Abstract
This study presents a photovoltaic (PV)-based electric vehicle (EV) charging system designed to optimize energy use and support isolated microgrid operations. The system integrates PV panels, DC/AC, AC/DC, and DC/DC converters, voltage and frequency droop control, and two energy management algorithms: Power Sharing and SEWP (Spread Energy with Priority). The DC/AC converter demonstrated high efficiency, with stable AC output and Total Harmonic Distortion (THD) limited to 1%. The MPPT algorithm ensured optimal energy extraction under both gradual and abrupt irradiance variations. The DC/DC converter operated in constant current mode followed by constant voltage regulation, enabling stable power delivery and preserving battery integrity. The Power Sharing algorithm, which distributes PV energy equally, favored vehicles with a higher initial state of charge (SOC), while leaving low-SOC vehicles at modest levels, reducing satisfaction under limited irradiance. In contrast, SEWP prioritized low-SOC EVs, enabling them to achieve higher SOC values compared to the Power Sharing algorithm, reducing SOC dispersion and enhancing fairness. The integration of voltage and frequency droop controls allowed the station to support microgrid stability by limiting reactive power injection to 30% of apparent power and adjusting charging current in response to frequency deviation.

Abstract
Recent technological advancements have increased computer crime, requiring public authorities to implement structured mitigation strategies. While initiatives exist to improve digital literacy on device security, they must also address the complexities of computer crime. Using Design Science Research, this study investigated the applicability of chatbots to raise awareness of computer crime in a public administration setting. A systematic literature review highlighted the issue's relevance and identified knowledge gaps. A scoping review gathered concepts, methodologies, technologies, architectures, and tools for developing and evaluating an effective chatbot. The design and development phase included a detailed proposal for a sophisticated chatbot architecture. During the demonstration and evaluation phases, the utility of the chatbot was tested in the domain of conversational flow efficiency and usability. The study's primary results and contributions are to assess the chatbot's effectiveness in raising awareness of computer crime on public websites. Future work should focus on implementing the chatbot in the actual context of public administration, proposing a network of specialized conversational assistants, and improving public service interoperability to enhance computer crime awareness.

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
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