The optimum condition for electric vehicles' battery powering factors to travel distance: A model-based approach

The development of electric vehicles (EVs) and their power source systems (PSS) is a rapidly growing field of technology. However, the EV's travel distance (range between charging stations) depends on the agility of the PSS, or battery capacity system. EV driving range and battery capacity are the two most significant technical challenges in commercializing EVs. This study aims to propose an integrated model that identifies the optimal energy factor orientation, enabling EVs to cover the maximum travel distance and reach the charging station for their next trip. Additionally, the artificial intelligence (AI) and statistical models were integrated and applied to predict, validate, and explain how energy factors affect the driving range of EVs. The developed models and validations revealed that maintaining precise assimilation of battery power factors can vary the EV's travel distance from 60 to 610 km. In this case, we have identified 77.5 kWh battery capacity and 14.5 kW charging capacity as the optimum power source factors. After 5.5 h of charging, various adjustments to power source factors allow for optimum battery performance. We have also proposed the central composite factorial design (CCFD) to compute the impact of energy factors on travel distance. The study used the response surface methodology (RSM) and an in-house-developed AI-based algorithm to achieve the research results. The alignment percentage between model-predicted data and real-time outputs showed an extremely high precision of over 95 % and confidence in the findings' reliability.

Unified Aedes aegypti Protein Resource Database (UAAPRD): An Integrated High-Throughput In Silico Platform for Comprehensive Protein Structure Modeling and Functional Target Analysis to Enhance Vector Control Strategies

A comprehensive examination of Aedes aegypti's proteome to detect key proteins that can be targeted with small molecules can disrupt blood feeding and disease transmission. However, research currently only focuses on finding repellent-like compounds, limiting studies on identifying unexplored proteins in its proteome. High-throughput analysis generates vast amounts of data, raising concerns about accessibility and usability. Establishing a dedicated database is a solution, centralizing information on identified proteins, functions, and modeled structures for easy access and research. This study focuses on scrutinizing key proteins in A. aegypti, modeling their structures using RaptorX standalone tool, identification of druggable binding sites using BiteNet, validating the models via Ramachandran plot studies and refining them via 50-ns molecular dynamic simulations using Schrodinger Maestro. By analyzing ~ 18 k proteins in the proteome of A. aegypti in our previous studies, all proteins involved in the light and dark circadian rhythm of the mosquito, inclusive of proteins in blood feeding, metabolism, etc. were chosen for the current study. The outcome is UAAPRD, a unique repository housing information on hundreds of previously unmodeled and un-simulated mosquito proteins. This robust MYSQL database ( https://uaaprd.onrender.com/user ) houses data on 309 modeled & simulated proteins of A. aegypti. It allows users to obtain protein data, view evolutionary analysis data of the protein categories, visualize proteins of interest, and send request to screen against the pharmacophore models present in UAAPRD against ligand of interest. This study offers crucial insights for developing targeted studies, which will ultimately contribute to more effective vector control strategies.