Youth offending is an increasingly major problem in many countries and cultures. Several theories imply that a subset of young people display delinquent behaviour at a young age and go on to have an extensive and serious criminal career. Recently, there has been interest in the literature in identifying these young people early on and carrying out interventions in order to deter them from a criminal career. Many studies have examined the development and usefulness of actuarial measures of risk of future violence or recidivism in adult offenders. However, the same attention has not been paid to the youth offender population. The present study gathered data from the population (N = 4307) of all young persons in New Zealand whose antisocial behaviour resulted in a Youth Justice intake from the Department of Child, Youth, and Family (CYF) in 2002. Information was obtained about this population from the CYF database, CYRAS, and from the Police National Intelligence Application database for a stratified random sample (N = 500). Three models were developed using Hierarchical Cox regression to predict recidivism, and they each used a different definition of recidivism. The performance of the models was assessed using ROC analysis and they were found to predict recidivism with a moderately good level of accuracy. A validation sample (N = 500), different from the sample on which the models were developed, was used to further assess the performance of the models by showing that they were able to generalize to a new data set and continue to perform at an adequate level. An actuarial model, like the one developed in the present study, could be used to help make decisions about which young people within the Youth Justice System require intervention in order to reduce the likelihood of subsequent reoffending.

Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

Youth offending is an increasingly major problem in many countries and cultures. Several theories imply that a subset of young people display delinquent behaviour at a young age and go on to have an extensive and serious criminal career. Recently, there has been interest in the literature in identifying these young people early on and carrying out interventions in order to deter them from a criminal career. Many studies have examined the development and usefulness of actuarial measures of risk of future violence or recidivism in adult offenders. However, the same attention has not been paid to the youth offender population. The present study gathered data from the population (N = 4307) of all young persons in New Zealand whose antisocial behaviour resulted in a Youth Justice intake from the Department of Child, Youth, and Family (CYF) in 2002. Information was obtained about this population from the CYF database, CYRAS, and from the Police National Intelligence Application database for a stratified random sample (N = 500). Three models were developed using Hierarchical Cox regression to predict recidivism, and they each used a different definition of recidivism. The performance of the models was assessed using ROC analysis and they were found to predict recidivism with a moderately good level of accuracy. A validation sample (N = 500), different from the sample on which the models were developed, was used to further assess the performance of the models by showing that they were able to generalize to a new data set and continue to perform at an adequate level. An actuarial model, like the one developed in the present study, could be used to help make decisions about which young people within the Youth Justice System require intervention in order to reduce the likelihood of subsequent reoffending.

Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

Development of temperature controlled packaging (TCP) systems involves a significant lead-time and cost as a result of the large number of tests that are carried out to understand system performance in different internal and external conditions. This MPhil project aims at solving this problem through the development of a transient spreadsheet based model using lumped system approach that predicts the performance of packaging systems under a wide range of internal configurations and dynamically changing environmental thermal conditions. Experimental tests are conducted with the aim of validating the predictive model. Testing includes monitoring system temperature in a wide range of internal configurations and external thermal environments. A good comparison is seen between experimental and model predicted results; increasing the mass of the chilled phase change material (PCM) in a system reduces the damping in product performance thereby reducing the product fluctuations or amplitude of the product performance curve. Results show that the thermal mathematical model predicts duration to failure within an accuracy of +/- 15% for all conditions considered.

Creatinine is a metabolic waste product, removed from the blood by the kidneys, and excreted in the urine. The measurement of creatinine is used in the assessment and monitoring of many medical conditions as well as in the determination or adjustment of absorbed dosage of pesticides. Earlier models to predict 24-hour urinary creatinine used ordinary least squares regression and assumed that the subjects' observations were uncorrelated. However, many of these studies had repeated creatinine measurements for each of their subjects. Repeated measures on the same subject frequently are correlated. Using data from the NIOSH-CDC "Pesticide Dose Monitoring in Turf Applicators" study, this thesis project built a model to predict 24-hour urinary creatinine using the Mixed Model methodology. A covariance structure, that permitted multiple observations for any one individual to be correlated, was identified and utilized. The predictive capabilities of this model were then compared to the earlier models investigated.

<p>Dental health is an important aspect of one’s health and well-being (American Dental Association, 2015). This research analyzes six factors (income level, weather, sales tax, population density, dentist density, and water quality) to examine their relationship with oral health conditions based on 2015 state-level data in the United States. The results show that these factors indeed affect oral health conditions. The analysis results clearly show that income level, dentist density, temperature, and water quality have significant positive effects while temperature has a negative effect effects on oral health at state level.</p><p><br></p> <p>Furthermore, this study uses a multilinear regression algorithm stepwise method to build three predictive models on different income groups, using the above factors to predict oral health. These models can be a helpful reference for further research in related areas, including but not limited to insurance companies, research institutes that work on improving public oral health, and government agencies.</p>

Phytoremediation is a relatively new remediation technology that may be useful in removing organic and inorganic pollutants from soils. Much research has focused on this type of remediation in the past few years due to its potential as an efficient and cost effective technology.<p>The purpose of this project was to extensively monitor phytoremediation of diesel-contaminated field soils in the laboratory under simulated field conditions. The main objectives were: to examine petroleum hydrocarbon (PHC) transfer and degradation processes involved in phytoremediation of contaminated field soils; to compare phytoremediation of contaminated field soils with intrinsic bioremediation; and, to develop a rationally-based model that could be used as a starting point for a quantitative prediction of the rate of PHC removal.<p>To realize these objectives a series of laboratory scale experiments were designed and carried out. The experiments reproduced pole planting of hybrid poplars into diesel contaminated field soils from a former bulk fuel station. The experiments were conducted in a closed and controlled environment over a 215-230 day period with numerous aspects of the system being monitored including volatilization of PHC from the tree and soil, and microbial activity of the soil.<p>Monitoring data indicated that microbial degradation of the contaminant was by far the most influential monitored degradation pathway, accounting for 96.3 to 98.7% of the mass removed for soils containing poplars. The monitoring data also indicated a significant difference in the mass of contaminant removed from the soil for soils containing poplars compared to those without. The total estimated mass of contaminant removed varied between 8.3 and 27.7% of the initial mass for soils containing poplars and between 6.0 and 6.1% of the initial mass for soils without poplars. Lastly, using the monitoring data and the below ground biomass of the poplars from each of the experimental test cells, a rationally-based model was developed to be used as a starting point for quantitative prediction of the rate of PHC removal.