Abstract
Hydrogen production through renewable energy-powered electrolysis is pivotal for fostering a sustainable future hydrogen market. In the electricity sector, hydrogen production bears an additional demand that affects electricity price, and mathematical models are needed for the joint simulation, analysis, and planning of electricity and hydrogen sectors. This study develops a Cournot and a perfect competition model to analyze the links of the electricity and hydrogen sectors. In contrast to other solving methods approaches, the Cournot model is solved by convex reformulation techniques, substantially easing the resolution. The case studies, focusing on the Iberian Peninsula, demonstrate the region's potential for competitive hydrogen production, and the advantages of perfect competition to maximize the use of renewable energies, in contrast to Cournot's oligopoly, where the exercise of market power raises electricity prices. Sensitivity analyses highlight the importance of strategic decision-making in mitigating market inefficiencies, with valuable insights for stakeholders and policymakers.

Abstract

Abstract
Identifying the maturation stage is an added value for olive oil producers and consumers, whether this is done to predict the best harvest time, give us more information about the olive oil, or even adapt techniques and extraction parameters in the olive oil mill. In this way, the proposed work presents a new method to identify and count the number of olives that enter the mill as well as their stage of maturation. It is based on artificial intelligence (AI) and deep learning algorithms, using the two most recent versions of YOLO, YOLOv7 and YOLOv8. The obtained results demonstrate the possibility of using this type of application in a real environment, managing to obtain a mAP of approximately 79% with YOLOv8 in the five maturation stages, with a processing rate of approximately 16 FPS increasing this with YOLOv7 to 36.5 FPS reaching a 66% mAP.

Abstract
We cannot neglect digital literacy because it is undeniable how much technology is part of our lives. Ignoring it and the tools and services it provides us, which greatly facilitate the human experience, is simply a mistake. Recognising the importance of digital literacy, primarily due to the digital transformation in Portugal, it will be necessary to have technological skills to overcome some limitations. Information and Communication Technologies are seen in this environment as a factor that can contribute, on a large scale, to the inclusion of individuals with a digital literacy deficit, both in the Portuguese Local Public Administration and in society in general. The growth of digital transformation causes almost all jobs to need digital skills and participation in society. It takes digitally intelligent employees who know not only to use but also innovate and lead to new technologies because digital transformation may not be successful without that capacity. Thus, it is pertinent to develop, propose and validate an explanatory model that improves the relationship between digital transformation in Portuguese Local Public Administration and the digital literacy of its employees.

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.