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Title: Functional Data Analysis and its application to cancer data.
Creator: Martinenko, Evgeny, Pensky, Marianna, Tamasan, Alexandru, Swanson, Jason, Richardson, Gary, University of Central Florida
Abstract / Description: The objective of the current work is to develop novel procedures for the analysis of functional dataand apply them for investigation of gender disparity in survival of lung cancer patients. In particular,we use the time-dependent Cox proportional hazards model where the clinical information isincorporated via time-independent covariates, and the current age is modeled using its expansionover wavelet basis functions. We developed computer algorithms and applied them to the dataset which is...
Show moreThe objective of the current work is to develop novel procedures for the analysis of functional dataand apply them for investigation of gender disparity in survival of lung cancer patients. In particular,we use the time-dependent Cox proportional hazards model where the clinical information isincorporated via time-independent covariates, and the current age is modeled using its expansionover wavelet basis functions. We developed computer algorithms and applied them to the dataset which is derived from Florida Cancer Data depository data set (all personal information whichallows to identify patients was eliminated). We also studied the problem of estimation of a continuousmatrix-variate function of low rank. We have constructed an estimator of such functionusing its basis expansion and subsequent solution of an optimization problem with the Schattennormpenalty. We derive an oracle inequality for the constructed estimator, study its properties viasimulations and apply the procedure to analysis of Dynamic Contrast medical imaging data.
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Date Issued: 2014
Identifier: CFE0005377, ucf:50447
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005377

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: USING STUDENT MOOD AND TASK PERFORMANCE TO TRAIN CLASSIFIER ALGORITHMS TO SELECT EFFECTIVE COACHING STRATEGIES WITHIN INTELLIGENT TUTORING SYSTEMS (ITS).
Creator: Sottilare, Robert, Proctor, Michael, University of Central Florida
Abstract / Description: The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to...
Show moreThe ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student's affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student's interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student's mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle's (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank's (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables.
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Date Issued: 2009
Identifier: CFE0002528, ucf:47644
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002528

Title: Investigating the Predictive Power of Student Characteristics on Success in Studio-mode, Algebra-based Introductory Physics Courses.
Creator: Pond, Jarrad, Rahman, Talat, Chini, Jacquelyn, Mucciolo, Eduardo, Butler, Malcolm, University of Central Florida
Abstract / Description: As part of a project to explore the differential success of similar implementations of the studio-mode of physics instruction, the objective of this work is to investigate the characteristics of students enrolled in algebra-based, studio-mode introductory physics courses at various universities in order to evaluate what effects these characteristics have on different measures of student success, such as gains in conceptual knowledge, shifts to more favorable attitudes toward physics, and...
Show moreAs part of a project to explore the differential success of similar implementations of the studio-mode of physics instruction, the objective of this work is to investigate the characteristics of students enrolled in algebra-based, studio-mode introductory physics courses at various universities in order to evaluate what effects these characteristics have on different measures of student success, such as gains in conceptual knowledge, shifts to more favorable attitudes toward physics, and final course grades. In my analysis, I explore the strategic self-regulatory, motivational, and demographic characteristics of students in algebra-based, studio-mode physics courses at three universities: the University of Central Florida (UCF), Georgia State University (GSU), and George Washington University (GW). Each of these institutions possesses varying student populations and differing levels of success in their studio-mode physics courses, as measured by students' overall average conceptual learning gains. In order to collect information about the students at each institution, I compiled questions from several existing questionnaires designed to measure student characteristics such as study strategies and motivations for learning physics, and organization of scientific knowledge. I also gathered student demographic information. This compiled survey, named the Student Characteristics Survey (SCS) was given at all three institutions. Using similar information collected from students, other studies (J. A. Chen, 2012; Nelson, Shell, Husman, Fishman, (&) Soh, 2015; Schwinger, Steinmayr, (&) Spinath, 2012; Shell (&) Husman, 2008; Shell (&) Soh, 2013; Tuominen-Soini, Salmela-Aro, (&) Niemivirta, 2011; Vansteenkiste, Soenens, Sierens, Luyckx, (&) Lens, 2009) have identified distinct learning profiles across varying student populations. Using a person-centered approach, I used model-based cluster analysis methods (Gan, Ma, (&) Wu, 2007) to organize students into distinct groups. From this analysis, I identified five distinct learning profiles in the population of physics students, similar to those found in previous research. In addition, student outcome information was gathered from both UCF and GSU. Conceptual inventory responses were gathered at both institutions, and attitudinal survey results and course grades were gathered at UCF. No student outcome data was gathered at GW; thus, GW is represented in analyses involving information compiled solely from the SCS, but GW is not represented in analyses involving student outcome information. Then, I use Automatic Linear Modeling, an application of multiple linear regression modeling (IBM, 2012, 2013), to identify which demographic variables (including the identified learning profiles) are the most influential in predicting student outcomes, such as scores on the Force Concept Inventory (FCI), the Conceptual Survey of Electricity and Magnetism (CSEM), and the Colorado Learning Attitudes about Sciences Survey (CLASS), both pre- and post-instruction. Modeling is conducted on the entire available dataset as a whole and is also conducted with the data disaggregated by institution in order to identify any differential effects that student characteristics may have at predicting student success at the different institutions. In addition, instructors teaching algebra-based, studio-mode introductory physics courses are interviewed about what makes students successful in order to better understand what instructors perceive is important for students to excel in their physics courses. Furthermore, student survey takers were interviewed to help verify their study strategies and motivations as measured by the SCS.The above analysis provides evidence that, on average, gaps in student understanding exist based on several demographic characteristics, such a gender, ethnicity, high school physics experience, and SAT Math score, and these results are generally consistent with those found in the literature. Disaggregation by institution reveals that differential effects from demographic variables exist; thus, similar groups of students at separate institutions attain different student outcomes. Overall, this is an undesirable observation, as the physics education research community strives to reduce such inequity in physics classrooms; however, identification of specific inequities and gaps in learning will help to inform further research investigations. Research should continue in the form of in-depth investigations into how individual instructors teach algebra-based studio-mode introductory physics courses, focusing on instructors' approaches to the studio-mode of instruction and uses of active learning techniques. Also, investigation of instructor awareness of demographic-driven gaps in student understanding would give insight into if and how instructors may be attempting to better understand the needs of different students. In addition, where a wide range of demographic data are available, I encourage institutions to conduct similar analyses as those presented here in order to identify any gaps in student understanding and place them in their institutional contexts for comparisons to other universities. Furthermore, as a result of my work, I find the identified learning profiles to have a significant association with students' attitudes toward physics, as measured by the CLASS questionnaire, both pre- and post-instruction. This relationship between learning profile and CLASS Pre-score is one that can help give instructors practical insight into students' study strategies and motivations at the very beginning of the physics course. By possessing knowledge of which students do and do not possess adaptive learning strategies early on, instructors can better optimize initial student groups by considering results of student outcome measures, adjust lesson plans, and assess students' needs accordingly.
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Date Issued: 2016
Identifier: CFE0006376, ucf:51515
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006376

Title: MODELING CRASH FREQUENCIES AT SIGNALIZED INTERSECTIONS IN CENTRAL FLORIDA.
Creator: Kowdla, Smitha, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description: A high percentage of highway crashes in the United States occur at intersections. These crashes result in property damage, lost productivity, injury, and even death. Identifying intersections associated with high crash rate is very important to minimize future crashes. The purpose of this study is to develop efficient means to evaluate intersections, which may require safety improvements. The area covered by the analysis in this thesis includes Orange and Seminole Counties and the City of...
Show moreA high percentage of highway crashes in the United States occur at intersections. These crashes result in property damage, lost productivity, injury, and even death. Identifying intersections associated with high crash rate is very important to minimize future crashes. The purpose of this study is to develop efficient means to evaluate intersections, which may require safety improvements. The area covered by the analysis in this thesis includes Orange and Seminole Counties and the City of Orlando. The aforementioned counties and city thus represent Central Florida. Each County/City provided data that consisted of signalized intersection drawings that were either in the form of electronic or hard copies, the county's extensive crash database and a list of intersections that underwent modifications during the study period. A total of 786 intersections were used in the analysis and the crash database was made up of 4271 crashes. From the signalized intersection drawings obtained from the county's traffic engineering department, a geometry database was created to classify all intersections by the number of through lanes, number of left turning lanes, Average Annual Daily Traffic and Posted Speed limits on the Major road of the intersection. In this research, crashes and their type, e.g., rear-end, left-turn and angle as well as total crashes were investigated. Numerous models were developed first using the Poisson regression and then using the Negative Binomial approach as the data showed overdispersion. The modeling process aimed to relate geometric and traffic factors to the frequency of crashes at intersections. Expected value analysis tables were also developed to determine if an intersection had an abnormally high number of crashes. These tables can be used in assisting Traffic Engineers in identifying serious safety problems at intersections. The general models illustrated that rear-end crashes were associated with high natural logarithm of AADT on the major road and the number of lanes (major intersections, e.g. 6x4/6x6), whereas AADT on the major road did not affect left-turn crashes. Intersections with the configuration 4x2/6x2 (2 through lanes at the minor roadway) or T intersections as another category experienced an increase in left-turn crashes. Angle crashes were most frequent at one-way intersections especially in the case of 4x4 intersections. Individual models that included interaction terms with one variable at a time concluded that AADT on the major road positively influenced rear-end crashes more compared to angle and left-turn crashes. As the speed increases on the minor road, the left turn crashes are affected more when compared to angle and rear-end crashes, therefore it can be concluded that left-turn crashes are most influenced by the speed limit on the minor road compared to angle crashes and then followed by rear-end crashes. As the total number of left turn lanes increased at the intersection, thereby increasing the size of the intersection, the number of rear-end crashes increased. An overall model that contained natural logarithm of AADT on major road, total number of left turn lanes at the intersection, number of through lanes on the minor road and configuration of the intersection, as independent variables, along with interaction terms, further concluded and supported the individual models that the number of crashes (rear-end, left-turn and angle) increased as the AADT on the major road increased and the number of crashes decreased as the total number of left turn lanes at the intersection increased. Also, crashes increased as the number of through lanes on the minor road increased. The variables' interaction effects with dummies representing rear-end and left-turn crashes in the final model showed that as the AADT on the major road increased, the number of rear-end crashes increased compared to left-turn and angle crashes and also that as the total number of left turn lanes at the intersection increased, the number of left-turn crashes decreased when compared to rear-end and angle crashes. Also the number of rear-end crashes increased at major four leg intersections e.g. 6x4, 6x6 etc. This thesis demonstrated the superiority of Negative Binomial regression in modeling the frequency of crashes at signalized intersections.
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Date Issued: 2004
Identifier: CFE0000267, ucf:46224
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000267

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: 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: 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

Title: Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level.
Creator: Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
Abstract / Description: This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based...
Show moreThis thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model's estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.
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Date Issued: 2018
Identifier: CFE0007358, ucf:52103
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007358

Title: COUNTER-TERRORISM: WHEN DO STATES ADOPT NEW ANTI-TERROR LEGISLATION?.
Creator: Clesca, Princelee, Dolan, Thomas, University of Central Florida
Abstract / Description: The intent of this thesis is to research the anti-terror legislation of 15 countries and the history of terrorist incidents within those countries. Both the anti-terror legislation and the history of terrorist incidents will be researched within the time period of 1980 to 2009, a 30 year span. This thesis will seek to establish a relationship between the occurrence of terrorist events and when states change their anti-terror legislation. Legislation enacted can vary greatly. Common changes in...
Show moreThe intent of this thesis is to research the anti-terror legislation of 15 countries and the history of terrorist incidents within those countries. Both the anti-terror legislation and the history of terrorist incidents will be researched within the time period of 1980 to 2009, a 30 year span. This thesis will seek to establish a relationship between the occurrence of terrorist events and when states change their anti-terror legislation. Legislation enacted can vary greatly. Common changes in legislation seek to undercut the financing of terrorist organizations, criminalize behaviors, or empower state surveillance capabilities. A quantitative analysis will be performed to establish a relationship between terrorist attacks and legislative changes. A qualitative discussion will follow to analyze specific anti-terror legislation passed by states in response to terrorist events.
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Date Issued: 2015
Identifier: CFH0004851, ucf:45451
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0004851

Title: ESTABLISHING DEGRADATION RATES AND SERVICE LIFETIME OF PHOTOVOLTAIC SYSTEMS.
Creator: Leyte-Vidal, Albert, Hickman, James, University of Central Florida
Abstract / Description: As fossil fuel sources continue to diminish, oil prices continue to increase, and global warming and CO2 emissions keep impacting the environment, it has been necessary to shift energy consumption and generation to a different path. Solar energy has proven to be one of the most promising sources of renewable energy because it is environmentally friendly, available anywhere in the world, and cost competitive. For photovoltaic (PV) system engineers, designing a PV system is not an easy task....
Show moreAs fossil fuel sources continue to diminish, oil prices continue to increase, and global warming and CO2 emissions keep impacting the environment, it has been necessary to shift energy consumption and generation to a different path. Solar energy has proven to be one of the most promising sources of renewable energy because it is environmentally friendly, available anywhere in the world, and cost competitive. For photovoltaic (PV) system engineers, designing a PV system is not an easy task. Research demonstrates that different PV technologies behave differently under certain conditions; therefore energy production varies not only with capacity of the system but also with the type of module. For years, researchers have also studied how these different technologies perform for long periods of time, when exposed out in the field. In this study, data collected by the Florida Solar Energy Center for periods of over four years was analyzed using two techniques, widely accepted by researchers and industry, to evaluate the long‐term performance of five systems. The performance ratio analysis normalizes system capacity and enables the comparison of performance between multiple systems. In PVUSA Regression analysis, regression coefficients are calculated which correspond to the effect of irradiance, wind speed, and ambient temperature, and these coefficients are then used to calculate power at a predetermined set of conditions. This study allows manufacturers to address the difficulties found on system lifetime when their modules are installed out on the field. Also allows for the further development and improvement of the different PV technologies already commercially available.
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Date Issued: 2010
Identifier: CFE0003326, ucf:48483
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003326

Title: The Identification and Segmentation of Astrocytoma Prior to Critical Mass, by means of a Volumetric/Subregion Regression Analysis of Normal and Neoplastic Brain Tissue.
Creator: Higgins, Lyn, Hughes, Charles, Morrow, Patricia Bockelman, Bagci, Ulas, Lisle, Curtis, University of Central Florida
Abstract / Description: As the underlying cause of Glioblastoma Multiforme (GBM) is presently unclear, this research implements a new approach to identifying and segmenting plausible instances of GBM prior to critical mass. Grade-IV Astrocytoma, or GBM, is an aggressive and malignant cancer arising from star-shaped glial cells, or astrocytes, where the astrocytes, functionally, assist in the support and protection of neurons within the central nervous system and spinal cord. Subsequently, our motivation for...
Show moreAs the underlying cause of Glioblastoma Multiforme (GBM) is presently unclear, this research implements a new approach to identifying and segmenting plausible instances of GBM prior to critical mass. Grade-IV Astrocytoma, or GBM, is an aggressive and malignant cancer arising from star-shaped glial cells, or astrocytes, where the astrocytes, functionally, assist in the support and protection of neurons within the central nervous system and spinal cord. Subsequently, our motivation for researching the ability to recognize GBM is that the underlying cause of the mutation is presently unclear, leading to the operative that GBM is only detectable through a combination of MRI and CT brain scans, cooperatively, along with a resection biopsy. Since astrocytoma only becomes evident at critical mass, when the cellular structure of the neoplasm becomes visible within the image, this research seeks to achieve earlier identification and segmentation of the neoplasm by evaluating the malignant area via a volumetric voxel approach to removing noise artifacts and analyzing voxel differentials. In order to investigate neoplasm continuity, a differential approach has been implemented utilizing a multi-polynomial/multi-domain regression algorithm, thus, ultimately, providing a graphical and mathematical analysis of the differentials within critical mass and non-critical mass images. Given these augmentations to MRI and CT image rectifications, we theorize that our approach will improve on astrocytoma recognition and segmentation, along with achieving greater accuracy in diagnostic evaluations of the malignant area.
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Date Issued: 2018
Identifier: CFE0007336, ucf:52111
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007336

Title: THE RELATIONSHIP BETWEEN GENRE CHOICE OF MUSIC AND ALTRUISTIC BEHAVIOR.
Creator: Hippler, Christine, Whitten, Shannon, University of Central Florida
Abstract / Description: ABSTRACT Extensive research has documented the relationship between listening to certain genres of music and negative effects on social behavior such as aggressive and antisocial behavior. The present study explored whether there are genres of music associated with altruism. Altruistic behavior is defined as behavior that is consistently more caring, helpful, considerate of other's feelings, and self- sacrificing. These behaviors promote our ability to thrive as a community. Yet, few studies...
Show moreABSTRACT Extensive research has documented the relationship between listening to certain genres of music and negative effects on social behavior such as aggressive and antisocial behavior. The present study explored whether there are genres of music associated with altruism. Altruistic behavior is defined as behavior that is consistently more caring, helpful, considerate of other's feelings, and self- sacrificing. These behaviors promote our ability to thrive as a community. Yet, few studies have addressed the relationship between music and altruism. Data was collected from 608 college students who completed a self-report altruism scale, music preference measure, the Marlowe Crowne social desirability scale, and a demographic information form in order to see if there is a relationship between choice of music and altruism. A multiple hierarchal regression analysis found music genre choice accounted for 15.9 percent of variance in self-reported altruism. Significant, positive correlations emerged also between altruism and several music genres including alternative, country, classical, and emo.
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Date Issued: 2011
Identifier: CFH0003820, ucf:44753
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0003820

machine learning (2)	+ -
simulation (2)	+ -
15 countries (1)	+ -
30 years (1)	+ -
3D noise reduction (1)	+ -

ARTERIAL CORRIDORS (1)	+ -
Accident analysis (1)	+ -
Altruism (1)	+ -
BY:HAZHAR TALAAT ABUBAKEER BLBAS FROM KURDISTAN-IRAQ (1)	+ -
CURVES (1)	+ -
Christine Hippler (1)	+ -
Christine Lynn Hippler (1)	+ -
Cluster Analysis (1)	+ -
Contingency Tables (1)	+ -
Counter-terrorism (1)	+ -
Cox regression (1)	+ -
Crash Frequency (1)	+ -
DC-AC Inverters (1)	+ -
DRIVER INJURY (1)	+ -
Data mining (1)	+ -
Energy Efficiency (1)	+ -
Expected Accident Value tables (1)	+ -
Florida PV (1)	+ -
GBM (1)	+ -
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Genre Choice (1)	+ -
Higgins (1)	+ -
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ETD Collection (11)	+ -
Honors Collection (2)	+ -

University of Central Florida (13)	+ -
Abdel-Aty, Mohamed (4)	+ -
Aroian, Karen (1)	+ -
Bagci, Ulas (1)	+ -
Blbas, Hazhar (1)	+ -

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