Abstract
Smart grids with EVs have been proposed as a great contribution to sustainability. Considering environmental sustainability is of great importance to humanity, it is essential to assess whether electrical vehicles (EVs) actually contribute to improving it. The objectives of the present study are, from a macro (broad-scope) perspective, to identify the sources of emissions and to create a framework for the calculation of CO2 emissions resulting from large-scale EV use. The results show that V2G mode increases emissions and therefore reduces the benefits of using EVs. The results also show that in the best scenario (NC mode), an EV will have 32.7% less emissions, and in the worst case (V2G mode), it will have 25.6% more emissions than an internal combustion vehicle (ICV), meaning that sustainability improvement is not always ensured. The present study shows that considering a macro perspective is essential to estimate a more comprehensive value of emissions. The main contributions of this work are the creation of a framework for identifying the main contributions to CO2 emissions resulting from large-scale EV integration, and the calculation of estimated CO2 emissions from a macro perspective. These are important contributions to future studies in the area of smart grids and large-scale EV integration, for decision-makers as well as common citizens.

Abstract
User studies are paramount for advancing science. In particular, the empirical evaluation of programmer-oriented tools is important to validate research ideas and prototypes, as well as production-ready tools. Previous research has collected several tools used by the software engineering and behavioral science communities to design and run studies. In this work, we study tools used in software engineering studies and identify their features. Furthermore, we analyze three behavioral science experiment tools to identify design ideas that might be adapted to programmer user studies. With this work, we present the set of features currently offered by software engineering tools to support researchers in the design and execution of programmer user studies. We also present the characteristics of some tools used in behavioral science experiments to identify design ideas that can be adapted to programmer user studies.

Abstract
Recommendation systems can play an important role in today’s digital content platforms by supporting the suggestion of relevant content in a personalised manner for each customer. Such content customisation has not been consistent across most media domains, and particularly on radio streaming and gaming aggregators, which are the two real-world application domains focused in this work. The challenges faced in these application areas are the dynamic nature of user preferences and the difficulty of generating recommendations for less popular content, due to the overwhelming choice and polarisation of available top content. We present the design and implementation of a Reinforcement Learning-based Recommendation System (RLRS) for web applications, using a Deep Deterministic Policy Gradient (DDPG) agent and, as a reward function, a weighted sum of the user Click Distribution (CD) across the recommended items and the Dwell Time (DT), a measure of the time users spend interacting with those items. Our system has been deployed in real production scenarios with preliminary but promising results. Several metrics are used to track the effectiveness of our approach, such as content coverage, category diversity, and intra-list similarity. In both scenarios tested, the system shows consistent improvement and adaptability over time, reinforcing its applicability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.

Abstract
There are still important theoretical and empirical gaps in understanding the role of best practices (BPs), such as quality management, lean and new product development, in generating firm's performance advantage and overcoming trade-offs across distinct performance dimensions. We examine these issues through the perspective of performance frontiers, integrating in novel ways the resource-based theory with the emergent practice-based view. Hypotheses on relationships between BPs, performance advantage, and trade-offs are developed and tested with stationary and longitudinal (recall) data from a global survey of manufacturing firms. We use data envelopment analysis, which overcomes limitations of mainstream methods based on central tendency. Our findings support the view that BPs may serve as a source of enduring competitive advantage, based on their ability to lead to a heterogeneous range of dominant and difficult-to-imitate competitive positions. The study provides new insights on contemporary debates about the role of BPs in generating performance advantage and how practitioners can sustain internal support and extract benefits from them.

Abstract
Currently, the transmission system operators (TSOs) from Portugal and Spain do not procure a frequency containment reserve (FCR) through market mechanisms. In this context, a virtual power plant (VPP) that aggregates sources, such as wind and solar power and hydrogen electrolyzers (HEs), would benefit from future participation in this ancillary service market. The methodology proposed in this paper allows for quantifying the revenues of a VPP that aggregates wind and solar power and HEs, considering the opportunity costs of these units when reserving power for FCR participation. The results were produced using real data from past FCR market sessions. Using market data from 2022, a VPP that aggregates half of the HEs and is expected to be connected in the country by 2025 will have revenues over EUR 800k, of which EUR 90k will be HEs revenues.