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
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

Abstract
Accurate dynamic modeling of ground robots (Unmanned Ground Vehicles - UGVs) is essential for robust control and navigation in agricultural environments, where variations in soil friction and rolling resistance significantly affect system dynamics. This work proposes a Linear Parameter-Varying (LPV) model parameterized by the friction coefficient, identified under different soil conditions using two excitation strategies: Amplitude-Pseudo-Random Binary Sequence (APRBS) and standard maneuvers (SM). A simulated ground robot - the Clearpath Husky - was used under multiple soil friction scenarios within the ROS 2 and Gazebo simulation environment. The results show that the LPV model effectively captures the influence of soil friction, with both LPV APRBS and LPV SM yielding similar RMSE values across scenarios. The results also highlight the feasibility of using SM-based excitation for identifying the robot dynamics. © 2026 IEEE.

Abstract
Abstract This paper covers the state-of-the-art perception and control technologies in precision spraying and mowing in permanent crops. The search was performed in six different databases, resulting in 1849 publications, from which only 94 were considered for inclusion in this review. The analysis highlighted the importance of canopy characteristics in precision spraying, focusing on parameters like height, width, leaf area, and volume, primarily using LiDAR sensors. Vision sensors also complemented LiDAR-based approaches, with diverse applications such as fruit detection and disease diagnosis. Despite valuable knowledge from studies on spray coverage assessment and real-time smartphone analysis, challenges persist, including dynamic environmental factors and the different collector materials used. Moreover, the review considers the cost of Variable Rate Technology (VRT) solutions in agriculture, enhancing their impact on accessibility, adoption, and sustainability. While conventional herbicide-based weed management prevails, interest in alternative techniques like mechanical mowing and organic mulches is growing, promising improved soil health and reduced environmental impact, particularly in permanent crops. To address these challenges, agricultural robotics play a crucial role in automating precision spraying and mowing, optimizing resource usage, and increasing operational precision. This systematic review highlights the state of precision agriculture in permanent crops and emphasizes the need for continued research and development to improve the sustainability and efficiency of precision spraying and mowing systems in orchards, vineyards, and other woody crop environments.