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
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
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.

Abstract
This paper proposes an innovative directional Benefit-of-the-Doubt (BoD) model for setting benchmarking targets on the frontier of the Production Possibility Set (PPS) in alignment with strategic goals defined a priori by experts or decision makers. The proposed model iteratively adjusts the directional vectors assigned to each Decision Making Unit (DMU) ensuring that once specific goals are achieved, further improvement efforts are directed towards indicators with remaining performance gaps. This mechanism enables a dynamic prioritization of improvement consistent with strategic objectives. Additionally, we define a Composite Indicator (CI) that measures the overall effectiveness of each DMU relative to a strategy-based reference. The CI can be decomposed into a technical score - reflecting proximity to the PPS frontier - and a strategical score - capturing the extent to which goals remain unmet upon reaching the frontier. The framework proposed is illustrated and validated through an empirical assessment of 27 European countries using 2022 data from the 'Education and Training 2030' indicators.

Abstract
This chapter explores the innovative development and integration of tailored user research instruments to inform digital health solutions for People Living with Amyotrophic Lateral Sclerosis (PALS) exhibiting characteristics of partial Locked-In Syndrome (LIS). Addressing the complex interplay of motor, cognitive, and emotional impairments typical of this population, the study proposes a synergistic framework combining three adapted instruments: the ALS Functional Rating Scale-Revised (ALSFRS-R/EX), the User Experience Questionnaire Plus (UEQ+), and a bespoke Cognitive-Motor-Emotional (CME) Observation Grid. These instruments were tailored to detect subtle variations in user function, affect, and interaction. Results show how embodied and sensory drawing participatory methods and customisation of instruments, along with semi-structured questionnaire and interviews with caregivers, can yield actionable insights for designing a model for solutions in neurodegenerative or communication-limiting contexts beyond Augmentative and Alternative Communication (AAC).