This study investigates the degree to which the cumulative risk index predicted school readiness in a Head Start population. In general, the reviewed studies indicated the cumulative risk model was efficacious in predicting adverse developmental outcomes. This study built on this literature by investigating how child, parent, and family risk factors predicted school readiness in Head Start children using two statistical models. Specific aims of this study included identifying 1) to what degree multiple predictors contributed to school readiness and 2) to what degree the cumulative risk index contributed to school readiness. Participants included 176 Head Start children ages 3 to 5 years. Data were analyzed using multivariate regression to determine if the cumulative risk model was a stronger predictor of school readiness than any risk factor in isolation. Hierarchical regression was also utilized to determine if individual risk factors contributed anything above and beyond the sum, the cumulative risk index. Multiple regression analysis revealed that older age and previous enrollment in Head Start predicted higher scores, while low income predicted lower scores, as did taking the test in Spanish. Analysis also revealed that higher scores on the cumulative risk index predicted lower test scores. The analysis revealed that the individual risk factors did not contribute to the model above and beyond the cumulative risk index. Adding the individual risk factors did not account for more variance than using gender, age, and the cumulative risk index as the only predictors. Similarly, the cumulative risk index did not account for more variance than using age and gender as the only predictors. The current study adds empirical support to the continued use of the cumulative risk model in predicting adverse developmental outcomes.

Quantum chromodynamics (QCD) is the modern theory of strong interactions. Quarks and antiquarks interact extit{via} gluons, the gauge bosons, and form well-know bound states, namely mesons and baryons. This non-Abelian gauge theory predicts the existence of unconventional states of quarks and gluons: Exotic hadrons. Their observation remains a difficult task since their properties are poorly known. Their theoretical study is a challenging task and will guide experimentalists in their work. This thesis is devoted to two main types of exotic hadrons: Hybrid mesons and glueballs. Hybrid mesons are mesons in which the gluon field is in an excited state. In constituent models, the excitation of the flux tube is interpreted in term of a constituent gluon. In this thesis, the connection between the two pictures, flux tube excitation and constituent gluon, is demonstrated. Using the technique of the auxiliary field, we reproduce, from the three-body system quark-antiquark-gluon, the excitations of the quark-antiquark potential observed in lattice QCD. We complete our study of mesons with exotic quantum numbers by performing a analysis of the low-lying spectrum. Glueballs are hadronic states with no valence quarks. Theoretical developments suggests that confined gluons gain a constituent mass inside glueballs. According to this, a gluon can be considered as a heavy spin-1 particle. Two- and three-gluon glueballs are then similar of heavy meson and baryon systems. We preformed a complete study of the low-lying spectrum of these glueballs. The comparison with the pure gauge results from lattice QCD showed a disagreement and led us to the conclusion that the gluon degree-of-freedom was not correctly taken into account. Indeed, the implementation of transverse gluons at the level of the wave functions solved this problem for two-gluon glueballs. For both systems, hybrid mesons and glueballs, we used semi-relativistic Hamiltonians since they remain valid for massless particles. A code in Gaussian basis was developed to solve our eigenvalue problems. For this purpose, we found the matrix elements between Gaussian functions for various operators.

Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy. The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics. As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified. In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community. Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.

As the number of vehicles increases rapidly, traffic congestion has become a serious problem in a city. Over the past years, a considerable number of studies have been made on traffic light setting. The traffic light setting problem is to investigate how to set the given traffic lights such that the total waiting time of vehicles on the roads is minimized. In this thesis, we use a graph model to represent the traffic network. On this model, some characteristics of the setting problem can be presented and analyzed. We first devise a branch and bound algorithm for obtaining the optimal solution of the traffic light setting problem. In addition, the genetic algorithm (GA), the particle swarm optimization (PSO) and the ant colony optimization (ACO) algorithm are also adopted to get the near optimal solution. Then, to extend this model, we add the assumption that each vehicle can change its direction. By comparing the results of various algorithms, we can study the impact of these algorithms on the traffic light setting problem. In our experiments, we also transform the map of Kaohsiung city into our graph model and test each algorithm on this graph.

In this work we examine a state constrained resource allocation model a with finite time horizon. Therefore, we use the necessary conditions of the Pontrjagin's Maximum Principle to find candidates for the solution and verify them later on using the sufficient conditions given by the duality concept of Klötzler. Moreover, we proof that the solution of the corresponding infinite horizon model does not fulfill the overtaking criterion of Weizsäcker.

The goal of this dissertation is to examine the theoretical and empirical implications of the inventory theoretic approach to the demand for money. Chapter 1 reviews the existing inventory theoretic frameworks and empirical money demand literature and provides an overview of this thesis. One of the main conclusions is that the elasticity results from the existing inventory theoretic models are not robust. Chapter 2 develops a partial equilibrium inventory theoretic model, in which a fixed cost is involved per cash transfer. The key feature is that a firm endogenously chooses the frequency of pay periods, which a household takes as given. When the firm must borrow working capital and pay wages by cheque, I show that both the firm and the household choose to transfer cash every payday only. The model keeps the basic result from the classical inventory theoretic approach that both the income and interest elasticity of money demand are 0.5. Chapter 3 extends the partial equilibrium model into a general equilibrium framework and shows that the partial equilibrium elasticity results no longer apply in the general equilibrium. First, the income elasticity is 1 in the general equilibrium. Second, the interest elasticity has two values depending on a threshold interest rate. When interest rates are below this threshold, the model is the Cash-In-Advance model with a constant income velocity of money and zero interest elasticity; otherwise the interest elasticity is close to 0.5 and the velocity fluctuates in response to variations in interest rates. Finally, the general equilibrium elasticity results are robust across alternative specifications of the agent's utility. Chapter 4 calibrates the general equilibrium model to the last 40 years of US data for M1. By constructing a residual measure of money transaction costs from the structural money demand function, I find that a structural break in the transaction costs occurred in 1981 might have been responsible for the instability of long-run money demand. The benefit of this approach is that it can explain this pattern of money demand without appealing to an exogenous structural break in the money demand function.

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.

The public nuisance and health concerns caused by odours from livestock facilities are among the key issues that affect neighbouring communities and the growth of the livestock industry across Canada. A setback distance is the common regulatory practice to reduce odour impact on the neighbouring areas. The air dispersion modeling method may be a more accurate tool for establishing setback distances since it considers site-specific airborne emissions, such as odour and gases from the animal production site as well as weather conditions and then estimates a concentration of the pollutant (odour, ammonia, etc.). Although various dispersion models have been studied to predict odour concentration from agricultural sources, limited field data exist to evaluate their applicability in agricultural odour dispersion. Thus, the purpose of this project was to evaluate the selected commercial air dispersion models with field plume measurements from swine operations. <p>Firstly, this thesis describes a sensitivity analysis of how the climatic parameters affect model simulations for four selected air dispersion models, ISCST3, AUSPLUME, CALPUFF, and CALPUFF. Under the steady state weather condition, mixing height had no effect on the livestock odour dispersion, while atmospheric stability, wind speed and wind direction had great effect on the livestock odour dispersion. Ambient temperature had a moderate effect compared with other parameters. Under variable weather conditions, the predicted odour concentrations were much lower than the results under steady state weather conditions. <p>A series of comparisons between model predictions of the same four models and field odour measurements were conducted. When using the livestock odour plume measurement data from University of Manitoba, three equations were used to convert the model predicted odour concentration to field measured odour intensity. The equations did not predict odour intensity very well. No model showed obvious better performance than the others. Scaling factors did not improve the results considerably. When using the odour plume measurement data from University of Minnesota, INPUFF2 performed better than CALPUFF. Scaling factors did improve the modeled results. When using the odour plume measurement data from University of Saskatchewan, INPUFF2 also performed better than CALPUFF. Scaling factors were still useful for the results improvements.<p>Finally, because CALPUFF is the US EPA preferred model and predicted the highest values under variable weather conditions in the sensitivity study, we used it to simulate odour plumes on selected three swine sites using hourly weather data from 1993 to 2002 in Yorkton, Saskatchewan. The maximum predicted distance were 2.9 km for 1 OU, which was lower than the recommended maximum setback distance of 3.2 km. <p>It is recommended that the variable weather conditions be used in the setback distance determination. CALPUFF is the preferred model and INPUFF2 is another option for field odour plume simulation, however scaling factors are needed to bring the model predictions close to the field measured results. Because the models evaluated were not developed for odour dispersion simulation, a model that can accurately predict livestock odour dispersion should be developed to take into account of the difference between odour and gas and wind direction shifts within the simulation time interval.

Using rich data collected from e-learning systems, it may be possible to build up just in time dynamic learner models to analyze learners' behaviours and to evaluate learners' performance in online education systems. The goal is to create metrics to measure learners' characteristics from usage data. To achieve this goal we need to use data mining methods, especially clustering algorithms, to find patterns from which metrics can be derived from usage data. In this thesis, we propose a six layer model (raw data layer, fact data layer, data mining layer, measurement layer, metric layer and pedagogical application layer) to create a just in time learner model which draws inferences from usage data. In this approach, we collect raw data from online systems, filter fact data from raw data, and then use clustering mining methods to create measurements and metrics.<p> In a pilot study, we used usage data collected from the iHelp system to create measurements and metrics to observe learners' behaviours in a real online system. The measurements and metrics relate to a learner's sociability, activity levels, learning styles, and knowledge levels. To validate the approach we designed two experiments to compare the metrics and measurements extracted from the iHelp system: expert evaluations and learner self evaluations. Even though the experiments did not produce statistically significant results, this approach shows promise to describe learners' behaviours through dynamically generated measurements and metric. Continued research on these kinds of methodologies is promising.

In this Masters dissertation, various doses of solubulized crude F4 fimbrial protein in conjunction with the adjuvants CpG ODN and porcine â-defensin 1 (pBD-1) were used to enhance the F4-specific intestinal immune response against Enterotoxigenic Escherichia coli (ETEC) F4 in post-weaned pigs. Using the mechanically shearing method we isolated the F4 fimbrial protein of ETEC with a molecular weight of 26 kDa. We verified this using a Western blot probed with a rabbit anti-F4 fimbrial antibody. Binding of the F4 fimbrial protein to the F4 receptor (F4R), present on the brush border of the villi in the small intestine of pigs, was demonstrated using an in vitro villus adhesion assay (IVVA). To demonstrate specificity rabbit polyclonal and mouse monoclonal anti-F4 antibodies, or the F4 protein were used to inhibit the adhesion of ETEC F4ac to F4R positive (F4Rpos) villi. <p>To examine immunogenicity of the 500 micrograms (ìg) of the F4 were administered into surgically created jejunal gut-loops in pigs. Three weeks later Peyers patches (PP) from immunized and control loops as well as gut-wall tissue were analyzed for their F4-specific antibody secreting cells (ASCs) by a modified enzyme linked immunosorbent spot (ELISPOT) assay. The F4-specific immune response in the serum was analyzed by an enzyme linked immunosorbant assay (ELISA). High numbers of F4-specific ASCs were isolated from the loops of pigs that contained high levels of the F4R. Conversely nominal or low numbers of F4-specific ASCs were found in loops of pigs expressing low levels of the F4R or no F4R (F4Rneg). The IVVA was used to categorize the pigs into either F4Rpos or F4Rneg animals. <p>Next three different concentrations of the crude F4 protein 50, 250, and 500 µg in the loops of individual pigs were used to analyze if dose affected the F4-specific immune response. Interestingly dose had no effect on the magnitude of the response. Therefore we hypothesized that the F4-specific immune response in the loops could be enhanced through the use of the adjuvants CpG ODN 2007 and pBD-1. The F4 protein was co-administered with either CpG ODN 2007 or pBD-1 and immune responses were assessed after 3 weeks. However neither CpG ODN 2007 nor PBD-1 at the doses used made an improvement in the immune response. Thus, these results demonstrated that the expression level of the F4R was the most important parameter for eliciting of the local immune response against the F4 protein. Furthermore our studies revealed that both F4Rneg and F4Rpos pigs responded to F4 immunization, however the former respond only nominally to F4-immunization in the loops. Moreover, an inverse relationship existed between the level of the F4-specific IgG in the serum and the F4-specific immune response seen in the loops. Thus our findings have important implications for oral vaccination using fimbrial based antigens (Ags) that utilize a receptor for their immunogenicity. Our results indicate that only animals with high levels of enterocyte F4R will have the ability to elicit high levels of protective F4-specific anti-fimbrial antibodies in their intestine after oral immunization. Therefore unless an effective adjuvant is available, animals with low to moderate levels of the fimbrial receptor in their small intestine will mount only weak immune responses making herd immunity after vaccination currently unattainable.