Abstract
Within the scope of the Mobilizing Agenda for the Development of Intelligent Green Mobility Products and Systems (A-MoVeR), PPS2 defined the presentation of a “new electric motorcycle, with high autonomy, aimed at promoting comfortable, efficient and efficient urban mobility. green”. In this context, the need to develop interfaces that meet the expectations of end users, promoting user experience and security are crucial. Therefore, following a User-Centered Design (DCU) methodology, a co-design perspective and UX data collection methods, this article presents the steps and preliminary results of the preparation, face-to-face session and subsequent analysis of results of a preliminary moment of acquiring knowledge on how to optimize motorcycle user interfaces. Specifically: script planning, requirements and analysis of user feedback collected through audiovisual recording, in a focus group, are described. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract

Abstract
Spectral analysis of cultural heritage materials offers valuable insights into the restoration and preservation of historical artifacts, revealing details about the materials used and the manufacturing techniques employed. However, given their historical and artistic significance, the extraction of elemental information from these fragile samples poses a unique challenge, as these objects must be examined using minimally invasive methods to prevent irreversible damage. Laser-induced Breakdown Spectroscopy (LIBS) is one such technique, providing a rapid and detailed elemental characterization. Yet, extensive LIBS analysis can still compromise the integrity of these delicate objects. In this work, a novel approach that integrates spectral and RGB data clustering to significantly reduce the number of LIBS measurements required is introduced. By segmenting the material into visually and chemically distinct clusters, this method enables targeted LIBS analysis using only a few representative shots per cluster, thus preserving the integrity of cultural heritage artifacts while still delivering reliable compositional insights. © 2026 The Author(s).

Abstract
Background: Online psychological interventions like behavioural activation (BA) can be provided with or without human support. Unguided online interventions require no human contact and are therefore easier to implement on a large scale than guided interventions. However, effectiveness and adherence rates to these interventions are generally lower. One way to increase adherence to unguided online interventions is to offer automated motivational support. Objective: This pilot randomized controlled trial (RCT) examined whether adherence to unguided online BA for low mood could be improved by adding automated support in the form of smartphone-delivered personalized motivational messages or a motivational virtual coach. Methods: A three-arm pilot RCT (n = 106) was conducted that compared an online intervention delivered with automated motivational support by a virtual coach (n = 35), or by automated personalized messages on their smartphone (n = 35), to the same intervention without support (control condition; n = 36). The primary outcome was level of adherence, operationalized as (1) the number of webpages of the intervention visited, and (2) the number of mood ratings completed on the smartphone application, both retrieved from participants' logfiles. Secondary outcomes were satisfaction with the intervention (CSQ-I), usability (SUS) depression scores (HADS), and motivation for treatment (SMFL), measured through online questionnaires administered at baseline or after 4 weeks. Results: Adherence was moderate overall, with participants visiting on average 23 pages of 55 webpages and completing on average 50 of 84 requested mood ratings. No evidence for differences in adherence rates were observed between the intervention conditions and the control condition. Satisfaction with the intervention was moderate to high. Usability scores were below the desirable threshold of 68. Depression symptoms did not change significantly across all participants (p = .053). No significant changes in motivation were found over time or between groups. Conclusions: Adding automated support to unguided online BA for depression did not improve overall adherence. The limited effectiveness may reflect a misalignment between the motivational strategies and the needs of the target population, who reported mild symptoms and high intrinsic motivation. The findings highlight the need to further improve both the quality of automated support and the usability of online platforms. Future research should explore additional adherence-related factors and investigate how personalization can better address different symptom severities in unguided mental health interventions. Trial registration: International Clinical Trials Registry Platform: trialsearch.who.int/Trial2.aspx?TrialID=NL8110. © 2025 The Authors

Abstract
Objective The aim of this scoping review was to map and describe the technological tools reported in the literature that have been designed for care management in Alzheimer's disease, with a particular focus on supporting patients living with the condition, their families, caregivers, and healthcare professionals. Methods The review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. A comprehensive literature search was performed across multiple databases, including Scopus, PubMed, Web of Science, and CINAHL, focusing on studies addressing technological resources aimed at supporting the care and management of Alzheimer's disease. Results A total of 23 studies were included in the final analysis. The most frequently utilized technologies were mobile applications and wearable devices. The most identified functionalities included cognitive training, location tracking, task reminders, communication support, fall detection, and vital signs monitoring, often integrated into comprehensive solutions to enhance patient care and safety. Conclusion Overall, these technologies were designed to support both patients and caregivers. However, despite the clear benefits and innovative potential of these technologies, significant limitations remain, particularly the lack of empirical validation in real-world clinical settings and the need to ensure greater usability for older adults and individuals with cognitive impairments.

Abstract
The growing availability of online data creates new opportunities to improve the timeliness and detail of official statistics, particularly in domains such as price monitoring and inflation measurement. However, leveraging web-scraped data for official use requires alignment with standardized classification frameworks such as the European Classification of Individual Consumption According to Purpose (ECOICOP). We train two natural-language models, a lightweight convolutional neural network (CNN) and a fine-tuned BERTimbau transformer, to classify Portuguese food and beverage items into ECOICOP categories. Using 100,000 product titles scraped from six national supermarket sites and labeled via a human-in-the-loop workflow, the CNN reaches a macro-F1 of 92.19 % with minimal computing cost, while the transformer attains 94.00 %, the first such result for Portuguese. Both models are published on Hugging Face, enabling reproducible inference at scale while the source data remain confidential. The study delivers the first open-source Portuguese ECOICOP classifiers for food and beverage products, a replicable low-resource labeling workflow, and a benchmark of accuracy-speed trade-offs to guide researchers in similar tasks.