Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R² = 0.898, Q²loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp²-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

N-myristoyltransferase (NMT) is an important eukaryotic monomeric enzyme which has emerged as an attractive target for developing a drug for cancer, leishmaniasis, ischemia-reperfusion injury, malaria, inflammation, etc. In the present work, statistically robust machine leaning models (QSAR (Quantitative Structure–Activity Relationship) approach) for Human NMT (Hs-NMT) inhibitory has been performed for a dataset of 309 Nitrogen heterocycles screened for NMT inhibitory activity. Hundreds of QSAR models were derived. Of these, the model 1 and 2 were chosen as they not only fulfil the recommended values for a good number of validation parameters (e.g., R² = 0.77–0.79, Q²_LMO = 0.75–0.76, CCC_ex = 0.86–0.87, Q²-F³ = 0.74–0.76, etc.) but also provide useful insights into the structural features that sway the Hs-NMT inhibitory activity of Nitrogen heterocycles. That is, they have an acceptable equipoise of descriptive and predictive qualities as per Organisation for Economic Co-operation and Development (OECD) guidelines. The developed QSAR models identified a good number of molecular descriptors like solvent accessible surface area of all atoms having specific partial charge, absolute surface area of Carbon atoms, etc. as important features to be considered in future optimizations. In addition, pharmacophore modeling has been performed to get additional insight into the pharmacophoric features, which provided additional results.

While Internet banking (IB) adoption after COVID-19 has become widespread in Egypt, understanding the factors that drive customers to continue using these services remains a critical and underexplored area. This surge in IB utilization poses a high risk for banks in future if it is not accompanied by a high adaptability of the Egyptians to this transformation. Previous studies demonstrate the Egyptian cultural preference for traditional banking relationships and personal interactions. Trust issues, perceived risks around privacy and security, and a conservative attitude towards digital finance might strongly influence IB continuous adoption behaviours (Fawzy and Esawai in J Bus Retail Manag Res 12, 2017). Thus, this study seeks to deepen the understanding of the factors influencing the continuous use of Internet banking in Egypt. This paper focuses on the significant direct and indirect drivers for continuous IB service usage during and after COVID-19 in Egypt. A conceptual model grounded in the extended technology acceptance model (TAM2) is developed and studied using the survey-based structural equation model. Data were collected using an online questionnaire from 300 Egyptian bank customers who adopt financial transactional Internet banking. Structural equation modelling (SEM) is utilized for data analysis. The study findings reveal that perceived risk of COVID-19 (PRC), perceived usefulness (PU), subjective norms (SNs), and expectation confirmation (CONF) are significant direct and indirect drivers for explaining customers’ IB continuous intentions during and after COVID-19. Accordingly, this research contributes to the existing literature by highlighting the significant determinants of IB post-adoption behaviour. In addition, the paper provides useful practical implications for bank managers, website developers, and policymakers on how to develop effective strategies to enhance the usefulness of IB and boost the IB users’ expectation confirmation that would strengthen their attitudes and their continuous intentions ensuring banks’ IB revenue stream continuity.

This paper presents a computational study for probabilistic fuzzy linear programming using machine learning. Two opposite probabilistic fuzzy constraints are considered, where the random variable in the two constraints is discrete with a Poisson distribution. The data was generated from a Poisson distribution under different scenarios. Five scenarios are investigated based on either the same mean parameter and different dispersions of the values of the random variable or the same values of the random variable and different mean parameters. While eight cases are derived by considering different combinations of fuzzy probabilities. These many configurations of different scenarios and cases allow us to compare how models perform while varying both the mean parameter and the range of the values through different combinations of fuzzy probabilities. This setup allows for a thorough evaluation of how these changes impact model performance using machine learning models. Nine machine learning models have been considered in this study for evaluating different scenarios and cases in predicting the target decision variables. Since the Poisson distribution is beneficial in fields such as telecommunications, healthcare, logistics, and reliability engineering, where the frequency of arrivals, failures, or demands exhibits Poisson-like behavior but is additionally impacted by ambiguity or incomplete information. Therefore, this study provides a useful tool to the decision-makers to carefully select the combinations of the fuzzy probabilities in the light of the possible values of the Poisson random variable, especially when the associated probabilities are not specified.

The present study examines the impact of the national culture on the country-level governance efficiency index. The dimensions of culture as an independent variable is measured using three different cultural frameworks: Hofstede, Globe and Trompenaar. Based on a secondary data collection, the governance efficiency is the dependent variable adopted in the analysis. As the current study is examining whether the national culture has an impact on the governance efficiency index, it is an attempt to understand the determinants of governance. An empirical study was conducted on data involving 177 different countries with different level of incomes. The analysis adopted is the linear regression to test the relationship between the independent and dependent variables and the machine learning models to classify data based on models which have been developed, the other purpose is to find out the best model for predicting the future outcomes. The paper at the end concluded with a discussion of some of the theoretical and practical implications from the results of the empirical study of governance in a number of different countries. It brings a valuable insight into a previously undocumented area of research stressing on the importance of national culture as an important factor and determinant for a better country-level governance.