Development of ``HealthServe'': A measure of customer service orientation in healthcare

An assessment tool designed to measure a customer service orientation among RN's and LPN's was developed using a content-oriented approach. Critical incidents were first developed by asking two samples of healthcare managers (n = 52 and 25) to identify various customer-contact situations. The critical incidents were then used to formulate a 121-item instrument. Patient-contact workers from 3 hospitals (n = 102) completed the instrument along with the NEO-FFI, a measure of the Big Five personality factors. Concurrently, managers completed a performance evaluation scale on the employees participating in the study in order to determine the predictive validity of the instrument.^ Through a criterion-keying approach, the instrument was scaled down to 38 items. The correlation between HealthServe and the supervisory ratings of performance evaluation data supported the instrument's criterion-related validity (r =.66, p $<$.0001). Incremental validity of HealthServe over the Big Five was found with HealthServe accounting for 46% of the variance.^ The NEO-FFI was used to assess the correlation between personality traits and HealthServe. A factor analysis of HealthServe suggested 4 factors which were correlated with the NEO-FFI scores. Results indicated that HealthServe was related to Extraversion, Openness to Experience, Agreeableness, Conscientiousness and negatively related to Neuroticism.^ The benefits of the test construction procedure used here over the use of broad-based measures of personality were discussed as well as the limitations of using a concurrent validation strategy. Recommendations for future studies were provided. ^

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

Foundational forensic techniques for cellular and ad hoc multi-hop networks

The Internet has become an integral part of our nation’s critical socio-economic infrastructure. With its heightened use and growing complexity however, organizations are at greater risk of cyber crimes. To aid in the investigation of crimes committed on or via the Internet, a network forensics analysis tool pulls together needed digital evidence. It provides a platform for performing deep network analysis by capturing, recording and analyzing network events to find out the source of a security attack or other information security incidents. Existing network forensics work has been mostly focused on the Internet and fixed networks. But the exponential growth and use of wireless technologies, coupled with their unprecedented characteristics, necessitates the development of new network forensic analysis tools. This dissertation fostered the emergence of a new research field in cellular and ad-hoc network forensics. It was one of the first works to identify this problem and offer fundamental techniques and tools that laid the groundwork for future research. In particular, it introduced novel methods to record network incidents and report logged incidents. For recording incidents, location is considered essential to documenting network incidents. However, in network topology spaces, location cannot be measured due to absence of a ‘distance metric’. Therefore, a novel solution was proposed to label locations of nodes within network topology spaces, and then to authenticate the identity of nodes in ad hoc environments. For reporting logged incidents, a novel technique based on Distributed Hash Tables (DHT) was adopted. Although the direct use of DHTs for reporting logged incidents would result in an uncontrollably recursive traffic, a new mechanism was introduced that overcome this recursive process. These logging and reporting techniques aided forensics over cellular and ad-hoc networks, which in turn increased their ability to track and trace attacks to their source. These techniques were a starting point for further research and development that would result in equipping future ad hoc networks with forensic components to complement existing security mechanisms.

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

The role of the health care organization in supporting nurses in the delivery of culturally competent care

This study was designed to explore ways in which health care organizations (HCOs) can support nurses in their delivery of culturally competent care. While cultural competence has become a priority for the federal government as well as the major health professional organizations, its integration into care delivery has not yet been realized. Health professionals cite a lack of educational preparation, time, and organizational resources as barriers. Most experts in the field agree that the cultural and linguistic needs of ethnic minorities pose challenges that individual care providers are unable to manage without the support of the health care organizations within which they practice. While several studies have identified implications for HCOs, there is a paucity of research on their role in this aspect of care delivery. Using a qualitative design with a case study approach, data collection included face-to-face interviews with 23 registered nurses, document analysis, and reports of critical incidents. The site chosen was a large health care system in South Florida that serves a culturally diverse population. Major findings from the study included language barriers, lack of training, difficulty with cultural differences, lack of organizational support, and reliance on culturally diverse staff members. Most nurses thought the ethnic mix was adequate, but rated other supports such as language services, training, and patient education materials as inadequate. Some of the recommendations for organizational performance were to provide the expectations and support for culturally competent care. Implications and recommendations for practice include nurses using trained interpreters instead of relying on coworkers or trying to "wing it", pursuing training, and advocating for organizational supports for culturally competent care. Implications and recommendations for theory included a blended model that combines both models in the conceptual framework. Recommendations for future research were for studies on the impact of language bathers on care delivery, develop and test a quantitative instrument, and to incorporate Gilbert's model into nursing research.

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Acceptable vs. marginal police officers' psychological ratings : a longitudinal comparison of job performance

Archival research was conducted on the inception of preemployment psychological testing, as part of the background screening process, to select police officers for a local police department. Various issues and incidents were analyzed to help explain why this police department progressed from an abbreviated version of a psychological battery, to a much more sophisticated and comprehensive set of instruments. While doubts about psychological exams do exist, research has shown that many are valid and reliable in predicting job performance of police candidates. During a three year period, a police department hired 162 candidates (133 males and 29 females) who received "acceptable" psychological ratings and 71 candidates (58 males and 13 females) who received "marginal" psychological ratings. A document analysis consisted of variables that have been identified as job performance indicators which police psychological testing tries to predict, and "screen in" or "screen out" appropriate applicants. The areas of focus comprised the 6-month police academy, the 4-month Field Training Officer (FTO) Program, the remaining probationary period, and yearly performance up to five years of employment. Specific job performance variables were the final academy grade average, supervisors' evaluation ratings, reprimands, commendations, awards, citizen complaints, time losses, sick time usage, reassignments, promotions, and separations. A causal-comparative research design was used to determine if there were significant statistical differences in these job performance variables between police officers with "acceptable" psychological ratings and police officers with "marginal" psychological ratings. The results of multivariate analyses of variance, t-tests, and chi-square procedures as applicable, showed no significant differences between the two groups on any of the job performance variables.

Foundational Forensic Techniques for Cellular and Ad Hoc Multi-hop Networks

The Internet has become an integral part of our nation's critical socio-economic infrastructure. With its heightened use and growing complexity however, organizations are at greater risk of cyber crimes. To aid in the investigation of crimes committed on or via the Internet, a network forensics analysis tool pulls together needed digital evidence. It provides a platform for performing deep network analysis by capturing, recording and analyzing network events to find out the source of a security attack or other information security incidents. Existing network forensics work has been mostly focused on the Internet and fixed networks. But the exponential growth and use of wireless technologies, coupled with their unprecedented characteristics, necessitates the development of new network forensic analysis tools. This dissertation fostered the emergence of a new research field in cellular and ad-hoc network forensics. It was one of the first works to identify this problem and offer fundamental techniques and tools that laid the groundwork for future research. In particular, it introduced novel methods to record network incidents and report logged incidents. For recording incidents, location is considered essential to documenting network incidents. However, in network topology spaces, location cannot be measured due to absence of a 'distance metric'. Therefore, a novel solution was proposed to label locations of nodes within network topology spaces, and then to authenticate the identity of nodes in ad hoc environments. For reporting logged incidents, a novel technique based on Distributed Hash Tables (DHT) was adopted. Although the direct use of DHTs for reporting logged incidents would result in an uncontrollably recursive traffic, a new mechanism was introduced that overcome this recursive process. These logging and reporting techniques aided forensics over cellular and ad-hoc networks, which in turn increased their ability to track and trace attacks to their source. These techniques were a starting point for further research and development that would result in equipping future ad hoc networks with forensic components to complement existing security mechanisms.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Mirror self-recognition in a gorilla (gorilla gorilla gorilla)

Psychologists have studied self-recognition in human infants as an indication of self-knowledge (Amsterdam, 1972) and the development of abstract thought processes. Gallup (1970) modified the mark test used in human infant work to examine if nonhuman primates showed similar evidence of mirror self-recognition. Chimpanzees (Pan troglodytes) and orangutans (Pongo pygmnaeus) pass the mirror self-recognition test with limited mirror training or exposure. Other species of primates, such as gorillas and monkeys, have not passed the mirror test, despite extensive mirror exposure and training (Gallup, 1979). This project examined a gorilla (G. gorilla gorilla) named Otto in the traditional mark test. Using the modified mark-test, there were more incidents of touching the marked area while Otto was in front of the mirror than when he was not in front of the mirror. These results indicated that Otto was able to show some evidence of selfawareness.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

A Multi-Criteria GIS-Based Route Selection Tool for Hazardous Material Transport: Consideration of Environmental Consequence, Traffic Congestions and Costs

Hazardous materials are substances that, if not regulated, can pose a threat to human populations and their environmental health, safety or property when transported in commerce. About 1.5 million tons of hazardous material shipments are transported by truck in the US annually, with a steady increase of approximately 5% per year. The objective of this study was to develop a routing tool for hazardous material transport in order to facilitate reduced environmental impacts and less transportation difficulties, yet would also find paths that were still compelling for the shipping carriers as a matter of trucking cost. The study started with identification of inhalation hazard impact zones and explosion protective areas around the location of hypothetical hazardous material releases, considering different parameters (i.e., chemicals characteristics, release quantities, atmospheric condition, etc.). Results showed that depending on the quantity of release, chemical, and atmospheric stability (a function of wind speed, meteorology, sky cover, time and location of accidents, etc.) the consequence of these incidents can differ. The study was extended by selection of other evaluation criteria for further investigation because health risk as an evaluation criterion would not be the only concern in selection of routes. Transportation difficulties (i.e., road blockage and congestion) were incorporated as important factor due to their indirect impact/cost on the users of transportation networks. Trucking costs were also considered as one of the primary criteria in selection of hazardous material paths; otherwise the suggested routes would have not been convincing for the shipping companies. The last but not least criterion was proximity of public places to the routes. The approach evolved from a simple framework to a complicated and efficient GIS-based tool able to investigate transportation networks of any given study area, and capable of generating best routing options for cargos. The suggested tool uses a multi-criteria-decision-making method, which considers the priorities of the decision makers in choosing the cargo routes. Comparison of the routing options based on each criterion and also the overall suitableness of the path in regards to all the criteria (using a multi-criteria-decision-making method) showed that using similar tools as the one proposed by this study can provide decision makers insights in the area of hazardous material transport. This tool shows the probable consequences of considering each path in a very easily understandable way; in the formats of maps and tables, which makes the tradeoffs of costs and risks considerably simpler, as in some cases slightly compromising on trucking cost may drastically decrease the probable health risk and/or traffic difficulties. This will not only be rewarding to the community by making cities safer places to live, but also can be beneficial to shipping companies by allowing them to advertise as environmental friendly conveyors.