Abstract
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also offers some advantages over real data, such as the large amount of data that can be generated or the ability to customize it to adapt to specific problem-solving needs. To effectively use such data, face recognition models should also be specifically designed to exploit synthetic data to its fullest potential. In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024. This is an ongoing challenge that provides researchers with an accessible platform to benchmark (i) the proposal of novel Generative AI methods and synthetic data, and (ii) novel face recognition systems that are specifically proposed to take advantage of synthetic data. We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition such as demographic bias, domain adaptation, and performance constraints in demanding situations, such as age disparities between training and testing, changes in the pose, or occlusions. Very interesting findings are obtained in this second edition, including a direct comparison with the first one, in which synthetic databases were restricted to DCFace and GANDiffFace.

Abstract

Abstract
Electric vehicles (EVs) are crucial in achieving a low-carbon transportation sector and can inherently offer demand-side flexibility by responding to price signals and incentives, yet real-world strategies to influence charging behavior remain limited. This paper combines bilevel optimization and causal machine learning as complementary tools to design and evaluate dynamic incentive schemes as part of a pilot project using a supermarket's EV charging station network. The bilevel model determines discount levels, while double machine learning quantifies the causal impact of these incentives on charging demand. The results indicate a marginal increase of 1.16 kW in charging demand for each one-percentage-point increase in discount. User response varies by hour and weekday, revealing treatment effect heterogeneity, insights that can inform business decision-making. While the two methods are applied independently, their combined use provides a framework for connecting optimization-based incentive design with data-driven causal evaluation. By isolating the impact of incentives from other drivers, the study sheds light on the potential of incentives to enhance demand-side flexibility in the electric mobility ecosystem.

Abstract
There are now multiple proposals for Conflict-free Replicated Data Types (CRDTs) in SQL databases aimed at distributed systems. Some, such as ElectricSQL, provide only relational tables as convergent replicated maps, but this omits semantics that would be useful for merging updates. Others, such as Pg\_crdt, provide access to a rich library of encapsulated column types. However, this puts merge and query processing outside the scope of the query optimizer and restricts the ability of an administrator to influence access paths with materialization and indexes. Our proposal, CRDV, overcomes this challenge by using two layers implemented as SQL views: The first provides a replicated relational table from an update history, while the second implements varied and rich types on top of the replicated table. This allows the definition of merge semantics, or even entire new data types, in SQL itself, and enables global optimization of user queries together with merge operations. Therefore, it naturally extends the scope of query optimization and local transactions to operations on replicated data, can be used to reproduce the functionality of common CRDTs with simple SQL idioms, and results in better performance than alternatives.

Abstract
The purpose of this study was to explore the influence of brand coolness on the intention to acquire NFTs within the luxury fashion market. To achieve this purpose, we developed a conceptual model offering a broader perspective regarding consumers' purchase intention of luxury brands' NFTs by including both emotional aspects related to the brand (brand love) and perceptions predominantly related to the financial nature of the investment (perceived risk). Word-of-mouth (WOM) and willingness to pay (WTP) are also analyzed as outcomes of brand coolness. The model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the proposed relationships. The findings show that brand coolness positively impacts brand love, WOM, and WTP. Although it was not possible to observe a significant relationship between brand coolness and consumers' purchase intention of luxury brands' NFTs, it has significant indirect effects through brand love. Guided by the unexpected findings of the quantitative study, this article also includes a follow-up qualitative study, whose main aim was to further explore the influence of brand coolness on the intention to acquire NFTs within the luxury fashion market. Participants were individuals with relevant knowledge and experience with NFTs. The qualitative study revealed that brand coolness alone is insufficient to drive NFT purchases, while brand love, tied to trust and symbolic value, plays a stronger role, helping explain the quantitative results. Overall, this study contributes to the literature by shedding light on the complex interplay between brand coolness, consumer behavior, and NFTs in the luxury fashion context.

Abstract
Social entrepreneurship is crucial for sustainable development as it blends innovative business models with a focus on economic, social and environmental impact. This synergy can potentially accelerate progress towards the sustainable development goals, creating a more equitable and sustainable future. This study aims to explore this phenomenon by carrying out a systematic review of literature. It is adopted the PRISMA framework to identify 54 relevant studies in this field. The findings characterize the evolution of articles in this field, the number of citations, the relationship between key terms, and the respective clusters. Moreover, seven contributions of social entrepreneurship for sustainable development are identified. Finally, the role of technology in promoting and supporting the interconnection between social entrepreneurship and sustainable development is explored. This study is relevant to enhance understanding of how technology supports social entrepreneurship and helps social entrepreneurs to achieve sustainable development goals.