Abstract

Abstract
The United Nations' 2030 Agenda for Sustainable Development highlights the importance of energy-efficient software to reduce the global carbon footprint. Programming languages and execution models strongly influence software energy consumption, with interpreted languages generally being less efficient than compiled ones. Lua illustrates this trade-off: despite its popularity, it is less energy-efficient than greener and faster languages such as C. This paper presents an empirical study of Lua's runtime performance and energy efficiency across 25 official interpreter versions and just-in-time (JIT) compilers. Using a comprehensive benchmark suite, we measure execution time and energy consumption to analyze Lua's evolution, the impact of JIT compilation, and comparisons with other languages. Results show that all LuaJIT compilers significantly outperform standard Lua interpreters. The most efficient LuaJIT consumes about seven times less energy and runs seven times faster than the best Lua interpreter. Moreover, LuaJIT approaches C's efficiency, using roughly six times more energy and running about eight times slower, demonstrating the substantial benefits of JIT compilation for improving both performance and energy efficiency in interpreted languages. © 2026 IEEE.

Abstract
The incorporation of large language models (LLMs) into clinical decision support systems (CDSS) offers potential for enhancing the quality of healthcare. Nevertheless, there is a dearth of comprehensive standards and understanding about the application of explainable and responsible AI practices in this context. The objective of this study is to present an overview of the literature on the utilization of LLMs in CDSSs, with an emphasis on Explainable AI (XAI) techniques and responsible AI principles. The present study was conducted through a systematic literature review analyzed of 36 studies on the application of LLMs in CDSS, with a focus on XAI and responsible AI. Human-centered AI is essential to design CDSSs that not only provide accurate recommendations but also align with clinicians' trust and workflow realities. The results suggest that while LLM-based CDSSs demonstrate potential in enhancing clinical decision-making, concerns regarding model interpretation into healthcare workflows persist as challenges.

Abstract
Robotic bin-picking is a critical operation in modern industry, which is characterised by the detection, selection, and placement of items from a disordered and cluttered environment, which can be boundary limited or not, e.g. bins, boxes or containers. In this context, perception systems are employed to localise, detect and estimate grasping points. Despite the considerable progress made, from analytical approaches to recent deep learning methods, challenges still remain. This is evidenced by the growing innovation proposing distinct solutions. This paper aims to review perception methodologies developed since 2009, providing detailed descriptions and discussions of their implementation. Additionally, it presents an extensive study, detailing each work, along with a comprehensive overview of the advancements in bin-picking perception.

Abstract
Manual selection of real estate properties can pose considerable challenges for agents since it needs a careful balance of various factors to satisfy client requirements while also manoeuvring through the complexities of the market. Although automated valuation models are widely used to estimate property market values, they are not designed to support property recommendation tasks. To address this gap, filteringbased recommendation methods have been explored, including collaborative and content-based approaches. However, these methods face several limitations in the real estate domain. This paper proposes a recommendation methodology designed to identify houses that closely resemble a given property, allowing agents to select the best matches based on geographical and physical characteristics. To assess the performance of the proposed methodology, we employ a range of evaluation metrics that measure different aspects of the model's effectiveness in ranking and recommending relevant items. The findings suggest that, while geographic features may slightly influence ranking behaviour, the model is capable of producing diverse and relevant recommendations consistently.

Abstract
The European Union Internal Electricity Market is undergoing major reforms to support the transition to a fully decarbonized energy system by 2050, where non-dispatchable renewable energy sources play a central role. To enhance market efficiency, renewable energy sources integration, and power system balancing, the European Union promotes increased cross-border interconnection and cooperation among Member States. This paper reviews existing literature and market models addressing multi-zone interconnection capacity allocation and proposes a novel inter-zonal co-optimization mechanism for the joint allocation of energy and automatic balancing reserve capacity based on system cost minimization. Unlike previous approaches that treat energy and reserve coordination separately or sequentially, this study introduces a unified optimization framework that captures the interdependencies of intra-and inter-zonal dispatch. The proposed mechanism is implemented within the CEVESA market model and applied to a realistic Iberian case study, assessing its economic and operational impacts under varying interconnection capacity scenarios. Results show that while energy coordination alone achieves significant cost reductions, joint coordination of energy and reserves delivers further efficiency gains, reduces reserve price volatility, and enhances cross-border system flexibility.