In some environments, there is a serious mismatch between the perceived (psychoacoustic) urgency of a warning and its situational urgency. In addition, many auditory warnings are not detectable within their environments. This research examined several prominent pulse parameters which affect the perceived urgency and detection time of auditory warning signals. These elements included pulse format (multitone sequential, multitone simultaneous, and rising sawtooth frequency-modulated pulse formats), pulse level (65 dBC and 79 dBC), and time between pulses (0 ms, 150 ms, and 300 ms). The environments of interest were those settings with steady-state broadband machinery noise. Conditions included a loading task which presented additional attentional demands upon the subject during the signal detection task. Free-modulus magnitude estimation quantified the relationship between auditory signal parameters and changes in perceived urgency. The method of paired comparisons was used to compare the perceived urgency of the auditory stimuli. Simple reaction time measured signal detectability. Signal effects were analyzed using a multivariate approach. Results indicated that there was a small but statistically significant relationship between perceived urgency and detection time. As perceived urgency increased, detection time decreased. Both perceived urgency and detection time were influenced by pulse level and format. The higher pulse level resulted in a greater perceived urgency of the signal and shorter detection time. Sequential signals were rated as less urgent than the other pulse formats, and subjects took longer to detect their occurrence. Under most conditions, there was no significant difference in the perceived urgency or detection time of simultaneous and frequency-modulated pulses. Time between pulses (inter-pulse interval) affected only perceived urgency, not detection time. The shorter the time between pulses, the greater the perceived urgency of the signal. / Ph. D.

Augmented reality (AR) has the potential to fundamentally change the way we interact with information. Direct perception of computer generated graphics atop physical reality can afford hands-free access to contextual information on the fly. However, as users must interact with both digital and physical information simultaneously, yesterday's approaches to interface design may not be sufficient to support the new way of interaction. Furthermore, the impacts of this novel technology on user experience and performance are not yet fully understood. Driving is one of many promising tasks that can benefit from AR, where conformal graphics strategically placed in the real-world can accurately guide drivers' attention to critical environmental elements. The ultimate purpose of this study is to reduce pedestrian accidents through design of driver interfaces that take advantage of AR head-up displays (HUD). For this purpose, this work aimed to (1) identify information requirements for pedestrian collision warning, (2) design AR driver interfaces, and (3) quantify effects of AR interfaces on driver performance and experience. Considering the dynamic nature of human-environment interaction in AR-supported driving, we took an ecological approach for interface design and evaluation, appreciating not only the user but also the environment. The requirement analysis examined environmental constraints imposed on the drivers' behavior, interface design translated those behavior-shaping constraints into perceptual forms of interface elements, and usability evaluations utilized naturalistic driving scenarios and tasks for better ecological validity. A novel AR driver interface for pedestrian collision warning, the virtual shadow, was proposed taking advantage of optical see-through HUDs. A series of usability evaluations in both a driving simulator and on an actual roadway showed that virtual shadow interface outperformed current pedestrian collision warning interfaces in guiding driver attention, increasing situation awareness, and improving task performance. Thus, this work has demonstrated the opportunity of incorporating an ecological approach into user interface design and evaluation for AR driving applications. This research provides both basic and practical contributions in human factors and AR by (1) providing empirical evidence furthering knowledge about driver experience and performance in AR, and, (2) extending traditional usability engineering methods for automotive AR interface design and evaluation. / Ph. D.

Roadway construction workers have to work in close proximity to construction equipment as well as high-speed traffic, exposing them to an elevated risk of collisions. This research aims to develop an innovative holistic solution to reduce the risk of collisions at roadway work zones. To this end, a connected hazard detection and prevention system is developed to detect potential unsafe proximities in highway work zones and provide warning and instructions of imminent threats. This connected system collects real-time information from all the actors inside and outside of the work zone and communicates it with a cloud server. A hazard detection algorithm is developed to identify potential proximity hazards between workers and connected/automated vehicles (CAV) and/or construction equipment. Detected imminent threats are communicated to in-danger workers and/or drivers. The trajectories and safety status of each actor is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based situational awareness tool, in real-time. To assure the accuracy of hazard detection, the algorithm accommodates various parameters including variant threat zones for workers-on-foot, vehicles, and equipment, the direction of movement, workers' distance to the work zone border, shape of road, etc. The designed system is developed and evaluated through various experiments on the Virginia's Smart Roads located at Virginia Tech. Data regarding activities of workers-on-foot was collected during experiments and was used and classified for activity recognition using supervised machine learning methods. A demonstration was held to evaluate the usability of the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions. The developed holistic system elevates safety of highway construction and maintenance workers at work sites. It also helps managers and inspectors to keep track of the real-time safety status of their work zone actors as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced. / Master of Science / In order to reduce the risk of collisions for roadway construction workers, this research aims to develop an innovative holistic solution at roadway work zones. In this research, a connected hazard detection and prevention system is developed to detect potential collision hazards in highway work zones and generate warning and instructions of imminent threats. This system collects real-time information from all the workers, construction equipment and connected/automated vehicles (CAV) of the work. A hazard detection algorithm is developed to identify potential proximity hazards between them as well as to recognize the activities of workers. The trajectories and safety status of each worker, equipment or vehicle is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based tool, in real-time. A demonstration was held to evaluate the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions. The developed holistic system helps managers and inspectors to keep track of the real-time safety status of their work zone worker, equipment and vehicles as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced.

Vehicle-related communications are a key application to be enabled by Fifth Generation (5G) wireless systems. The communications enabled by the future Internet of Vehicles (IoV) that are connected to every wireless device are referred to as Vehicle-to-Everything (V2X) communications. A major application of V2X communication systems will be to provide emergency warnings. This thesis evaluates Long-Term Evolution (LTE) and Dedicated Short Range Communications (DSRC) in terms of service quality and latency, and provides guidelines for design of cooperative LTE-DSRC systems for V2X communications. An extensive simulation analysis shows that (1) the number of users in need of warning has an effect on latency, and more so for LTE than for DSRC, (2) the DSRC priority parameter has an impact on the latency, and (3) wider system bandwidths and smaller cell sizes reduce latency for LTE. The end-to-end latency of LTE can be as high as 1.3 s, whereas the DSRC latency is below 15 ms for up to 250 users. Also, improving performance of systems is as much as important as studying about latency. One method to improving performance is using a better suitable antenna for physical communication. The mobility of vehicles results in a highly variable propagation channel that complicates communication. Use of a smart, steerable antenna can be one solution. The most commonly used antennas for vehicular communication are omnidirectional. Such antennas have consistent performance over all angles in the horizontal plane; however, rapidly steerable directional antennas should perform better in a dynamic propagation environment. A linear array antenna can perform dynamical appropriate azimuth pattern by having different weights of each element. The later section includes (1) identifying beam pattern parameters based on locations of a vehicular transmitter and fixed receivers and (2) an approach to find weights of each element of linear array antenna. Through the simulations with our approach and realistic scenarios, the desired array pattern can be achieved and array element weights can be calculated for the desired beam pattern. Based on the simulation results, DSRC is preferred to use in the scenario which contains large number of users with setup of higher priority, and LTE is preferred to use with wider bandwidth and smaller cell size. Also, the approach to find the controllable array antenna can be developed to the actual implementation of hardware with USRP. / Master of Science

Yes / Hospital-acquired Acute Kidney Injury (H-AKI) is a common cause of avoidable morbidity and mortality. To determine if the patients’ vital signs data as defined by a National Early Warning Score (NEWS), can predict H-AKI following emergency admission to hospital. Methods: Analyses of emergency admissions to York hospital over 24-months with NEWS data. We report the area under the curve (AUC) for logistic regression models that used the index NEWS (model A0), plus age and sex (A1), plus subcomponents of NEWS (A2) and two-way interactions (A3). Likewise for maximum NEWS (models B0,B1,B2,B3). Results: 4.05% (1361/33608) of emergency admissions had H-AKI. Models using the index NEWS had the lower AUCs (0.59 to 0.68) than models using the maximum NEWS AUCs (0.75 to 0.77). The maximum NEWS model (B3) was more sensitivity than the index NEWS model (A0) (67.60% vs 19.84%) but identified twice as many cases as being at risk of H-AKI (9581 vs 4099) at a NEWS of 5. Conclusions: The index NEWS is a poor predictor of H-AKI. The maximum NEWS is a better predictor but seems unfeasible because it is only knowable in retrospect and is associated with a substantial increase in workload albeit with improved sensitivity. / The Health Foundation

Yes / Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods: We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results: Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p

Yes / The novel coronavirus SARS-19 produces 'COVID-19' in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version NEWS2 is a simple physiological scoring system used in hospitals, which may be useful in the early identification of COVID-19 patients. We investigate the performance of multiple enhanced NEWS2 models in predicting the risk of COVID-19. Our cohort included unplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020 ) from two hospitals (YH for model development; SH for external model validation). We used logistic regression to build multiple prediction models for the risk of COVID-19 using the first electronically recorded NEWS2 within ± 24 hours of admission. Model M0' included NEWS2; model M1' included NEWS2 + age + sex, and model M2' extends model M1' with subcomponents of NEWS2 (including diastolic blood pressure + oxygen flow rate + oxygen scale). Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥ 5. The prevalence of COVID-19 was higher in SH (11.0 %=277/2520) than YH (8.7 %=343/3924) with a higher first NEWS2 scores ( SH 3.2 vs YH 2.8) but similar in-hospital mortality (SH 8.4 % vs YH 8.2 %). The c-statistics for predicting the risk of COVID-19 for models M0',M1',M2' in the development dataset were: M0': 0.71 (95 %CI 0.68-0.74); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.78 (95 %CI 0.75-0.80)). For the validation datasets the c-statistics were: M0' 0.65 (95 %CI 0.61-0.68); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.72 (95 %CI 0.69-0.75) ). The calibration slope was similar across all models but Model M2' had the highest sensitivity (M0' 44 % (95 %CI 38-50 %); M1' 53 % (95 %CI 47-59 %) and M2': 57 % (95 %CI 51-63 %)) and specificity (M0' 75 % (95 %CI 73-77 %); M1' 72 % (95 %CI 70-74 %) and M2': 76 % (95 %CI 74-78 %)) for the validation dataset at NEWS2 ≥ 5. Model M2' appears to be reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions. / The Health Foundation (Award No 7380) and the National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC) (Award No PSTRC-2016-006) / Research Development Fund Publication Prize Award winner, Aug 2021.

Horizontal curves on roadways are the site of numerous crashes and motorist deaths each year. Traditional methods to warn drivers of curve hazards, including static roadside signs, are sometimes ineffective at influencing driver behavior for reasons such as driver acclimation to inconsistency in posted advisory speeds. In-vehicle curve warning devices (CWDs) may be an effective alternative for reducing the number of collisions at curves. Multi-modality displays have elicited positive driver results and should be further explored. The objective of this study was to determine the most effective curve warning system using on-road performance and subjective evaluation. Two top-performing warning stimulus presentations, as determined by a recent simulator study, were tested at the Virginia Smart Road closed test highway. Both warnings exhibited auditory (speech) and visual (Heads Down Display) stimuli, however one included a throttle pushback haptic stimulus and the other did not. No on-road studies of this type of haptic stimulus, nor of CWDs, have been published to date. Forty-eight individuals, 24 age 18-25 years and 24 age >60 years, participated in the study. A 2 (Age) x 3 (Stimulus Presentation) between-subjects design was used to examine participant performance and ratings for the first "surprise" experience with the stimulus while driving on the Smart Road, and a 2x3 mixed factors design examined stimulus as a repeated measure. Participant braking reaction times, speed, and subjective evaluations were compared between stimulus presentations as well as driver age. Throttle reaction times and brake reaction times were significantly quicker, and curve entry speed significantly closer to an advisory speed for participants receiving a warning presentation than those without a warning presentation at alpha=0.05. No statistical differences between objective measures were found between the stimulus presentation with the haptic and the stimulus presentation without the haptic stimulus. Age was a significant main effect as older drivers reached more appropriate curve entry speeds than younger drivers. Driver risk-taking style was significantly related to age and to curve entry speed. During an interview, participants demonstrated higher comprehension when presented with the stimulus lacking the haptic component, and ranked this presentation higher, though ratings gathered from questionnaires were not significantly different between the two stimulus presentations. Driver comments were examined using a content analysis technique organized by design guideline topics. Discussion was presented in terms of four main research questions and recommendations toward CWD design guidelines were developed based on the objective and subjective results of this study. In addition, this research developed a foundation for further on-road testing of CWDs and other multi-modal in-vehicle warning systems. / Master of Science

Mozambique is one of the countries most affected by natural hazards in the world. Therefore it can benefit greatly from enhancements on its early warning system. Cell broadcast, which is a technology able to send simultaneous alert messages to all mobile phones in a geographical area has gained attention of emergency authorities since various catastrophes in the years 2000’s and increasing diffusion of the mobile network. This research has looked into the disaster risk management routines in Mozambique, interviewing relevant institutions, to identify the currently in use early warning system and analyze the circuit of information from detecting a hazard until the transmission of alert messages to the population. The goal of this research has been to identify how alert messages are sent to the population and, based on currently available infrastructure, analyze the possibilities to use Cell Broadcast to target alerts to all subscribers on specific geographic zones. It has been identified that the country already uses a solution that sends SMS to a list of phone numbers registered in a database. Nonetheless, telecommunication operators in the country are willing to cooperate with emergency authorities to design a solution in which Cell Broadcast can be used to strategically target alerts to subscribers at designated areas of risk. In this way, enabling enhanced accuracy and efficiency of the public alert system in Mozambique, with reduced time between detection and the simultaneously delivery of public alert messages to the entire population or only to people located on relevant geographic zones. Furthermore, the results also allowed to speculate on the viability of automated solutions, which can be used in combination to the enhancements that Cell Broadcast can bring to disaster risk management routines.

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