Abstract
Cities worldwide have agreed on ambitious goals regarding carbon neutrality. To do so, policymakers seek ways to foster smarter and cleaner transportation solutions. However, citizens lack awareness of their carbon footprint and of greener mobility alternatives such as public transports. With this, three main challenges emerge: (i) increase users’ awareness regarding their carbon footprint, (ii) provide personalized recommendations and incentives for using sustainable transportation alternatives and, (iii) guarantee that any personal data collected from the user is kept private. This paper addresses these challenges by proposing a new methodology. Created under the FranchetAI project, the methodology combines federated Artificial Intelligence (AI) and Greenhouse Gas (GHG) estimation models to calculate the carbon footprint of users when choosing different transportation modes (e.g., foot, car, bus). Through a mobile application that keeps the privacy of users’ personal information, the project aims at providing detailed reports to inform citizens about their impact on the environment, and an incentive program to promote the usage of more sustainable mobility alternatives.

Abstract
This research seeks to raise awareness about the challenges faced by people with visual impairments by immersing users in a virtual environment that simulates 18 different visual conditions. Through a series of tests, participants are tasked with performing simple activities while navigating the complexities of these impairments. The study, validated by 60 users, uses objective metrics like reaction time and accuracy to measure the impact of these conditions on task performance. An online pre- and post-test questionnaire also reveals a significant increase in empathy among participants. The results highlight the importance of direct experience in understanding the challenges of people with visual impairments and demonstrate the potential of such simulations to foster empathy and awareness. Ultimately, this application contributes to a broader understanding of visual impairments and underscores the need for universal design initiatives.

Abstract
In uplift modeling, the goal is to identify high-value customers based on persuadable customers, those who make a purchase only if contacted. To achieve this, uplift modeling combines machine learning techniques with causal inference, allowing businesses to refine their customer targeting strategies and focus efforts where they are most profitable. This study proposes a practical and reproducible two-phase procedure for identifying high-value customers. In the first phase, customers are segmented using decision trees, which offer a transparent and data-driven approach to grouping individuals with similar characteristics. This segmentation lays the groundwork for a meaningful interpretation of customer behavior. In the second phase, uplift is calculated for each customer segment by comparing the outcomes of the treatment and control groups. This enables the identification of customer groups with the highest uplift. A real-world use case further illustrates the value and applicability of the proposed method. To validate model performance, the procedure employs established metrics such as the Qini index and Cohen's kappa, which provide insights into both the effectiveness and reliability of the uplift estimates. This work presents a decoupled procedure for uplift modeling that leverages well-established libraries, fostering transparency and a clear understanding of the analytical process. A key contribution to uplift modeling and causal inference is the use of decision trees for stratification, which enables the creation of meaningful segments and their evaluation through the average treatment effect. By integrating theory with practical implementation, this work offers a comprehensive framework for uplift modeling that bridges academic rigor and business usability. © 2025 Elsevier B.V., All rights reserved.

Abstract
Since publication of the 2020 survey of surveys, Finding the gaps about uses of immersive learning environments: a survey of surveys, the field of immersive learning environments has experienced substantial growth and diversification. This updated review systematically maps recent developments by analyzing 64 new literature surveys published after the original corpus date, significantly expanding the corpus from 47 to 111 reviews. Through thematic content analysis, our study identifies and integrates five new educational use themes-Games, Observation, Personification, Storytelling, and Student Authoring-and revises existing categories based on recent research. We observed shifts in the prevalence of themes, most notably an increase in uses related to data collection, interactive exploration and manipulation, contextual/media integration, and physical world simulation. We also discussed these changes in relation to recent technological advancements and the influence of emergency remote teaching during the COVID-19 pandemic. Moreover, our results provide an updated representation of immersive learning uses within the conceptual framework of immersion dimensions (system, narrative, agency), updating current research clusters and persistent gaps. By illustrating areas with limited exploration, such as highly interactive narrative experiences, or low-technology interactive uses, this paper informs future research directions and contributes to an understanding of how immersive environments are being employed for learning. This comprehensive mapping thus serves as a resource for researchers and educators aiming to leverage immersive learningenvironments. This paper builds on a shorter version accepted for inclusion in the proceedings of the iLRN 2025 conference, offering expanded results, additional analyses, and extended discussion that clarifies and deepens the original findings.

Abstract
This study introduces a secure, adaptable, and decentralized learning framework empowered by blockchain technology to enhance smart grid security and efficiency. Security is achieved through blockchain's ledger, ensuring data integrity, privacy, and resilience. Adaptability refers to the framework's ability to adjust to changing conditions, supporting multiple learning paradigms. Decentralization enhances fault tolerance by distributing control across nodes. Our framework excels in scalability, data-exchange security, and rapid response times, aiming to establish an intelligent blockchain-based smart grid supporting centralized learning (CL), federated learning (FL), and active federated learning (AFL). We present an innovative blockchain-based architecture customized to optimize information sharing and security within the blockchain. Our solution addresses various learning paradigm requirements by: (i) Selecting reliable entities for participation based on high-quality training data models; (ii) Acquiring a reliable subset of data for CL and AFL, balancing learning performance, latency, and cost; (iii) Adjusting blockchain configuration to align with specific learning paradigm requirements. Results from real-world datasets demonstrate superior performance compared to existing solutions. Our framework achieves high learning performance while minimizing latency and blockchain costs.

Abstract
The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations.