Abstract
Globally Important Agricultural Heritage Systems (GIAHS) are models of sustainability, as they ensure a balance between human activity and ecosystem conservation. The Barroso region in Portugal is part of this network, as it follows traditional natural resource management and resilience practices by local communities. Given the threats posed by environmental degradation, it is urgent to adopt technological solutions for monitoring these conditions. Thus, throughout this article, the main threats to the integrity of these territories will be analyzed, and various methodologies and solutions for environmental monitoring will be presented. Based on the knowledge acquired, we will present an architecture for a digital solution that includes sensors, the Internet of Things (IoT), processing units, and platforms for real-time data visualization and alarm management.

Abstract
Text-to-SQL has gained increasing attention with Large Language Models (LLMs). While existing architectures have demonstrated the potential of multi-agent systems there remains significant room for improvement. In this work, we extend the CHESS framework by integrating In-Context Learning (ICL) techniques into the Candidate Generator module, evaluating three strategies: Zero-Shot, Few-Shot Learning, and Retrieval-Augmented Generation (RAG). We implement the system using GPT-4o, and perform experiments on the financial dataset from BIRD-SQL. Results show that Few-Shot Learning and RAG significantly outperform the standard approach. Compared to Zero-Shot (59.31% Execution Accuracy (EX), 0.412 ROUGE-1), RAG significantly boosted performance, increasing EX to 69.48% and ROUGE-1 to 0.652.

Abstract

Abstract
The growing number of senior experts leaving the workforce (especially in more developed economies, such as in Europe), combined with the ubiquitous access to artificial intelligence (AI), is triggering organizations to review their knowledge transfer programs, motivated by both financial and management perspectives. Our study aims to contribute to the field by analyzing options to integrate intergenerational tacit knowledge transfer (InterGenTacitKT) with AI-driven approaches, offering a novel perspective on sustainable Knowledge and Human Resource Management in organizations. We will do this by building on previous research and by extracting findings from 36 in-depth semi-structured interviews that provided success factors for junior/senior tandems (JuSeTs) as one notable format of tacit knowledge transfer. We also refer to the literature, in a grounded theory iterative process, analyzing current findings on the use of AI in tacit knowledge transfer and triangulating and critically synthesizing these sources of data. We suggest that adding AI into a tandem situation can facilitate collaboration and thus aid in knowledge transfer and trust-building. We posit that AI can offer strong complementary services for InterGenTacitKT by fostering the identified success factors for JuSeTs (clarity of roles, complementary skill sets, matching personalities, and trust), thus offering organizations a powerful means to enhance the effectiveness and sustainability of InterGenTacitKT that also strengthens employee productivity, satisfaction, and loyalty and overall organizational competitiveness.

Abstract
In our study, we conducted a thorough analysis of the spectral characteristics of a D-shaped surface plasmon resonance (SPR) photonic crystal fiber (PCF) refractive index sensor, incorporating a full width at half maximum (FWHM) analysis. We explored four distinct plasmonic materials—silver (Ag), gold (Au), Ga-doped zinc oxide (GZO), and an Ag-nanowire metamaterial—to understand their impact on sensor performance. Our investigation encompassed a comprehensive theoretical modeling and analysis, aiming to unravel the intricate relationship between material composition, sensor geometry, and spectral response. By scrutinizing the sensing properties offered by each material, we laid the groundwork for designing multiplasmonic resonance sensors. Our findings provide valuable insights into how different materials can be harnessed to tailor SPR sensing platforms for diverse applications and environmental conditions, fostering the development of advanced and adaptable detection systems. This research not only advances our understanding of the fundamental principles governing SPR sensor performance but also underscores the potential for leveraging varied plasmonic materials to engineer bespoke sensing solutions optimized for specific requirements and performance metrics. © 2025 SBMO/SBMag.

Abstract
There is a strong need to motivate students to learn science, technology, engineering, and mathematics (STEM) subjects. This is a problem not only at lower educational levels, but also at college institutions. With this idea in mind, the School of Engineering of the Porto Polytechnic (ISEP) Electrical Engineering Department decided, in 2021, to launch a robotics competition in order to foster students' interest in the areas of robotics and automation. This event, named Robotics@ISEP Open, aims to raise awareness of the area of electronics, computing, and robotics among students, involving them in the use of techniques and tools in this area, and encompasses three distinct robotics competitions covering both manipulator arms and mobile robots. It is based on two main points of interest: (i) robotic competitions and (ii) outside class training in robotics, aimed at students who want support to participate in competitions. Since its first edition, the event has grown and internationalized and has already become a milestone in the academic life of ISEP. This paper presents the motivations that led to the creation of this event, its main organizational aspects, and the competitions that are part of it, as well as some results gathered from the experience accumulated in organizing it.