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

Abstract
PurposeThis study aims to understand the perceived emotions of human-artificial intelligence (AI) interactions in the private sector. Moreover, this research discusses the transferability of these lessons to the public sector.Design/methodology/approachThis research analysed the comments posted between June 2022 and June 2023 in the global open Reddit online community. A data mining approach was conducted, including a sentiment analysis technique and a qualitative approach.FindingsThe results show a prevalence of positive emotions. In addition, a pertinent percentage of negative emotions were found, such as hate, anger and frustration, due to human-AI interactions.Practical implicationsThe insights from human-AI interactions in the private sector can be transferred to the governmental sector to leverage organisational performance, governmental decision-making, public service delivery and the creation of economic and social value.Originality/valueBeyond the positive impacts of AI in government strategies, implementing AI can elicit negative emotions in users and potentially negatively impact the brand of private and government organisations. To the best of the authors' knowledge, this is the first research bridging the gap by identifying the predominant negative emotions after a human-AI interaction.

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.