The pandemic SARS-CoV-2 is highly transmittable with its proliferation among nations. This study aims to design and exploring the efficacy of novel nirmatrelvir derivatives as SARS entry inhibitors by adapting a molecular modeling approach combined with theoretical design. The study focuses on the preparation of these derivatives and understanding their effectiveness, with a special focus on their binding affinity to the S protein, which is pivotal for the virus’s access to the host cell. Considering molecular docking aspects in the scope of a study on nirmatrelvir derivatives and S protein, dynamics simulations with 25 nanoseconds of their binding are explored. The study shows that these derivatives might work as effective antivi

A new Azo‐Schiff base ligand L was prepared by reaction of m‐hydroxy benzoic acid with (Schiff base B) of 3‐[2‐(1H–indol‐3‐yl)‐ethylimino]‐1.5‐dimethyl‐2‐phenyl‐2,3‐dihydro‐1H‐pyrazol‐4‐ylamine. This synthesized ligand was used for complexation with different metal ions like Ni(II), Co(II), Pd(II) and Pt(IV) by using a molar ratio of ligand: metal as 1:1. Resulted compounds were characterized by NMR (¹H and ¹³C), UV–vis spectroscopy, TGA, FT‐IR, MS, elemental analysis, magnetic moment and molar conductivity studies. The activation thermodynamic parameters, such as ΔE*, ΔH^*, ΔS^*, ΔG^*and

Comparative Analysis of Economic Policy Stability between Monarchical and Republican Systems: A Theoretical Fundamental Research

The accurate determination of nuclear radius is fundamental to understanding nuclear structure and interactions. The present study conducts a comprehensive theoretical analysis of nuclear radius measurements using various nuclear structure models, including the empirical mass-number scaling model, the Hartree-Fock approach, and the relativistic mean-field (RMF) theory. These models are systematically compared against experimental nuclear radii to evaluate their predictive accuracy and assess their strengths and limitations. The study also incorporates an uncertainty analysis to quantify the reliability of theoretical predictions, employing Monte Carlo simulations and Bayesian inference techniques to refine estimations. The results r

The 3-aminoacetophenone and 4-aminoantipyrine were used as ‎precursors to prepare new six ligands. The three new ligands (L1,L2 ‎and L3) were synthesis by reacting one mole of 3-aminoacetophenone ‎with one mole of (Acetyl chloride), (benzoyl chloride), (4-‎methoxybenzoyl chloride) and ammonium thiocyanat in acetone as a ‎solvent, they are:-‎ L1 (AAA) =[N-(3-acetylphenylcarbamothioyl)acetamide]‎ L2 (BAA) =[N-(3-acetylphenylcarbamothioyl)benzamide]‎ L3 (MAA) =[N-(3-acetylphenylcarbamothioyl)-4-methoxy benzamide]‎ Also three new derivatives of 4-aminoantipyrine were synthesis by ‎reacting one mole of 4-aminoantipyrine with one mole of (Acetyl ‎chloride), (benzoyl chloride), (4-methoxybenzoyl chloride) and ‎ammonium thio

Optimization is essentially the art, science and mathematics of choosing the best among a given set of finite or infinite alternatives. Though currently optimization is an interdisciplinary subject cutting through the boundaries of mathematics, economics, engineering, natural sciences, and many other fields of human Endeavour it had its root in antiquity. In modern day language the problem mathematically is as follows - Among all closed curves of a given length find the one that closes maximum area. This is called the Isoperimetric problem. This problem is now mentioned in a regular fashion in any course in the Calculus of Variations. However, most problems of antiquity came from geometry and since there were no general methods to solve suc

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial