Abstract
In today's rapidly evolving and competitive business environment, organizations must continuously innovate, leading to the development of new optimization techniques, methods, and tools to support decision-making. In project scheduling management, efficiency and effectiveness are crucial for organizational success, and the tools developed are designed to improve these two critical factors. This paper focuses on applying optimization techniques to project scheduling, with a particular emphasis on metaheuristics, specifically Simulated Annealing. A mathematical model was developed, incorporating the specific requirements of resource constrained project scheduling. A prototype was then implemented based on this model and tested using academic data to assess its effectiveness. The results demonstrated that the prototype could generate effective schedules and exhibited remarkable flexibility, adapting to different types of projects and multi-project environments. This article concludes that using metaheuristics, such as Simulated Annealing, provides a powerful and effective approach to solving complex project scheduling problems, offering significant advantages for organizations operating in dynamic and highly constrained environments.

Abstract
Formal verification has recently been increasingly used to prove the correctness and security of many applications. It is attractive because it can prove the absence of errors with the same certainty as mathematicians proving theorems. However, while most security experts recognize the value of formal verification, the views of non-technical users on this topic are unknown. We designed and implemented two experiments to address this issue to understand how formal verification impacts users. Our approach started with a formative study involving 15 participants, followed by the main quantitative study with 200 individuals. We focus on the application domain of Password Managers (PMs) since it has been documented that the lack of trust in PMs might lead to lower adoption. Moreover, recent efforts have focused on formally verifying (parts of) PMs. We conclude that formal verification is seen as desirable by users and identify three actionable recommendations to improve formal verification communication efforts.

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.