Bangladesh University (BU) (Bengali: বাংলাদেশ বিশ্ববিদ্যালয়) is a non-profit, private university. Its main campus is located near the Jatiyo Sangshad Bhaban in Dhaka, Bangladesh. The university was established in 2001 under the Private University Act 1992. The university's founder was Quazi Azher Ali, who served as first vice chancellor of the university from 2001 to 2009.
The sustained expansion of the world’s population has led to a remarkable escalation in renewable energy consumption as global economies adopt green energy systems; therefore, high-fidelity renewable energy consumption forecasting emerges as a crucial factor for efficient energy management and sustainable economic development. This study proposes a hybrid framework for forecasting renewable energy consumption. Machine learning (ML) predictive model integrated statistical models such as simple exponential smoothing (SES), the holt-winters (HW), and the autoregressive integrated moving average (ARIMA) model to form the hybrid framework. The explainable artificial intelligence (XAI) interprets the best predictive model’s feature contributions. The developed forecasting models were evaluated on a hold-out test set and validated across a 24-month future forecast period, along with comparative plots and metrics. The proposed system demonstrates 2.68
Heart disease continues to pose a critical worldwide health issue, more specifically in areas with insufficient access to healthcare infrastructure and diagnostic systems. Conventional diagnostic approaches often fall short in accurately detecting and managing heart disease risks, resulting in unfavorable outcomes. Machine learning presents a powerful means to boost the precision and reliability of cardiovascular disease prognosis and diagnosis. In this research, we introduced a unified approach incorporating classification techniques for detecting heart disease and regression techniques for forecasting associated risks. The analysis utilized the dataset, named Heart Disease, containing 1,035 instances. To mitigate the problem of data disproportion, the SMOTE was implemented, producing 100,000 additional synthetic samples. Evaluation metrics such as F1-score, recall, precision, accuracy, MAE, RMSE, MSE, and R2 were adopted to evaluate the performance of the models. Among the classification algorithms, Random Forest delivered the most notable results, attaining an accuracy of 0.972 on actual data and 0.976 on artificially generated data. For prediction modeling, for both synthetic and real samples, linear regression produced the best R2 values of 0.992 and 0.984, respectively, along with the least amount of measurement errors. Furthermore, Explainable AI methods were utilized to improve the comprehensibility of the model outcomes. This paper emphasizes the transformative capabilities of machine learning for diagnosing cardiovascular disease and estimating risk levels, thereby supporting timely interventions and enhancing clinical settings.
Quantum machine learning is often proposed for richer feature representations, yet most evidence rests on idealized simulation rather than real noisy intermediate-scale quantum (NISQ) hardware. This research presents a controlled comparison of classical and quantum-enhanced diagnostic pipelines on three clinical binary classification tasks: Mammographic Mass, Anemia, and Diabetic Retinopathy. All pipelines share standardized preprocessing, principal component analysis (PCA), and a fixed extreme gradient boosting (XGBoost) classifier, so differences arise only from the feature representation. Four quantum encodings (angle, phase, basis, and the ZZ feature map) are each run on two backends: a noiseless simulator and the real IBM Heron r2 processor (156 qubits). Across nine performance metrics, compared with the classical pipeline, the performance decreases in the quantum hardware execution for all datasets, with a more significant reduction observed for the Anemia dataset. In contrast, compared with the simulated pipeline, the hardware execution shows a slight performance decrease for the Mammographic and Diabetic Retinopathy datasets. The exceptions are the angle and phase encodings for the Mammographic dataset, where the hardware result improves slightly compared with the simulator. For the Anemia dataset, the transition from simulation to real quantum hardware results in a considerable performance reduction. These findings show that current quantum feature maps through the encode-measure-boost pipeline on NISQ hardware do not yet outperform a well-designed classical pipeline.
This study presents a detailed investigation of the two-dimensional magnetohydrodynamic (MHD) flow of a Casson hybrid nanofluid with chemical reactions through a perpendicular porous channel under sinusoidal boundary conditions. The introduction of periodic MHD effects and oscillatory wall motion represents the key novelty of this work. The governing nonlinear partial differential equations are transformed into non-dimensional form and solved using a hybrid analytical–numerical approach, with stability and convergence analyses confirming the reliability of the solution. Flow and heat transfer characteristics are analyzed through streamline and isotherm visualizations. The results reveal that the Grashof number and heat source parameter enhance skin friction, while higher Prandtl number, magnetic parameter, porosity, and chemical reaction rate suppress it. Notably, a 25% reduction in velocity is observed as the magnetic parameter increases from 1 to 5, with similar trends evident for other parameters. The findings exhibit strong agreement with existing studies and highlight the model’s practical relevance to biomedical fluid transport, thermal management in electronic systems, and various industrial and manufacturing applications.
Curcuma genus has been widely used in folk medicine for its diverse medicinal properties. This study explored the in vitro and in vivo anti-inflammatory activity of the chloroform fraction of Curcuma wallichii (CFCW) and assessed the drug-likeness of identified compounds using computational tools. Phytochemical screening, gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC) analyses of the CFCW confirmed the presence of different bioactive compounds. The in vitro methods include the human red blood cell (HRBC) membrane stabilization and egg albumin denaturation assays, where the fraction exhibited substantial anti-inflammatory activity in a dose-dependent manner. In vivo evaluation using the formalin-induced licking test and acetic acid-induced writhing test in Swiss albino mice showed noteworthy anti-inflammatory effects. The pharmacokinetic profile of the identified compounds was analyzed using pkCSM and SwissADME, which evaluated their drug-likeness properties and toxicity. Molecular docking studies were conducted to recognize possible drug candidates by predicting the binding affinity of the compounds against COX-2, using diclofenac sodium (DS) as a reference. Several molecules demonstrated even stronger binding affinities than the standard DS. Findings suggest that the plant C. wallichii contains bioactive compounds with significant anti-inflammatory effects. Further research is necessary to analyze their bioactivity, and clinical trials will be essential for the development of new anti-inflammatory drugs.
