Abstract
Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) in adults can present challenges in learning and work environments, by impacting focus, organization, social interaction, and self-esteem. The aim of this study is the potential of Human-Computer Interaction (HCI) in the empowerment of adults with ADHD and ASD. Specific difficulties faced in educational and professional settings were found through qualitative interviews with six participants. HCI seems to offer a pathway towards a more inclusive future, as educational technology solutions built on HCI principles can create better and alternative learning environments with fewer distractions and gamification for increased engagement. Assistive technologies can address challenges related to focus and organization (like task management apps, time tracking tools). Additionally, features promoting social interaction and communication can empower individuals with ASD. Technologies arising nowadays like Augmented and Virtual Reality (AR/VR) can create interactive learning experiences. Through the use of HumanComputer Interaction principles, more inclusive learning and work environments that empower individuals with ADHD and ASD can originate, while improving engagement and efficiency for all.

Abstract
This work aimed to develop methodologies for analysing statistical correlations among wind data series using various Measure-Correlate-Predict (MCP) methods, with the goal of selecting the most suitable method for extrapolating long-term data with minimal associated uncertainty. It was analysed the minimum time required for a wind measurement campaign when applying this methodology. Fifteen local wind measurement stations were selected. The long-term wind data reanalysis series that exhibited the strongest correlation with the measured wind data at each station was then chosen. Multiple tests were conducted with different simultaneous periods between the measured data series and the long-term series. Fifteen correlation algorithms were tested for each concurrent period. The performance of each model was evaluated using the RMSE (Root Mean Square Error) and MBE (Mean Bias Error) associated with each MCP. Analysis of the errors identified measurement periods with the lowest associated error ranging from 1 to 5 years and a single-factor ANOVA analysis was conducted. Finally, t-significance tests were performed. The study concluded that the Neural Network was the most effective MCP method. Additionally, it was determined that the minimum number of years required for a local measurement campaign should be between 2 and 3 years. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

Abstract
Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
The growing demand for sustainable and environment-friendly energy sources resulted in extensive research in the field of renewable energy. Biomass, derived from organic materials such as agricultural waste, forestry products, and wastewater treatment plant (WWTP) sludge, holds great potential as a renewable energy resource that can reduce greenhouse gas emissions and offer sustainable solutions for energy production. This study focused on diverse biomass materials, including sludge from WWTPs, forest biomass, swine waste, cork powder, and biochar. Chemical and physicochemical characterizations were performed to understand their energy potential, highlighting their elemental composition, proximate analysis, and calorific values. Results showed that different biomasses have varying energy content, with biochar and cork powder emerging as high-energy materials with net heating values of 32.56 MJ/kg and 25.73 MJ/kg, respectively. WWTP sludge also demonstrated considerable potential with net heating values of around 14.87 MJ/kg to 17.44 MJ/kg. The relationships between biomass compositions and their heating values were explored, indicating the significance of low nitrogen and sulphur content and favourable carbon, hydrogen, and moisture balances for energy production. Additionally, this study looked into the possibility of mixing different biomasses to optimize their use and overcome limitations like high ash and moisture contents. Mixtures, such as 75% Santo Emiliao WWTP Sludge + 25% Biochar, showed impressive net heating values of approximately 21.032 MJ/kg and demonstrated reduced emissions during combustion. The study's findings contribute to renewable energy research, offering insights into efficient and sustainable energy production processes and emphasizing the environmental benefits of biomass energy sources with low nitrogen and sulphur content.

Abstract
Assembly lines are at the core of many manufacturing systems, and planning for a well-balanced flow is key to ensure long-term efficiency. However, in flexible configurations such as Multi-Manned Assembly Lines (MMAL), the balancing problem also becomes more challenging. Due to the increased relevance of these assembly lines, this work aims to investigate the MMAL balancing problem, to contribute for a more effective decision-making process. Therefore, a new approach is proposed based on Deep Reinforcement Learning (DRL) embedded in a Digital Twin architecture. The proposed approach provides a close-to-reality training environment for the agent, using Discrete Event Simulation to simulate the production system dynamics. This methodology was tested on a real-world instance with preliminary results showing that similar solutions to the ones obtained using optimization-based strategies are achieved. This research provides evidence of success in terms of dynamic resource assignment to tasks and workers as a basis for future developments.

Abstract