole genome sequence diversity of E. coli in urban based tertiary care hospital wastewater, emphasizing the genetic variation and plasmid diversity in these E. coli strains, which may have implications in public health and microbial ecology of the environment.
The rapid expansion of Internet of Things (IoT) devices has transformed industry and everyday lives by facilitating widespread connection and data interchange. This increase in connection has generated significant security weaknesses, rendering IoT systems more vulnerable to advanced cyber-attacks. This research introduces a novel ensemble learning architecture focused on at improving the detection of IoT attacks. The proposed approach utilizes advanced machine learning methods, namely the extra trees classifier, and implements intensive preprocessing and hyperparameter optimization to examine datasets including CICIoT-2023, IoTID20, BotNeTIoT-L01, ToN_IoT, N-BaIoT, and BoT-IoT. The findings demonstrate remarkable performance, with the model attaining near-optimal metrics, including recall, accuracy, and precision, while maintaining incredibly low error rates. These findings demonstrate the model's efficiency above existing techniques, offering an effective choice for securing IoT environments. This research establishes a new benchmark for IoT security, providing a robust basis for future progress in protecting networked devices from emerging cyber threats.
This study investigated how interactive teaching, constructive feedback, technology use, and teacher-student relationships influence student participation in Bangladeshi universities. Using a quantitative, cross-sectional design, data were collected from 417 students across public and private universities through structured questionnaires. Analyses using descriptive statistics, Spearman’s correlation, ANOVA, and regression models tested ten hypotheses. Results showed significant positive associations between interactive teaching and participation (ρ = 0.386, p < .001), and between teacher feedback and motivation (ρ = 0.415, p < .001). Technology use was positively associated with both interactive teaching and student participation, indicating a mediating relationship. The findings suggest that technology use is more strongly associated with participation when combined with interactive pedagogy and emotional support. However, not all hypotheses were supported; particularly the moderation effect of technology use (H₅), and demographic differences by gender and academic discipline (H₈, H₉), which were not statistically significant. Despite limitations such as self-reported data and a single-country focus, the study contributes to understanding how pedagogy, technology, and relational factors jointly foster engagement. It offers practical insights for educators and policymakers to design more interactive, inclusive, and motivating learning environments.
Progressive loss of neurons, oxidative stress, neuroinflammation, and mitochondrial dysfunction are hallmarks of neurodegenerative diseases (NDs), such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Resveratrol, a polyphenolic phytoalexin mainly found in grapes and red wine, is a promising treatment candidate due to its diverse biological effects and neuroprotective properties. This review demonstrates the regulatory effects of resveratrol on cellular signaling pathways linked to NDs and its neuroprotective mechanisms. Resveratrol enhances neuronal survival, boosts mitochondrial biogenesis, and mitigates oxidative stress by affecting key molecular pathways, including SIRT1/AMPK, PI3K/Akt, MAPK, and Nrf2/ARE. The PI3K/Akt and ERK1/2 pathways promote neuronal regeneration by modulating pro-apoptotic and anti-apoptotic factors. Resveratrol inhibits NF-κB, reducing cytokine release and microglial activation, thereby exhibiting anti-inflammatory properties. It improves cognitive function, synaptic plasticity, and neuronal survival. Despite an increasing pharmacological profile, its practical applicability is limited by inadequate bioavailability, rapid metabolism, and restricted brain penetration. This review demonstrates resveratrol's effect on interconnected signaling networks related to neurodegeneration. We critically compare evidence from preclinical and clinical studies, demonstrating both therapeutic potential and translational limitations. Emerging nanotechnology-based delivery strategies are demonstrated to overcome bioavailability and blood–brain barrier penetration challenges. These insights provide a translational perspective for the future development of resveratrol-based interventions in NDs.
Robust and personalized anomaly detection is essential due to the rapid growth of UAV deployments in critical infrastructure, logistics, and surveillance. Distributed, non-IID, and sensitive UAV communication scenarios pose challenges for traditional centralized learning. To address these issues, this work presents BANCO-FL, a balanced and optimized federated learning framework combining a lightweight neural network with adaptive aggregation methods, FedAdam, FedMedian, and ClusterAvg. Experiments conducted on a real-world UAV dataset containing 2.35 million communication records demonstrate that BANCO-FL achieves a peak accuracy of 99.98%, 99.98% precision, 99.98% recall, and a 99.98% F1-score in 3-client and 9-client non-IID scenarios. Compared to standard baselines, BANCO-FL reduces misclassification rates by over 35%, improves training stability, and enhances fairness across clients. These findings show that BANCO-FL is a practical, scalable, and communication-efficient solution for real-world UAV anomaly detection.
