Abstract
Self and co-regulation of learning (SCRL) are strategies that students can adopt to become more active and committed to their learning. Encouraging students to adopt these strategies is a challenge for teachers that can be met by using narratives as a teaching resource. To support teachers in this process, we present a method for evaluating, classifying, and reflecting on excerpts from immersive narratives for SCRL, so they objectively base their decision-making. The method was developed as an artifact of Design Science Research (DSR). In the Design stage of DSR, a 4-stage scheme was developed, and 38 criteria were described to identify and classify narratives that guide or encourage students to adopt SCRL strategies. In the DSR demonstration stage, we tested the method in an asynchronous e-learning curricular unit in Portuguese higher education, which uses a narrative-oriented immersive learning approach for SCRL, called e-SimProgramming. The results show that the graphic visualization of the classification made it possible to perceive the occurrence of the SCRL categories in the narratives, enabling the teacher to be inspired and reflect on the categories to be enhanced for necessary changes in the narrative in line with their pedagogical objectives.

Abstract
Obstructive sleep apnea (OSA) is a sleep disorder marked by repeated episodes of airway obstruction, leading to apneas (complete blockage) or hypopneas (partial blockage) during sleep. The standard diagnostic metric, the apnea-hypopnea index (AHI), quantifies the number of these events per hour of sleep but has limitations, such as its dependence on manual interpretation and lack of attention to event duration, which can be clinically significant. To address these issues, this study developed an algorithm to detect respiratory events from nasal airflow signals and measure their duration, using data from 22 patients at St. Vincent's University Hospital, sourced from the PhysioNet dataset. Signal processing techniques, including filtering and envelope analysis, were applied to extract features, and apnea/hypopnea events were identified based on American Academy of Sleep Medicine (AASM) guidelines. Events were classified by duration into three groups: 10-20 seconds, 20-40 seconds, and over 40 seconds. Preliminary results showed detection accuracy of 60% for apnea and 93% for hypopnea events. The study also explored relations between event duration and demographic factors, such as age, gender, body mass index (BMI), and Epworth Sleepiness Scale (ESS) scores, to assess whether longer events were linked to greater severity. These findings suggest that incorporating event duration and automated detection into OSA diagnosis could improve accuracy and provide better insight into the condition, potentially leading to more personalized treatments.

Abstract
This paper discusses the potential effects of Attention-Capture Damaging Patterns (ACDPs) in designing socially and culturally sensitive interfaces based on their mechanisms and psychological impacts on users. Building on the concept of “dark patterns” and examining how they contribute to social polarization, this study explores the intersection between digital interface design, digital wellbeing, and polarization. The paper analyzes several examples of ACDPs present in popular social media apps and platforms such as Instagram, TikTok, WhatsApp, and Facebook, proposing a new taxonomic approach based on three main categories. In addition, a set of alternative design strategies that promote healthier interactions on digital platforms are discussed to mitigate the negative effects of these patterns and promote a more balanced digital environment.

Abstract
This work introduces a web application for extracting, processing, and visualizing data from sleep studies reports. Using Optical Character Recognition (OCR) and Natural Language Processing (NLP), the pipeline extracts over 75 key data points from four types of sleep reports. The web application offers an intuitive interface to view individual reports' details and aggregate data from multiple reports. The pipeline demonstrated 100% accuracy in extracting targeted information from a test set of 40 reports, even in cases with missing data or formatting inconsistencies. The developed tool streamlines the analysis of OSA reports, reducing the need for technical expertise and enabling healthcare providers and researchers to utilize sleep study data efficiently. Future work aims to expand the dataset for more complex analyses and imputation techniques.

Abstract
This paper presents a method for position correction in collaborative robots, applied to a case study in an industrial environment. The case study is aligned with the GreenAuto project and aims to optimize industrial processes through the integration of various hardware elements. The case study focuses on tightening a specific number of nuts onto bolts located on a partition plate, referred to as “Cloison”, which is mounted on commercial vans produced by Stellantis, to secure the plate. The main challenge lies in deviations that may occur in the plate during its assembly process, leading to uncertainties in its fastening to the vehicles. To address this and optimize the process, a collaborative robot was integrated with a 3D vision system and a screwdriving system. By using the 3D vision system, it is possible to determine the bolts’ positions and adjust them within the robot’s frame of reference, enabling the screwdriving system to tighten the nuts accurately. Thus, the proposed method aims to integrate these different systems to tighten the nuts effectively, regardless of the deviations that may arise in the plate during assembly. © 2025 by the authors.

Abstract
The active involvement of marginalized and vulnerable groups such as migrants and newly arrived refugees in the development of local communities has been part of many agendas across the EU and around the world. Despite the lessons gleaned from more than three decades of IUI research, there is still a shortage of systematic understanding and concrete guidance on how to design more socially inclusive and culturally sensitive interfaces targeted to these populations. In this paper, we argue that community-based citizen science approaches hold the potential to foster people-place bonds and inform the design of inclusive interactions since these initiatives are typically open to a wide audience regardless of race, ethnicity, gender, and education. From portable environmental monitoring devices to open databases providing place-related data about species observations and environmental threats, citizen scientists have a socially transformative and place-development potential that is often overlooked from an interaction design perspective. This research investigates this gap by examining digital interactions in citizen science through a systematic literature review addressing interaction possibilities for digitally enhanced place-belongingness. The results indicate three interaction themes within citizen science literature contributing to digitally enhanced sense of place-belonginess: place awareness and involvement, experience sharing, and collaboration encouragement. In addition, we found that the inclusivity goals in citizen science initiatives typically vary from urban and rural development to cultural purposes and environmental engagement and conservation. The interaction themes, along with the negative impacts of digital technologies, are discussed regarding their potential to inform technology design for place-belongingness in HCI.