Abstract

Abstract
Companies are increasingly adopting technologies such as Robotic Process Automation (RPA) to reduce costs and improve productivity. RPA is deployed in areas like accounting, payroll, and finance to automate business processes. While RPA does not necessarily result in unemployment, it has notable effects on employees and company governance. This study explores the impact of RPA implementation on employees and company governance, using a qualitative methodology based on thirteen semi-structured interviews with RPA experts from four multinational companies. The results indicate that the impacts of RPA vary depending on the automation strategy adopted (task-oriented or process-oriented). In task-oriented strategies, citizen developers often play a central role, contributing to rapid implementation. In contrast, process-oriented strategies tend to rely on professional developers and require more structured governance. The findings also point out that RPA influences not only task execution but also employee upskilling, job role redefinition, and the evolution of governance models. The study proposes an integrated framework linking automation strategy, governance, upskilling, and employee adaptation, offering both practical insights and theoretical contributions to digital transformation research and for managing risks and enhancing workforce capabilities. It also advances academic understanding by linking real-world RPA implementations to organisational and technological impacts.

Abstract
Large Language Models (LLMs) have become an essential tool for providing assistance, including in the form of virtual assistants that support everyday tasks. However, recent evidence suggests that LLMs can exhibit confirmation bias, responding in ways that reinforce beliefs and assumptions. This behavior negatively influences clinical decision-making processes within Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy and treatment recommendations. This study aims to quantify the prevalence of confirmation bias in LLMs, the conditions under which it occurs, and its causes. The methodology consisted of an experiment in which 52 biased, health-related queries were submitted to ten of the most widely used LLM models, and the responses were evaluated for bias. Statistical analyses were performed to indicate the presence of confirmation bias. Specifically, our results show that confirmation bias is present in all of the LLMs tested, with a prevalence ranging from 15% to 67%. Our findings suggest that LLMs' biases arise from factors such as the training dataset, the Transformer architecture's inherent characteristics, the prompt engineering and fine-tuning instructions added by developers. This study contributes to ongoing efforts towards the development of trustworthy LLMs. © 2026 IEEE.

Abstract
The wine industry, with its high resource demands, is facing increasing pressure to improve operational sustainability by reducing water and energy use due to climate change, limited resources, and changing regulations. This study is a critical narrative review that followed a structured literature identification and screening approach to synthesise evidence on monitoring, instrumentation, and digital solutions for water and energy efficiency in vineyards and wineries. The review and critical analysis focus on sensor technologies, IoT-based systems, and smart management platforms applicable to the winemaking process. The evidence highlights the underexplored water-energy nexus within the wine sector, demonstrating bidirectional dependencies and potential co-benefits of integrated resource management, particularly in water-intensive and energy-intensive operations (e.g., cleaning/CIP, cooling/refrigeration, and pumping). Despite advancements in agriculture and industrial sectors, the integration of smart monitoring systems in wineries remains limited due to fragmented implementation, lack of standardized indicators, and socioeconomic barriers affecting scalability. Based on the synthesis, this work proposes actionable measures and implementation pathways, including baseline metering and sub-metering with clear KPIs, real-time monitoring and anomaly/leak detection, optimisation of cleaning/ CIP and cooling control, water reuse/recycling opportunities, and integrated energy management via dashboards/decision-support tools to enable data-driven operation. These findings help bridge technical innovations with practical adoption pathways, supporting the transition toward sustainable, low-impact, and climate-resilient wine production.

Abstract
Systematic reviews and mapping studies are critical to synthesize research, identify gaps, and guide future work, but are often labor-intensive and time-consuming. Existing tools provide partial support for specific steps, leaving much of the process manual and error-prone. We present ProfOlaf, a semi-automated tool designed to streamline systematic reviews while maintaining methodological rigor. ProfOlaf supports iterative snowballing for article collection with human-in-the-loop filtering and uses large language models to help select articles, extract key topics, and answer queries about the content of articles. By combining automation with guided manual effort, ProfOlaf enhances the efficiency, quality, and reproducibility of systematic reviews across research fields. ProfOlaf can be used both as a CLI tool and in web application format. A video demonstrating ProfOlaf is available at: https://youtu.be/R-gY4dJlN3s © 2026 Copyright held by the owner/author(s).

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.