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
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.

Abstract
Marine litter is a growing environmental threat, with severe ecological and socio-economic impacts. Most monitoring strategies rely on optical sensors to detect surface pollution, however these approaches fail to capture submerged plastics dispersed throughout the water column. Multibeam acoustic imaging offers a complementary solution, but the scarcity of annotated sonar datasets and the high noise levels of acoustic imagery make automated detection and tracking particularly challenging. This study presents a comparative evaluation of deep learning based multi-object tracking (MOT) algorithms applied to water column acoustic data. Pre-trained YOLOv8 detectors were integrated with tracking-by-detection frameworks including BoT-SORT, OC-SORT, ByteTrack, and DeepOC-SORT. Performance was assessed across acoustic frequencies and preprocessing strategies using standard MOT metrics. Results show that adaptive Gaussian thresholding and opening morphology improved robustness at lower frequencies ( 950 kHz and 1200 kHz ), while unprocessed inputs proved more resilient to severe clutter at 1400 kHz . BoostTrack and ByteTrack achieved the most consistent tracking, effectively managing intermittent detections to maximise MOTA and IDF1. In contrast, OC-SORT underperformed, struggling with fragmented sonar trajectories. Furthermore, while efficient Nano models dominated at lower frequencies, Medium models were required under higher noise. These findings demonstrate the feasibility of applying MOT methods to sonar-based litter monitoring. Future work will explore unsupervised learning approaches to leverage intrinsic sonar data structure, reduce annotation needs, and enable scalable marine litter tracking.

Abstract
We address a classification problem where data are not single-valued, but distributions. The objective is to identify Internet traffic re-direction. Each observation consists of a block of 10 measurements of round-trip-times (RTT) measured at each of a set of probes, and is represented by the corresponding empirical distribution. The proposed approach relies on a method for discriminant analysis of distributional data that uses fractional programming, and where distributions are represented by quantile functions, under specific assumptions. A linear discriminant function is defined, that allows obtaining a score for each unit, in the form of a quantile function. This is then used to classify the units in a priori groups, using the Mallows distance. Results show that proposed approach works well, allowing for the identification of the diverted traffic.

Abstract
Inspecting reflective parts is challenging due to strong specular reflections that conceal small porosities and reduce defect visibility. This work presents a framework that combines augmented reality with a deep learning detector. An augmented reality headset is used to capture multi-view images under natural illumination, enabling the operator to adjust the viewpoint and obtain angles that reduce glare. The collected data form a 640 × 480 dataset used to train a yolov8 detection model, integrated into a Robot Operating System 2 architecture for real-time processing. Testing on an independent set of unseen parts yields a precision of 86.70 %, a recall of 87.26 %, and an F1-score of 86.97 %. Additional qualitative examples confirm that the model can identify low-contrast porosities despite reflective surfaces. The results demonstrate the feasibility of AR-assisted acquisition combined with deep learning for real-time inspection of machined aluminum components in a laboratory case study. © 2026 IEEE.

Abstract