Abstract
Corporate Social Responsibility (CSR) has consolidated itself as one of the strategic pillars of organizations, influencing reputation, competitiveness, and the relationship with consumers. This study examines the attitudes of Generations X, Y, and Z towards CSR practices, exploring the impact of these perceptions on purchase intention. In addition, it investigates the perception of the greenwashing phenomenon, with a particular focus on Generation Z, which is often considered more sensitive to sustainability issues. The research adopted a quantitative approach through an online questionnaire administered to 223 respondents, of which 218 were considered valid and distributed across the three generations under analysis (1965–2009). Data were processed using SPSS software, allowing the evaluation of knowledge levels, attitudes, and the influence of CSR on consumer behavior. The results show that Generation X demonstrates greater knowledge of CSR practices, while Generation Z reveals comparatively lower levels of concern. Generation Y displays balanced and consistent values across the different dimensions studied. These findings contribute to the intergenerational understanding of the relationship between CSR, trust, and purchase intention, offering relevant insights for companies and marketing professionals in designing ethical and differentiating strategies.

Abstract
Renewable Energy Communities (RECs) hold potential for enhancing local energy flexibility and supporting a just energy transition. Yet most operate without intelligent coordination, limiting technical performance and raising concerns over fairness and the distribution of benefits. This study examines both the performance and equity dimensions of RECs by combining a critical review of technical, regulatory, and social barriers with simulation-based analysis of a real-world demonstrator developed in the EU-funded OPEVA project. Using real consumption and generation data, we model baseline, rule-based, and intelligent coordination scenarios, as well as expansion cases that integrate additional batteries or EV chargers into underserved households, setting to answer these two questions: Who benefits most from current and future deployments of flexibility technologies? And how can REC systems be expanded to not only aggregate performance gains but also equitable and fair outcomes for all participants? Results show that intelligent control reduces community-level peak demand, ramping, and energy costs while improving renewable self-consumption. However, these benefits are unevenly distributed, concentrated among participants already equipped with flexible assets or with higher demand. Expansion scenarios improve both technical performance and fairness, but inequities persist without deliberate policy intervention. We conclude with open challenges and propose policy and technical measures to ensure that RECs deliver not only efficiency gains but also just and inclusive outcomes.

Abstract
Developing information retrieval (IR) systems that enable access across multiple languages is crucial in multilingual contexts. In Timor-Leste, where Tetun, Portuguese, English, and Indonesian are official and working languages, no cross-lingual information retrieval (CLIR) solutions currently exist to support information access across these languages. This study addresses that gap by investigating CLIR approaches tailored to the linguistic landscape of Timor-Leste. Leveraging an existing monolingual Tetun document collection and ad-hoc text retrieval baselines, we explore the feasibility of CLIR for Tetun. Queries were manually translated into Portuguese, English, and Indonesian to create a multilingual query set. These were then automatically translated back into Tetun using Google Translate and several large language models, and used to retrieve documents in Tetun. Results show that Google Translate is the most reliable tool for Tetun CLIR overall, and the Hiemstra LM consistently outperforms BM25 and DFR BM25 in cross-lingual retrieval performance. However, overall effectiveness remains up to 26.95% points lower than that of the monolingual baseline, underscoring the limitations of current translation tools and the challenges of developing an effective CLIR for Tetun. Despite these challenges, this work establishes the first CLIR baseline for Tetun ad-hoc text retrieval, providing a foundation for future research in this under-resourced setting.

Abstract
Cancer is a leading cause of mortality worldwide, with breast and lung cancer being the most prevalent globally. Early and accurate diagnosis is crucial for successful treatment, and medical imaging techniques play a pivotal role in achieving this. This paper proposes a novel pipeline that leverages generative artificial intelligence to enhance medical images by combining synthetic image generation and super-resolution techniques. The framework is validated in two medical use cases (breast and lung cancers), demonstrating its potential to improve the quality and quantity of medical imaging data, ultimately contributing to more precise and effective cancer diagnosis and treatment. Overall, although some limitations do exist, this paper achieved satisfactory results for an image size which is conductive to specialist analysis, and further expands upon this field's capabilities.

Abstract
An all-passive, multipoint, and multiparameter optical monitoring system was developed and deployed in an industrial environment for the simultaneous measurement of methane concentration and other physical parameters. Methane is detected via rapid wavelength modulation spectroscopy (WMS) at 1648.2 nm and 4 MHz frequency. An attenuation invariant quantity defined by the peaks at 0, 4, and 8 MHz of the fast Fourier transform (FFT) of temporal signal is employed, characterized, and validated. Other parameters can concomitantly be measured by fiber Bragg grating (FBG) sensors operating in the 1520-1590 nm range. In the deployed system, the tested parameter was the temperature, which is an important quantity for gas monitoring. The system features a modular architecture that enables scalability up to 16 384 sensing points with an estimated less than 20-min acquisition cycle. In its current deployment, it monitors methane and temperature at eight locations using a single optical network. The system is intended to be used onshore and offshore platforms where the usual monitoring protocol consists of manual measurements usually performed three to four times a year and involves personal displacement and risky situations. Field tests at an onshore natural gas treatment unit (NGTU) demonstrated reliable performance and effective event detection, including undocumented nocturnal emissions, maneuvers at main shut-off valve, and partial plant shutdowns and restarts.

Abstract
Understanding user behavior in search systems is essential for improving retrieval effectiveness and user satisfaction. While prior research has extensively examined general-purpose web search engines, domain-specific contexts-such as sports information-remain comparatively underexplored. In this study, we analyze over 400,000 interaction log entries from a sports-oriented search engine collected over a two-week period. Our analysis combines classic query-level metrics (e.g., frequency distributions, query lengths) with a detailed examination of click behavior, including entropy-based intent variability and a custom query quality scoring model. Compared to established baselines from general and specialized search environments, we observe a high proportion of new and single-term queries, as well as a notable lack of representativeness among top queries. These findings reveal patterns shaped by the event-driven and entity-centric nature of sports content, offering actionable insights for the design of domain-specific retrieval systems.