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
The integration of electric vehicles (EVs) into the power grid poses significant challenges and opportunities for energy management systems. This is especially concerning for parking lots or private building condominiums in which refurbishing is not possible or is costly. This paper presents a real-time monitoring approach to EV charging dynamics with battery storage support over a 24 h period. By simulating EV demand, state of charge (SOC), and charging and discharging events, we provide insights into the operational strategies for energy storage systems to ensure maximum charging simultaneity factor through internal power enhancement. The study uses a time-series analysis of EV demand, contrasting it with the battery's SOC, to dynamically adjust charging and discharging actions within the constraints of the upstream infrastructure capacity. The model incorporates parameters such as maximum power capacity, energy storage capacity, and charging efficiencies, to reflect realistic conditions. Results indicate that real-time SOC monitoring, coupled with adaptive charging strategies, can mitigate peak demands and enhance the system's responsiveness to fluctuating loads. This paper emphasizes the critical role of real-time data analysis in the effective management of energy resources in existing parking lots and lays the groundwork for developing intelligent grid-supportive frameworks in the context of growing EV adoption.

Abstract
This study proposes a strategy for short-term operational planning of active distribution systems to minimize operating costs and greenhouse gas (GHG) emissions. The strategy incorporates network reconfiguration, switchable capacitor bank operation, dispatch of fossil fuel-based and renewable distributed energy resources, energy storage devices, and a demand response program. Uncertain operational conditions, such as energy costs, power demand, and solar irradiation, are addressed using stochastic scenarios derived from historical data through a k-means technique. The mathematical formulation adopts a stochastic scenario-based mixed-integer second-order conic programming (MISOCP) model. To handle the computational complexity of the model, a neighborhood-based matheuristic approach (NMA) is introduced, employing reduced MISOCP models and a memory strategy to guide the optimization process. Results from 69 and 118-node distribution systems demonstrate reduced operational costs and GHG emissions. Moreover, the proposed NMA outperforms two commercial solvers. This work provides insights into optimizing the operation of distribution systems, yielding economic and environmental benefits.

Abstract

Abstract
This paper presents an approach to address the challenges of manual inspection using multirotor Unmanned Aerial Vehicles (UAV) to detect olive tree flies (Bactrocera oleae). The study employs computer vision techniques based on the You Only Look Once (YOLO) algorithm to detect insects trapped in yellow chromotropic traps. Therefore, this research evaluates the performance of the YOLOv7 algorithm in detecting and quantify olive tree flies using images obtained from two different digital cameras in a controlled environment at different distances and angles. The findings could potentially contribute to the automation of insect pest inspection by UAV-based robotic systems and highlight potential avenues for future advances in this field. In view of the experiments conducted indoors, it was found that the Arducam IMX477 camera acquires images with greater clarity compared to the TelloCam, making it possible to correctly highlight the set of Bactrocera oleae in different prediction models. The presented results in this research demonstrate that with the introduction of data augmentation and auto label techniques on the set of images of Bactrocera oleae, it was possible to arrive at a prediction model whose average detection was 256 Bactrocera oleae in relation to the corresponding ground truth value to 270 Bactrocera oleae.