One of the main challenges that impact transportation systems electrification is their batteries' charging process. This work presents the development of a three-phase ultra-fast Electric Vehicle (EV) charger based on the SEPIC converter. Since SEPIC operating in Discontinuous Conduction Mode (DCM) is usually recommended for low-power applications, this work proposes a scheme for its employment in high-power EV chargers. This is achieved through three single-phase modules of interleaved SEPIC converters. The presented scheme ensures reducing the stresses on the semiconductor devices since the power is divided over the interleaved modules. The design addresses DCM operation in terms of both capacitor voltage and inductor current (DCVM and DICM, respectively). This paper examines the analysis of the proposed converter and the small-signal modelling. Also, the converter efficiency is assessed. A Constant Current (CC) charging approach is deployed for charging the EV battery. The validation of the designs is explored through simulation results using MATLAB/Simulink platform. A 4 kW experimental prototype for the interleaved SEPIC DC-DC converter is built to verify the claimed contributions with 92% efficiency.
Background: The potential of Artificial intelligent (AI) models to process and interpret large health datasets at scale could revolutionize public health and epidemiology, providing a foundation for public health. Ethics has been recognized as a priority concern in the development and deployment of AI. Because AI technology can jeopardize patient safety, privacy, and posing a new set of ethical problems that must be addressed. Objectives: We aim to provide a holistic view on what are the different ethical and legal principles that was addressed in the included studies regarding the use of AI in public health and what are the ethical challenges that can arise.Methods: Following PRISMA guideline, five bibliographic databases were used in our search: PubMed, Scopus, JSTOR, IEEE Xplore, and Google Scholar from 2015 to February 2022. Four reviewers carried out study selection and data extraction, and the data extracted was synthesized by a narrative approach. Results: This review included 23 unique publications out of a total of 1123 items that were initially identified. Different ethical principles regarding the uses of AI in public health and community health were identified and discussed distinctly in the current review. The common ethical and legal themes that this review focused on are equity, bias, privacy and security, safety, transparency, confidentiality, accountability, social justice, and autonomy. In addition, five ethical challenges were mentioned. Conclusion: Research regarding ethical and legal principles and challenges about using AI in public health specifically consider a new filed, because all previous themes are concerning the physical and patients’ area where it focuses only on the clinical settings.
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