<p> In the United States, 397,000 children received foster care services in 2012. Some states successfully achieved permanent homes for children with diagnosed disabilities who exited care while others were less successful. Using change theory and social ecological theory as the foundations, the purpose of this study was to determine the impact that diagnosed disabilities had on permanent home placements among Pennsylvania&rsquo;s foster care children who were discharged and were between ages of 0 to 6 years in 2012. Hernandez&rsquo;s and Hodges&rsquo;s theory of change was used to evaluate the 1982 standards that license foster care agencies, while Stokol&rsquo;s ecological theory provided the framework to assess whether there were measurable increases in child welfare outcomes for permanent placements among children with diagnosed disabilities. Following a retrospective, nonexperimental, quantitative design, data were acquired from a purposive sample of 344 archived foster care files across the state. These data were analyzed using bivariate correlation procedures to evaluate the strength of the relationship between medically diagnosed conditions and permanent placement. The findings indicated a statistically significant association between medically diagnosed conditions and permanent placements (p=0.01). Additionally, length of stay in care was also found to be statistically associated with permanent placement (p=0.019). The theoretical constructs evaluation with a theory of change found the 1982 standards were outdated to authorize the licensing of foster care agencies; the social ecological theory identified evidence for change to achieve the intended goal. Findings of this study may provide guidance to policymakers in term of improving standards related to oversight and licensing foster care agencies in order to better support permanent placement of children with disabilities.</p><p>

Parents modulate the phenotypes of their offspring, beyond the effects of the genes they pass on. These parental effects can have impacts on the fitnesses of those offspring, as well as the fitness of the parents themselves. Parental investment in offspring is expected to be under antagonistic selection through its beneficial effects to offspring, and its detrimental effects on the parent's own fitness. Evolutionary conflict over parental care is therefore expected to occur, and may cause evolutionary stasis. Furthermore, selection is also expected to act on offspring traits, in order to maximise offspring fitness within a given parental environment, generating predictions of parent-offspring coadaptation. I tested the predictions of conflict and coadaptation in parent-offspring interactions, using a population of blue tits (Cyanistes caeruleus), a species in which adults provide biparental care to their offspring. I found evolutionary conflict over offspring body mass, which may explain stasis in this trait. I also used a cross-fostering design to test for coadaptation between parents and offspring, and siblings. I did not find evidence for parent-offspring coadaptation, nor did I find that siblings were important through either direct interactions, or in mediating parent-offspring interactions, suggesting that there is little family coadaptation in this species. In addition, I investigated whether a maternal effect on hatching time was a passive consequence of environmental changes, or was an anticipatory maternal effect actively placed in eggs to manipulate hatching time. The results from this analysis suggest the latter to be the case, and mothers appear to actively manipulate offspring hatching time to reduce the extent of hatching asynchrony, which may reduce fitness costs to the offspring. By measuring the effects of interactions between individuals on phenotypes and fitness measures, I was able to show how parental effects on offspring can affect evolutionary dynamics. Such evidence of evolutionary conflicts has not previously been found, due to methodological issues with the ways in which selection has been measured. Thus, I highlight how gaps in knowledge about the evolutionary consequences of parental effects can be addressed using appropriate statistical tools and measures of fitness.

Recent advances in information and communications technology have led to a surge in the popularity of artificial Collective Adaptive Systems (CAS). Such systems, comprised by many spatially distributed autonomous entities with decentralised control, can often achieve discernible characteristics at the global level; a phenomenon sometimes termed emergence. Examples include smart transport systems, smart electricity power grids, robot swarms, etc. The design and operational management of CAS are of vital importance because different configurations of CAS may exhibit very large variability in their performance and the quality of services they offer. However, due to their complexity caused by varying degrees of behaviour, large system scale and highly distributed nature, it is often very difficult to understand and predict the behaviour of CAS under different situations. Novel modelling and quantitative analysis methodologies are therefore required to address the challenges posed by the complexity of such systems. In this thesis, we develop a process algebraic modelling formalism that can be used to express complex dynamic behaviour of CAS and provide fast and scalable analysis techniques to investigate the dynamic behaviour and support the design and operational management of such systems. The major contributions of this thesis are: (i) development of a novel high-level formalism, PALOMA, the Process Algebra for Located Markovian Agents for the modelling of CAS. CAS specified in PALOMA can be automatically translated to their underlying mathematical models called Population Continuous-Time Markov Chains (PCTMCs). (ii) development of an automatic moment-closure approximation method which can provide rapid Ordinary Differential Equation-based analysis of PALOMA models. (iii) development of an automatic model reduction algorithm for the speed up of stochastic simulation of PALOMA/PCTMC models. (iv) presenting a case study, predicting bike availability in stations of Santander Cycles, the public bike-sharing system in London, to show that our techniques are well-suited for analysing real CAS.

No description available.

Most human traits are influenced by a combination of genetic and environmental effects. Heritability expresses the proportion of trait variance that can be explained by genetic factors, and the 1980s heralded the beginning of studies that aimed to pinpoint genetic loci that contribute to trait variation, also known as quantitative trait loci (QTLs). Subsequently, the availability of cheap, high-resolution genotyping chips ushered in the era of genome-wide association studies (GWAS). These genetic studies have discovered many associations between single-nucleotide polymorphisms (SNPs) and complex traits, but these associations do not explain the genetic component of these traits entirely. This is known as the ‘missing heritability’ problem. Within this thesis, 40 medically-relevant human complex traits are studied in order to identify new QTLs. These traits include eye biometric traits, blood biochemical traits and anthropometric traits measured in approximately 28,000 individuals belonging to family-based samples from the general Scottish population (the Generation Scotland study) or from population isolates from Croatian (Korčula, Vis) or Scottish (Shetland, Orkney) islands. These individuals had been genotyped using commercially-available arrays, and unobserved genotypes were imputed using the Haplotype Reference Consortium (HRC) dataset. In parallel to standard GWAS, these traits are analysed using two other statistical genetics approaches: variance component linkage analysis and regional heritability (RH) mapping. Each study is analysed separately, in order to detect study-specific genetic effects that may not generalise across populations. At the same time, because most traits are available in several studies, this also enables meta-analysis, which boosts the power of discovery and can reveal cross-study genetic effects. These methods are a priori complementary to each other, exploiting different aspects of human genetic variation, such as the segregation of variants within families (identity by descent, IBD), or the presence of the same variant throughout the general population (identity by state, IBS). The strengths and weaknesses of these methods are systematically assessed by applying them to real and simulated datasets.

No description available.

abstract: Statistical mediation analysis has been widely used in the social sciences in order to examine the indirect effects of an independent variable on a dependent variable. The statistical properties of the single mediator model with manifest and latent variables have been studied using simulation studies. However, the single mediator model with latent variables in the Bayesian framework with various accurate and inaccurate priors for structural and measurement model parameters has yet to be evaluated in a statistical simulation. This dissertation outlines the steps in the estimation of a single mediator model with latent variables as a Bayesian structural equation model (SEM). A Monte Carlo study is carried out in order to examine the statistical properties of point and interval summaries for the mediated effect in the Bayesian latent variable single mediator model with prior distributions with varying degrees of accuracy and informativeness. Bayesian methods with diffuse priors have equally good statistical properties as Maximum Likelihood (ML) and the distribution of the product. With accurate informative priors Bayesian methods can increase power up to 25% and decrease interval width up to 24%. With inaccurate informative priors the point summaries of the mediated effect are more biased than ML estimates, and the bias is higher if the inaccuracy occurs in priors for structural parameters than in priors for measurement model parameters. Findings from the Monte Carlo study are generalizable to Bayesian analyses with priors of the same distributional forms that have comparable amounts of (in)accuracy and informativeness to priors evaluated in the Monte Carlo study. / Dissertation/Thesis / Doctoral Dissertation Psychology 2017

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