Abstract
As technology continues to evolve, organizations face growing and complex challenges and opportunities that affect their ability to govern, manage and harness data as a key source of competitive advantage. Equally, data are considered a powerful and unique source of success for organizations, which in turn, can impact their decision-making capabilities and play a critical role in their success. Hence, this article aims to provide a detailed identification, analysis and discussion over the current data governance context and its existing frameworks, highlighting their commonalities, differences and gaps, including ones related to data governance relationship with Generative Artificial Intelligence (GenAI). This article conducts an extensive methodological and in-depth analysis over a set of sixteen data governance frameworks based on different key data governance attributes, denoting that although there are numerous frameworks, they hold weaknesses, limitations and challenges which prevent them from being capable of incorporating and governing the use and management of AI, particularly the demands originating from GenAI. Our findings provide and propose a new and enhanced data governance framework which integrates the best features and ideas from the existing ones and initiatives derived from the advancements and particularities of AI and GenAI models, systems, and overall usage.

Abstract
Within the scope of the Mobilizing Agenda for the Development of Intelligent Green Mobility Products and Systems (A-MoVeR), PPS2 defined the presentation of a “new electric motorcycle, with high autonomy, aimed at promoting comfortable, efficient and efficient urban mobility. green”. In this context, the need to develop interfaces that meet the expectations of end users, promoting user experience and security are crucial. Therefore, following a User-Centered Design (DCU) methodology, a co-design perspective and UX data collection methods, this article presents the steps and preliminary results of the preparation, face-to-face session and subsequent analysis of results of a preliminary moment of acquiring knowledge on how to optimize motorcycle user interfaces. Specifically: script planning, requirements and analysis of user feedback collected through audiovisual recording, in a focus group, are described. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Context. Software testing is a critical aspect of the software development lifecycle, yet it remains underrepresented in academic curricula. Despite advances in pedagogical practices and increased attention from the academic community, challenges persist in effectively teaching software testing. Understanding these challenges from the teachers' perspective is crucial to aligning education with industry needs. Objective. To analyze the characteristics, practices, tools, and challenges of software testing courses in higher education, from the perspective of educators, and to assess the integration of recent pedagogical approaches in software testing education. Method. A structured survey consisting of 52 questions was distributed to 143 software testing educators across Western European universities, resulting in 49 valid responses. The survey explored topics taught, course organization, teaching practices, tools and materials used, gamification approaches, and teacher satisfaction. Results. The survey revealed significant variability in course content, structure, and teaching methods. Most dedicated software testing courses are offered at the master's level and are elective, whereas testing is introduced earlier in less specialized (NST) courses. There is low adoption of formal guidelines (e.g., ACM, SWEBOK), limited integration of non-functional testing types, and a high diversity in textbooks and tools used. While modern practices like Test-Driven Development and automated assessment are increasingly adopted, gamification and active learning approaches remain underutilized. Teachers expressed a need for improved and more consistent teaching materials. Conclusion. The study highlights a mismatch between academic practices and industry expectations in software testing education. Greater integration of standardized curricula, broader adoption of modern teaching tools, and increased support for teachers through high-quality, adaptable teaching materials are needed to enhance the effectiveness of software testing education.

Abstract
This study examines the role of online health communities in Brazil dedicated to cannabis treatments for chronic diseases as platforms for evidence-based activism. Using a mixed-methods approach, the research combines qualitative analysis with computational techniques, including Latent Dirichlet Allocation (LDA) topic modeling, to analyze six online groups from WhatsApp and Facebook. Key themes emerging from the analysis include treatment per pathology, treatment effects, access barriers, peer support, and advocacy efforts. The findings reveal how these communities act as epistemic networks, where patients and caregivers co-produce knowledge by sharing personal experiences and engaging in dialogue with healthcare professionals. This study highlights how online health communities transform experience sharing into structured evidence, enabling collective action to address barriers such as limited access to cannabis-based treatments. It underscores the potential of digital platforms to empower patients, foster collaboration with healthcare professionals, and influence health governance.

Abstract
Online health communities enable patients and caregivers to share experiences, seek advice, and collaboratively generate knowledge about treatments and condition. However, accessing relevant information often proves challenging due to platform limitations like insufficient search functionalities. A previous study identified key topics discussed in Brazilian online health groups centered on cannabis treatments for chronic diseases. Building on these findings, this study introduces a proof-of-concept chatbot designed to enhance access to the collective knowledge within these communities. The chatbot prototype, built using Google Dialogflow, was tailored to provide contextually relevant, accurate, and user-friendly responses. A user study involving 38 participants evaluated its performance, showing high user satisfaction, task completion rates, and trust in the information provided. The results highlight the chatbot’s potential enhance knowledge accessibility, promote patient engagement, and support evidence-based activism by organizing and disseminating community-generated content effectively.

Abstract
This paper solves the resource-constrained project scheduling problem (RCPSP) with a satisfiability problem (SAT) solver. This paper builds further on various existing SAT models for this well-known project scheduling problem and extends them with two methods to satisfy the resource constraints. Specifically, we use the wellknown minimal forbidden sets and compare them with the so-called covers that are traditionally used in SAT implementations. Moreover, we also implement an existing binary decision trees approach under various settings and extend the model with networks with adders, so far never used for solving the RCPSP, to guarantee that resource constraints are satisfied. The algorithms are tested under different settings on a set of 13,413 project instances with diverse network and resource structures, and the experiments demonstrate that a combination of these approaches help in finding better solutions within a reasonable time. Moreover, 393 new lower bounds, 62 new upper bounds, and 290 optimally solved instances (including 18 from the PSPLIB) have been discovered, which, to the best of our knowledge, had not been found before. The strong performance of the new algorithm motivated additional experiments, and the preliminary results suggest several promising directions for future research.