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Title: NON-ACUTE COGNITIVE SEQUELAE ASSOCIATED WITH RECREATIONAL ECSTASY USE: A META-ANALYSIS.
Creator: Linkovich Kyle, Tiffany, Dunn, Michael, University of Central Florida
Abstract / Description: Studies using animal models have found considerable evidence of neurological damage resulting from exposure to 3,4- methylenedioxymethamphetamine (MDMA, ecstasy). Yet, studies comparing the cognitive performance of human recreational ecstasy users to ecstasy naïve controls have produced inconsistent results. The present study is a meta-analysis of the published empirical literature on the cognitive sequelae of human recreational ecstasy use. The pooled effect size estimate for combined...
Show moreStudies using animal models have found considerable evidence of neurological damage resulting from exposure to 3,4- methylenedioxymethamphetamine (MDMA, ecstasy). Yet, studies comparing the cognitive performance of human recreational ecstasy users to ecstasy naïve controls have produced inconsistent results. The present study is a meta-analysis of the published empirical literature on the cognitive sequelae of human recreational ecstasy use. The pooled effect size estimate for combined cognitive domains was statistically significant and moderate in size. Small to large, statistically significant aggregate effect sizes resulted for eight of the nine cognitive ability domains included in the analysis. Moderator analyses suggested that frequent ecstasy use is associated with greater cognitive impairment, cognitive impairment can occur after relatively low amounts of total lifetime cumulative use, and recovery of functioning does not occur within one year post cessation.
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Date Issued: 2005
Identifier: CFE0000701, ucf:46614
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000701

Title: DETERMINING SEX AND ANCESTRY OF THE HYOID FROM THE ROBERT J. TERRY ANATOMICAL COLLECTION.
Creator: Kindschuh, Sarah, Dupras, Tosha, University of Central Florida
Abstract / Description: One of the basic goals of the physical anthropologist is to create a biological profile, consisting of sex, ancestry, age, and stature, from the skeletal material that they are presented with. This thesis seeks to explore size and shape differences related to sex and ancestry from the hyoid bones of the Robert J. Terry Anatomical Collection in order to gauge its usefulness in the process of developing a biological profile. A series of measurements were taken from 398 hyoids and analysis was...
Show moreOne of the basic goals of the physical anthropologist is to create a biological profile, consisting of sex, ancestry, age, and stature, from the skeletal material that they are presented with. This thesis seeks to explore size and shape differences related to sex and ancestry from the hyoid bones of the Robert J. Terry Anatomical Collection in order to gauge its usefulness in the process of developing a biological profile. A series of measurements were taken from 398 hyoids and analysis was conducted using a number of statistical methods. Independent samples t-tests were used to examine size differences between sexes and ancestries, while linear regression analysis and principle component analysis were used to examine shape differences. Discriminant function analysis was employed to test the ability of the hyoids to be classified by sex or ancestry. The ultimate goal of the thesis is to provide physical anthropologists with a series of discriminant function equations that can be used to estimate the sex and ancestry of a hyoid. Five equations ranging in accuracy from 83-88% were developed to determine sex of a hyoid, while four equations ranging in accuracy from 70-89% can be used to determine ancestry. In addition, the t-tests, regression analyses, and principle component analysis have identified several variations in size and shape between sexes and ancestries. These analyses have provided further knowledge as to the morphological form of the hyoid, as well as a method that can be easily used by physical anthropologists to assess sex and ancestry.
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Date Issued: 2009
Identifier: CFE0002599, ucf:48282
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002599

Title: SEX DETERMINATION USING DISCRIMINANT FUNCTION ANALYSIS OF CARPALS FROM MAYA SITES IN BELIZE FROM PRE-CLASSIC TO SPANISH COLONIAL PERIOD.
Creator: Labbe, Michelle D, Williams, Lana, University of Central Florida
Abstract / Description: The sexing of human skeletal remains is important for identification and demographic purposes. It is made more difficult when elements such as the skull and pelvis are not recovered or are in too poor of a condition to assess. Previous studies have used carpal (wrist) bones of contemporary populations to assess the viability of these skeletal elements exhibiting sexual dimorphism, as these bones are small, compact elements that are usually recovered in good condition. This study evaluates the...
Show moreThe sexing of human skeletal remains is important for identification and demographic purposes. It is made more difficult when elements such as the skull and pelvis are not recovered or are in too poor of a condition to assess. Previous studies have used carpal (wrist) bones of contemporary populations to assess the viability of these skeletal elements exhibiting sexual dimorphism, as these bones are small, compact elements that are usually recovered in good condition. This study evaluates the use of carpal bones recovered from an ancient Maya population from Belize to determine the biological sex of individuals. The study sample is part of the Maya Archaeological Skeletal Collection (MASC), which contains individuals from the sites of Lamanai, San Pedro, Altun Ha, and Marco Gonzalez and dates from the Late Maya Pre-Classic (400 BC-AD 250) to the Spanish Colonial period (AD 1521-1821). Multiple measurements were taken on 36 capitate, 34 lunate, 34 scaphoid, 27 trapezium, 24 hamate, 22 triquetral, 22 trapezoid, and 16 pisiform bones from several individuals. Discriminant function analysis was used to determine if sexual dimorphism is measurable in this population using these elements. Previous studies used populations with known identities, assessing individuals from crypts, graveyards, or medical collections from the last few centuries. This study varies from previous studies as it utilizes archaeological remains, making this study one of the first to evaluate non-contemporary remains with unknown sex. Results of this study demonstrate that this population exhibits sexual dimorphism and discriminant function analysis can be used to distinguish between two groups. This demonstrates that carpals could be used to help determine biological sex of archaeological populations as well as a tool to help with identification in forensic cases.
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Date Issued: 2019
Identifier: CFH2000562, ucf:45645
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH2000562

Title: OPTIMAL DUAL FRAMES FOR ERASURES AND DISCRETE GABOR FRAMES.
Creator: Lopez, Jerry, Han, Deguang, University of Central Florida
Abstract / Description: Since their discovery in the early 1950's, frames have emerged as an important tool in areas such as signal processing, image processing, data compression and sampling theory, just to name a few. Our purpose of this dissertation is to investigate dual frames and the ability to find dual frames which are optimal when coping with the problem of erasures in data transmission. In addition, we study a special class of frames which exhibit algebraic structure, discrete Gabor frames. Much work...
Show moreSince their discovery in the early 1950's, frames have emerged as an important tool in areas such as signal processing, image processing, data compression and sampling theory, just to name a few. Our purpose of this dissertation is to investigate dual frames and the ability to find dual frames which are optimal when coping with the problem of erasures in data transmission. In addition, we study a special class of frames which exhibit algebraic structure, discrete Gabor frames. Much work has been done in the study of discrete Gabor frames in $\mathbb^n$, but very little is known about the $\ell^2(\mathbb)$ case or the $\ell^2(\mathbb^d)$ case. We establish some basic Gabor frame theory for $\ell^2(\mathbb)$ and then generalize to the $\ell^2(\mathbb^d)$ case.
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Date Issued: 2009
Identifier: CFE0002614, ucf:48274
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002614

Title: IMPROVING PROJECT MANAGEMENT WITH SIMULATION AND COMPLETION DISTRIBUTION FUNCTIONS.
Creator: Cates, Grant, Mollaghasemi, Mansooreh, University of Central Florida
Abstract / Description: Despite the critical importance of project completion timeliness, management practices in place today remain inadequate for addressing the persistent problem of project completion tardiness. Uncertainty has been identified as a contributing factor in late projects. This uncertainty resides in activity duration estimates, unplanned upsetting events, and the potential unavailability of critical resources. This research developed a comprehensive simulation based methodology for conducting...
Show moreDespite the critical importance of project completion timeliness, management practices in place today remain inadequate for addressing the persistent problem of project completion tardiness. Uncertainty has been identified as a contributing factor in late projects. This uncertainty resides in activity duration estimates, unplanned upsetting events, and the potential unavailability of critical resources. This research developed a comprehensive simulation based methodology for conducting quantitative project completion-time risk assessments. The methodology enables project stakeholders to visualize uncertainty or risk, i.e. the likelihood of their project completing late and the magnitude of the lateness, by providing them with a completion time distribution function of their projects. Discrete event simulation is used to determine a project's completion distribution function. The project simulation is populated with both deterministic and stochastic elements. Deterministic inputs include planned activities and resource requirements. Stochastic inputs include activity duration growth distributions, probabilities for unplanned upsetting events, and other dynamic constraints upon project activities. Stochastic inputs are based upon past data from similar projects. The time for an entity to complete the simulation network, subject to both the deterministic and stochastic factors, represents the time to complete the project. Multiple replications of the simulation are run to create the completion distribution function. The methodology was demonstrated to be effective for the on-going project to assemble the International Space Station. Approximately $500 million per month is being spent on this project, which is scheduled to complete by 2010. Project stakeholders participated in determining and managing completion distribution functions. The first result was improved project completion risk awareness. Secondly, mitigation options were analyzed to improve project completion performance and reduce total project cost.
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Date Issued: 2004
Identifier: CFE0000209, ucf:46243
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000209

Title: Exploration and development of crash modification factors and functions for single and multiple treatments.
Creator: Park, Juneyoung, Abdel-Aty, Mohamed, Radwan, Essam, Eluru, Naveen, Wang, Chung-Ching, Lee, JaeYoung, University of Central Florida
Abstract / Description: Traffic safety is a major concern for the public, and it is an important component of the roadway management strategy. In order to improve highway safety, extensive efforts have been made by researchers, transportation engineers, Federal, State, and local government officials. With these consistent efforts, both fatality and injury rates from road traffic crashes in the United States have been steadily declining over the last six years (2006~2011). However, according to the National Highway...
Show moreTraffic safety is a major concern for the public, and it is an important component of the roadway management strategy. In order to improve highway safety, extensive efforts have been made by researchers, transportation engineers, Federal, State, and local government officials. With these consistent efforts, both fatality and injury rates from road traffic crashes in the United States have been steadily declining over the last six years (2006~2011). However, according to the National Highway Traffic Safety Administration (NHTSA, 2013), 33,561 people died in motor vehicle traffic crashes in the United States in 2012, compared to 32,479 in 2011, and it is the first increase in fatalities since 2005. Moreover, in 2012, an estimated 2.36 million people were injured in motor vehicle traffic crashes, compared to 2.22 million in 2011. Due to the demand of highway safety improvements through systematic analysis of specific roadway cross-section elements and treatments, the Highway Safety Manual (HSM) (AASHTO, 2010) was developed by the Transportation Research Board (TRB) to introduce a science-based technical approach for safety analysis. One of the main parts in the HSM, Part D, contains crash modification factors (CMFs) for various treatments on roadway segments and at intersections. A CMF is a factor that can estimate potential changes in crash frequency as a result of implementing a specific treatment (or countermeasure). CMFs in Part D have been developed using high-quality observational before-after studies that account for the regression to the mean threat. Observational before-after studies are the most common methods for evaluating safety effectiveness and calculating CMFs of specific roadway treatments. Moreover, cross-sectional method has commonly been used to derive CMFs since it is easier to collect the data compared to before-after methods.Although various CMFs have been calculated and introduced in the HSM, still there are critical limitations that are required to be investigated. First, the HSM provides various CMFs for single treatments, but not CMFs for multiple treatments to roadway segments. The HSM suggests that CMFs are multiplied to estimate the combined safety effects of single treatments. However, the HSM cautions that the multiplication of the CMFs may over- or under-estimate combined effects of multiple treatments. In this dissertation, several methodologies are proposed to estimate more reliable combined safety effects in both observational before-after studies and the cross-sectional method. Averaging two best combining methods is suggested to use to account for the effects of over- or under- estimation. Moreover, it is recommended to develop adjustment factor and function (i.e. weighting factor and function) to apply to estimate more accurate safety performance in assessing safety effects of multiple treatments. The multivariate adaptive regression splines (MARS) modeling is proposed to avoid the over-estimation problem through consideration of interaction impacts between variables in this dissertation. Second, the variation of CMFs with different roadway characteristics among treated sites over time is ignored because the CMF is a fixed value that represents the overall safety effect of the treatment for all treated sites for specific time periods. Recently, few studies developed crash modification functions (CMFunctions) to overcome this limitation. However, although previous studies assessed the effect of a specific single variable such as AADT on the CMFs, there is a lack of prior studies on the variation in the safety effects of treated sites with different multiple roadway characteristics over time. In this study, adopting various multivariate linear and nonlinear modeling techniques is suggested to develop CMFunctions. Multiple linear regression modeling can be utilized to consider different multiple roadway characteristics. To reflect nonlinearity of predictors, a regression model with nonlinearizing link function needs to be developed. The Bayesian approach can also be adopted due to its strength to avoid the problem of over fitting that occurs when the number of observations is limited and the number of variables is large. Moreover, two data mining techniques (i.e. gradient boosting and MARS) are suggested to use 1) to achieve better performance of CMFunctions with consideration of variable importance, and 2) to reflect both nonlinear trend of predictors and interaction impacts between variables at the same time. Third, the nonlinearity of variables in the cross-sectional method is not discussed in the HSM. Generally, the cross-sectional method is also known as safety performance functions (SPFs) and generalized linear model (GLM) is applied to estimate SPFs. However, the estimated CMFs from GLM cannot account for the nonlinear effect of the treatment since the coefficients in the GLM are assumed to be fixed. In this dissertation, applications of using generalized nonlinear model (GNM) and MARS in the cross-sectional method are proposed. In GNMs, the nonlinear effects of independent variables to crash analysis can be captured by the development of nonlinearizing link function. Moreover, the MARS accommodate nonlinearity of independent variables and interaction effects for complex data structures. In this dissertation, the CMFs and CMFunctions are estimated for various single and combination of treatments for different roadway types (e.g. rural two-lane, rural multi-lane roadways, urban arterials, freeways, etc.) as below:1) Treatments for mainline of roadway: - adding a thru lane, conversion of 4-lane undivided roadways to 3-lane with two-way left turn lane (TWLTL)2) Treatments for roadway shoulder: - installing shoulder rumble strips, widening shoulder width, adding bike lanes, changing bike lane width, installing roadside barriers3) Treatments related to roadside features: - decrease density of driveways, decrease density of roadside poles, increase distance to roadside poles, increase distance to trees Expected contributions of this study are to 1) suggest approaches to estimate more reliable safety effects of multiple treatments, 2) propose methodologies to develop CMFunctions to assess the variation of CMFs with different characteristics among treated sites, and 3) recommend applications of using GNM and MARS to simultaneously consider the interaction impact of more than one variables and nonlinearity of predictors.Finally, potential relevant applications beyond the scope of this research but worth investigation in the future are discussed in this dissertation.
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Date Issued: 2015
Identifier: CFE0005861, ucf:50914
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005861

Title: The effects of chronic sleep deprivation on sustained attention: A study of brain dynamic functional connectivity.
Creator: He, Yiling, Karwowski, Waldemar, Xanthopoulos, Petros, Hancock, Peter, Mikusinski, Piotr, University of Central Florida
Abstract / Description: It is estimated that about 35-40% of adults in the U.S. suffer from insufficient sleep. Chronic sleep deprivation has become a prevalent phenomenon because of contemporary lifestyle and work-related factors. Sleep deprivation can reduce the capabilities and efficiency of attentional performance by impairing perception, increasing effort to maintain concentration, as well as introducing vision disturbance. Thus, it is important to understand the neural mechanisms behind how chronic sleep...
Show moreIt is estimated that about 35-40% of adults in the U.S. suffer from insufficient sleep. Chronic sleep deprivation has become a prevalent phenomenon because of contemporary lifestyle and work-related factors. Sleep deprivation can reduce the capabilities and efficiency of attentional performance by impairing perception, increasing effort to maintain concentration, as well as introducing vision disturbance. Thus, it is important to understand the neural mechanisms behind how chronic sleep deprivation impairs sustained attention.In recent years, more attention has been paid to the study of the integration between anatomically distributed and functionally connected brain regions. Functional connectivity has been widely used to characterize brain functional integration, which measures the statistical dependency between neurophysiological events of the human brain. Further, evidence from recent studies has shown the non-stationary nature of brain functional connectivity, which may reveal more information about the human brain. Thus, the objective of this thesis is to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic functional connectivity.A modified spatial cueing paradigm was used to assess human sustained attention in rested wakefulness and chronic sleep deprivation conditions. Partial least squares approach was applied to distinguish brain functional connectivity for the experimental conditions. With the integration of a sliding-window approach, dynamic patterns of brain functional connectivity were identified in two experimental conditions. The brain was modeled as a series of dynamic functional networks in each experimental condition. Graph theoretic analysis was performed to investigate the dynamic properties of brain functional networks, using network measures of clustering coefficient and characteristics path length.In the chronic sleep deprivation condition, a compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed. Specifically, a highly clustered organization of brain functional networks was illustrated with a large clustering coefficient. This organization suggested that brain utilizes more connections to maintain attention in the chronic sleep deprivation condition. A smaller impact of clustering coefficient variation on characteristics path lengths indicated an ineffective adaptability of brain functional networks in the chronic sleep deprivation condition. In the rested wakefulness condition, brain functional networks showed the small-world topology in general, with the average small-world topology index larger than one. Small-world topology was identified as an optimal network structure with the balance between local information processing and global integration. Given the fluctuating values of the index over time, small-world brain networks were observed in most cases, indicating an effective adaptability of the human brain to maintain the dominance of small-world networks in the rested wakefulness condition. On the contrary, given that the average small-world topology index was smaller than one, brain functional networks generally exhibited random network structure. From the perspective of dynamic functional networks, even though there were few cases showing small-world brain networks, brain functional networks failed to maintain the dominance of small-world topology in the chronic sleep deprivation condition.In conclusion, to the best of our knowledge this thesis was the first to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic brain functional connectivity. A compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed in the chronic sleep deprivation condition. Furthermore, chronic sleep deprivation impaired sustained attention by reducing the effectiveness of brain functional networks' adaptability, resulting in the disrupted dominance of small-world brain networks.
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Date Issued: 2015
Identifier: CFE0006036, ucf:50990
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006036

Title: Do programs designed to train working memory, other executive functions, and attention benefit children with ADHD? A meta-analytic review of cognitive, academic, and behavioral outcomes.
Creator: Orban, Sarah, Rapport, Mark, Beidel, Deborah, Cassisi, Jeffrey, University of Central Florida
Abstract / Description: Children with ADHD are characterized frequently as possessing underdeveloped executive functions and sustained attentional abilities, and recent commercial claims suggest that computer-based cognitive training can remediate these impairments and provide significant and lasting improvement in their attention, impulse control, social functioning, academic performance, and complex reasoning skills. The present review critically evaluates these claims through meta-analysis of 25 studies of...
Show moreChildren with ADHD are characterized frequently as possessing underdeveloped executive functions and sustained attentional abilities, and recent commercial claims suggest that computer-based cognitive training can remediate these impairments and provide significant and lasting improvement in their attention, impulse control, social functioning, academic performance, and complex reasoning skills. The present review critically evaluates these claims through meta-analysis of 25 studies of facilitative intervention training (i.e., cognitive training) for children with ADHD. Random effects models corrected for publication bias and sampling error revealed that studies training short-term memory alone resulted in moderate magnitude improvements in short-term memory (d= 0.63), whereas training attention did not significantly improve attention and training mixed executive functions did not significantly improve the targeted executive functions (both nonsignificant: 95% confidence intervals include 0.0). Far transfer effects of cognitive training on academic functioning, blinded ratings of behavior (both nonsignificant), and cognitive tests (d= 0.14) were nonsignificant or negligible. Unblinded raters (d= 0.48) reported significantly larger benefits relative to blinded raters and objective tests (both p (<) .05), indicating the likelihood of Hawthorne effects. Critical examination of training targets revealed incongruence with empirical evidence regarding the specific executive functions that are (a) most impaired in ADHD, and (b) functionally related to the behavioral and academic outcomes these training programs are intended to ameliorate. Collectively, meta-analytic results indicate that claims regarding the academic, behavioral, and cognitive benefits associated with extant cognitive training programs are unsupported in ADHD. The methodological limitations of the current evidence base, however, leaves open the possibility that cognitive training techniques designed to improve empirically documented executive function deficits may benefit children with ADHD.
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Date Issued: 2013
Identifier: CFE0005040, ucf:49962
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005040

Title: The Relationship Between Married Partners' Individual and Relationship Distress: An Actor-Partner Analysis of Low-income, Racially and Ethnically Diverse Couples in Relationship Education.
Creator: Munyon, Matthew, Young, Mark, Hagedorn, William, Daire, Andrew, Sivo, Stephen, University of Central Florida
Abstract / Description: Couples experiencing relationship distress often require professional help, such as counseling and couple and relationship education (CRE). Research recently discovered that among couples in counseling, a circular relationship exists between individual and relationship distress(-)stress begets stress. Until this study, a similar examination had not been conducted among couples selecting CRE. This study examined the relationship between individual and relationship distress among married...
Show moreCouples experiencing relationship distress often require professional help, such as counseling and couple and relationship education (CRE). Research recently discovered that among couples in counseling, a circular relationship exists between individual and relationship distress(-)stress begets stress. Until this study, a similar examination had not been conducted among couples selecting CRE. This study examined the relationship between individual and relationship distress among married couples that had children, were from predominantly low-income and racially and ethnically diverse backgrounds, and selected CRE. A correlational research design was employed and framed in the social interdependence theory. The actor-partner interdependence model was conducted within a three-level hierarchical model. The results confirmed that a circular relationship exists between individual and relationship distress(-)distress begets distress. Within the circular model of individual and relational functioning, personal individual distress predicted partner individual distress as well as personal and partner relationship distress, and personal relationship distress predicted personal individual distress and partner relationship distress. The extent to which distress begot distress was stronger among women, those with low income, and those who were unemployed. The results also revealed a continuum of individual and relational functioning. Dyad members interact along a continuum from intrapersonal individual functioning to interpersonal relational functioning. The continua meet at the nexus of negotiation or the heart of interpersonal interaction, where dyad members communicate and make decisions, among other actions. Implications related to the findings of this study as well as inspirations for future research are discussed.
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Date Issued: 2012
Identifier: CFE0004284, ucf:49529
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004284

Title: Development of Traffic Safety Zones and Integrating Macroscopic and Microscopic Safety Data Analytics for Novel Hot Zone Identification.
Creator: Lee, JaeYoung, Abdel-Aty, Mohamed, Radwan, Ahmed, Nam, Boo Hyun, Kuo, Pei-Fen, Choi, Keechoo, University of Central Florida
Abstract / Description: Traffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic...
Show moreTraffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic crashes are still one of the leading causes of deaths, according to the Centers for Disease Control and Prevention (CDC). In recent years, efforts to incorporate traffic safety into transportation planning has been made, which is termed as transportation safety planning (TSP). The Safe, Affordable, Flexible Efficient, Transportation Equity Act (-) A Legacy for Users (SAFETEA-LU), which is compliant with the United States Code, compels the United States Department of Transportation to consider traffic safety in the long-term transportation planning process. Although considerable macro-level studies have been conducted to facilitate the implementation of TSP, still there are critical limitations in macroscopic safety studies are required to be investigated and remedied. First, TAZ (Traffic Analysis Zone), which is most widely used in travel demand forecasting, has crucial shortcomings for macro-level safety modeling. Moreover, macro-level safety models have accuracy problem. The low prediction power of the model may be caused by crashes that occur near the boundaries of zones, high-level aggregation, and neglecting spatial autocorrelation.In this dissertation, several methodologies are proposed to alleviate these limitations in the macro-level safety research. TSAZ (Traffic Safety Analysis Zone) is developed as a new zonal system for the macroscopic safety analysis and nested structured modeling method is suggested to improve the model performance. Also, a multivariate statistical modeling method for multiple crash types is proposed in this dissertation. Besides, a novel screening methodology for integrating two levels is suggested. The integrated screening method is suggested to overcome shortcomings of zonal-level screening, since the zonal-level screening cannot take specific sites with high risks into consideration. It is expected that the integrated screening approach can provide a comprehensive perspective by balancing two aspects: macroscopic and microscopic approaches.
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Date Issued: 2014
Identifier: CFE0005195, ucf:50653
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005195

Title: THE EFFECTS OF THE ATTAINMENT OF FUNCTIONAL ASSESSMENT SKILLS BY PRESCHOOL TEACHERS AND THEIR ASSISTANTS ON STUDENTS' CLASSROOM BEHAVIOR.
Creator: Wagner, Karen, Cross, Lee, University of Central Florida
Abstract / Description: The purpose of this study was to examine the effects of teaching functional assessment skills to three Prekindergarten teachers and their teaching assistants. The effects were measured by examining the behavior of the teachers and assistants, as well as the behaviors of the students; before, during and after the delivery of three, two-hour functional assessment classes. The teaching staff videotaped themselves and their students during a regular class time, predetermined by the researcher and...
Show moreThe purpose of this study was to examine the effects of teaching functional assessment skills to three Prekindergarten teachers and their teaching assistants. The effects were measured by examining the behavior of the teachers and assistants, as well as the behaviors of the students; before, during and after the delivery of three, two-hour functional assessment classes. The teaching staff videotaped themselves and their students during a regular class time, predetermined by the researcher and each teacher prior to the onset of baseline data collection, over an approximate nine-week period. The video was taken in twelve-minute segments every day. Later, the video was coded for specific behaviors. Although there were gains in appropriate intervention strategies from teachers and assistants during the intervention phase, the interventions generally peaked a week or two after the classes ended and gradually declined. Teacher skills were retained however, as most ratios of appropriate interventions maintained at higher rates than baseline. Relationships between student behavior and correct teacher interventions were established and maintained. The intervention resulted in changes in staff behavior, but results did not sustain at high levels over time. The realization that escape maintained some student behavior, and teaching skills to "test" for function, were likely the most important concepts for many of the participants. Further research should include adding a behavior coach to assist in shaping the teaching staffs' emerging skills and to provide a sounding board when developing specific student interventions.
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Date Issued: 2008
Identifier: CFE0002088, ucf:47575
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002088

Title: Secondary and Postsecondary Calculus Instructors' Expectations of Student Knowledge of Functions: A Multiple-case Study.
Creator: Avila, Cheryl, Ortiz, Enrique, Dixon, Juli, Hynes, Michael, Andreasen, Janet, Mohapatra, Ram, University of Central Florida
Abstract / Description: This multiple-case study examines the explicit and implicit assumptions of six veteran calculus instructors from three types of educational institutions, comparing and contrasting their views on the iteration of conceptual understanding and procedural fluency of pre-calculus topics. There were three components to the research data recording process. The first component was a written survey, the second component was a (")think-aloud(") activity of the instructors analyzing the results of a...
Show moreThis multiple-case study examines the explicit and implicit assumptions of six veteran calculus instructors from three types of educational institutions, comparing and contrasting their views on the iteration of conceptual understanding and procedural fluency of pre-calculus topics. There were three components to the research data recording process. The first component was a written survey, the second component was a (")think-aloud(") activity of the instructors analyzing the results of a function diagnostic instrument administered to a calculus class, and for the third component, the instructors responded to two quotations. As a result of this activity, themes were found between and among instructors at the three types of educational institutions related to their expectations of their incoming students' prior knowledge of pre-calculus topics related to functions. Differences between instructors of the three types of educational institutions included two identifiable areas: (1) the teachers' expectations of their incoming students and (2) the methods for planning instruction. In spite of these differences, the veteran instructors were in agreement with other studies' findings that an iterative approach to conceptual understanding and procedural fluency are necessary for student understanding of pre-calculus concepts.
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Date Issued: 2013
Identifier: CFE0004809, ucf:49758
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004809

Title: ENTERPRISE BUSINESS ALIGNMENT USING QUALITY FUNCTION DEPLOYMENT, MULTIVARIATE DATA ANALYSIS AND BUSINESS MODELING TOOLS.
Creator: GAMMOH, DIALA, Elshennawy, Ahmad, University of Central Florida
Abstract / Description: This dissertation proposes two novel ideas to enhance the business strategy alignment to customer needs. The proposed business alignment clock is a new illustration to the relationships between customer requirements, business strategies, capabilities and processes. To line up the clock and reach the needed alignment for the enterprise, a proposed clock mechanism is introduced. The mechanism integrates the Enterprise Business Architecture (EBA) with the House of Quality (HoQ). The relationship...
Show moreThis dissertation proposes two novel ideas to enhance the business strategy alignment to customer needs. The proposed business alignment clock is a new illustration to the relationships between customer requirements, business strategies, capabilities and processes. To line up the clock and reach the needed alignment for the enterprise, a proposed clock mechanism is introduced. The mechanism integrates the Enterprise Business Architecture (EBA) with the House of Quality (HoQ). The relationship matrix inside the body of the house is defined using multivariate data analysis techniques to accurately measure the strength of the relationships rather than defining them subjectively. A statistical tool, multivariate data analysis, can be used to overcome the ambiguity in quantifying the relationships in the house of quality matrix. The framework is proposed in the basic conceptual model context of the EBA showing different levels of the enterprise architecture; the goals, the capabilities and the value stream architecture components. In the proposed framework, the goals and the capabilities are inputs to two houses of quality, in which the alignment between customer needs and business goals, and the alignment between business goals and capabilities are checked in the first house and the second house, respectively. The alignment between the business capabilities and the architecture components (workflows, events and environment) is checked in a third HoQ using the performance indicators of the value stream architecture components, which may result in infrastructure expansion, software development or process improvement to reach the needed alignment by the enterprise. The value of the model was demonstrated using the Accreditation Board of Engineering and Technology (ABET) process at the Industrial Engineering and Management Systems department at the University of Central Florida. The assessment of ABET criteria involves an evaluation of the extent to which the program outcomes are being achieved and results in decisions and actions to improve the Industrial Engineering program at the University of Central Florida. The proposed framework increases the accuracy of measuring the extent to which the program learning outcomes have been achieved at the department. The process of continuous alignment between the educational objectives and customer needs becomes more vital by the rapid change of customer requirements that are obtained from both internal and external constituents (students, faculty, alumni, and employers in the first place).
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Date Issued: 2010
Identifier: CFE0003298, ucf:48506
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003298

Title: Automatic Detection of Brain Functional Disorder Using Imaging Data.
Creator: Dey, Soumyabrata, Shah, Mubarak, Jha, Sumit, Hu, Haiyan, Weeks, Arthur, Rao, Ravishankar, University of Central Florida
Abstract / Description: Recently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity,...
Show moreRecently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity, which are all subjective.Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool to understand the functioning of the brain such as identifying the brain regions responsible for different cognitive tasks or analyzing the statistical differences of the brain functioning between the diseased and control subjects. ADHD is also being studied using the fMRI data. In this dissertation we aim to solve the problem of automatic diagnosis of the ADHD subjects using their resting state fMRI (rs-fMRI) data.As a core step of our approach, we model the functions of a brain as a connectivity network, which is expected to capture the information about how synchronous different brain regions are in terms of their functional activities. The network is constructed by representing different brain regions as the nodes where any two nodes of the network are connected by an edge if the correlation of the activity patterns of the two nodes is higher than some threshold. The brain regions, represented as the nodes of the network, can be selected at different granularities e.g. single voxels or cluster of functionally homogeneous voxels. The topological differences of the constructed networks of the ADHD and control group of subjects are then exploited in the classification approach.We have developed a simple method employing the Bag-of-Words (BoW) framework for the classification of the ADHD subjects. We represent each node in the network by a 4-D feature vector: node degree and 3-D location. The 4-D vectors of all the network nodes of the training data are then grouped in a number of clusters using K-means; where each such cluster is termed as a word. Finally, each subject is represented by a histogram (bag) of such words. The Support Vector Machine (SVM) classifier is used for the detection of the ADHD subjects using their histogram representation. The method is able to achieve 64% classification accuracy.The above simple approach has several shortcomings. First, there is a loss of spatial information while constructing the histogram because it only counts the occurrences of words ignoring the spatial positions. Second, features from the whole brain are used for classification, but some of the brain regions may not contain any useful information and may only increase the feature dimensions and noise of the system. Third, in our study we used only one network feature, the degree of a node which measures the connectivity of the node, while other complex network features may be useful for solving the proposed problem.In order to address the above shortcomings, we hypothesize that only a subset of the nodes of the network possesses important information for the classification of the ADHD subjects. To identify the important nodes of the network we have developed a novel algorithm. The algorithm generates different random subset of nodes each time extracting the features from a subset to compute the feature vector and perform classification. The subsets are then ranked based on the classification accuracy and the occurrences of each node in the top ranked subsets are measured. Our algorithm selects the highly occurring nodes for the final classification. Furthermore, along with the node degree, we employ three more node features: network cycles, the varying distance degree and the edge weight sum. We concatenate the features of the selected nodes in a fixed order to preserve the relative spatial information. Experimental validation suggests that the use of the features from the nodes selected using our algorithm indeed help to improve the classification accuracy. Also, our finding is in concordance with the existing literature as the brain regions identified by our algorithms are independently found by many other studies on the ADHD. We achieved a classification accuracy of 69.59% using this approach. However, since this method represents each voxel as a node of the network which makes the number of nodes of the network several thousands. As a result, the network construction step becomes computationally very expensive. Another limitation of the approach is that the network features, which are computed for each node of the network, captures only the local structures while ignore the global structure of the network.Next, in order to capture the global structure of the networks, we use the Multi-Dimensional Scaling (MDS) technique to project all the subjects from an unknown network-space to a low dimensional space based on their inter-network distance measures. For the purpose of computing distance between two networks, we represent each node by a set of attributes such as the node degree, the average power, the physical location, the neighbor node degrees, and the average powers of the neighbor nodes. The nodes of the two networks are then mapped in such a way that for all pair of nodes, the sum of the attribute distances, which is the inter-network distance, is minimized. To reduce the network computation cost, we enforce that the maximum relevant information is preserved with minimum redundancy. To achieve this, the nodes of the network are constructed with clusters of highly active voxels while the activity levels of the voxels are measured based on the average power of their corresponding fMRI time-series. Our method shows promise as we achieve impressive classification accuracies (73.55%) on the ADHD-200 data set. Our results also reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.So far, we have only used the fMRI data for solving the ADHD diagnosis problem. Finally, we investigated the answers of the following questions. Do the structural brain images contain useful information related to the ADHD diagnosis problem? Can the classification accuracy of the automatic diagnosis system be improved combining the information of the structural and functional brain data? Towards that end, we developed a new method to combine the information of structural and functional brain images in a late fusion framework. For structural data we input the gray matter (GM) brain images to a Convolutional Neural Network (CNN). The output of the CNN is a feature vector per subject which is used to train the SVM classifier. For the functional data we compute the average power of each voxel based on its fMRI time series. The average power of the fMRI time series of a voxel measures the activity level of the voxel. We found significant differences in the voxel power distribution patterns of the ADHD and control groups of subjects. The Local binary pattern (LBP) texture feature is used on the voxel power map to capture these differences. We achieved 74.23% accuracy using GM features, 77.30% using LBP features and 79.14% using combined information.In summary this dissertation demonstrated that the structural and functional brain imaging data are useful for the automatic detection of the ADHD subjects as we achieve impressive classification accuracies on the ADHD-200 data set. Our study also helps to identify the brain regions which are useful for ADHD subject classification. These findings can help in understanding the pathophysiology of the problem. Finally, we expect that our approaches will contribute towards the development of a biological measure for the diagnosis of the ADHD subjects.
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Date Issued: 2014
Identifier: CFE0005786, ucf:50060
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005786

Title: APPLICATION OF THE EMPIRICAL LIKELIHOOD METHOD IN PROPORTIONAL HAZARDS MODEL.
Creator: HE, BIN, Ren, Jian-Jian, University of Central Florida
Abstract / Description: In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring such as double censoring and partly interval-censoring in survival data makes model assessment...
Show moreIn survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring such as double censoring and partly interval-censoring in survival data makes model assessment difficult, and the existing tests for goodness-of-fit do not have direct extension to these complicated types of censored data. In this work, we use empirical likelihood (Owen, 1988) approach to construct goodness-of-fit test and provide estimates for the Cox model with various types of censored data.Specifically, the problems under consideration are the two-sample Cox model and stratified Cox model with right censored data, doubly censored data and partly interval-censored data. Related computational issues are discussed, and some simulation results are presented. The procedures developed in the work are applied to several real data sets with some discussion.
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Date Issued: 2006
Identifier: CFE0001099, ucf:46780
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001099

discriminant function analysis (2)	+ -
meta-analysis (2)	+ -
ABET (1)	+ -
Attention Deficit Hyperactive Disorder (1)	+ -
Attributed Graph (1)	+ -

Bag of Words (1)	+ -
Bayesian inference (1)	+ -
Before-After Analysis (1)	+ -
Belize (1)	+ -
Bootstrap (1)	+ -
Chronic sleep deprivation (1)	+ -
Conceptual understanding (1)	+ -
Convolutional Neural Network (1)	+ -
Cox model (1)	+ -
Crash Modification Factors (1)	+ -
Crash Modification Functions (1)	+ -
Cross-Sectional Analysis (1)	+ -
Diagnostic assessment (1)	+ -
Discrete Gabor Frames (1)	+ -
Dual Frames (1)	+ -
Dynamic functional connectivity (1)	+ -
EBA (1)	+ -
Error analysis (1)	+ -
Frames (1)	+ -
Functional Analysis (1)	+ -
GIS (1)	+ -
Gabor Analysis (1)	+ -
Geographic Information System (1)	+ -
Graph theoretic analysis (1)	+ -
HOQ (1)	+ -

ETD Collection (14)	+ -
Honors Collection (1)	+ -

University of Central Florida (15)	+ -
Abdel-Aty, Mohamed (2)	+ -
Lee, JaeYoung (2)	+ -
Andreasen, Janet (1)	+ -
Avila, Cheryl (1)	+ -

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Cassisi, Jeffrey (1)	+ -
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Choi, Keechoo (1)	+ -
Cross, Lee (1)	+ -
Daire, Andrew (1)	+ -
Dey, Soumyabrata (1)	+ -
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Dunn, Michael (1)	+ -
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Kindschuh, Sarah (1)	+ -
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