This comprehensive literature review analyzes the current state-of-the-art research on 5G applications and technologies in smart grids. Adhering to PRISMA guidelines, the review presents a detailed taxonomy of applications and methodologies being utilized in this field. The review searched various electronic databases from 2012 to 2022, and conducted bibliometric analysis to identify research trends, influential authors, journals, and institutions in this field. The review identifies various 5G applications and technologies used in Smart grids, and the potential benefits of 5G technology in enhancing the efficiency, reliability, and security of smart grid systems. The study provides valuable insights into the research trends, research gaps, and future research directions in this field, and can guide researchers and practitioners in developing and implementing 5G-enabled smart grid systems.
In engineering and electrical engineering higher education, in its practice-oriented laboratory training background, the use of virtual methods is very common, in an environment where the main goal is of course to strengthen and develop practical attitudes.Without neglecting the importance of the computer-based possibilities of simulation and emulation and their practical relevance, there is a danger, one could say with some malice, that we are using virtual tools to train engineers who will be fully capable of solving virtual problems virtually.To avoid all this, we continue to believe that it is important to use real instruments in electrical engineering training, whether they are measuring instruments or electronic circuits to be measured. In this paper, we aim to present an application that supports analog electrical engineering, the operation of analog circuits, where we offer a high degree of flexibility to teachers and students in a real electronic circuit measurement environment.Thus, we will continue to use real instruments, generators, power supplies, oscilloscopes, spectrum analysers, measuring instruments, but we will design the object to be measured using FPAA (Field Programmable Analog Array) devices, by creating different analog circuits to be measured. This allows us to set different circuit parameters, even per measurement place, for the same circuit.
This systematic review aims to explore how the critical role of human participation in cyberspace is reassessed, given the growing importance of Industry 4.0 and 5.0. The focus of the review will be to explore the implications for the labor market and the training of the workforce, in particular the need for the development of soft skills and cyber security awareness. Literature is lacking on the role of cyberspace, cyberawareness and soft skills, their interdependencies and the implications and consequences of developing them. This systematic review provides an overview of the empirical research on soft skills and cybersecurity awareness in the context of industrial revolutions. The development of soft skills and cybersecurity awareness is key to successful adaptation and competitiveness for both individuals and the labor market. In accordance with the screening criteria, 32 articles were identified and were selected for inclusion in the final summary for the period 2017-2022 from a total of 5921 articles. This study examines the human-machine collaboration of Industry 4.0 and 5.0 in cyberspace from a labor market and workforce training perspective. The study highlights the increasing importance of combining technical and soft skills, including digital literacy, emotional intelligence, empathy, and adaptability, for engineers and IT specialists working in this industry. To address the digital and cybersecurity skills gap, the study also highlights the need for continuous skills development, on-the-job learning, and mentoring support.
With the accelerating adoption of artificial intelligence and machine learning in medicine, algorithmic fairness, and the demonstration of the validity, accuracy and clinical relevance of models is becoming increasingly important. EQ-5D-5L is one of the most widely used instrument for measuring health-related quality of life. For the evaluation, selection, and adoption of machine learning models used for the prediction of healt-related quality of life expressed in EQ-5D-5L index scores, we propose a new metric called G as a single measure of model goodness that consolidates measures such as accuracy, bias, and fairness in a value ranging from 0 to 1. Fairness is conceived as the independence of prediction error from EQ-5D-5L, and protected variables, such as sex, age, education, income and work status. The G metric ignores prediction errors that cannot be perceived by patients (e.g., smaller than the minimum clinically important difference (MCID) for EQ-5D-5L). For the computation of G, we estimated the Hungarian MCID for EQ-5D-5L as 0.066617. The proposed metric was tested on simulated prediction errors in a real-world sample of 2000 individuals and synthetic dataset. Consistently with the expectations, in the real-world dataset, highest G values were found with most accurate predictions and independent errors from the protected variables. However, using the same error simulation parameters, the performance of G was inconsistent in the synthetic dataset. Further research is needed to derive a G metric robust to data distribution shifts.
