Abstract
The integration of inverter-based distributed generation challenges the implementation of an reliable protection This work proposes an adaptive protection method for coordinating protection systems using directional overcurrent relays, where the settings depend on the distribution network operating conditions. The coordination problem is addressed through a specialized genetic algorithm, aiming to minimize the total operating times of relays with time-delayed operation. The pickup current is also optimized. Coordination diagrams from diverse fault scenarios illustrate the method's adaptability to different operational conditions, emphasizing the importance of employing multiple setting groups for optimal protection system performance. The proposed technique provides high-quality solutions, enhancing reliability compared to traditional protection schemes.

Abstract

Abstract
With the proliferation of Internet of Things (IoT) devices, there is an increasing need to prioritize their security, especially in the context of identity and authentication mechanisms. However, IoT devices have unique limitations in terms of computational capabilities and susceptibility to hardware attacks, which pose significant challenges to establishing strong identity and authentication systems. Paradoxically, the very hardware constraints responsible for these challenges can also offer potential solutions. By incorporating hardware-based identity implementations, it is possible to overcome computational and energy limitations, while bolstering resistance against both hardware and software attacks. This research addresses these challenges by investigating the vulnerabilities and obstacles faced by identity and authentication systems in the IoT context, while also exploring potential technologies to address these issues. Each identified technology underwent meticulous investigation, considering known security attacks, implemented countermeasures, and an assessment of their pros and cons. Furthermore, an extensive literature survey was conducted to identify instances where these technologies have effectively supported device identity. The research also includes a demonstration that evaluates the effectiveness of hardware trust anchors in mitigating various attacks on IoT identity. This empirical evaluation provides valuable insights into the challenges developers encounter when implementing hardware-based identity solutions. Moreover, it underscores the substantial value of these solutions in terms of mitigating attacks and developing robust identity frameworks. By thoroughly examining vulnerabilities, exploring technologies, and conducting empirical evaluations, this research contributes to understanding and promoting the adoption of hardware-based identity and authentication systems in secure IoT environments. The findings emphasize the challenges faced by developers and highlight the significance of hardware trust anchors in enhancing security and facilitating effective identity solutions.

Abstract
The decline in student attendance and engagement in Higher Education (HE) is a pressing concern for educational institutions worldwide. Traditional lecture-style teaching is no longer effective, and students often become disinterested and miss classes, impeding their academic progress. While Gamification has improved learning outcomes, the integration of Artificial Intelligence (AI) has the potential to revolutionise the educational experience. The combination of AI and Gamification offers numerous research opportunities and paves the way for updated academic approaches to increase student engagement and attendance. Extensive research has been conducted to uncover the correlation between student attendance and engagement in HE. Studies consistently reveal that regular attendance leads to better academic performance. On the other hand, absenteeism can lead to disengagement and poor academic performance, stunting a student's growth and success. This position paper proposes integrating Gamification and AI to improve attendance and engagement. The approach involves incorporating game-like elements into the learning process to make it more interactive and rewarding. AI-powered tools can track student progress and provide personalised feedback, motivating students to stay engaged. This approach fosters a more engaging and fruitful educational journey, leading to better learning outcomes. This position paper will inspire further research in AI-Gamification integration, leading to innovative teaching methods that enhance student engagement and attendance in HE.

Abstract
Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating energy storage systems (ESSs) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This article compares the performance of hybrid energy storage systems (HESSs) to a single battery, evaluating their energy supply cost and environmental impact through optimization problems. The optimization model is based on a MILP incorporating the energy and degradation terms. It generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load. Seven technologies are assessed, with an example applied to an industrial site combining a vanadium redox flow battery (VRFB) and lithium battery considering the demand of a local load (building). The results indicate that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

Abstract
With technological advancements, much data is being captured by sensors, smartphones, wearable devices, and so forth. These vast datasets are stored in data centres and utilized to forge data-driven models for the condition monitoring of infrastructures and systems through future data mining tasks. However, these datasets often surpass the processing capabilities of traditional information systems and methodologies due to their significant size. Additionally, not all samples within these datasets contribute valuable information during the model training phase, leading to inefficiencies. The processing and training of Machine Learning algorithms become time-consuming, and storing all the data demands excessive space, contributing to the Big Data challenge. In this paper, we propose two novel techniques to reduce large time-series datasets into more compact versions without undermining the predictive performance of the resulting models. These methods also aim to decrease the time required for training the models and the storage space needed for the condensed datasets. We evaluated our techniques on five public datasets, employing three Machine Learning algorithms: Holt-Winters, SARIMA, and LSTM. The outcomes indicate that for most of the datasets examined, our techniques maintain, and in several instances enhance, the forecasting accuracy of the models. Moreover, we significantly reduced the time required to train the Machine Learning algorithms employed.