Gono University or Gono Bishwabidyalay (Bengali: গণ বিশ্ববিদ্যালয়, abbreviated as GB) is a private university in Savar, Bangladesh which was established on 14 July 1994. It is now operating their academic and administrating activity on their permanent campus at Nolam, Savar, Dhaka. It was approved by University Grants Commission (UGC) on 1998. Laila Parveen Banu is the vice-chancellor of the university.
Climate change has become central to global policy and public debate, but it's less clear whether this shift shows up in the more guarded language of scientific writing itself. Because standard sentiment analysis tools don't work well on formal academic prose, we instead build a topic tone framework that pairs Latent Dirichlet Allocation with custom lexicon-based scores for uncertainty, certainty, and risk intensity capturing how confidently and how urgently claims are made, rather than emotional tone. Using climate-related abstracts from the arXiv metadata corpus (1995–2026), we model topics, score tone, and run regression and change point analyses to track trends over time. We finds a gradual but statistically meaningful rise in risk-oriented language, apparently accelerating after 2015, while uncertainty fluctuates rather than steadily declining possibly reflecting the growing complexity of climate modeling. Topics also carry distinct linguistic signatures: policy related work shows more risk language, while modeling heavy work hedges more. None of this points to scientific writing becoming "alarmist," but it does suggest a real, incremental shift in how climate risk is discussed one this reproducible framework could help track in other fields too.
Type 2 diabetes mellitus (T2DM) is driven by interconnected disturbances involving postprandial glucose regulation, insulin resistance, lipid imbalance, and progressive metabolic dysfunction, making single-target interventions only partially effective in many settings. In this context, the present study investigated the antidiabetic potential of the phthalic acid ester derivative, 2-O-(3,5-dimethylphenyl) 1-O-(4-formylphenyl) benzene-1,2-dicarboxylate, as a predicted dual modulator of α-glucosidase and peroxisome proliferator-activated receptor gamma (PPARγ) using an integrated in silico workflow. Biological activity prediction suggested possible involvement in lipid regulation, metabolic signaling, and cytoprotective pathways, while physicochemical and ADMET analyses indicated acceptable drug-likeness without Lipinski violations and comparatively restrained toxicity alerts. Molecular docking revealed favorable binding toward both targets, with interaction energies of -10.3 kcal/mol against PPARγ and − 9.2 kcal/mol against α-glucosidase, exceeding those of pioglitazone and acarbose, respectively. Interaction mapping suggested that ligand accommodation was influenced less by hydrogen-bond abundance and more by cooperative aromatic packing and hydrophobic stabilization within receptor microenvironments. Density functional theory analysis showed a narrow HOMO-LUMO energy gap (0.17228 eV), low hardness (0.08614 eV), and elevated softness (5.80 eV⁻¹), which may indicate moderate electronic adaptability during receptor interaction. Molecular dynamics simulations further appeared to support sustained ligand retention and favorable interaction persistence over 100 ns. Although entirely computational, these findings suggest that phthalic acid ester frameworks may represent an underexplored chemical space for multitarget antidiabetic investigation and warrant subsequent experimental validation.
Cardiovascular disease (CVD) is a leading cause of mortality worldwide and detecting it early can make all the difference between a manageable condition and a medical emergency. In this study set out to build a smarter prediction system by combining several feature selection techniques Chi-Square, Recursive Feature Elimination, Lasso regression, and Random Forest importance to narrow down the most clinically meaningful predictors from the UCI Heart Disease dataset (303 patients, 13 attributes). We then applied Fuzzy C-Means clustering to uncover hidden patient subgroups, validating the choice of three clusters through four independent checks (Elbow method, Silhouette score, Dunn index, and Gap statistic), all of which agreed. This cluster information was added as an extra feature before training five classifiers: Logistic Regression, SVM, Decision Tree, Random Forest, and Naive Bayes. The Support Vector Machine came out on top with 90.2% accuracy, followed closely by Logistic Regression and Random Forest. Chest pain type, ST depression, heart rate, and the cluster label itself proved to be the strongest predictors. Overall, our results suggest that thoughtful preprocessing not just fancier algorithms can meaningfully improve how well we predict cardiovascular risk.
