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REAL-TIME TREE SIMULATION USING VERLET INTEGRATION
Title
REAL-TIME TREE SIMULATION USING VERLET INTEGRATION.
Creator
Manavi, Bobak, Kincaid, J. Peter, University of Central Florida
Abstract / Description
One of the most important challenges in real-time simulation of large trees and vegetation is the vast number of calculations required to simulate the interactions between all the branches in the tree when external forces are applied to it. This paper will propose the use of algorithms employed by applications like cloth and soft body simulations, where objects can be represented by a finite system of particles connected via spring-like constraints, for the structural representation and...
Show moreOne of the most important challenges in real-time simulation of large trees and vegetation is the vast number of calculations required to simulate the interactions between all the branches in the tree when external forces are applied to it. This paper will propose the use of algorithms employed by applications like cloth and soft body simulations, where objects can be represented by a finite system of particles connected via spring-like constraints, for the structural representation and manipulation of trees in real-time. We will then derive and show the use of Verlet integration and the constraint configuration used for simulating trees while constructing the necessary data structures that encapsulate the procedural creation of these objects. Furthermore, we will utilize this system to simulate branch breakage due to accumulated external and internal pressure.
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Date Issued
2007
Identifier
CFE0001802, ucf:47381
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001802
Factors Influencing Hypoglycemia Care Utilization and Outcomes Among Adult Diabetic Patients Admitted to Hospitals: A Predictive Model
Title
Factors Influencing Hypoglycemia Care Utilization and Outcomes Among Adult Diabetic Patients Admitted to Hospitals: A Predictive Model.
Creator
Kattan, Waleed, Wan, Thomas, Ramirez, Bernardo, Gurupur, Varadraj, Stevenson, Robyne, Pratley, Richard, University of Central Florida
Abstract / Description
Diabetes Miletus (DM) is one of the major health problems in the United States. Despite all efforts made to combat this disease, its incidence and prevalence are steadily increasing. One of the common and serious side effects of treatment among people with diabetes is hypoglycemia (HG), where the level of blood glucose falls below the optimum level. Episodes of HG vary in their severity. Nevertheless, many require medical assistance and are usually associated with higher utilization of...
Show moreDiabetes Miletus (DM) is one of the major health problems in the United States. Despite all efforts made to combat this disease, its incidence and prevalence are steadily increasing. One of the common and serious side effects of treatment among people with diabetes is hypoglycemia (HG), where the level of blood glucose falls below the optimum level. Episodes of HG vary in their severity. Nevertheless, many require medical assistance and are usually associated with higher utilization of healthcare resources such as frequent emergency department visits and physician visits. Additionally, patients who experience HG frequently have poor outcomes such as higher rates for morbidities and mortality.Although many studies have been conducted to explore the risk factors associated with HG as well as others that looked into the level of healthcare utilization and outcomes among patients with HG, most of these studies failed to establish a theoretical foundation and integrate a comprehensive list of personal risk factors. Therefore, this study aimed to employ Andersen's health Behavior Model of health care utilization (BM) as a framework to examine the problems of HG. This holistic approach facilitates enumerating predictors and examining differential risks of the predisposing (P), enabling (E) and need-for-care (N) factors influencing HG and their effects on utilization (U) and outcomes (O). The population derived from the national inpatient sample of the Healthcare Cost and Utilization Project (HCUP) database and included all non-pregnant adult diabetic patients admitted to hospitals' Emergency Departments (EDs) with a diagnosis of HG from 2012-2014. Based on the BM framework, different factors influencing HG utilization and outcome were grouped under the P, E, or N component. Utilization was measured by patients' length of stay (LoS) in the hospital and the total charges incurred for the stay. Outcome was assessed based on the severity ranging from mortality (the worst), severe complications, mild complications, to no complications (the best). Structural Equation Modeling (SEM) followed by Decision Tree Regression (DTREG) were performed. SEM helped in testing multiple hypotheses developed in the study as well as exploring the direct and indirect impact of different risk factors on utilization and outcome. The results of the analysis show that N is the most influential component of predictors of U and O. This is parallel to what was repeatedly found in different studies that employed the BM. Regarding the other two components, P was found to have some effect on O, while E influences the total charge. Interaction effects of predictors were noted between some components, which indicate the indirect effect of these components on U and O. Subsequently, DTREG analysis was conducted to further explore the probability of the different predictor variables on LoS, total charge, and outcome. Results of this study revealed that the presence of renal disease and DM complications among HG patients play a key role in predicting U and O. Furthermore, age, socio-economic status (SES), and the geographical location of the patients were also found to be vital factors in determining the variability in U and O among HG patients.In conclusion, findings of this study lend support to the use of the BM approach to health services use and outcomes and provide some practical applications for healthcare providers in terms of using the predictive model for targeting patient subgroups (HG patients) for interventions among diabetic patients. Moreover, policy implications, particularly related to the Central Florida area, for decision makers regarding how to approach the growing problem of DM can be drawn from the study results.
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Date Issued
2017
Identifier
CFE0006611, ucf:51304
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006611
ANALYSIS OF TYPE AND SEVERITY OF TRAFFIC CRASHES AT SIGNALIZED INTERSECTIONS USING TREE-BASED REGRESSION AND ORDERED PROBIT MODELS
Title
ANALYSIS OF TYPE AND SEVERITY OF TRAFFIC CRASHES AT SIGNALIZED INTERSECTIONS USING TREE-BASED REGRESSION AND ORDERED PROBIT MODELS.
Creator
Keller, Joanne Marie, Abdel-Aty, Mohamed, University of Central Florida
Abstract / Description
Many studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes...
Show moreMany studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes, by type of collision as well as injury level, at signalized intersections. The first objective was to determine if there is a difference between important variables for models based on individual crash types or severity levels and aggregated models. The second objective of this research was to investigate the quality and completeness of the crash data and the effect that incomplete data has on the final results. A detailed and thorough data collection effort was necessary for this research to ensure the quality and completeness of this data. Multiple agencies were contacted and databases were crosschecked (i.e. state and local jurisdictions/agencies). Information (including geometry, configuration and traffic characteristics) was collected for a total of 832 intersections and over 33,500 crashes from Brevard, Hillsborough and Seminole Counties and the City of Orlando. Due to the abundance of data collected, a portion was used as a validation set for the tree-based regression.Hierarchical tree-based regression (HTBR) and ordered probit models were used in the analyses. HTBR was used to create models for the expected number of crashes for collision type as well as injury level. Ordered probit models were only used to predict crash severity levels due to the ordinal nature of this dependent variable. Finally, both types of models were used to predict the expected number of crashes.More specifically, tree-based regression was used to consider the difference in the relative importance of each variable between the different types of collisions. First, regressions were only based on crashes available from state agencies to make the results more comparable to other studies. The main finding was that the models created for angle and left turn crashes change the most compared to the model created from the total number of crashes reported on long forms (restricted data usually available at state agencies). This result shows that aggregating the different crash types by only estimating models based on the total number of crashes will not predict the number of expected crashes as accurately as models based on each type of crash separately. Then, complete datasets (full dataset based on crash reports collected from multiple sources) were used to calibrate the models. There was consistently a difference between models based on the restricted and complete datasets. The results in this section show that it is important to include minor crashes (usually reported on short forms and ignored) in the dataset when modeling the number of angle or head-on crashes and less important to include minor crashes when modeling rear-end, right turn or sideswipe crashes. This research presents in detail the significant geometric and traffic characteristics that affect each type of collision.Ordered probit models were used to estimate crash injury severity levels for three different types of models; the first one based on collision type, the second one based on intersection characteristics and the last one based on a significant combination of factors in both models. Both the restricted and complete datasets were used to create the first two model types and the output was compared. It was determined that the models based on the complete dataset were more accurate. However, when compared to the tree-based regression results, the ordered probit model did not predict as well for the restricted dataset based on intersection characteristics. The final order
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Date Issued
2004
Identifier
CFE0000074, ucf:52857
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000074
Modeling Mass Care Resource Provision Post Hurricane
Title
Modeling Mass Care Resource Provision Post Hurricane.
Creator
Muhs, Tammy, Kincaid, John, Rollins, David, Dorman, Teresa, Taylor, Gregory, University of Central Florida
Abstract / Description
Determining the amount of resources needed, specifically food and water, following a hurricane is not a straightforward task. Through this research effort, an estimating tool was developed that takes into account key demographic and evacuation behavioral effects, as well as hurricane storm specifics to estimate the number of meals required for the first fourteen days following a hurricane making landfall in the State of Florida. The Excel based estimating tool was created using data collected...
Show moreDetermining the amount of resources needed, specifically food and water, following a hurricane is not a straightforward task. Through this research effort, an estimating tool was developed that takes into account key demographic and evacuation behavioral effects, as well as hurricane storm specifics to estimate the number of meals required for the first fourteen days following a hurricane making landfall in the State of Florida. The Excel based estimating tool was created using data collected from four hurricanes making landfall in Florida during 2004-2005. The underlying model used in the tool is a Regression Decision Tree with predictor variables including direct impact, poverty level, and hurricane impact score. The hurricane impact score is a hurricane classification system resulting from this research that includes hurricane category, intensity, wind field size, and landfall location. The direct path of a hurricane, a higher than average proportion of residents below the poverty level, and the hurricane impact score were all found to have an effect on the number of meals required during the first fourteen days following a hurricane making landfall in the State of Florida.
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Date Issued
2011
Identifier
CFE0004143, ucf:49053
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004143