Abstract
Introduction: Urban centers face increasing congestion and pollution due to population growth driven by jobs, education, and entertainment. Promoting active modes like walking and cycling offers healthier and less polluting alternatives. Understanding perceptions of comfort (green areas, commercial areas, crowd density, noise, thermal sensation, air quality, allergenics), safety and security (street illumination, traffic volume, surveillance, visual appearance, and speed limits) are crucial for encouraging active modes adoption. This study categorizes user groups based on these indicators, supporting policymakers in the development of targeted strategies. Methods: We developed a questionnaire to support our empirical study and collected 653 responses. We have analyzed the data using clustering methods such as Affinity Propagation, BIRCH, Bisecting K-means, HAC, K-means, Mini-Batch K-means, and Spectral clustering. The best performing method (K-means) was used to identify the user groups while a random forest model evaluated the relative importance of indicators for each group. Results: The study identified five user groups based on urban mobility indicators for safety and security, comfort, and distance and time. Conclusions: These groups, distinguished by sociodemographic features, include: Street Aesthetes (young men valuing visual appeal), Safety Seekers (employed men prioritizing speed limits), Working Guardians (employed men focused on surveillance and green spaces), Urban Explorers (young women valuing air quality and low traffic), and Comfort Connoisseurs (employed women prioritizing noise reduction and aesthetics).

Abstract
In order to facilitate the adoption of deep learning in areas where decisions are of critical importance, understanding the model's internal workings is paramount. Nevertheless, since most models are considered black boxes, this task is usually not trivial, especially when the user does not have access to the network's intermediate outputs. In this paper, we propose IBISA, a model-agnostic attribution method that reaches stateof-the-art performance by optimizing sampling masks using the Information Bottleneck Principle. Our method improves on the previously known RISE and IBA techniques by placing the bottleneck right after the image input without complex formulations to estimate the mutual information. The method also requires only twenty forward passes and ten backward passes through the network, which is significantly faster than RISE, which needs at least 4000 forward passes. We evaluated IBISA using a VGG-16 and a ResNET-50 model, showing that our method produces explanations comparable or superior to IBA, RISE, and Grad-CAM but much efficiently.

Abstract
Road safety is a global issue, with road-related accidents being one of the biggest leading causes of death. Motorcyclists are especially susceptible to injuries and death when there is an accident, due to the inherent characteristics of motorcycles. Accident prevention is paramount. To improve motorcycle safety, this paper discusses and proposes a preliminary architecture of a system composed of various sensors, to assist and warn the rider of potentially dangerous situations such as front and back collision warnings, pedestrian collision warnings, and road monitoring.

Abstract
Digital twins are increasingly used, as they allow the creation of detailed virtual representations of physical products and systems. They face, however, significant challenges such as heterogeneous data integration and high costs. This article presents an innovative methodology that uses Large Language Models to unify information and automate the generation of Digital Twin models. The proposal comprises several modules, covering the stages of data collection, semantic processing, modular construction and validation of the Digital Twin. In this way, the proposed model guarantees interoperability, efficiency and scalability for various domains. © 2025 IEEE.

Abstract
Solid-state batteries are prominent in today's research landscape due to their advantages in capacity and safety. This work explores anode-less all-solid-state batteries, a configuration with industrial benefits as it avoids handling alkali metal anodes, albeit with room for improvement. To elucidate the intricacies of these batteries, Laser-Induced Breakdown Spectroscopy (LIBS) served as a pivotal analytical tool, primarily focusing on the negative current collector surface where Li+ nucleation occurs from the Li-rich electrolyte. The use of a fiber-laser for breakdown spectroscopy offers advantages over conventional lasers by producing high beam quality, enabling minimal spot size, and ensuring excellent spatial resolution. LIBS is an asset to verify Li presence, discerning its source, assessing nucleation and distinguishing it from electrolyte-derived Li. For instance, in this work utilizing Li2.99Ba0.005ClO as the electrolyte, LIBS is crucial to elucidate the relationship between Li and other elements like Cl, Zn, or Fe, shedding light on key battery performance aspects. LIBS demonstrated a high potential for verifying in situ Li metal nucleation in anode-less cells. This study highlights its effectiveness in conceptual and product development and advanced quality testing. The application of a clustering method enhanced result interpretability and the distinction between electrolyte and in situ anode regions.

Abstract
The pervasiveness of mobile devices has fostered a multitude of services and applications, but also raised serious privacy concerns. In order to avoid users' tracking and/or users' fingerprinting, smartphones have been tightening the access to unique identifiers. Nevertheless, smartphone applications can still collect diverse data from available sensors and smartphone resources. Using real-world data from a field study we performed, this paper demonstrates the possibility of fingerprinting users from Wi-Fi data in mobile devices and the consequent privacy impact. From the performed analysis, we concluded that a single snapshot of a set of scanned Wi-Fi BSSIDs (MAC addresses) per user is enough to uniquely identify about 99% of the users. In addition, the most frequent Wi-Fi BSSID is sufficient to re-identify more than 90% of the users, a percentage that goes up to 97% of the users with the top-2 scanned BSSIDs. The Wi-Fi SSID (network name) also leads to a re-identification risk of about 83% and 97% with 1 and 2 of the strongest Wi-Fi Access Points (APs), respectively.