The benefits of a digital twin city have been assessed based on real-time data collected from preinstalled Internet of Things (IoT) sensors (e.g., traffic, energy use, air pollution, water quality) for managing the complex systems of cities, but the sensor-based reality information is likely insufficient to provide dynamic spatiotemporal information about physical vulnerabilities. Understanding cities’ current states of physical vulnerability can support city decision makers in analyzing associated potential risk in urban areas for data-driven infrastructure management in extreme weather events. As a step toward creating a digital twin city for effective risk-informed decision-making, this paper proposes a new framework to bring crowdsourced visual data-based reality information into a three-dimensional (3D) virtual city for a model update with interactive and immersive visualization. Unstructured visual data are collected from participatory sensing and analyzed to estimate the geospatial information of vulnerable objects in the distance representing physical vulnerability in cities. The crowdsourced visual data–based reality information of physical vulnerability in a given region is then integrated with a 3D virtual city model, and the updated 3D city model is fed into a computer-aided virtual environment (CAVE) for immersive visualization to enable users to navigate the intersection of reality and virtuality. To test the proposed framework, case studies were conducted on Houston. The outcomes demonstrate that the proposed method has the potential to make the virtual city model live in terms of local vulnerability. The digital twin city building on crowdsourced visual data is expected to contribute to risk-informed decision-making for infrastructure management in cities and help analyze various what-if scenarios in disaster situations with increased visibility of hazard and city interactions.

Inspecting and assessing existing utility poles has become increasingly important for reducing the vulnerability of power distribution infrastructure systems in disaster situations, which can enhance community resilience. Although vision-based systems have been applied to detect faults in power distribution infrastructures, little research currently exists on assessing component- and network-level failures of utility poles based on their geometric and environmental information. This paper aims to propose a new data-driven approach to support risk-informed decision-making for utility maintenance under extreme wind conditions. Large-scale open-source imagery from Google Street View is used to assess geometric properties of utility poles (i.e., leaning angle). Then the failure probability of utility poles is analyzed under varying conditions (e.g., age, leaning angle, and wind loads) in a three-dimensional virtual city model. The proposed method is tested through case studies in Texas to (1) validate an algorithm for estimating leaning angles of utility poles and (2) understand the progress of failures of leaning utility poles from a network perspective. The outcomes of the case studies demonstrate that the proposed method has the potential to leverage large-scale open-source visual data to assess the vulnerability of utility pole networks that may lead to cascading failures in power distribution infrastructure systems. Based on the proposed virtual environment, the method is expected to enable practitioners to facilitate risk-informed decision-making against disaster situations, which creates an opportunity for prioritizing maintenance tasks regarding power distribution infrastructures.

Urban built environments often include many negative stimuli (e.g., unleashed dogs, dead animals, litter, graffiti, abandoned vehicles) that are linked with stress symptomatology among urban populations. Biosignals (e.g., electrodermal activity, gait patterns, and blood volume pulse) can help assess pedestrian distress levels induced by negative environmental stimuli by overcoming the measurement limitations of traditional self-reporting methods and field observations. Despite their potential, biosignals from naturalistic outdoor environments are often contaminated by uncontrollable extraneous factors (e.g., movement artifacts, physiological reactivity due to unintended stimuli, and individual variability). Thus, more quantitative evidence and novel methodological approaches are required to accurately capture pedestrian environmental distress resulting from negative environmental stimuli. In this context, we investigate the interplay between pedestrians’ biosignal data and image-based data (built environment feature information and perceptual distress levels identified from images) in a machine learning model. Results from the statistical model estimated with the biosignal data demonstrated significant physiological responses to the negative environmental stimuli. The use of the features from image-based data increased the prediction accuracy of the computational model. This method can be applied to geospatial intelligence, further advancing built environmental assessments and evidence-based approaches to promote walking and walkable communities.

Enhancing safety communication within the construction industry is of paramount importance due to its potential in curtailing occupational injuries and improving the overall well-being of construction field workers. While the importance of improved communication is apparent, few studies have been focused on identifying the factors that positively influence communication, particularly in the context of safety. Especially in the case of foreign construction field workers (FCFWs), who often face communication challenges stemming from language and cultural differences, performing labor tasks in harsh and constantly changing environments is contributing significantly to the increasing rate of industrial accidents. Therefore, this study is aimed at investigating the vital factors that impact safety communication among FCFWs. A phenomenological qualitative method was applied to determine the vital factors influencing the safety communication among FCFWs. On applying the analytical hierarchy process, the factors and their importance were determined, and the vital factors were identified. Quantitative assessment through the analytic hierarchy process (AHP) established that extrinsic motivation (weight: 0.513), management communication style (0.264), and visible safety information (0.127) significantly overshadow other factors in safety communication effectiveness, validating their pivotal roles. However, a major limitation is that interview data were collected from workers of only four nationalities. Future studies should address this by expanding the range of nationalities included to enhance the diversity of experiences and perspectives from a broader variety of foreign construction field workers.

We elaborated a new ultra low-power nanometer circuit design methodology by introducing statistical fluctuations in advanced technology nodes as noise sources causing computational errors. The modeling is performed on sub-50 nm technology node to create a statistical performance metric. The relationship between the probability of error and the circuit noise for a variety of different configurations and of circuit topologies is explored. Input-coupled noise has the dominant effect in terms of error and is analyzed through AC and high-frequency properties of the inverter transfer characteristics. Gate-level implementation of the probabilistic CMOS logic is validated with circuit simulations using a commercial 45-nm SOI CMOS process technology. Using a 32-bit adder where voltages can be scaled from MSB to LSB as an example, simulation results show the power of the technique. A calculation error of 10^-6, a number quite appropriate for many computational tasks, occurs with a total power reduction of more than 40 %.

Clinical Scenario : Lateral ankle sprains are common musculoskeletal injuries, especially in active individuals, with up to 70% cases leading to chronic ankle instability (CAI). CAI is characterized by recurrent ankle sprains, persistent dysfunction, and an increased risk of long-term joint degeneration. Strength deficits at the ankle are one of the hallmark symptoms of CAI, directly impacting joint stability and functional performance. Clinical Question : Does incorporating blood flow restriction (BFR) training, either applied passively without concurrent exercise or combined with rehabilitation exercises, enhance ankle strength in individuals with CAI compared with traditional rehabilitation methods? Summary of Key Findings : (1) Three studies were reviewed to evaluate the effects of BFR training on ankle strength in individuals with CAI. (2) Results showed that although BFR alone did not result in consistent improvements in ankle strength, significant improvements in ankle strength across all 4 directions (plantarflexion, dorsiflexion, eversion, and inversion) were found when combined with rehabilitation or other therapeutic modalities. Clinical Bottom Line : BFR training might be most effective when incorporated into a comprehensive rehabilitation plan aimed at improving ankle strength. Although passive BFR (applied without concurrent exercises) demonstrates limited effectiveness, combining BFR with traditional rehabilitation or therapeutic exercises significantly enhances muscle strength at the ankle joint in individuals with CAI. Strength of Recommendation : Grade B evidence supports the inclusion of BFR in rehabilitation programs for individuals with CAI to enhance muscle strength.