Exploring physical and phycological condition of Jahangirnagar University students residing near university area during COVID-19 pandemic

Background: Due of the closure of all educational institute as well as lockdown, the pandemic has affected physical and psychological health of all level students specifically university level students. Although the online based education was started but that brought different challenge to them. Thus, the study aimed to explore the physical and psychological problems faced by Jahangirnagar University students who resides near university area. Methods: Data was collected through an online questionnaire (Google form) from Jahangirnagar University students who reside near the university area using convenient sampling method. To analyse the data, descriptive statistics, Chi-square test and ordinal logistic regression was executed along with graphical representations. Results: The study showed about 92.5% (moderately: 41.2 %, extremely severe: 18%, severe: 24.7%, mild: 8.6%) students were depressed while 94.8% extremely severe: 49.8%, mild: 2.2%, moderate: 15.4% and severe: 27.3%) students were suffering from anxiety problems during pandemic. Chi-square and ordinal logistic suggested “infected by COVID-19”, “sleeping time”, and “time usually spent on physical activity” were the risk factors for depression and anxiety. The study revealed 73.8 percent of respondents have long-term health-related complications where half (52.8%) of the respondents think that the COVID-19 pandemic has had an effect on their physical health. Conclusions: This study shows that throughout the COVID-19 period, a substantial percentage of Jahangirnagar University students experienced physical and psychological health issues. Proper initiatives should be taken by government and policymakers to boost up the mental and physical health condition of students.

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

In this study, four different soft computing AI techniques were tested for the prediction of sediment yield based on hydro-meteorological variables at Jondhara station, Seonath stream in Rajnandgaon district, India. In order to fulfill this purpose, the models namely, multilayer perceptron (MLP), support vector machine (SVM), multilayer perceptron coupled with genetic algorithm (MLP-GA), and support vector machine coupled with genetic algorithm (SVM-GA) models were employed. To select the optimal input variables, a statistical method such as the Gamma test was considered among several methods. Based on the results of the analysis, all models were evaluated by using the following statistical indices: Coefficient of Correlation (CC), room mean square error (RMSE) and percent bias (PBAIS). Overall, the performance of the studied models indicates that all of them are capable of simulation sediments yield at Jondhara station, Seonath river basin in a satisfactory manner. Comparison of results showed that the MLP-GA with CC = 0.988, RMSE = 0.006 and PBIAS = 0.000 in training period and CC= 0.990, RMSE = 0.007 and PBIAS = 0.000 in testing period for S-6 model and CC = 0.986, RMSE = 0.025 and PBIAS = -0.001 in training period and CC = 0.988, RMSE = 0.029 and PBIAS = -0.001 in testing period for S-13 model were able to yield better results than the other models considered. Furthermore, an SVM model is also observed to have some advantages over MLP models and SVM-GA models since it can represent the output data in a continuous manner by fitting a linear regression function to the output data, which has the advantage of making the model more precise than MLP and SVM-GA models.

In-vitro relationship between protein-binding and free drug concentrations of a water-soluble selective beta-adrenoreceptor antagonist (atenolol) and its interaction with arsenic.

The degree of binding of a drug to plasma proteins has a marked effect on its distribution, elimination, and pharmacological effect since only the unbound fraction is available for distribution into extra-vascular space. The protein-binding of atenolol was measured by equilibrium dialysis in the bovine serum albumin (BSA). Free atenolol concentration was increased due to addition of arsenic which reduced the binding of the compounds to BSA. During concurrent administration, arsenic displaced atenolol from its high-affinity binding Site I, and free concentration of atenolol increased from 4.286 +/- 0.629% and 5.953 +/- 0.605% to 82.153 +/- 1.924% and 85.486 +/- 1.158% in absence and presence of Site I probe respectively. Thus, it can be suggested that arsenic displaced atenolol from its binding site resulting in an increase of the free atenolol concentration in plasma.