Aïsha Sahaï, Nicolas Souliman, Natacha Métayer

The deployment of driverless vehicles in urban environments raises concerns about pedestrian safety due to the loss of traditional driver communication cues. This study investigated the subjective crossing experience of young and older adult pedestrians facing two different driverless vehicles in a shared space. In a virtual reality setting, 44 participants (24 young adults and 20 older adults) were asked to cross while a driverless car and a driverless shuttle approached. The complexity of the crossing was manipulated so that the two driverless vehicles either had the same behavior (i.e., both yielding or both passing) or different behaviors (i.e., one yielding, the other passing). Additionally, the two vehicles could both be equipped with an external Human-Machine Interface (eHMI) indicating their respective intention (i.e., to yield or to pass) using visual and sound signals, or had none. After each crossing, the participants rated their perceived safety and understanding of the vehicles’ intentions using questionnaires. Semi-structured interviews were conducted post-experiment to gather qualitative feedback on the participants’ crossings and the bimodal eHMI. Firstly, our results indicated that the older adults reported better understanding of both the cars and shuttle’s intentions than the young adults, likely due to their broader integration of environmental cues. Moreover, a learning effect among the older adults was found, indicating improved understanding of the car’s intentions over time when the two vehicles exhibited different behaviors, reflecting preserved learning abilities in normal ageing that support adaptation to complex traffic scenarios. Furthermore, both age groups report ed an initial loss of perceived safety when the two vehicles behave differently, which diminished with repeated exposure, suggesting an adaptive learning process in complex traffic scenarios. Finally, t he presence of the bimodal eHMI on driverless v ehicles demonstrated a positive impact at different levels of the pedestrian crossing experience. These findings are further discussed.

Ravishankar Sharma, Dhanjoo N. Ghista, Edmund Evangelista

Background: Stress-related disorders and chronic diseases are increasing globally, highlighting the need for integrative health indices that go beyond isolated physiological signals. Objective: We introduce the Physiological Wellness Index (PWI), a wearable-compatibl e composite that integrates autonomic, respiratory, and electrodermal domains into a single interpretable score for real-time wellness monitoring. Methods: The PWI synthesizes heart rate variability (HRV, proxied through PRV RMSSD), respiratory rate (RR, a s the operational proxy for Breathing Efficacy), and electrodermal activity (EDA) through a weighted normalization framework. Each signal is scaled per participant, with RR and EDA inverted so that higher component values consistently reflect better physiology. The index outputs a 0–100 score, mapped into three actionable categories: restful (70–100), active (40–69), and distressed (0–39). The validation was performed on the ZU-PWD ’25 dataset, comprising 28 adults, 15 months of monitoring, and over 900,000 minute-level records collected using Empatica Embrace Plus devices. Results: Despite heterogeneous data coverage, the PWI reliably distinguished baseline from stress days and tracked acute episodes characterized by EDA spikes, suppressed HRV, and elevated RR. The composite index classified data with greater stability and offered trajectories that were more easily interpreted compared to single-signal measures. Conclusions: The PWI represents a transparent, sensor -driven alternative to traditional fitness and wellbeing metrics. By addressing raw multimodal physiology with actionable states, it supports preventive healthcare, workplace and educational wellness, and clinical decision -making, marking a significant step forward on the IoMT landscape.

Chin-Wei Wang, Yu-Shih Lee

With the growing emphasis on global digitalization strategies, and in the context of both the diverse needs of a super -aged society and the shortage of nursing personnel, this study integrated the Resource -Based View (RBV) and the Unified Theory of Acceptance and Use of Technology (UTAUT) models to examine how internal resources and capabilities of long -term care institutions affect digital maturity. It also analyzed the factors influencing professionals’ intention to adopt dig ital technologies. Within the framework of digital maturity, behavioral intention, and digital transformation application, digital maturity is identified as the most critical component. The results indicated that technology has a significant impact on digi tal maturity, while cultural and organizational factors do not. Personal innovativeness and performance expectancy significantly affect behavioral intention, whereas effort expectancy and social influence show no significant effect. The empirical analysis validated the key factors influencing digital transformation, providing managerial implications for leaders in formulating digital transformation strategies, and establishing a foundation for how long- term care institutions can leverage digital technologies to enhance service quality and efficiency.

Romain Ferrer, Sorana Cimpan, Hinata Yokoyama

Curriculum Design in competency-based education often lacks means to capture relationships between competencies and courses intended to develop them . In this paper, we present ForestED an interactive visualization tool which addresses this issue. Applied to curricula, ForestED displays visualizations of the curricula that render explicit the links between competencies and courses. While the visualization reflects the static curriculum definition, it also allows inference of competency development over time through the course timeline.

Gabriel Lenz Balatka, Rafael Stubs Parpinelli

The point cloud part segmentation task consists of segmenting an object, represented by a point cloud, into its constituent parts, such as a chair that is segmented into seat, backrest, and legs. The most recent computational strategies use Artificial Neural Networks to perform this task, but the architectures used are developed generically and therefore do not consider the specific patterns of each category of objects. Thus, this work proposes to analyze the contribution of building specific architectures based on the optimization of hyperparameters of the PointNet architecture, which is well established in the literature. The dataset used was the PartNet, and four case studies were employed. In addition, we also studied the impact of point cloud size on th is segmentation task, performing the optimization process in each category studied in three different point cloud sizes: 512, 1,024, and 2,048. From the results obtained, an average improvement of 2% in the test accuracy metric was achieved in the Table -1, Chair-1, and Lamp-1 categories and 6% in the StorageFurniture -1 category. The impact of point cloud size was low, and statistically significant improvements were observed in Table -1, Chair -1, and StorageFurniture -1 categories. Thus, hyperparameter optimization proved to be consistent, achieving satisfactory results.

Pedro Isaías

Editorial preface for Volume 20, Issue 2 of the IADIS International Journal on Computer Science and Information Systems (IJCSIS). This issue brings together peer-reviewed original research contributions covering the wide spectrum of Information Systems, Artificial Intelligence, Software Engineering, and Digital Transformation.