Energy security is currently one of the most important topics worldwide. Maintaining a reliable energy supply is one of the biggest challenges in security science. Additionally, defending energy infrastructure from cyberattacks is an ongoing issue. Understanding the vulnerabilities of energy infrastructure, especially the Smart Grid, which relies on information technology and communications, is a significant advantage. Understanding which system vulnerabilities lead to specific cyber threats presents a significant opportunity, enhancing the defence of energy infrastructure. This paper uses a systematic literature review to identify the most common cyber threat and Smart Grid vulnerability mentioned and researched in the literature from 2018 to 2025. This paper also aims to map the vulnerabilities that allow for cyber threats to occur, with the idea that if we know what causes a weak spot, we can effectively prevent it. Identifying specific weaknesses that could lead to cyber threats allows us to mitigate these dangers by addressing and correcting those vulnerabilities.

The aim of this paper is to identify models for preventive risk recognition as tool for crine prevention in the renewable energy sector through a systematic review of the available literature. These models should represent a formalized set of steps or protocols that can be used at any stage of renewable energy projects, particularly in the construction of renewable energy power plants, to mitigate and minimize the risk of criminal offenses. Models of preventive risk recognition ensure that the power plant construction project progresses smoothly from start to finish without unnecessary financial losses due to their ability to identify all weak points in the entire process. With the help of the models described in this paper, the transition to green energy is accelerated and sustainable development is fully supported,

In the modern days, the amount of the data and information is increasing along with their accessibility and availability, due to the Internet and social media. To be able to search this vast data set and to discover unknown useful data patterns and predictions, the data mining method is used. Data mining allows for unrelated data to be connected in a meaningful way, to analyze the data, and to represent the results in the form of useful data patterns and predictions that help and predict future behavior. The process of data mining can potentially violate sensitive and personal data. Individual privacy is under attack if some of the information leaks and reveals the identity of a person whose personal data were used in the data mining process. There are many privacy‐preserving data mining (PPDM) techniques and methods that have a task to preserve the privacy and sensitive data while providing accurate data mining results at the same time. PPDM techniques and methods incorporate different approaches that protect data in the process of data mining. The methodology that was used in this article is the systematic literature review and bibliometric analysis. This article identifieds the current trends, techniques, and methods that are being used in the privacy‐preserving data mining field to make a clear and concise classification of the PPDM methods and techniques with possibly identifying new methods and techniques that were not included in the previous classification, and to emphasize the future research directions.

Introduction: Quantifier Elimination gives us perfect insight into the most basic world of the computer, its origin, its primer functions and it basic operations. Carefully designed and programmed Algorithm for Quantifier Elimination makes the quantifier elimination from the quantified formulas much easier and much more comprehensive Aim: This paper explains how Quantifier Elimination algorithm can be used in the field of Biology, or to be more specific, in the field of Epidemiology. Material and methods: Exemplary formulas needed for the algorithm are all the formulas from the Mathematical Logic field. JavaScript programming language was used in order to program fast and effective algorithm for Quantifier Elimination. Results: Solving the certain problems from the field of Epidemiology using the Quantifier Elimination method, proved to be very successful in the past, because it made possible for the results to be extracted very fast. Doing the exact thing using the newer generation algorithm might be even more effective. Conclusion: The most basic concepts of Mathematical Logic can be implemented in order to solve the one of the most important questions in Epidemiology.