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
The use of gamified crowdsourcing mechanisms through serious games and games with a purpose (GWAPs) has emerged as an effective motivational strategy for enhancing performance in human intelligence tasks (HITs). In this systematic literature review, we examine the underlying characteristics of competitive and cooperative player-oriented GWAPs and how they can be leveraged to optimize crowdsourcing performance in completing batches of HITs. By exploring gamified crowdsourcing elements in GWAPs, we can evaluate the impact of these two types of player behaviors (i.e., competition and cooperation) on motivation and performance. We reviewed 27 publications and grouped them into five categories: player orientation, game elements and motivation, crowd work optimization, gamified knowledge collection, and comparative studies and best practices. Our research pinpoints the significance of intuitive task instructions, alignment of game elements with player motivations, and the role of competitive and cooperative dynamics in enhancing engagement and performance.

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.

Abstract
Explainable Artificial Intelligence (XAI) seeks to enhance the interpretability of Artificial Intelligence (AI) systems, ensuring that algorithmic decisions and their underlying data are comprehensible to non-technical stakeholders. While advanced Machine Learning (ML) models, such as deep neural networks, have significantly improved AI capabilities, their complexity poses challenges for XAI, particularly in handling large datasets required for training and interpretation. In particular, the application of Shapley Additive Explanations (SHAP), although widely recognized for its effectiveness, often incurs a high computational cost when applied to large-scale data. Addressing this issue, our previous work proposed a novel approach that leverages K-Means clustering to identify representative data instances, applied after the forecasting phase to refine SHAP-based explanations and reduce computational costs while preserving their fidelity. This extended study further optimizes the clustering strategy and evaluates its applicability across broader use cases in sustainable energy systems. We apply our method to forecast photovoltaic (PV) generation in buildings, a critical aspect of energy management in e-mobility and smart grids. The results show that clustering reduces execution time by more than 50 % compared to random sampling while maintaining comparable explanatory stability. These findings highlight the potential of data-driven clustering techniques in enhancing the explainability of ML models in energy forecasting, contributing to more accessible and practical AI solutions for real-world applications.