Abstract
Abstract Social network analysis offers a powerful method for comprehending intricate systems created through agent-based computational models. Scholars contend that intricate agent networks have the capacity to grasp both the dynamics at an individual level and the overarching characteristics at a global level within a complex system. Consequently, they can contribute to a deeper comprehension of the underlying principles governing such systems. Within this endeavour, we undertake a comprehensive examination of the existing body of literature that establishes a connection between agent-based models and social network analysis. The focal aim of this exploration is to cater to the domain of management studies. We define a baseline for a network topology for a taxonomy of ‘macro’ and ‘micro’ characteristics of social interaction networks. We apply and extend this taxonomy for agent-based models and classify existing models in macro structures, macro patterns of interaction, micro interactions and multilayers, and micro level (clustering and local interaction). We also emphasize the learning methods for agent-based models of social networks.

Abstract
An athlete’s sporting experience is a factor associated with better-coping strategies and emotional regulation, especially concerning competitive anxiety and its symptoms. To verify whether more experienced athletes have lower rates of negativism and higher levels of self-confidence, we compared the means of these two variables between athletes with more and less experience. A meta-analysis was performed, following the PRISMA model. Seven articles were selected that measured, through the Competitive State Anxiety Inventory – 2 (CSAI-2 or its shortened version, CSAI-2R), the levels of self-confidence and negativism of high-performance athletes with different sports experiences. Significant statistical differences were found regarding the levels of negativism between athletes with more and less experience (p < .001). The same occurred with the levels of self-confidence between athletes with more and less sports experience (< .001). The results align with our initial hypothesis, formulated by Martens et al. (1990), that athletes with more experience would have higher self-confidence and lower negativism averages. One of the reasons may be using more effective coping strategies that are improved during the career years.

Abstract
Wireless networks have undergone significant advancements with the rapid progress in Software Defined Networks (SDN), Open RAN (O-RAN) and 5G technology. Among the most notable developments is the emergence of network slicing, which leverages the concept of virtual networks to separate the end-to-end network and computational resources into individual network slices per one or more services/tenants. However, determining the appropriate allocation of computational and network resources for each network slice to meet the specific requirements of each service remains a challenge, especially with continuously changing demands and varying Key Performance Indicators (KPIs) per service and the risk of insufficient or excessive resource allocation. To address these issues, this paper proposes an intelligent predictive framework based on Deep Reinforcement Learning (DRL). Our framework utilizes historical demand and KPI requirements to predict and reserve optimized network slices for multiple services in the future, considering unique constraints, dynamic pricing and under-provisioning. Using different datasets, we validated our system's effectiveness, scalability, and adaptiveness by comparing it with different baselines and state-of-the-art approaches. Our results affirm the efficiency of the proposed solution, showcasing a minimum cost reduction of 15% compared to different baselines and state-of-the-art solutions while incurring less than 2% additional resource consumption. Furthermore, our system demonstrates excellent scalability and adaptability across varying network conditions.

Abstract
Simultaneous Localization and Mapping (SLAM) algorithms are a key component in enabling autonomous navigation for robotic systems. This study presents a comprehensive assessment of state-of-the-art SLAM algorithms, focusing exclusively on those with Robot Operating System (ROS) support. The study aims to provide insights into the computational performance of these algorithms by leveraging the benchmark results reported in their respective studies. Each algorithm's performance metrics, as reported in their benchmark studies, are analyzed and compared. This comparative analysis not only highlights the strengths and weaknesses of individual algorithms but also provides a broader understanding of their applicability across diverse robotic platforms and environments. Overall, this study contributes to the advancement of SLAM research by offering a comparative evaluation tailored to ROS-supported algorithms. The findings serve as a valuable resource to make informed decisions regarding the selection and implementation of SLAM solutions in real-world applications.

Abstract
Immersive Virtual Reality (iVR) is a promising educational tool for learning a second/foreign language. However, interactive iVR studies remain in their infancy, with more research required to validate what and how it can be implemented. This study focuses on the English listening dimension and evaluates the impact of a realistic interactive iVR compared to traditional listening exercises. The results were favourable and indicated that interactive iVR positively impacts the users' knowledge retention compared to a traditional listening approach. Likewise, the users revealed a preference for using iVR for learning when compared to traditional listening exercises, as well as higher user satisfaction with the iVR experiment.

Abstract
Solar wind forecasting is a core component of Space Weather, a field that has been the target of many novel machine-learning approaches. The continuous monitoring of the Sun has provided an ever-growing ensemble of observations, facilitating the development of forecasting models that predict solar wind properties on Earth and other celestial objects within the solar system. This enables us to prepare for and mitigate the effects of solar wind-related events on Earth and space. The performance of some simulation-based solar wind models depends heavily on the quality of the initial guesses used as initial conditions. This work focuses on improving the accuracy of these initial conditions by employing a Recurrent Neural Network model. The study's findings confirmed that Recurrent Neural Networks can generate better initial guesses for the simulations, resulting in faster and more stable simulations. In our experiments, when we used predicted initial conditions, simulations ran an average of 1.08 times faster, with a statistically significant improvement and reduced amplitude transients. These results suggest that the improved initial conditions enhance the numerical robustness of the model and enable a more moderate integration time step. Despite the modest improvement in simulation convergence time, the Recurrent Neural Networks model's reusability without retraining remains valuable. With simulations lasting up to 12 h, an 8% gain equals one hour saved per simulation. Moreover, the generated profiles closely match the simulator's, making them suitable for applications with less demanding physical accuracy.