Abstract
In the field of engineering education, hands-on experiences in laboratories are essential for effective learning. However, traditional laboratories face challenges such as cost and infrastructure, hindering access. And virtual laboratories are believed to have significant potential to offer efficient and cost-effective solutions for practical lab work compared to traditional labs. Moreover, currently there is a growing implementation of these solutions in various educational and professional areas where practical knowledge is crucial, especially due to the COVID-19 pandemic. Therefore, the current research focuses on the design and development of a 3D virtual laboratory for computer hardware classes aiming to provide a solution to the problem of the lack of physical equipment for conducting practical IT laboratory activities. Additionally, to develop the virtual laboratory, tools such as Blender, Sculptris, and ZBrush were used for modelling, and the Unity Game Engine was used for programming and rendering. This simulated environment allows interactions with digital computer hardware components and circuits, promoting accessibility, scalability, and cost savings.

Abstract
To meet the growing demand of robotics applications in industry high efforts are placed on research and education for robotics fundamentals, as a more intuitive and easier access to robotics must be ensured. Based on this, the aim of this work is to make a contribution to investigate new approaches for teaching robotics fundamentals using immersive technologies. In this work an existing digital twin for a desktop robot was extended by adding a feature for the positioning of way-points for path planning in an existing Augmented Reality (AR) application as well as update the robot controller with the inverse kinematics for the corresponding motion implementation. The performance of the new functionality was evaluated by means of a pick-and-place application and validated via a test series with an associated questionnaire. The conducted experiments have been carried out in a comparative study between the AR based digital twin and an ABB industrial robot where a clear benefit of the proposed systems for entry into robotics as well as for teaching was identified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The telecommunications industry is particularly competitive and characterized by very high churn rates. The literature on the topic is vast, but studies on the determinants of churn behavior are dispersed, failing to provide a comprehensive view of the state of the art. Based on this research gap, this article aims to contribute to developing the literature on customer churn in the telecommunications sector by summarizing the current state of research, and identifying the main determinants of churn and switching intentions. It provides a systematic literature review (SLR) of 37 articles on the topic published between 1999 and 2022. The results reveal the existence of two research streams. The first, in which the studies are based on surveys examining the alleged intentions of subscribers to change operators, with criteria such as satisfaction and attitudes as predictors, and the second, dealing with subscribers' actual switching behavior and relating this to behaviors and characteristics extracted from internal customer management systems. All independent variables used to explain switching intention or real churn were mapped. It was found that age, gender, satisfaction, switching costs and barriers, and service quality are the most important determinants highlighted by the literature. Our study also outlines some insightful practical implications, which could be extended to other service sectors. The paper ends with a research agenda for future studies according to the gaps detected by the study's results. Among its limitations, this research excludes papers related to predictive models and studies not in English.

Abstract
The growing adoption of microgrids necessitates efficient management of electrical energy storage units to ensure reliable and sustainable power supply. This paper investigates a thermal management system (TMS) for maintaining the longevity of large-scale batteries. To streamline the thermal modeling of batteries, the McCormick relaxation method is employed to linearize a nonlinear and interdependent heat generation model. The thermal model of the battery follows a nonlinear behavior where the generated heat makes the battery system temperature soar, thereby affecting the thermal performance of the battery. To showcase the efficacy of the proposed approach, four distinct case scenarios are studied, highlighting the critical importance of batteries within microgrid operations. A comparative analysis is conducted between linear and nonlinear models for TMS performance. A quantitative assessment based on simulation results demonstrates the precision of the linearized model, particularly in a multitemporal optimal power flow and day-ahead scheduling of microgrids incorporating energy storage units. Controlling the battery temperature within a permissible range (from 15 degrees C to 40 degrees C) is achieved by using a heating, ventilation, and air conditioning (HVAC) system. The paper explores the economic implications of energy storage units in microgrids by extracting and comparing daily operational costs with and without battery integration. The findings reveal that the inclusion of energy storage units yields substantial economic benefits, with potential profit margins of approximately 20 % during typical working days and 60 % on weekends.

Abstract
In recent years, there has been a significant growth in the use of technologies that rely on natural resources (wind, solar, etc.) as primary sources of energy. The generation originating from renewable sources brings an increased need for adaptation in power electrical systems. Predicting the amount of energy produced by these technologies is a complex task due to the uncertainty associated with natural resources. This uncertainty hinders decision-making, both at the system level and for consumers themselves who are increasingly using this type of technology for self-consumption. This study focuses on classifying solar intensity using imbalanced data, which means that some of the data categories are more prevalent than others. Oversampling techniques are be employed to increase the amount of data, thereby allowing for balanced training data and improving the performance of prediction models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
In this work, the implementation of an efficient multi-threading algorithm for calculating the power flow in electricity distribution networks is carried out using recursion and parallel programming. With the integration of renewable energy, energy storage systems and distributed generation, the ability of power flow simulations becomes a crucial factor in finding the best solution in the shortest possible time. We propose the direct use of graph theory to represent distribution network topologies. In this data structure, the traversal algorithms are inherently recursive, thus enabling the development of algorithms with parallel programming to obtain the power flow calculation faster and more efficiently. Results under a 809 buses test system show that the implementation provides additional computation efficiency of 32% with recursion techniques and 27% with parallel programming, due the expense of threads' allocation the combined gain reaches 50%.