Abstract
This paper presents the design, deployment and evaluation of a GPT-4o-based grading and feedback pipeline for a Master's-level Information Systems Project Management course, developed following the Design Science Research Methodology. The artefact orchestrates submission collection (Google Forms), rubric-based scoring with rubric-linked qualitative feedback (OpenAI GPT-4o via a Cloud Function) and analytics storage (BigQuery), across six consecutive case-study assignments. The AI grader supplemented rather than replaced instructor grading: official grades remained the instructor's, while AI grades and feedback served as a second-opinion signal and additional formative feedback. The evaluation followed a pre-specified analysis plan combining a quantitative comparison of AI and instructor grades, a voluntary student survey and an automated assessment of feedback quality on the full corpus. Across 362 submissions from 66 students, the AI reproduced the instructor's mark within the pre-specified tolerance of 0.5 points on a five-point scale in 83% of cases, with a mean absolute error (MAE) of 0.31 points and a small systematic bias of-0.16 points (95% CI [-0.19, -0.12]). Chance-corrected agreement, measured by the intraclass correlation coefficient (ICC), was moderate at best: ICC(2,1) = 0.49 (95% CI [0.33,0.61]), spanning the poor-to-moderate range. Among the 112 survey responses (31% response rate), about 80% rated the feedback useful and 77% accurate. The pipeline operated at EUR 0.55 per student per assignment and delivered feedback within 24 h. The findings support using LLMs as scalable assistants for formative assessment, as a supplement to rather than a substitute for instructor evaluation.

Abstract
Evaluating diffusion properties of novel optical clearing (OC) agents is critical for advancing medical imaging. Tartrazine (TTZ), a strong absorbing dye, has shown promise in enhancing tissue transparency, yet its diffusion properties remain uncharacterized. In this work, OC treatments with TTZ-water solutions with varying osmolarities were performed, and the diffusion times (tau) that characterize the tissue dehydration and the RI matching mechanisms were estimated. From kinetic T-c measurements during treatment, tau values of water and TTZ were estimated in muscles as 60.0 s and 416.0 s, respectively. Corresponding diffusion coefficients (D) were derived from sample thickness data measured during treatments where the unique fluxes of TTZ and water occur. The respective D values were then calculated as 1.9 x 10(-6) cm(2)/s for water and 3.6 x 10(-7) cm(2)/s for TTZ. These findings provide key insights into TTZ diffusion in skeletal muscle and support its potential as an effective OC agent.

Abstract
Kidney paired donation (KPD) programs are organized in various countries to facilitate the donation of kidneys from willing but incompatible donors by matching them with pairs in similar situations. These programs often struggle with an accumulation of difficult-to-match recipients and small pools of incompatible pairs. To address this, several international collaborations have emerged to expand the pool sizes and increase the number of transplants by exchanging donors' kidneys across countries. We identified 4 established international KPD programs, each supported by protocols and agreements signed by the participating parties. Each program is presented separately, detailing its historical establishment, operational aspects, and statistics on pool characteristics and performance. Following this, we provide a comparative analysis of key aspects across the 4 programs. Each program has its unique context and specificities. Even though 3 of 4 collaborations started just before the COVID-19 pandemic, they have collectively facilitated >450 transplants. This underscores the importance of further developing these collaborations to share practices and experiences, and to facilitate more transplants, particularly for difficult-to-match recipients. Three of the 4 presented collaborations are either fully operated or led by European countries. This highlights the crucial role of ongoing international cooperation in the development of KPDs, in particular in Europe. By further promoting collaboration among countries, we can facilitate pan-European exchanges and improve access to live kidney transplants for patients in need.

Abstract
The Visual-Action Reinforcement Integrator for Actor-Critic with Temporal Observations (VIRIATO) is a compact multi-input feature-extractor architecture designed to enable robust visual navigation of Unmanned Aerial Vehicles (UAVs) conducting close-range inspection of photovoltaic arrays. The target task of low-altitude flight over dynamic, visually variable surfaces without privileged information is inherently partially observable. VIRIATO augments stacked image observations with a short history of recent past actions, producing a richer latent state for the Soft Actor-Critic (SAC) agent. Training is performed with domain randomization to expose the policy to diverse lighting, backgrounds and panel layouts. In simulation, VIRIATO yields faster learning and improved sample efficiency compared to a standard image-only Convolutional Neural Network (CNN) feature extractor, achieving lower position and yaw errors and substantially better robustness under image perturbations while retaining high task completion rates. The architecture is intentionally simple and general: it improves temporal awareness without adding complex recurrence, and it could be adapted to other perception-driven robotic tasks. These results demonstrate that integrating historical action data with visual encoding, together with domain randomization, is an effective way to achieve reliable autonomous vision-based navigation. © 2026 IEEE.

Abstract

Abstract
This study investigates how pandemic-induced digitalisation, understood as the transition to remote work combined with the enforced use of digital tools and the reconfiguration of tasks and digital skills at the job level, has affected job security and job quality in Portugal. In 2022, a nationwide survey was administered to employees in companies registered in the country, yielding 2001 valid responses through a stratified random sampling strategy that ensured representation across different firm sizes. Structural equation modelling (PLS-SEM) was used to examine the relationships between digitalisation (independent construct) and perceived job quality and job security (dependent constructs), while controlling for demographic, organisational, and work-regime characteristics. Digitalisation had a significant positive effect on perceived job quality but no systematic effect on perceived job security. The results also revealed more positive perceptions of job security among women, employees in smaller firms, and those working on-site, whereas directors and workers in the Lisbon Metropolitan Area reported greater negative effects. These findings underscore the importance of contextual factors in shaping how workers experience digitalisation and provide evidence to inform public policies aimed at promoting job security and job quality in a post-COVID-19 labour market.