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Background: Video-based training has been widely adopted by private organizations and public authorities to educate occupants on various types of building emergencies. However, the effectiveness of video-based training for preparing occupants for building emergencies has not been rigorously studied nor has the impact of emergency type been investigated on training effectiveness. Objectives: This study examines whether video-based training is an effective method to prepare occupants for building emergencies and how the effectiveness differs in the context of different building emergencies. Methods: We simulated fire and active shooter emergencies in a virtual office building and conducted evacuation experiments to examine participants' emergency responses using both objective and subjective metrics. A total of 108 participants were recruited and responded to the fire or active shooter incident with or without video-based training. Results and Conclusions: The results revealed that participants with video-based training more often chose to follow other recommendations when responding to building emergencies instead of simply following others. Results from ANOVA showed that training increased participants' self-efficacy significantly, especially for those in the active shooter group. Moreover, participants in the active shooter simulation had a higher level of response efficacy than those in the fire emergency simulation. Our results also demonstrated the influence of emergency type on participants' final decisions and considerations of the recommendations. Implications: Our results suggested that video-based training is effective in improving participants' emergency preparedness and changing their behaviour patterns to a certain extent such as reducing following behaviour and encouraging safe evacuations. Additionally, statistically significant interactions between video-based training and emergency types suggested that training effectiveness should be considered in accordance with the emergency type.

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F1-scores for predicting eustress and distress, respectively. Gender-based models resulted in an average increase of 2-4% in eustress and distress prediction accuracy. Finally, a model to predict the simultaneous assessment of eustress and distress, distinguishing between pure eustress, pure distress, eustress-distress coexistence, and the absence of stress achieved a moderate F1-score of 65.12%. The results of this study lay the foundation for work management interventions to maximize eustress and minimize distress in the workplace. [ABSTRACT FROM AUTHOR]

For a graph G,P( G,λ)denotes the chromatic polynomial of G. Two graphs G and H are said to be chromatically equivalent, denoted by G∼ H, if P(G,λ)= p(H,λ). Let [ G]={ H| H ∼ G}. If [ G]={ G}, then G is said to be chromatically unique. For a complete 5-partite graph G with 5 n vertices, define ϑ( G)=( α( G,6) − 2 n−1 − 2 n−1+5)/2 n−2, where α( G, 6) denotes the number of 6-independent partitions of G. In this paper, the authors show that ϑ( G)⩾0 and determine all graphs with ϑ( G)=0, 1, 2, 5/2, 7/2, 4, 17/4. By using these results the chromaticity of 5-partite graphs of the form G − S with ϑ(G)=0, 1, 2, 5/2, 7/2, 4, 17/4 is investigated, where S is a set of edges of G. Many new chromatically unique 5-partite graphs are obtained. [ABSTRACT FROM AUTHOR]