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Title: SAFETY AND OPERATIONAL EVALUATION OF DYNAMIC LANE MERGING IN WORK ZONES.
Creator: Harb, Rami, Radwan, Essam, University of Central Florida
Abstract / Description: Traffic safety and mobility of roadway work zones have been considered to be one of the major concerns in highway traffic safety and operations in Florida. In intent to expose Florida's work zones crash characteristics, the Florida Traffic Crash Records Database for years 2002, 2003 and 2004 were explored. Statistical models were estimated and Florida's work zone crash traits for single vehicle crashes and two-vehicle crashes were drawn. For the single-vehicle crashes, trucks were...
Show moreTraffic safety and mobility of roadway work zones have been considered to be one of the major concerns in highway traffic safety and operations in Florida. In intent to expose Florida's work zones crash characteristics, the Florida Traffic Crash Records Database for years 2002, 2003 and 2004 were explored. Statistical models were estimated and Florida's work zone crash traits for single vehicle crashes and two-vehicle crashes were drawn. For the single-vehicle crashes, trucks were found more likely to be involved in single vehicle crashes in freeway work zones compared to freeways without work zones. Straight level roadways are significantly affected by the presence of work zones. The lighting condition is also one of the risk factors associated with work zone single-vehicle crashes. In fact, at work areas with poor or no lighting during dark conditions, motor vehicles are more prone for crashes compared to non-work zone locations with poor or no lighting during dark. The weather condition is positively associated with single-vehicle work zone crashes. Results showed that during rainy weather, drivers are less likely to be involved in work zone crashes compared to the same weather conditions in non-work zone locations. This fact may be due to the vigilant driving pattern during rain at work zones. For the two-vehicle work zone crashes, results showed that drivers younger than 25 years of age and drivers older than 75 years old have the highest risk to be the at-fault driver in a work zone crash. Male drivers have significantly higher risk than female drivers to be the at-fault driver. The model conspicuously shows that drivers under the influence of narcotics/alcohol are more likely to cause crashes (i.e. at-fault driver) at work zones. Road geometry and the lighting condition were significant risk factors associated with two-vehicle work zone crashes. Freeways straight segments are more susceptible to crashes in work zone areas. Poor lighting or no lighting at all during dark can lead to significantly higher crash hazard at work zones. Foggy weather causes a significant mount in work zone crash risk compared to non-work zone locations. In addition to that, work zones located in rural areas have higher crash potential than work zones located in urban areas. After examining the current Florida work zone Maintenance of Traffic (MOT) plans, known as the Motorist Awareness System (MAS), it was realized that this system is static hence does not react to changing traffic conditions. An ITS-based dynamic lane management system, known as dynamic lane merging system, was explored to supplement the existing MAS plans. Two forms of dynamic lane management were recognized as dynamic lane merging namely the early merge and the late merge. These two systems were designed to advise drivers on definite merging locations. Previously deployed dynamic lane merging systems comprise several Portable Changeable Message Signs (PCMS) and traffic sensors. The addition of multiple PCMSs to the current MAS plans may encumber the latter and usually requires relatively extensive equipment installation and relocation which could be inefficient for short term movable work zones. Therefore, two Simplified Dynamic Lane Merging Systems (SDLMS) were designed, deployed, and tested on Florida's short term movables work zones. The first SDLMS was a simplified dynamic early merge system (early SDLMS) and the second SDLMS was a simplified dynamic late merge system (late SDLMS). Both SDLMS consisted of supplementing the MAS plans used in Florida work zones with an ITS-based lane management system. From the two-to-one work zone configuration (first site), it was noted that the ratio of the work zone throughput at the onset of congestion over the demand volume was significantly the highest for the early SDLMS compared to the MAS and late SDLMS. Travel time through the work was the lowest for the early SDLMS, followed by the late SDLMS, and then MAS. However, the differences in mean travel times were not statistically significant. It was also concluded that the early SDLMS resulted in higher early merging compared to the MAS and that the late SDLMS in higher late merging compared to the MAS. The first site was used as a pilot for testing the system since data collection was limited to two days for each MOT type. Hence, operational measures of effectiveness (MOEs) could not be evaluated under different demand volumes. It should also be noted that the RTMS was not available during the MAS data collection which disabled us from collecting speed data. From the three-to-two work zone configuration site, data was collected extensively relative to the first site. The RTMS was available for all three MOT types tested which enabled the collection of the speed data that are used as a safety surrogate measure. The mean speed fluctuation in the closed lane was the highest under the MAS system for all demand volumes and in all three lanes. Comparing the dynamic early merge and the dynamic late merge mean speed fluctuations in the closed lane and the middle lane, results showed that the mean speed fluctuation for the early merge are lower than those of the late merge under all demand volumes. However, the difference in the mean speed fluctuation is only statistically significant under demand volume ranging between 1 and 500 veh/hr. As for the shoulder lane, it was noted that the speed mean speed fluctuation is significantly the lowest for demand volumes ranging between 1500 veh/hr and 2000 veh/hr under the late SDLMS compared to the early SDLMS and the MAS. The ratio of the throughput over demand volume was taken as the operational MOE. Results showed that the Dynamic early merge performs significantly better than the regular MAS under demand volume ranging between 500 veh/hr and 2000 veh/hr. Results also showed that the dynamic late merge perform better than the MAS under volumes ranging between 1500 veh/hr and 2000 veh/hr and significantly poorer than the MAS under low volumes. Therefore, the late SDLMS is not recommended for implementation under low volumes. Results also showed that the late SDLMS performs better than the early SDLMS under higher volume (ranging between 1500 veh/hr to 2000 veh/hr). A simulated work zone with a two-to-one lane closure configuration was coded in VISSIM and operational and safety MOEs under MAS, early SDLMS, and late SDLMS were compared under different drivers' adherence rate to the merging instructions, truck percentage in the traffic composition, and traffic demand volumes. Results indicated that throughputs are higher in general under the early SDLMS, travel times are lower under the early SDLMS. However, overall, the early SDLMS resulted in the highest speed variance among MOT types. The MAS resulted in the lowest speed variances overall
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Date Issued: 2009
Identifier: CFE0002741, ucf:48159
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002741

Title: SEXUAL DIMORPHISM OF THE POSTERIOR PELVIS OF THE ROBERT J. TERRY ANATOMICAL COLLECTION AND THE WILLIAM M. BASS DONATED SKELETAL COLLECTION.
Creator: Novak, Lauren, Schultz, John, University of Central Florida
Abstract / Description: Studies of sexual dimorphism of the sacrum have generally been conducted as part of broader population research or on living persons and cadavers, making the anthropological literature sparse. The greater sciatic notch and the preauricular sulcus of the ilium have both been found to show sexual dimorphism, although studies of these traits often have ambiguous definitions of characteristics and lack the standardization of measurements. This research was designed to reexamine and test the...
Show moreStudies of sexual dimorphism of the sacrum have generally been conducted as part of broader population research or on living persons and cadavers, making the anthropological literature sparse. The greater sciatic notch and the preauricular sulcus of the ilium have both been found to show sexual dimorphism, although studies of these traits often have ambiguous definitions of characteristics and lack the standardization of measurements. This research was designed to reexamine and test the accuracy of standard scoring systems and measurements of the posterior pelvis used to determine sex and to establish new formulae combining traits and measurements to accurately determine sex using logistic regression analysis. A series of metric measurements and morphological scores were recorded for 104 males and 106 females of both European- and African-American ancestry from the William M. Bass and Terry Collections. In order to reexamine previous research conducted on the posterior pelvis, standard ratios of metric measurements were analyzed to determine ranges and cut-off values for males and females in this sample. The ratio of ala width to the maximum transverse diameter of the sacral base and the ratio of the length and width of the sciatic notch have proven to be the most useful ratios in sex determination, though not as accurate as the formulae created using logistic regression. These data were also analyzed in SPSS using logistic regression to assess the usefulness of metric measurements and morphological scores of the posterior pelvis in sex determination. Using step-wise logistic regression, a combination of traits for both the sacrum and posterior ilium that are the most reliable and accurate for sex determination have been determined. The values for these selected traits can be incorporated into the log odds formulas which will classify an individual as male or female. The ultimate goal of this research was to provide physical anthropologists with logistic regression equations that can be used to estimate the sex of the posterior ilium and sacrum. Two equations ranging in accuracy from 79-84% were developed to determine sex of the posterior pelvis.
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Date Issued: 2010
Identifier: CFE0003157, ucf:48615
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003157

Title: Analysis of the Congruency between Educational Choices and Community College Student Degree Aspirations.
Creator: Quathamer, Mark, Owens, J. Thomas, Cintron Delgado, Rosa, Cox, Thomas, Marshall, Nancy, LAMB, ROBERT, University of Central Florida
Abstract / Description: This research explored variables that influence community college student degree aspirations and students purpose for enrolling and pursuing specific degree types. The study was conducted using secondary data for students pursuing Associate in Arts, Associate in Science, and Bachelor of Applied Science degrees at a single community college. A logistic regression test was used to test graduate and baccalaureate degree aspirations of the entire sample of students and separately by degree type....
Show moreThis research explored variables that influence community college student degree aspirations and students purpose for enrolling and pursuing specific degree types. The study was conducted using secondary data for students pursuing Associate in Arts, Associate in Science, and Bachelor of Applied Science degrees at a single community college. A logistic regression test was used to test graduate and baccalaureate degree aspirations of the entire sample of students and separately by degree type. Significant predictors of degree aspirations included age, gender, credits enrolled in, participation in student groups, academic course planning, receipt of scholarship, and college GPA. In general, community college students had high degree aspirations. Younger students tended to be on the collegiate transfer track and older students tended to want to pursue baccalaureate degrees locally. In addition to having high degree aspirations, a large proportion of students attended the college for occupational purposes and created intermediate and long-term goals related to their academic aspirations. The findings of the research confirm findings of previous studies on college student degree aspirations, and add to the understanding of variables contribute to students' educational goals. Recommendations for practice and future research are presented.
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Date Issued: 2014
Identifier: CFE0005539, ucf:50327
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005539

Title: Psychophysiology meets computer science: predicting the magnitude of participant physiological response with machine learning.
Creator: Parchment, Avonie, Wiegand, Rudolf, Matthews, Gerald, Yan, Xin, Abich, Julian, Greenwood-Ericksen, Adams, University of Central Florida
Abstract / Description: The present inquiry uses methods from psychophysiology and machine learning to reduce overall error in classification models. The field of psychophysiology, though rooted in decades of experimentation, has never reached the same level of precision as some aspects of medical inquiry. In fact, while some medical regression models, when determining some way to classify a patient's illness based on certain symptoms, can result in highly significant results with large effect sizes, equal levels...
Show moreThe present inquiry uses methods from psychophysiology and machine learning to reduce overall error in classification models. The field of psychophysiology, though rooted in decades of experimentation, has never reached the same level of precision as some aspects of medical inquiry. In fact, while some medical regression models, when determining some way to classify a patient's illness based on certain symptoms, can result in highly significant results with large effect sizes, equal levels are virtually unheard of in psychophysiology. The present investigation attempts to unravel some part of this mystery and determines some possible reasons for the difficulty in finding similar effect sizes, especially concerning methods that match participant state with physiological response. Of particular focus are two areas: baseline research and experimental data analysis methods. The role of baselining techniques in relation to overall quality of response is the first emphasis and this interest stems from the Law of Initial Value that indicates some relationship between baseline and experimental response. Though this relationship has been continually investigated and found to be lacking for many physiological measures, experimental condition heart rate response has been consistently shown to rely heavily on baseline response. This finding influences the second half of the present inquiry, which deals with the overall analysis of experimental data and the role that traditional statistics could play in the present problem. By comparing logistic regression and support vector models, it is expected that researchers would use the preferred method, based on their goals, to flag potentially highly influential cases that may greatly skew data and make modeling difficult. Additionally, demographic characteristics that could also help identify these influential cases in the future before modeling are shown.
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Date Issued: 2018
Identifier: CFE0007355, ucf:52106
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007355

Title: Forecasting Volcanic Activity Using An Event Tree Analysis System and Logistic Regression.
Creator: Junek, William, Jones, W, Simaan, Marwan, Foroosh, Hassan, Woods, Mark, University of Central Florida
Abstract / Description: Forecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into...
Show moreForecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into a single set of models provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. A bootstrapping analysis of the training dataset allowed for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and high eruption frequency. The cross validation process produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78 - 0.81, which indicate the algorithm has good predictive capabilities. In addition, ROC curves also allowed for the determination of a false positive rate and optimum detection threshold for each stage of the algorithm. The results demonstrate the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information. The incorporation of source modeling results into the event tree's decision making process has begun the transition of volcano monitoring applications from simple mechanized pattern recognition algorithms to a physical model based forecasting system.
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Date Issued: 2012
Identifier: CFE0004253, ucf:49517
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004253

Title: A Predictive Model To Identify Caregivers At Risk Of Musculoskeletal Disorders.
Creator: Ali, Abdulelah, Lee, Gene, Elshennawy, Ahmad, Rabelo, Luis, Rahal, Ahmad, University of Central Florida
Abstract / Description: Healthcare systems face several challenges due to the aging workforce, recruitment shortages, increasing patient acuteness, and increasing patient size and weight. The most costly, leading, and prevalent problem in the healthcare industry and nursing professions is work-related Musculoskeletal Disorders (MSDs). MSDs are common among caregivers because of the nature of their work, which requires repetitive heavy physical activity. The development of MSDs among caregivers negatively impacts the...
Show moreHealthcare systems face several challenges due to the aging workforce, recruitment shortages, increasing patient acuteness, and increasing patient size and weight. The most costly, leading, and prevalent problem in the healthcare industry and nursing professions is work-related Musculoskeletal Disorders (MSDs). MSDs are common among caregivers because of the nature of their work, which requires repetitive heavy physical activity. The development of MSDs among caregivers negatively impacts the quality of care, and incurs high costs such as worker compensation, days away from work, turnover, rehabilitation, and lower productivity. Therefore, it is essential to determine the factors that contribute to musculoskeletal disorder injuries among caregivers, in order to reduce or eliminate risks within healthcare environments which might cause such ramifications. This dissertation develops a framework to identify risk factors for MSDs and to determine which ones show significant contribution to be included in a developed predictive model. The data was obtained from caregivers who work in Saudi Arabian healthcare institutions, with 104 participating nurses to determine which risk factors would be included in the predictive model. Logistic regression analysis was used to investigate the association of the identified work related and non-work related risk factors for musculoskeletal disorders in healthcare organizations among caregivers. The development of the predictive model provides insights into risk factors which can guide the development of policies and recommendations to reduce and eliminate the development of MSDs among caregivers.
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Date Issued: 2016
Identifier: CFE0006065, ucf:50967
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006065

Title: Predicting Undergraduate Retention in STEM Majors Based on Demographics, Math Ability, and Career Development Factors.
Creator: Belser, Christopher, Shillingford-Butler, Ann, Van Horn, Stacy, Taylor, Dalena, Daire, Andrew, Witta, Eleanor, University of Central Florida
Abstract / Description: Science, technology, engineering, and math (STEM) fields are currently facing a crisis with respect to filling jobs with qualified workers (NSF, 2013; NAS, 2011). While advancements in these industries have translated into job growth, post-secondary declaration and retention rates within STEM majors lag behind industry needs (Carnevale et al., 2011; Chen, 2013; Koenig et al., 2012). Although researchers previously investigated demographic variables and math-related variables in the context of...
Show moreScience, technology, engineering, and math (STEM) fields are currently facing a crisis with respect to filling jobs with qualified workers (NSF, 2013; NAS, 2011). While advancements in these industries have translated into job growth, post-secondary declaration and retention rates within STEM majors lag behind industry needs (Carnevale et al., 2011; Chen, 2013; Koenig et al., 2012). Although researchers previously investigated demographic variables and math-related variables in the context of STEM retention (Beasley (&) Fischer, 2012; CollegeBoard, 2012; Cundiff et al., 2013; Gayles (&) Ampaw, 2014; Le et al., 2014; Nosek (&) Smyth, 2011; Riegle-Crumb (&) King, 2010), the need exists for additional research examining the impact of career-related variables (Belser et al., 2017; Folsom et al., 2004; Parks et al., 2012; Reardon et al., 2015). Additionally, prior STEM retention research primarily focused on students with declared STEM majors, as opposed to undeclared students considering STEM majors. In the present study, the researcher sought to determine the degree to which demographic variables (gender and ethnicity), math ability variables (SAT Math scores and Math Placement Test--Algebra scores), and career development related variables (initial major, STEM course participation, and Career Thoughts Inventory [CTI] change scores) could predict undergraduate retention in STEM for participants in a STEM recruitment and retention program. Using binary logistic regression, the researcher found that initially having a declared STEM major was the best predictor of STEM retention. Higher scores on math variables consistently predicted higher odds of STEM success, and the data revealed higher odds of STEM retention for ethnic minority students. Gender only showed to be a significant predictor of STEM attrition with the undecided students with first-to-third year retention. Finally, larger decreases in CTI scores predicted increased odds of STEM retention. Implications from the findings relate to a variety of professionals from higher education, counseling, and research. The findings provide guidance and new perspectives on variables associated with better rates of STEM retention, and as such, inform STEM initiatives targeting undergraduate STEM recruitment and retention.
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Date Issued: 2017
Identifier: CFE0006565, ucf:51326
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006565

Title: ASSESSING CRASH OCCURRENCE ON URBAN FREEWAYS USING STATIC AND DYNAMIC FACTORS BY APPLYING A SYSTEM OF INTERRELATED EQUATIONS.
Creator: Pemmanaboina, Rajashekar, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Traffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models...
Show moreTraffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models for different categories of crashes divided based on type of crash, or condition in which they occur, 3) safety models to determine the probability of crash occurrence, including a rainfall index that has been estimated using a logistic regression model. The study corridor is a 36.25 mile stretch of Interstate 4 in Central Florida. For the first two sections, crash cases from 1999 through 2002 were considered. Conventionally most of the crash frequency analysis model all crashes, instead of dividing them based on type of crash, peaking conditions, availability of light, severity, or pavement condition, etc. Also researchers traditionally used AADT to represent traffic volumes in their models. These two cases are examples of macroscopic crash frequency modeling. To investigate the microscopic models, and to identify the significant factors related to crash occurrence, a preliminary study (first analysis) explored the use of microscopic traffic volumes related to crash occurrence by comparing AADT/VMT with five to twenty minute volumes immediately preceding the crash. It was found that the volumes just before the time of crash occurrence proved to be a better predictor of crash frequency than AADT. The results also showed that road curvature, median type, number of lanes, pavement surface type and presence of on/off-ramps are among the significant factors that contribute to crash occurrence. In the second analysis various possible crash categories were prepared to exactly identify the factors related to them, using various roadway, geometric, and microscopic traffic variables. Five different categories are prepared based on a common platform, e.g. type of crash. They are: 1) Multiple and Single vehicle crashes, 2) Peak and Off-peak crashes, 3) Dry and Wet pavement crashes, 4) Daytime and Dark hour crashes, and 5) Property Damage Only (PDO) and Injury crashes. Each of the above mentioned models in each category are estimated separately. To account for the correlation between the disturbance terms arising from omitted variables between any two models in a category, seemingly unrelated negative binomial (SUNB) regression was used, and then the models in each category were estimated simultaneously. SUNB estimation proved to be advantageous for two categories: Category 1, and Category 4. Road curvature and presence of On-ramps/Off-ramps were found to be the important factors, which can be related to every crash category. AADT was also found to be significant in all the models except for the single vehicle crash model. Median type and pavement surface type were among the other important factors causing crashes. It can be stated that the group of factors found in the model considering all crashes is a superset of the factors that were found in individual crash categories. The third analysis dealt with the development of a logistic regression model to obtain the weather condition at a given time and location on I-4 in Central Florida so that this information can be used in traffic safety analyses, because of the lack of weather monitoring stations in the study area. To prove the worthiness of the weather information obtained form the analysis, the same weather information was used in a safety model developed by Abdel-Aty et al., 2004. It was also proved that the inclusion of weather information actually improved the safety model with better prediction accuracy.
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Date Issued: 2005
Identifier: CFE0000587, ucf:46468
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000587

Title: SEVERITY ANALYSIS OF DRIVER CRASH INVOLVEMENTS ON MULTILANE HIGH SPEED ARTERIAL CORRIDORS.
Creator: Nevarez-Pagan, Alexis, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: Arterial roads constitute the majority of the centerline miles of the Florida State Highway System. Severe injury involvements on these roads account for a quarter of the total severe injuries reported statewide. This research focuses on driver injury severity analysis of statewide multilane high speed arterials using crash data for the years 2002 to 2004. The first goal is to test different ways of analyzing crash data (by road entity and crash types) and find the best method of driver...
Show moreArterial roads constitute the majority of the centerline miles of the Florida State Highway System. Severe injury involvements on these roads account for a quarter of the total severe injuries reported statewide. This research focuses on driver injury severity analysis of statewide multilane high speed arterials using crash data for the years 2002 to 2004. The first goal is to test different ways of analyzing crash data (by road entity and crash types) and find the best method of driver injury severity analysis. A second goal is to find driver, vehicle, road and environment related factors that contribute to severe involvements on multilane arterials. Exploratory analysis using one year of crash data (2004) using binary logit regression was used to measure the risk of driver severe injury given that a crash occurs. A preliminary list of significant factors was obtained. A massive data preparation effort was undertaken and a random sample of multivehicle crashes was selected for final analysis. The final injury severity analysis consisted of six road entity models and twenty crash type models. The data preparation and sampling was successful in allowing a robust dataset. The overall model was a good candidate for the analysis of driver injury severity on multilane high speed roads. Driver injury severity resulting from angle and left turn crashes were best modeled by separate non-signalized intersection crash analysis. Injury severity from rear end and fixed object crashes was best modeled by combined analysis of pure segment and non-signalized intersection crashes. The most important contributing factors found in the overall analysis included driver related variables such as age, gender, seat belt use, at-fault driver, physical defects and speeding. Crash and vehicle related contributing factors included driver ejection, collision type (harmful event), contributing cause, type of vehicle and off roadway crash. Multivehicle crashes and interactions with intersection and off road crashes were also significant. The most significant roadway related variables included speed limit, ADT per lane, access class, lane width, roadway curve, sidewalk width, non-high mast lighting density, type of friction course and skid resistance. The overall model had a very good fit but some misspecification symptoms appeared due to major differences in road entities and crash types by land use. Two additional models of crashes for urban and rural areas were successfully developed. The land use models' goodness of fit was substantially better than any other combination by road entity or the overall model. Their coefficients were substantially robust and their values agreed with scientific or empirical principles. Additional research is needed to prove these results for crash type models found most reliable by this investigation. A framework for injury severity analysis and safety improvement guidelines based on the results is presented. Additional integration of road characteristics (especially intersection) data is recommended for future research. Also, the use of statistical methods that account for correlation among crashes and locations are suggested for use in future research.
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Date Issued: 2008
Identifier: CFE0002080, ucf:47591
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002080

Title: THE USE OF THE UCF DRIVING SIMIULATOR TO TEST THE CONTRIBUTION OF LARGER SIZE VEHICLES (LSVS) IN REAR-END COLLISIONS AND RED LIGHT RUNNING ON INTERSECTIONS.
Creator: Harb, Rami, Radwan, Essam, University of Central Florida
Abstract / Description: Driving safety has been an issue of great concern in the United States throughout the years. According to the National Center for Statistics and Analysis (NCSA), in 2003 alone, there were 6,267,000 crashes in the U.S. from which 1,915,000 were injury crashes, including 38,764 fatal crashes and 43,220 human casualties. The U.S. Department of Transportation spends millions of dollars every year on research that aims to improve roadway safety and decrease the number of traffic collisions. In...
Show moreDriving safety has been an issue of great concern in the United States throughout the years. According to the National Center for Statistics and Analysis (NCSA), in 2003 alone, there were 6,267,000 crashes in the U.S. from which 1,915,000 were injury crashes, including 38,764 fatal crashes and 43,220 human casualties. The U.S. Department of Transportation spends millions of dollars every year on research that aims to improve roadway safety and decrease the number of traffic collisions. In spring 2002, the Center for Advanced Traffic System Simulation (CATSS), at the University of Central Florida, acquired a sophisticated reconfigurable driving simulator. This simulator, which consists of a late model truck cab, or passenger vehicle cab, mounted on a motion base capable of operation with six degrees of freedom, is a great tool for traffic studies. Two applications of the simulator are to study the contribution of Light Truck Vehicles (LTVs) to potential rear-end collisions, the most common type of crashes, which account for about a third of the U.S. traffic crashes, and the involvement of Larger Size Vehicles (LSVs) in red light running. LTVs can obstruct horizontal visibility for the following car driver and has been a major issue, especially at unsignalized intersections. The sudden stop of an LTV, in the shadow of the blindness of the succeeding car driver, may deprive the following vehicle of a sufficient response time, leading to high probability of a rear-end collision. As for LSVs, they can obstruct the vertical visibility of the traffic light for the succeeding car driver on signalized intersection producing a potential red light running for the latter. Two sub-scenarios were developed in the UCF driving simulator for each the vertical and horizontal visibility blockage scenarios. The first sub-scenario is the base sub-scenario for both scenarios, where the simulator car follows a passenger car, and the second sub-scenario is the test sub-scenario, where the simulator car follows an LTV for the horizontal visibility blockage scenario and an LSV for the vertical visibility blockage scenario. A suggested solution for the vertical visibility blockage of the traffic light problem that consisted of adding a traffic signal pole on the right side of the road was also designed in the driving simulator. The results showed that LTVs produce more rear-end collisions at unsignalized intersections due to the horizontal visibility blockage and following car drivers' behavior. The results also showed that LSVs contribute significantly to red light running on signalized intersections and that the addition of a traffic signal pole on the right side of the road reduces the red light running probability.
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Date Issued: 2005
Identifier: CFE0000626, ucf:46513
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000626

Title: DRIVING SIMULATOR VALIDATION AND REAR-END CRASH RISK ANALYSIS AT A SIGNALISED INTERSECTION.
Creator: Chilakapati, Praveen, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: In recent years the use of advanced driving simulators has increased in the transportation engineering field especially in evaluating safety countermeasures. The driving simulator at UCF is a high fidelity simulator with six degrees of freedom. This research aims at validating the simulator in terms of speed and safety with the intention of using it as a test bed for high risk locations and to use it in developing traffic safety countermeasures. The Simulator replicates a real world...
Show moreIn recent years the use of advanced driving simulators has increased in the transportation engineering field especially in evaluating safety countermeasures. The driving simulator at UCF is a high fidelity simulator with six degrees of freedom. This research aims at validating the simulator in terms of speed and safety with the intention of using it as a test bed for high risk locations and to use it in developing traffic safety countermeasures. The Simulator replicates a real world signalized intersection (Alafaya trail (SR-434) and Colonial Drive (SR-50)). A total of sixty one subjects of age ranging from sixteen to sixty years were recruited to drive the simulator for the experiment, which consists of eight scenarios. This research validates the driving simulator for speed, safety and visual aspects. Based on the overall comparisons of speed between the simulated results and the real world, it was concluded that the UCF driving simulator is a valid tool for traffic studies related to driving speed behavior. Based on statistical analysis conducted on the experiment results, it is concluded that SR-434 northbound right turn lane and SR-50 eastbound through lanes have a higher rear-end crash risk than that at SR-50 westbound right turn lane and SR-434 northbound through lanes, respectively. This conforms to the risk of rear-end crashes observed at the actual intersection. Therefore, the simulator is validated for using it as an effective tool for traffic safety studies to test high-risk intersection locations. The driving simulator is also validated for physical and visual aspects of the intersection as 87.10% of the subjects recognized the intersection and were of the opinion that the replicated intersection was good enough or realistic. A binary logistic regression model was estimated and was used to quantify the relative rear-end crash risk at through lanes. It was found that in terms of rear-end crash risk SR50 east- bound approach is 23.67% riskier than the SR434 north-bound approach.
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Date Issued: 2006
Identifier: CFE0001391, ucf:46964
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001391

Title: A deep learning approach to diagnosing schizophrenia.
Creator: Barry, Justin, Valliyil Thankachan, Sharma, Gurupur, Varadraj, Jha, Sumit Kumar, Ewetz, Rickard, University of Central Florida
Abstract / Description: In this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep...
Show moreIn this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep learning techniques using three hidden layers in the model. The results obtained indicate that deep learning provides state-of-the-art accuracy in diagnosing schizophrenia. Based on these observations, there is a possibility that deep learning may provide a paradigm shift in diagnosing schizophrenia.
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Date Issued: 2019
Identifier: CFE0007429, ucf:52737
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007429

Title: Analysis of Remote Tripping Command Injection Attacks in Industrial Control Systems Through Statistical and Machine Learning Methods.
Creator: Timm, Charles, Caulkins, Bruce, Wiegand, Rudolf, Lathrop, Scott, University of Central Florida
Abstract / Description: In the past decade, cyber operations have been increasingly utilized to further policy goals of state-sponsored actors to shift the balance of politics and power on a global scale. One of the ways this has been evidenced is through the exploitation of electric grids via cyber means. A remote tripping command injection attack is one of the types of attacks that could have devastating effects on the North American power grid. To better understand these attacks and create detection axioms to...
Show moreIn the past decade, cyber operations have been increasingly utilized to further policy goals of state-sponsored actors to shift the balance of politics and power on a global scale. One of the ways this has been evidenced is through the exploitation of electric grids via cyber means. A remote tripping command injection attack is one of the types of attacks that could have devastating effects on the North American power grid. To better understand these attacks and create detection axioms to both quickly identify and mitigate the effects of a remote tripping command injection attack, a dataset comprised of 128 variables (primarily synchrophasor measurements) was analyzed via statistical methods and machine learning algorithms in RStudio and WEKA software respectively. While statistical methods were not successful due to the non-linearity and complexity of the dataset, machine learning algorithms surpassed accuracy metrics established in previous research given a simplified dataset of the specified attack and normal operational data. This research allows future cybersecurity researchers to better understand remote tripping command injection attacks in comparison to normal operational conditions. Further, an incorporation of the analysis has the potential to increase detection and thus mitigate risk to the North American power grid in future work.
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Date Issued: 2018
Identifier: CFE0007257, ucf:52193
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007257

Title: A comparative analysis of different Dilemma Zone countermeasures at signalized intersections based on Cellular Automaton Model.
Creator: Wu, Yina, Abdel-Aty, Mohamed, Lee, JaeYoung, Eluru, Naveen, University of Central Florida
Abstract / Description: In the United States, intersections are among the most frequent locations for crashes. One of the major problems at signalized intersection is the dilemma zone, which is caused by false driver behavior during the yellow interval. This research evaluated driver behavior during the yellow interval at signalized intersections and compared different dilemma zone countermeasures. The study was conducted through four stages.First, the driver behavior during the yellow interval were collected and...
Show moreIn the United States, intersections are among the most frequent locations for crashes. One of the major problems at signalized intersection is the dilemma zone, which is caused by false driver behavior during the yellow interval. This research evaluated driver behavior during the yellow interval at signalized intersections and compared different dilemma zone countermeasures. The study was conducted through four stages.First, the driver behavior during the yellow interval were collected and analyzed. Eight variables, which are related to risky situations, are considered. The impact factors of drivers' stop/go decisions and the presence of the red-light running (RLR) violations were also analyzed. Second, based on the field data, a logistic model, which is a function of speed, distance to the stop line and the lead/follow position of the vehicle, was developed to predict drivers' stop/go decisions. Meanwhile, Cellular Automata (CA) models for the movement at the signalized intersection were developed. In this study, four different simulation scenarios were established, including the typical intersection signal, signal with flashing green phases, the intersection with pavement marking upstream of the approach, and the intersection with a new countermeasure: adding an auxiliary flashing indication next to the pavement marking. When vehicles are approaching the intersection with a speed lower than the speed limit of the intersection approach, the auxiliary flashing yellow indication will begin flashing before the yellow phase. If the vehicle that has not passed the pavement marking before the onset of the auxiliary flashing yellow indication and can see the flashing indication, the driver should choose to stop during the yellow interval. Otherwise, the driver should choose to go at the yellow duration. The CA model was employed to simulate the traffic flow, and the logistic model was applied as the stop/go decision rule. Dilemma situations that lead to rear-end crash risks and potential RLR risks were used to evaluate the different scenarios. According to the simulation results, the mean and standard deviation of the speed of the traffic flow play a significant role in rear-end crash risk situations, where a lower speed and standard deviation could lead to less rear-end risk situations at the same intersection. High difference in speed are more prone to cause rear-end crashes. With Respect to the RLR violations, the RLR risk analysis showed that the mean speed of the leading vehicle has important influence on the RLR risk in the typical intersection simulation scenarios as well as intersections with the flashing green phases' simulation scenario.Moreover, the findings indicated that the flashing green could not effectively reduce the risk probabilities. The pavement marking countermeasure had positive effects on reducing the risk probabilities if a platoon's mean speed was not under the speed used for designing the pavement marking. Otherwise, the risk probabilities for the intersection would not be reduced because of the increase in the RLR rate. The simulation results showed that the scenario with the pavement marking and an auxiliary indication countermeasure, which adds a flashing indication next to the pavement marking, had less risky situations than the other scenarios with the same speed distribution. These findings suggested the effectiveness of the pavement marking and an auxiliary indication countermeasure to reduce both rear-end collisions and RLR violations than other countermeasures.
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Date Issued: 2014
Identifier: CFE0005562, ucf:50291
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005562

Title: Predicting Gun Ownership in America: Birth Cohort, Political Views, and Attitudes Towards Gun Control Legislation.
Creator: Adams, Jared, Gay, David, Donley, Amy, Corzine, Harold, University of Central Florida
Abstract / Description: With mass shootings occurring with frightening regularity, research into gun ownership behavior is becoming increasingly important for public policy creation and public safety. While extant research tells us that firearm ownership is woven deep into the historical fabric of American culture, scholarship has yet to fully explore predictors for gun ownership. Employing 2015 Pew Research Center political survey data, this study examines the predictive effects of birth cohort, political ideology,...
Show moreWith mass shootings occurring with frightening regularity, research into gun ownership behavior is becoming increasingly important for public policy creation and public safety. While extant research tells us that firearm ownership is woven deep into the historical fabric of American culture, scholarship has yet to fully explore predictors for gun ownership. Employing 2015 Pew Research Center political survey data, this study examines the predictive effects of birth cohort, political ideology, and attitudes towards gun control legislation on gun ownership, with and without controls, using hierarchical binary logistic regression models. The presented models examine three separate cohorts: The Millennials, Generation X, and the Baby Boomers. Findings reveal that Millennials, liberal political ideology, attitudes which stress the importance of controlling, as opposed to protecting, gun ownership are significantly less likely to own a firearm. Furthermore, gender, household income, population density, southern residency, and race were also found to significantly influence gun ownership. Implications, limitations, and recommendations for future research are also discussed. While this research cannot perfectly predict individual gun ownership, it does effectively highlight several important facts to consider. From the fog of media speculation, political grandstanding, and overly simplistic and unwarranted assumptions, the results of this study bring into full view the inherent complexity of American gun ownership.
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Date Issued: 2017
Identifier: CFE0006706, ucf:51913
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006706

Title: FLORIDA SCHOOL INDICATOR REPORT DATA AS PREDICTORS OF HIGH SCHOOL ADEQUATE YEARLY PROGRESS (AYP).
Creator: Carr, John, Bozeman, William, University of Central Florida
Abstract / Description: The focus of this research was to identify variables reported in the 2008-2009 Florida School Indicator Report (FSIR) that had a statistical impact, positive or negative, on the likelihood that a school would achieve Adequate Yearly Progress (AYP) in reading or mathematics using the logistic regression technique. This study analyzed four broad categories reported by the FSIR to include academic, school, student, and teacher characteristics. FSIR and AYP data was collected for 468 Florida high...
Show moreThe focus of this research was to identify variables reported in the 2008-2009 Florida School Indicator Report (FSIR) that had a statistical impact, positive or negative, on the likelihood that a school would achieve Adequate Yearly Progress (AYP) in reading or mathematics using the logistic regression technique. This study analyzed four broad categories reported by the FSIR to include academic, school, student, and teacher characteristics. FSIR and AYP data was collected for 468 Florida high schools that were categorized by the Florida Department of Education as presenting a comprehensive curriculum to grades 9-12 or grades 10-12. It was determined in this study that academic data associated with ACT results and the grade 11 FCAT Science were effective predictors of a school's academic health in reading and mathematics. Student absenteeism showed the greatest impact on a school obtaining AYP in reading while the percentage of students qualifying for free and disabled populations within a school showed the greatest impact on a school obtaining AYP in mathematics. Teachers teaching out of field were identified as having a negative influence on AYP in reading and mathematics while a teacher's experience was considered a positive influence on AYP in mathematics only. Further research is necessary to fully explore the use of logistic regression as a predictive tool at the state, school district, and school level.
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Date Issued: 2011
Identifier: CFE0003638, ucf:48848
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003638

Title: Factors Contributing to Low Adequate Prenatal Care Rates in Orange County, Florida.
Creator: Daniel, Lauren, Donley, Amy, Hinojosa, Melanie, University of Central Florida
Abstract / Description: In 2017, only 56% of births in Orange County, Florida, received adequate prenatal care(-)care that has been shown to prevent maternal and infant death. The Florida Department of Health uses the Kotelchuck Index to determine care adequacy. This index rates care adequacy based on when the mother first receives care, and how many recommended appointments she attends. Prenatal care is rated (")inadequate(") if it starts after the fourth month of pregnancy, and/or if less than half of the...
Show moreIn 2017, only 56% of births in Orange County, Florida, received adequate prenatal care(-)care that has been shown to prevent maternal and infant death. The Florida Department of Health uses the Kotelchuck Index to determine care adequacy. This index rates care adequacy based on when the mother first receives care, and how many recommended appointments she attends. Prenatal care is rated (")inadequate(") if it starts after the fourth month of pregnancy, and/or if less than half of the recommended appointments are attended. Receiving earlier and consistent prenatal care has been shown to be an effective way to improve birth outcomes.In Florida, counties that have low adequate prenatal care rates like Orange County's tend to be less populous and rural. However, Orange County stands out with its large population of 1.3 million and more urban environment; other Florida counties similar in population and environment to Orange tend to have rates like that of the state's, at approximately 70%.The objective of this study is to determine which factors contribute most significantly to prenatal care inadequacy in Orange, Duval, Hillsborough, Miami-Dade, and Pinellas counties; determine the differences between the most significant factors in Orange County and those in the other four counties; and to determine if residing in Orange County in of itself a risk factor for inadequate prenatal care, using logistic regression. By identifying factors that may lead to low adequacy rates, interventions intended to increase care adequacy in Orange County can be better targeted towards populations in need.
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Date Issued: 2019
Identifier: CFE0007447, ucf:52715
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007447

Title: The effectiveness of Child Restraint and Bicycle Helmet Policies to Improve Road Safety.
Creator: Bustamante, Claudia, Abdel-Aty, Mohamed, Eluru, Naveen, Lee, JaeYoung, University of Central Florida
Abstract / Description: Analyzing the effect of legislation in children's safety when they travel as motor-vehicle passengers and bicycle riders can allow us to evaluate the effectiveness in transportation policies. The Child Restraint Laws (CRL) and Bicycle Helmet Laws (BHL) were studied by analyzing the nationwide Fatality Analysis Reporting System (FARS) to estimate the fatality reduction as well as drivers' decisions to use Child Restraint Systems (CRS) and bicycle helmets respectively. Differences in...
Show moreAnalyzing the effect of legislation in children's safety when they travel as motor-vehicle passengers and bicycle riders can allow us to evaluate the effectiveness in transportation policies. The Child Restraint Laws (CRL) and Bicycle Helmet Laws (BHL) were studied by analyzing the nationwide Fatality Analysis Reporting System (FARS) to estimate the fatality reduction as well as drivers' decisions to use Child Restraint Systems (CRS) and bicycle helmets respectively. Differences in legislation could have different effects on traffic fatalities. Therefore, this study presents multiple methodologies to study these effects. In the evaluation of traffic safety issues, several proven statistical models have shown to be effective at estimating risky factors that might influence crash prevention. These proven models and predictive data analysis guided the process to attempt different models, leading to the development of three specific models used in this study to best estimate the effectiveness of these laws. Then, it was found that legislation in Child Safety Policy has consequences in traffic fatalities. A negative binomial model was created to analyze the CRL influence at the state-level in fatal crashes involving children, and showed that legislating on CRS can reduce the number of fatalities by 29% for children aged 5 to 9. Additionally, at the drivers-level a logistic regression model with random effects was used to determine the significant variables that influence the driver's decision to restrain his/her child. Such variables include: driver's restraint use, road classification, weather condition, number of occupants in the vehicle, traffic violations and driver's and child's age. It was also shown that drivers from communities with deprived socio-economic status are less likely to use CRS. In the same way, a binary logistic regression model was developed to evaluate the effect of BHL in bicycle helmet-use. Findings from this model show that bicyclists from states with the BHL are 236 times more likely to wear a helmet compared to those from states without the BHL. Moreover, the bicyclist's age, gender, education, and income level also influences bicycle helmet use. Both studies suggest that enacting CRL and BHL at the state-level for the studied age groups can be combined with education, safety promotion, enforcement, and program evaluation as proven countermeasures to increase children's traffic safety. This study evidenced that there is a lack of research in this field, especially when policy making requires having enough evidence to support the laws in order to not become an arbitrary legislation procedure affecting child's protection in the transportation system.
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Date Issued: 2017
Identifier: CFE0006571, ucf:51315
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006571

Title: STATISTICAL ANALYSIS OF DEPRESSION AND SOCIAL SUPPORT CHANGE IN ARAB IMMIGRANT WOMEN IN USA.
Creator: Blbas, Hazhar, Uddin, Nizam, Nickerson, David, Aroian, Karen, University of Central Florida
Abstract / Description: Arab Muslim immigrant women encounter many stressors and are at risk for depression. Social supports from husbands, family and friends are generally considered mitigating resources for depression. However, changes in social support over time and the effects of such supports on depression at a future time period have not been fully addressed in the literature This thesis investigated the relationship between demographic characteristics, changes in social support, and depression in Arab Muslim...
Show moreArab Muslim immigrant women encounter many stressors and are at risk for depression. Social supports from husbands, family and friends are generally considered mitigating resources for depression. However, changes in social support over time and the effects of such supports on depression at a future time period have not been fully addressed in the literature This thesis investigated the relationship between demographic characteristics, changes in social support, and depression in Arab Muslim immigrant women to the USA. A sample of 454 married Arab Muslim immigrant women provided demographic data, scores on social support variables and depression at three time periods approximately six months apart. Various statistical techniques at our disposal such as boxplots, response curves, descriptive statistics, ANOVA and ANCOVA, simple and multiple linear regressions have been used to see how various factors and variables are associated with changes in social support from husband, extended family and friend over time. Simple and multiple regression analyses are carried out to see if any variable observed at the time of first survey can be used to predict depression at a future time. Social support from husband and friend, husband's employment status and education, and depression at time one are found to be significantly associated with depression at time three. Finally, logistic regression analysis conducted for a binary depression outcome variable indicated that lower total social support and higher depression score of survey participants at the time of first survey increase their probability of being depressed at the time of third survey.
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Date Issued: 2014
Identifier: CFE0005133, ucf:50676
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005133

logistic regression (6)	+ -
Logistic Regression (3)	+ -
Logistic regression (2)	+ -
SVM (2)	+ -
simulation (2)	+ -

ARTERIAL CORRIDORS (1)	+ -
Academic Persistence (1)	+ -
Attitudes (1)	+ -
BY:HAZHAR TALAAT ABUBAKEER BLBAS FROM KURDISTAN-IRAQ (1)	+ -
Binary Logistic Regression (1)	+ -
Birth Cohort (1)	+ -
CMF (1)	+ -
CURVES (1)	+ -
Career Development (1)	+ -
Community college baccalaureate (1)	+ -
Contingency Tables (1)	+ -
Crash Frequency (1)	+ -
DRIVER INJURY (1)	+ -
Deep Learning (1)	+ -
Dilemma zone (1)	+ -
Driving Simulator (1)	+ -
Driving simulator validation (1)	+ -
Event Tree (1)	+ -
Florida School Indicator Report (1)	+ -
Gun Culture (1)	+ -
Gun Legislation (1)	+ -
Gun Ownership (1)	+ -
Gun Rights (1)	+ -
Human Factors and Ergonomics (1)	+ -
INVOLVEMENTS (1)	+ -

University of Central Florida (19)	+ -
Abdel-Aty, Mohamed (5)	+ -
Donley, Amy (2)	+ -
Eluru, Naveen (2)	+ -
Harb, Rami (2)	+ -

Lee, JaeYoung (2)	+ -
Radwan, Essam (2)	+ -
Wiegand, Rudolf (2)	+ -
Abich, Julian (1)	+ -
Adams, Jared (1)	+ -
Ali, Abdulelah (1)	+ -
Aroian, Karen (1)	+ -
Barry, Justin (1)	+ -
Belser, Christopher (1)	+ -
Blbas, Hazhar (1)	+ -
Bozeman, William (1)	+ -
Bustamante, Claudia (1)	+ -
Carr, John (1)	+ -
Caulkins, Bruce (1)	+ -
Chilakapati, Praveen (1)	+ -
Cintron Delgado, Rosa (1)	+ -
Corzine, Harold (1)	+ -
Cox, Thomas (1)	+ -
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Daniel, Lauren (1)	+ -
Elshennawy, Ahmad (1)	+ -
Ewetz, Rickard (1)	+ -
Foroosh, Hassan (1)	+ -
Gay, David (1)	+ -
Greenwood-Ericksen, Adams (1)	+ -

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