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In this paper, we explore the mutual effect of prior background expectations and visibility afforded by the 3D configuration of the physical environment on wayfinding efficiency and strategy in multilevel buildings. We perform new analyses on data from 149 participants who performed six unaided and directed wayfinding tasks in virtual buildings with varying degrees of visibility. Our findings reveal that the interaction between visibility and prior background expectations significantly affects wayfinding efficiency and strategy during between-floor wayfinding tasks. We termed this interaction effect strategic visibility, which emphasizes the importance of the strategic allocation of visibility towards actionable building elements in promoting efficient wayfinding and shaping wayfinding strategy. Our study highlights the significance of strategic visibility in promoting inclusive and accessible built environments for neurodiversity. Finally, we provide an open-source dataset that can be used to develop and test new wayfinding theories and models to advance research in the emerging field of human-building interaction. [ABSTRACT FROM AUTHOR]

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur. [ABSTRACT FROM AUTHOR]

Based on the interrelationship between the built environment and spatial–temporal distribution of population density, this paper proposes a method to predict the spatial–temporal distribution of urban population density using the depth residual network model (ResNet) of neural network. This study used the time-sharing data of mobile phone users provided by the China Mobile Communications Corporation to predict the time–space sequence of the steady-state distribution of population density. Firstly, 40 prediction databases were constructed according to the characteristics of built environment and the spatial–temporal distribution of population density. Thereafter, the depth residual model ResNet was used as the basic framework to construct the behaviour–environment agent model (BEM) for model training and prediction. Finally, the average percentage error index was used to evaluate the prediction results. The results revealed that the accuracy rate of prediction results reached 76.92% in the central urban area of the verification case. The proposed method can be applied to prevent urban public safety incidents and alleviate pandemics. Moreover, this method can be practically applied to enable the construction of a "smart city" for improving the efficient allocation of urban resources and traffic mobility. [ABSTRACT FROM AUTHOR]

The integration of human-centric approaches has gained more attention recently due to more automated systems being introduced into our built environments (buildings, roads, vehicles, etc.), which requires a correct understanding of how humans perceive such systems and respond to them. This paper introduces an Immersive Virtual Environment-based method to evaluate the infrastructure design with psycho-physiological and behavioral responses from the vulnerable road users, especially for pedestrians. A case study of pedestrian mid-block crossings with three crossing infrastructure designs (painted crosswalk, crosswalk with flashing beacons, and a smartphone app for connected vehicles) are tested. Results from 51 participants indicate there are differences between the subjective and objective measurement. A higher subjective safety rating is reported for the flashing beacon design, while the psychophysiological and behavioral data indicate that the flashing beacon and smartphone app are similar in terms of crossing behaviors, eye tracking measurements, and heart rate. In addition, the smartphone app scenario appears to have a lower stress level as indicated by eye tracking data, although many participants do not have prior experience with it. Suggestions are made for the implementation of new technologies, which can increase public acceptance of new technologies and pedestrian safety in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Non-thermal plasma (NTP) is a promising technology for the improvement of indoor air quality (IAQ) by removing volatile organic compounds (VOCs) through advanced oxidation process (AOP). In this paper, authors developed a laboratory scale dielectric barrier discharge (DBD) reactor which generates atmospheric NTP to study the removal of low-concentration formaldehyde (HCHO), a typical indoor air VOC in the built environment associated with cancer and leukemia, under different processing conditions. Strong ionization NTP was generated between the DBD electrodes by a pulse power zero-voltage switching flyback transformer (ZVS-FBT), which caused ionization of air molecules leading to active species formation to convert HCHO into carbon dioxide (CO2) and water vapor (H2O). The impact of key electrical and physical processing parameters i.e. discharge power (P), initial concentration (Cin), flow rate (F), and relative humidity (RH) which affect the formaldehyde removal efficiency (ɳ) were studied to determine optimum conditions. Results show that, the correlation coefficient (R2) of removal efficiency dependence on the processing parameters follow the order R2 (F) = 0.99 > R2 (RH) = 0.96, > R2 (Cin) = 0.94 > R2 (P) = 0.93. The removal efficiency reached 99% under the optimum conditions of P = 0.6 W, Cin = 0.1 ppm, F = 0.2 m3/h, and RH = 65% with no secondary pollution. The study provided a theoretical and experimental basis for the application of DBD plasma for air purification in the built environment. [ABSTRACT FROM AUTHOR]

Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model. [ABSTRACT FROM AUTHOR]

As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications. [ABSTRACT FROM AUTHOR]

Research has demonstrated the positive effect of natural environment on human restoration and well-being. Time spent in nature can often alleviate both physiological and psychological stress. However, few studies have discussed the environmental health effects of the nature's components and characteristics. Sixty volunteers were recruited and one manufactured environment and five different natural environments were randomly assigned to them, including coniferous forests (pure coniferous forest-PC and mixed coniferous forest-MC), broad-leaved forests (pure broad-leaved forest-PB and mixed broad-leaved forest-MB), and mixed forest (mixed coniferous and broad-leaved forest-MCB). Each volunteer sat in a built or natural environment and looked around the environment for 15 min. Physiological (HR, HRV, BP, pulse rate and salivary cortisol) and psychological indicators (POMS and STAI) were used to evaluate the changes in their stress level. Results indicated a strong difference in HR, HRV, POMS and STAI between the built and natural environment, which showed that natural environment can lower the stress level. MC had the best effect on relieving physiological stress, whereas MCB is most successful in improving emotional state and reducing anxiety. Broad-leaved forest and mixed forest significantly affected the DBP and vigor level of the subjects, respectively. While coniferous forest did significantly increase the concentration of salivary cortisol in subjects. The study confirmed that compared to the built environment, the natural environment can relieve the human body's physical and psychological stress and negative emotions, while significantly increasing vitality. And different plant communities also have different effects on the physiological and psychological indicators of the subjects. These results will provide scientific basis for the construction and improvement of urban green space environment. [ABSTRACT FROM AUTHOR]

Compared to microbiomes on other skin sites, the bacterial microbiome of the human hand has been found to have greater variability across time. To increase understanding regarding the longitudinal transfer of the hand microbiome to objects in the built environment, and vice versa, 22 participants provided skin microbiome samples from their dominant hands, as well as from frequently and infrequently touched objects in their office environments. Additional longitudinal samples from home environments were obtained from a subset of 11 participants. We observed stability of the microbiomes of both the hand and built environments within the office and home settings; however, differences in the microbial communities were detected across the two built environments. Occupants' frequency of touching an object correlated to that object having a higher relative abundance of human microbes, yet the percent of shared microbes was variable by participants. Finally, objects that were horizontal surfaces in the built environment had higher microbial diversity as compared to objects and the occupants' hands. This study adds to the existing knowledge of microbiomes of the built environment, enables more detailed studies of indoor microbial transfer, and contributes to future models and building interventions to reduce negative outcomes and improve health and well-being. [ABSTRACT FROM AUTHOR]

Biosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of traditional self-reported surveys and field audits. However, to date, most prior work has been conducted in a controlled setting and there has been little investigation into utilizing biosignals captured in real-life settings. This research examines the usability of biosignals (electrodermal activity, gait patterns, and heart rate) acquired from real-life settings to capture the environmental distress experienced by pedestrians. We collected and analyzed geocoded biosignals and self-reported stimuli information in real-life settings. Data was analyzed using spatial methods with statistical and machine learning models. Results show that the machine learning algorithm predicted location-based collective distress of pedestrians with 80% accuracy, showing statistical associations between biosignals and the self-reported stimuli. This method is expected to advance our ability to sense and react to not only built environmental issues but also urban dynamics and emergent events, which together will open valuable new opportunities to integrate human biological and physiological data streams into future built environments and/or walkability assessment applications. [ABSTRACT FROM AUTHOR]

Physical activity is known to be one of the most health-beneficial behaviors, and salutogenic design modifications to the built environment can facilitate increased physical activity. Unfortunately, it is not often clear in advance which environmental and urban design implementations will generate increases in activities such as walking, and which will have little impact or even reduce walking. The present study tested the feasibility and acceptability of a virtual reality (VR) model for pre-testing urban designs for their impact on walking. Using a wearable VR head-mounted display/computer, young adults (n = 40) walked freely through a large indoor gymnasium, simultaneously walking through a virtual model of an urban streetscape that was designed to be modifiable and allow for testing impacts on walking of various changes to the urban environment. The majority of participants found the experience to be acceptable: pleasant and nonaversive, and they walked freely through the VR model for approximately 20 min, on average. Using modifiable VR models to pre-test built-environment changes for their impacts on walking behavior appears to be a feasible and acceptable approach and worthy of continued research investigation. [ABSTRACT FROM AUTHOR]

Cognitive buildings use data on how occupants respond to the built environment to proactively make occupant-centric adjustments to lighting, temperature, ventilation, and other environmental parameters. However, sensors that unobtrusively and ubiquitously measure occupant responses are lacking. Here we show that Doppler-radar based sensors, which can sense small physiological motions, provide accurate occupancy detection and estimation of vital signs in challenging, realistic circumstances. Occupancy was differentiated from an empty room over 93% of the time in a 3.4 m × 8.5 m conference room with a single sensor in both wall and ceiling-mounted configurations. Occupancy was successfully detected while an occupant was under the table, visibly blocked from the sensor, a scenario where infrared, ultrasound, and video-based occupancy sensors would fail. Heart and respiratory rates were detected in all seats in the conference room with a single ceiling-mounted sensor. The occupancy sensor can be used to control HVAC and lighting with a short, 1–2 min delay and to provide information for space utilization optimization. Heart and respiratory rate sensing could provide additional feedback to future human-building interactive systems that use vital signs to determine how occupant comfort and wellness is changing with time. [ABSTRACT FROM AUTHOR]

This study examines the associations between perceived urban environment attributes and obesity indices by country using data from an eight-nation study from Latin America. The data were collected from 8185 adults. The Neighbourhood Environment Walkability-abbreviated scale was used to assess perceived urban environment attributes. Obesity indices considered were body mass index, waist circumference, neck circumference, a body shape index and waist-to-height ratio. The perception of a more and better land use mix-diversity (β − 0.44; 95% CI − 0.59, − 0.28), traffic safety (− 0.39; − 0.66, − 0.12), and safety from crime (− 0.36; − 0.57, − 0.15) was associated with lower body mass index across the entire sample. Land use mix-diversity (− 1.21; − 1.60, − 0.82), street connectivity (− 0.26; − 0.37, − 0.15), and traffic safety (− 0.79; − 1.47, − 0.12) were negatively associated with waist circumference. Land use mix-diversity (− 0.11; − 0.20, − 0.03), land use mix-access (− 0.23; − 0.34, 0.12), walking/cycling facilities (− 0.22; − 0.37, − 0.08), and safety from crime (− 0.27; − 0.42, − 0.12) were negatively associated with neck circumference. No associations between perceived urban environment attributes and a body shape index were found. Land use mix-diversity (− 0.01; − 0.02, − 0.01), aesthetics (− 0.02; − 0.03, − 0.01), and safety from crime (− 0.02; − 0.04, − 0.01) were associated with waist-to-height ratio. Environmental interventions involving urban environment attributes are associated with obesity indices and, therefore, may help decrease the prevalence of overweight and obesity. [ABSTRACT FROM AUTHOR]

Bullet impacts are a ubiquitous form of damage to the built environment resulting from armed conflicts. Bullet impacts into stone buildings result in surficial cratering, fracturing, and changes to material properties, such as permeability and surface hardness. Controlled experiments into two different sedimentary stones were conducted to characterise surface damage and to investigate the relationship between the impact energy (a function of engagement distance) and crater volumes. Simplified geometries of crater volume using only depth and diameter measurements showed that the volume of a simple cone provides the best approximation (within 5%) to crater volume measured from photogrammetry models. This result suggests a quick and efficient method of estimating crater volumes during field assessments of damage. Impact energy has little consistent effect on crater volume over the engagement distances studied (100–400 m), but different target materials result in an order of magnitude variation in measured crater volumes. Bullet impacts in the experiments are similar in appearance to damage caused by hypervelocity experiments, but crater excavation is driven by momentum transfer to the target rather than a hemispherical shock wave. Therefore in contrast to predictions of impact scaling relationships for hypervelocity experiments, target material plays the dominant role in controlling damage, not projectile energy. [ABSTRACT FROM AUTHOR]

Wildfire events have resulted in unprecedented social and economic losses worldwide in the last few years. Most studies on reducing wildfire risk to communities focused on modeling wildfire behavior in the wildland to aid in developing fuel reduction and fire suppression strategies. However, minimizing losses in communities and managing risk requires a holistic approach to understanding wildfire behavior that fully integrates the wildland's characteristics and the built environment's features. This complete integration is particularly critical for intermixed communities where the wildland and the built environment coalesce. Community-level wildfire behavior that captures the interaction between the wildland and the built environment, which is necessary for predicting structural damage, has not received sufficient attention. Predicting damage to the built environment is essential in understanding and developing fire mitigation strategies to make communities more resilient to wildfire events. In this study, we use integrated concepts from graph theory to establish a relative vulnerability metric capable of quantifying the survival likelihood of individual buildings within a wildfire-affected region. We test the framework by emulating the damage observed in the historic 2018 Camp Fire and the 2020 Glass Fire. We propose two formulations based on graph centralities to evaluate the vulnerability of buildings relative to each other. We then utilize the relative vulnerability values to determine the damage state of individual buildings. Based on a one-to-one comparison of the calculated and observed damages, the maximum predicted building survival accuracy for the two formulations ranged from 58 - 64 % for the historical wildfires tested. From the results, we observe that the modified random walk formulation can better identify nodes that lie at the extremes on the vulnerability scale. In contrast, the modified degree formulation provides better predictions for nodes with mid-range vulnerability values. [ABSTRACT FROM AUTHOR]

According to the research status of Software Defined Network (SDN) control layer traffic scheduling, we find the current common problems, including single path, easy congestion, Quality of Service (QoS) requirements and high delay. To solve these four problems, we design and implement a QoS-oriented global multi-path traffic scheduling algorithm for SDN, referred to as QOGMP. First, we propose a link weight calculation algorithm based on the idea of traction links and deep reinforcement learning, and conduct experimental verifications related to traction links. The algorithm considers QoS requirements and alleviates the problems of easy congestion and high delay. Then, we propose a traffic scheduling algorithm based on link weight and multi-path scheme, which also considers QoS requirements and solves the problem of single path. Finally, we combined the link weight calculation algorithm and the traffic scheduling algorithm to implement QOGMP, and carried out comparative experiments in the built simulation environment. The experimental results show that QOGMP is better than the two comparison algorithms in terms of delay and rescheduling rate. [ABSTRACT FROM AUTHOR]

The brachistochrone (shortest-time) curve is the path connecting two points that enables the shortest travel time. This work explores the "brachistochrone path" of fire spread connecting two points at the same altitude and with a fixed path length. The starting and ending points are connected by both thermally thin fuels (thin wires) and thermally thick fuels (PMMA bars). Flame-spread paths of triangular, rectangular, and circular shapes with different heights and inclinations are explored. Results show that having a local maximum flame-spread rate does not result in the shortest overall travel time. For thin-wire paths, the fastest overall-path fire spread occurs, when the upward spread path is vertical, and the path height reaches a maximum, as demonstrated by the theoretical analysis. Differently, for thick PMMA-bar paths, the brachistochrone condition occurs when the path length of the vertical upward spread reaches the maximum, because the upward spread is about ten times faster than the downward spread. This study extends the conventional problem of the fastest fire spread to the shortest-time problem of the whole fire path, and it may help optimize the fuel distribution inside the built environment and estimate available safe egress time in building and wildland fires. [ABSTRACT FROM AUTHOR]

The built environment represents the stage surrounding our everyday life activities. To investigate how architectural design impacts individuals' affective states, we measured subjective judgments of perceived valence (pleasant and unpleasant) and arousal after the dynamic experience of a progressive change of macro visuospatial dimensions of virtual spaces. To this aim, we developed a parametric model that allowed us to create 54 virtual architectural designs characterized by a progressive change of sidewalls distance, ceiling and windows height, and color of the environment. Decreasing sidewalls distance, ceiling height variation, and increasing windows height significantly affected the participants' emotional state within virtual environments. Indeed, such architectural designs generated high arousing and unpleasant states according to subjective judgment. Overall, we observed that valence and arousal scores are affected by all the dynamic form factors which modulated the spaciousness of the surrounding. Showing that the dynamic experience of virtual environments enables the possibility of measuring the emotional impact of macro spatial architectural features, the present findings may lay the groundwork for future experiments investigating the effects that the architectural design has on individuals' mental state as a fundamental factor for the creation of future spaces. [ABSTRACT FROM AUTHOR]

Although the built environment may affect physical activity, there is little evidence on how neighborhood walkability attributes influence post-stroke physical activity. This study aimed to explore associations between objectively measured physical activity and neighborhood walkability attributes in community-dwelling patients with stroke. This cross-sectional study recruited patients who could ambulate outside free of assistance. We assessed objectively measured physical activity comprising the number of steps taken and time spent in moderate-to-vigorous physical activity (MVPA) with an accelerometer. Neighborhood walkability attributes were evaluated using the Walk Score. Multiple linear regression analyses were used to determine whether the Walk Score was independently associated with the number of steps taken or MVPA. Eighty participants with a mean age of 65.9 ± 11.1 years were included. The participants took an average of 5900.6 ± 2947.3 steps/day and spent an average of 19.7 ± 21.7 min/day in MVPA. The mean Walk Score was 71.4 ± 17.2. Multiple linear regression analyses showed that no significant associations were found between the Walk Score and the number of steps taken or MVPA. No associations were found between objectively measured physical activity and neighborhood walkability attributes in community-dwelling patients with stroke in an Asian area. [ABSTRACT FROM AUTHOR]

Mobile robots are deployed in the built environment at increasing rates. However, lack of considerations for a robot-inclusive planning has led to physical spaces that would potentially pose hazards to robots, and contribute to an overall productivity decline for mobile service robots. This research proposes the use of an adapted Failure Mode and Effects Analysis (FMEA) as a structured tool to evaluate a building's level of robot-inclusivity and safety for service robot deployments. This Robot-Inclusive FMEA (RIFMEA) framework, is used to identify failures in the built environment that compromise the workflow of service robots, assess their effects and causes, and provide recommended actions to alleviate these problems. The method was supported with a case study of deploying telepresence robots in a university campus. The study concluded that common failures were related to poor furniture design, a lack of clearance and hazard indicators, and sub-optimal interior planning. [ABSTRACT FROM AUTHOR]

The built environment affects mental health outcomes, but this relationship is less studied and understood. This article proposes a novel multi-level scenario-based predictive analytics framework (MSPAF) to explore the complex relationships between community mental health outcomes and the built environment conditions. The MSPAF combines rigorously validated interpretable machine learning algorithms and scenario-based sensitivity analysis to test various hypotheses on how the built environment impacts community mental health outcomes across the largest metropolitan areas in the US. Among other findings, our results suggest that declining socio-economic conditions of the built environment (e.g., poverty, low income, unemployment, decreased access to public health insurance) are significantly associated with increased reported mental health disorders. Similarly, physical conditions of the built environment (e.g., increased housing vacancies and increased travel costs) are significantly associated with increased reported mental health disorders. However, this positive relationship between the physical conditions of the built environment and mental health outcomes does not hold across all the metropolitan areas, suggesting a mixed effect of the built environment's physical conditions on community mental health. We conclude by highlighting future opportunities of incorporating other variables and datasets into the MSPAF framework to test additional hypotheses on how the built environment impacts community mental health. [ABSTRACT FROM AUTHOR]