Abstract
E-learning has been important in higher education, enabling people to continue their education with more flexibility. Virtual laboratories play a crucial role in Computer Science distance learning degrees, by enabling students to study at their rhythm and getting practical answers to practical problems immediately. Theoretical models such as finite automata, pushdown automata, context-free grammars, Turing machines, etc., are essential for understanding the grounds of languages and computability and are also the basis for the implementation of compilers. In this paper, a new virtual laboratory is presented, UAbALL-Automata Learning Lab, developed at Universidade Aberta (UAb), the Portuguese Open University. This virtual laboratory has already been tested in the curricular unit of Languages and Computation, with good feedback from the students. A comparison to other tools was performed showing that UAbALL is more complete in terms of tools provided.

Abstract
Green hydrogen is expected to play an important role for decarbonizing hard-to-abate sectors but faces regulatory, economic, and operational barriers. In the EU, strict renewable energy usages requirements and temporal and geographical criteria constrain green hydrogen production and complicate integration with electricity markets. Support mechanisms (SMs), such as premiums and quotas, aim to boost hydrogen production, yet their impacts on coupled electricity-hydrogen systems remain underexplored. This paper extends a previous joint electricity-hydrogen Cournot equilibrium model to represent and analyze the impact of different green hydrogen production SMs. Different SMs lead to different equilibrium models that were solved using equivalent quadratic optimization problems and applied to real-size Iberian case studies. Results reveal how different SMs influence hydrogen and electricity prices, production and emissions, highlighting trade-offs among stakeholders. The findings provide guidance for designing balanced policies that stimulate green hydrogen while minimizing unintended consequences and offer flexible tools to assess regulatory and economic interactions in emerging hydrogen markets

Abstract
Robust robotic manipulation is an essential task for the progress of automation, yet clothing handling remains a major challenge for robots. Despite currently going through rapid technological advancement, the textile industry still faces many obstacles regarding textile manipulation, which often requires a lot of testing and resources to build robust systems. Simulation is often explored as an alternative to real-life testing, but the unpredictability of this type of material makes the development of reliable simulated environments with fabrics very difficult. This paper presents an advancement made in textile models in the MuJoCo simulator by extending an existing macro for realistic rectangular cloth generation to support garments of arbitrary shapes. Both a T-shirt and a square cloth were placed in real manipulation scenarios, and the setup was replicated afterwards in simulation, taking 3D scans of the final state. Several recorded metrics show similarities between the two tests, with the simulated models mimicking most of the relevant features and behaviour of real-life scenarios. The results indicate that the proposed approach shows great potential as an alternative for a reliable simulation framework for robotic garment manipulation.

Abstract
The forest-to-bioenergy supply chain is significantly vulnerable to natural disruptions, including wildfires, heavy snowfall, and windstorms. The increased occurrence of these disruptive events has caused severe challenges in forest biomass harvesting and transportation processes, which are difficult to manage. With the need to support decision-makers in designing resilient supply chains (SCs), we propose a Decision Support System (DSS) combining a two-stage stochastic programming framework with various flexibility mechanisms, such as dynamic network reconfiguration and operations postponement. The DSS incorporates an AI-based methodology to identify the most appropriate datasets and resilience metrics, capturing different supply chain dimensions (supply, demand, and operations). This integrated framework supports the selection of effective resilience-enhancing strategies to mitigate large-scale disruptions, with a particular focus on wildfires. The proposed approach is applied in a real case study in Portugal, where the most significant risk factor is wildfires. We perform computational studies and sensitivity analysis to evaluate the applicability and performance of the model and to drive managerial insights. The results show that adopting the model solutions can significantly reduce supply chain logistics and operational costs under more severe disruptive scenarios. Moreover, the results indicate up to a 60% increase in the tons of forest residues that can be removed and processed.

Abstract
The increasing complexity and distributed nature of Robot Operating System (ROS)-based robotic systems require advanced Fault Detection and Diagnosis (FDD) approaches that operate autonomously with minimal human intervention. The goal of this systematic literature review is to investigate how observability-driven FDD can be automated in ROS-based robotic systems to minimise human effort. Through this lens, the review surfaces four recurring gaps that collectively limit observability-driven automation: rich telemetry sources-logs, traces, and metrics-exist in isolation and are rarely integrated into real-time detection pipelines or leveraged collectively to improve failure diagnostics; online monitoring enables automatic fault detection but depends heavily on predefined rules and expert configuration and interpretation; failure explanations are generated post hoc and rely heavily on logs; and systems remain largely reactive, lacking the continuous monitoring infrastructure needed to anticipate faults before they propagate. Although Large Language Models (LLMs) show considerable promise for automated fault explanation and natural language interaction with robotic systems, current implementations fall short of comprehensive, real-time monitoring that unifies logs, traces, metrics, and sensor streams with Artificial Intelligence (AI) reasoning. To address these gaps, this paper motivates hybrid architectures that combine observability-first design, runtime monitoring, static analysis, and agentic LLM-based reasoning, laying the groundwork for more proactive and autonomous fault management in ROS-based systems.

Abstract
This study aims to explore the integration challenges faced by immigrant entrepreneurs in Portugal. It employed a multiple case study approach, drawing on semi-structured interviews with nine immigrant entrepreneurs from three distinct communities in Portugal. The findings of this study highlight the role of social networks in enabling and shaping the entrepreneurial journeys of immigrants in Portugal. These networks act as a bridge to help immigrants overcome barriers such as unfamiliarity with local markets, restricted access to resources, and cultural differences. In this context, community knowledge and referrals play a particularly significant role. Furthermore, the findings also identify five types of challenges faced by these communities including the financial, regulatory, social, institutional, and psychological dimensions. This study is relevant due to the role of immigrants in fostering economic growth and social cohesion. Understanding and addressing the integration challenges is key to enabling their success, which in turn strengthens local economies and promotes inclusive growth. Additionally, exploring these issues helps policymakers and organizations develop targeted strategies to support immigrant entrepreneurs, ensuring they can fully realize their potential and contribute positively to the host society.