Abstract
Daylight Saving Time (DST) has long been justified as a measure to reduce electricity consumption by better aligning human activity with daylight. However, its effectiveness under current energy-use patterns remains uncertain. This study provides a comprehensive assessment of the impact of DST on electricity-related needs for lighting and climatization across multiple European countries.The analysis is based on a unified methodological framework that evaluates the number of hours requiring artificial lighting and thermal conditioning, using empirical solar irradiance and temperature data. By focusing on the extensive margin, the approach enables consistent comparisons between DST and non-DST scenarios under alternative legal time configurations.The results show that DST reduces lighting needs, but these reductions are modest when realistic irradiance data are taken into account. In contrast, DST increases climatization requirements, primarily due to higher heating demand. As these effects operate in opposite directions, the overall impact of DST on electricity-related needs is limited. Additionally, the analysis indicates that adopting a uniform legal time across the European Union may be suboptimal from an energy perspective, because the alignment between civil time and environmental conditions varies with longitude.Overall, the findings indicate that the energy implications of DST are relatively small and context-dependent, and that legal time settings should account for regional differences.

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
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.