Abstract
This research examines the impact of the COVID-19 pandemic and the Ukraine-Russia conflict on Portugal and Spain's national energy and climate plans. Both countries have updated their plans in response to these events, emphasizing energy efficiency, renewable energy investment, and circular economy principles. Portugal focused on addressing energy poverty and digitalization, while Spain accelerated its energy transition to align with the European Green Deal. Additionally, the Ukraine-Russia conflict prompted measures to enhance energy security and NECPs in both countries. Through a semi-systematic literature review spanning 2020-2023, our study analyzes how these global events shaped national energy and climate plans. The case studies of Portugal and Spain highlight the importance of flexibility and resilience in crafting sustainable energy strategies during such a complex crisis.

Abstract
Retailers depend on accurate sales forecasts to effectively plan operations and manage supply chains. These forecasts are needed across various levels of aggregation, making hierarchical forecasting methods essential for the retail industry. As competition intensifies, the use of promotions has become a widespread strategy, significantly impacting consumer purchasing behavior. This study seeks to improve forecast accuracy by incorporating promotional data into hierarchical forecasting models. Using a sales dataset from a major Portuguese retailer, base forecasts are generated for different hierarchical levels using ARIMA models and Multi-Layer Perceptron (MLP) neural networks. Reconciliation methods including bottom-up, top-down, and optimal reconciliation with OLS and WLS (struct) estimators are employed. The results show that MLPs outperform ARIMA models for forecast horizons longer than one day. While the addition of regressors enhances ARIMA's accuracy, it does not yield similar improvements for MLP. MLPs present a compelling balance of simplicity and efficiency, outperforming ARIMA in flexibility while offering faster training times and lower computational demands compared to more complex deep learning models, making them highly suitable for practical retail forecasting applications.

Abstract
Existe uma ampla variedade de ferramentas e ambientes disponíveis para aplicações de realidade virtual imersiva, passíveis de utilização em contexto educativo. Para proporcionar uma perceção panorâmica das potencialidades disponíveis, este estudo efetuou um levantamento e categorização dessas ferramentas educativas, classificando-as por áreas de aplicação: exploração geográfica, entretenimento, ciência, arte e outras. Recorreu-se metodologicamente ao protocolo de levantamento (scoping review) proposto por Morgado & Beck. Com base neste protocolo efetuaram-se os processos de definição e desenvolvimento das buscas, da seleção e análise de elementos e extração das conclusões. As ferramentas foram também analisadas face à tipologia de usos de ambientes imersivos dos mesmos autores, segundo a qual constatámos que o tipo de ferramentas mais prevalente é o referente a “Manipulação Interativa e Exploração”, seguido pelas de “Interação Multimodal” e “Treino de Competências”. São também comuns as ferramentas de Colaboração. Algumas categorias menos prevalentes, como “Ver o Invisível”, “Envolvimento”, “Simulação do Mundo Físico” e outras, permitem ainda assim ter uma perceção de como se concretizam essas tipologias de usos enquanto experiências de aprendizagem possíveis em ambientes virtuais imersivos.

Abstract
The article provides a comparison of Convolutional Neural Network (CNN) and Reinforcement Learning (RL) applied to the field of autonomous driving within the CARLA (CAr Learning to Act) simulator for training and evaluation. The analysis of results revealed CNNs better overall performance, as it demonstrated a more refined driving experience, shorter training durations, and a more straightforward learning curve and optimization process. However, it required data labelling. In contrast, RL relayed on an exhaustive (unsupervised) exploration of different models, ultimately selecting the model at timestep 600,000, which had the highest mean reward. Nevertheless, RL’s approach revealed its susceptibility to excessive oscillations and inconsistencies, necessitating additional optimization and tuning of hyperparameters and reward functions. This conclusion is further substantiated by a range of used performance metrics (objective and subjective), designed to assess the performance of each approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Food waste is responsible for severe environmental, social, and economic issues and therefore it is imperative to prevent or at least minimize its generation. The main cause of food waste is poor demand forecasting and so it is essential to improve the accuracy of the tools tasked with these forecasts. The present work proposes four models meant to help food catering services predict food demand accurately and thus avoid overproducing or underproducing. Each model is based on a different machine learning technique. Two baseline models are also proposed to mimic how food catering services estimate future demand and to infer the added value of employing machine learning in this context. To verify the impact of the proposed models, they were tested on data from the three different canteens chosen as case studies. The results show that the models based on the random forest algorithm and the long short-term memory neural network produced the best forecasts, which would lead to a 14% to 52% reduction in the number of wasted meals. Furthermore, by basing their decisions on these forecasts, the food catering services would be able to reduce unmet demand by 3% to 16% when compared with the forecasts of the baseline models. Thus, employing machine learning to forecast future demand can be very beneficial to food catering services. These forecasts can increase the service level of food services and reduce food waste, mitigating its environmental, social, and economic consequences.

Abstract
The relationship between hydrogen and electricity has gained attention due to their interconnected roles in the energy transition. Existing joint electricity and hydrogen market models often overlook the dependence between electricity and hydrogen prices. Indeed, while electrolyzers production can raise electricity prices, electricity price significantly impacts the costs of hydrogen production. Considering this price-based interdependency, this study compares a Cournot equilibrium and a perfect competition market model for electricity and hydrogen integration. Both models are transformed into new quadratic optimization problems to facilitate resolution. The analysis highlights the potential of the Iberian region for hydrogen production. Furthermore, it is evident that, under conditions of perfect competition, renewable generation is given priority for meeting electricity demand, leading to a decrease in both electricity and hydrogen prices on a global scale compared to the Cournot scenario.