Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Havidan Rodriguez, Dept. of Sociology & Criminal Justice. Includes bibliographical references.

A new zone was added to the existing Livestock Early Warning System (LEWS), which is a subproject of the USAID Global Livestock Collaborative Research Support Program. LEWS uses the PHYGROW model and satellite imagery of weather and vegetation to estimate the availability of forage to livestock and wildlife. Drought advisories are then distributed to governments, development organizations, and pastoralists via the Internet, satellite radios, and written reports. The Laikipia zone was established in 2001 to provide drought early warning for the arid pastoral rangelands of the Ewaso Ngiro ecosystem in the Laikipia and southern Samburu Districts, Kenya. Field verification of PHYGROW estimates of standing crop was conducted in 2002. In addition, research was conducted to determine the ability of the warning system to provide significant advance notice of emerging drought conditions. Results of this study indicate that LEWS is capable of providing accurate estimates of forage availability on East African rangelands. There is also evidence that the use of LEWS advisories could accelerate drought response by pastoralists as much as three to seven weeks.

Collision warning systems represent a promising means to reduce rear-end crash involvement. However, these systems experience failures in the real-world that may promote driver distrust and diminish drivers' willingness to comply with warnings. Recent research suggests that not all false alarms (FAs) are detrimental to drivers. However, very few studies have examined how different alarms influence different driving populations. The purpose of this research was to examine how younger, middle-aged, and older drivers (with and without UFOV impairments) evaluated and responded to four different alarm contexts - false alarm (FA), nuisance alarm (NA), unnecessary alarm (UA) and true alarm (TA) - when they did and did not receive warnings. FA contexts represent out-of-path conflict scenarios where it is difficult for the driver to identify the source of the alarm. NA contexts represent out-of-path conflict scenarios that occur in a predictable manner that allows drivers to identify the source of the alarm. UA contexts are transitioning host conflict scenarios where the system issues an alert but the situation resolves itself before the driver needs to intervene. TA contexts represent in-host conflict scenarios where the situation requires the driver to intervene to avoid a collision. The results suggest that alarm context does matter. Compared to response data that differentiates FA and NA from UA and TA, subjective data shows greater sensitivity and differentiates between all four alarm contexts (FA Younger drivers indicated a high degree of confidence in their own ability across the different conditions. While they adopted a similar response pattern as middle-aged drivers during the TA contexts, these drivers responded less frequently than middle-aged and older drivers during the UA context. Diminished hazard perception ability and the tendency to consider these situations less hazardous likely account for the fewer responses made during these situations by younger drivers. Older drivers with and without UFOV impairments indicated similar hazard ratings for UA and TA contexts, yet drivers with UFOV impairments responded less frequently in both alarm contexts. Diminished hazard perception ability, slower simple response times, and degraded contrast sensitivity likely account for the fewer and slower responses. Interestingly older drivers with impairments did respond more frequently when warned during the TA context. They also rated FAs and NAs more positively than the other driver groups. The results of this study suggest applying signal detection theory without concern for the alarm context and driver characteristics is insufficient for understanding how different alarms influence operators and that subjective data can inform design. Researchers are encouraged to combine multiple perspectives that incorporate of both an engineering and human perspective.

A significant number of aircraft incidents and accidents have been caused, in part, by flightcrew failure to properly manage cockpit activities, such as failure to initiate activities at the appropriate time, misprioritization of activities, or the failure to appropriately monitor activities and terminate them when required. To facilitate the management of the cockpit activities, a computational aid, the Agenda Manager (AM) has been developed for use in simulated cockpit environments in an investigation which was one aspect of a more extensive research project supported by the NASA Ames Research Center. The AM is directed at the management of goals and functions, the actors who perform those functions, and the resources used by these actors. Development of an earlier AM version, the Cockpit Task Management System (CTMS), demonstrated that it could be used to assist flightcrews in the improvement of cockpit activity management under experimental conditions, assuming that the AM determined pilot goals accurately as well as the functions performed to achieve those goals. To overcome AM limitations based on that assumption, a pilot goal communication method (GCM) was developed to facilitate accurate recognition of pilot goals. Embedded within AM, the GCM was used to recognize pilot goals and to declare them to the AM. Two approaches to the recognition of pilots goals were considered: (1) The use of an Automatic Speech Recognition (ASR) system to recognize overtly or explicitly declared pilot goals, and (2) inference of covertly or implicitly declared pilot goals via use of an intent inferencing mechanism. These two methods were integrated into the AM to provide a rich environment for the study of human-machine interactions in the supervisory control of complex dynamic systems. Through simulated flight environment experimentation, the proposed GCM has demonstrated its capability to accurately recognize pilot goals and to handle incorrectly declared goals, and was validated in terms of subjective workload and pilot flight control performance. / Graduation date: 1997

Intrastate conflicts in Sub-Saharan Africa are a development tragedy and a security dilemma that requires more prevention and better intervention from the international community. Such engagement necessitates a robust early warning system, which can determine, with a sufficient degree of accuracy, the countries most at risk of experiencing intrastate conflict. This research summarizes and critiques current efforts to conceptualize intrastate conflict in Sub-Saharan Africa and determine what factors best explain the likelihood of intrastate conflict onset. The research examines the challenges of empirically modelling the human behaviour that underlies intrastate conflict, as well as some promising avenues for overcoming challenges posed by data issues and existing methodological shortcomings. The research concludes that with improved data and research design, and more attention being paid to how statistical significance reflects pathway(s) to violence, the development of an intrastate conflict early warning system is possible.

Thesis (M.Sc.)--University of Waikato, 2007. / Title from PDF cover (viewed April 8, 2008) Includes bibliographical references (p. 114-119)

Thesis (M.A.)--Baylor University, 2008. / Includes bibliographical references (p. 121-126).

In partial fulfillment of requirements for the degree of Master of Arts in Security Studies / "June 2010." Includes bibliographic references, p. 43-45.

Failure to rescue (FTR) outcomes may be one consequence of the relationship be-tween healthcare provider behaviors and attitudes, organizational factors, and environ-mental factors that intersect to potentially threaten patient safety. Early warning systems (EWS) were designed as surveillance systems to reduce failure to rescue events and avoid morbidity and mortality. Challenges with EWS include lack of standardization, organiza-tional barriers, such as culture and supports, and human factor attributes such as intuition, expertise, and experience. The experience and perceptions of nurses using EWS technolo-gy as it relates to their clinical assessment, critical thinking, and decision-making skills has yet to be undertaken. This study adds to the body of EWS and FTR literature and the broader culture of safety literature in acute care environments. The purposes of this exploratory qualitative descriptive study was to explore the experiences of nurses using EWS in acute care practice settings and how they perceive it impacts on their critical thinking and clinical decision-making processes. The study identi-fied three informative findings: a) EWS has added value particularly with novice nurses or nurses new to practice settings, b) EWS provides benefits to nurses working in acute clinical environments that experience high volumes and high acuity of patients by alerting or reminding them about potential FTR situations, and c) Existing EWS may require mod-ification to improve adequacy, reduce redundancy, and reduce alarm fatigue. Based on the evidence reviewed, a qualitative study to increase our understanding of the experi-ence of nurses and their perception of the impact of EWS and related technology on their critical thinking and other nursing practice processes has the potential to contribute to a wider evaluation of EWS systems and to improve patient outcomes. / Thesis / Master of Science (MSc) / Before patients on general medical or surgical hospital units become so ill that they need to be transferred to an intensive care unit, they have abnormal vital signs and other physiological changes that can go unnoticed for 2448 hours. As these changes can be hard to detect and serious illness can begin very slowly at first, early warning systems have been developed to help health care professionals respond to patient’s conditions before they need to be in an intensive care unit. These early warning systems can be in the form of new technology and assist nurses with preventing a serious illness from becoming critical. It is not well research or understood how nurses experience such early warning systems technology and it is not well understood how nurses think this technology impacts their nursing practice. The overall aim of the study is to learn more about the experience and perception of nurses who use this technology, how they believe it informs their nursing practice and how it supports them in making clinical decisions about patient care.

Risk indicators are metrics that are widely used in risk management to indicate how risky an activity is. Among different types of risk indicators, early warning systems are designed to help decision makers predict and be prepared for catastrophic events. Especially, in complex systems where outcomes are often difficult to predict, early warnings can help decision makers manage possible risks and take a proactive approach. Early prediction of catastrophic events and outcomes are at the heart of risk management, and help decision makers take appropriate actions in order to mitigate possible effects of such events. For example, physicians would like to prevent any adverse events for their patients and like to use all pieces of information that help accurate early diagnosis and interventions. In this research, first we study risk assessment for occupational injuries using accident severity grade as an early warning indicator. We develop a new severity scoring system which considers multiple injury severity factors, and can be used as a part of a novel three-dimensional risk assessment matrix which includes an incident's severity, frequency, and preventability. Then we study the predictability of health outcome based on early risk indicators. A systems model of patient health outcomes and hospital length of stay is presented based on initial health risk and physician assessment of risk. The model elaborates on the interdependent effects of hospital service and a physician's subjective risk assessment on length of stay and mortality. Finally, we extend our research to study the predictive power of early warning systems and prognostic risk indicators in predicting different outcomes in health such as mortality, disease diagnosis, adverse outcomes, care intensity, and survival. This study provides a theoretical framework on why risk indicators can or cannot predict healthcare outcomes, and how better predictors can be designed. Overall, these three essays shed light on complexities of risk assessments, especially in health domain, and in the contexts where individuals continuously observe and react to the risk indicators. Furthermore, our multi-method research approach provides new insights into improving the design and use of the risk measures. / Ph. D.