Human Computer Interaction (Switzerland)

Abstract Social virtual reality (SVR) allows real individuals to communicate and engage in simulated physical environments and activities. These systems simultaneously afford social and physical presence. However, the precise nature of various presence states in SVR is unclear. This study addresses this conceptual gap through interviews with SVR users and interpretative phenomenological analysis of the individual responses. This research shows that physical and social presence are only occasionally co-experienced; presence in SVR often depends on the individual experiences/backgrounds, perceived affordances, or specific goal of technology use. These findings contribute to a deeper understanding of presence and its dynamic nature in SVR shaped by the users’ individual experiences. This facilitates how we will study and measure presence in SVR in the future.

The engagement and burnout profiles of preschool teachers are closely linked to young children's developmental outcomes. This study investigated engagement and burnout profiles among 529 Chinese preschool teachers in relation to their emotional states, varying experiences, and professional backgrounds. The sample predominantly consisted of early-career educators, with 47.8% aged between 21 and 30 years and 33.1% having 0-5 years of work experience. Using a quantitative cross-sectional design and latent profile analysis (LPA), this study identified four distinct profiles: slightly exhausted (48.58%), moderately burned out (18.53%), engaged (25.90%), and highly burned out (6.99%). Positive emotional states, such as enjoyment, were associated with higher work engagement, while anxiety was associated with a higher probability of belonging to burnout profiles. In contrast, perceived career success and negative emotions like anger did not significantly predict work engagement and burnout profiles. Teachers with extensive teaching experience and pre-service early childhood education (ECE) training were more likely to maintain high work engagement. This study highlights the critical role of emotional states and professional ECE training in promoting preschool teachers' work engagement and sustainable practice, particularly among early-career teachers.

The Magic Coat Hanger is a robotic coat hanger designed to help university students transition into their home environments by helping them to develop a ‘homecoming ritual.’ It adopts a nature-inspired aesthetic, expressive movement, and dynamic LED lighting to engage users and help them refocus their attention. Following the development of a high-fidelity prototype, a user study was conducted with university students to evaluate if The Magic Coat Hanger could potentially foster a homecoming ritual. Overall, this work demonstrates how an ordinary moment can become restorative by transforming a simple household object into an interactive, robotic form.

Abstract. Missing values are ubiquitous in atmospheric monitoring due to instrument drift, calibration cycles, operational interruptions, and other random malfunctions. Such gaps can undermine the reliability of subsequent analyses and introduce systematic biases. Conventional imputation methods, such as K-nearest neighbor (KNN), Bayesian principal component analysis (BPCA), and deep learning architectures, rely primarily on statistical correlations, requiring auxiliary inputs, and offer limited physical interpretability. To address this issue, we propose a novel source–receptor informed Positive Matrix Factorization Reconstruction (PMFr) method that leverages PMF-derived source–receptor relationships, rather than purely statistical interpolation, to impute missing PM2.5 speciation data without requiring auxiliary data. Benchmarking against commonly used imputation techniques KNN, BPCA, and deep learning predictive model demonstrates that PMFr achieves superior accuracy and robustness under all real-world missing scenarios, with a mean coefficient of determination (R2) of 0.81, index of agreement (IoA) of 0.92, and mean absolute percentage error (MAPE) of 22.8 %, reducing MAPE by 25.5–29.1 %, particularly for key PM2.5 species, highlighting its potential as a robust tool for recovering reliable data in air quality studies.

ABSTRACT Intelligent technologies have shifted operator tasks from physical to cognitive demands, making it crucial to understand the impact of task load on visual search tasks. This study examined the effects of auditory working memory and visual perceptual load on inspection tasks using electroencephalography, electrooculography, and electromyography (EMG). Working memory load was varied by an N‐back task, and perceptual load by interference presence. Results indicated: (1) Excessive auditory working memory load induces interference suppression, with event‐related potential P300 and P200 amplitudes indicating sensitivity to perceptual load; (2) Delta, theta, alpha, and low beta band power spectral densities (PSDs) are sensitive to working memory load, with alpha being most sensitive; (3) Eye blink rate (EBR) increases with working memory load; (4) EMG activity detection may not effectively detect working memory load despite observed trends; (5) Strong positive correlations exist within PSD bands and between P300/P200 amplitudes at certain electrodes, with significant correlations between different modality indicators, such as negative correlations between EBR and low beta band PSD and positive correlations between root mean square, integrated EMG (iEMG), and F4 electrode P300 amplitude.