The present study attempted to answer two general questions: (1) what is the relation between expected and actual reactions to a spider in individuals afraid of spiders? and (2) are inaccurate expectancies updated on the basis of experience? Behavioral and cognitive-behavioral learning models of fear, treatment protocols developed in relation to these, and recent findings from our laboratory necessitated answers to two additional questions: (3) does the expectation accuracy of individuals who meet DSM-IV criteria for diagnosis with a specific phobia differ from that of individuals who are fearful but do not meet criteria? and (4) does expectation accuracy vary as a function of context? Two final questions were obvious: (5) do the actual reactions of individuals who meet criteria for diagnosis differ predictably from those of fearful individuals? and (6) do reactions vary contextually? Student participants reported and tested a series of trial-specific expectancies about their reactions to a live, mechanical, or virtual tarantula over seven trials. Participants then completed three final trials in the presence of a live tarantula. Participants poorly anticipated the quality and intensity of their initial reactions, but expectation accuracy increased quickly. No clear tendencies for over- or under-prediction emerged. Participants updated expectancies in relation to prior trial expectation accuracy, either increasing or decreasing their predicted reactions relative to the original expectancy. Participants who met criteria for diagnosis with a specific phobia consistently anticipated and reported more intense reactions than did those who were fearful, but diagnostic status was not predictive of expectation accuracy. Participants in the live and virtual spider groups reported similar levels of fear that were greater than those in the mechanical spider group. Participants in the virtual spider group more readily reduced the distance maintained between themselves and the spider stimulus than did those in the live or mechanical spider groups. Expectation accuracy did not vary contextually. Results are discussed in light of the theoretical models presented, with findings lending greater support to behavioral models of fear learning relative to cognitive models that postulate a substantial role for conscious processing and appraisal in specific fear. Practical recommendations are made to researchers and clinicians based on present findings.

This thesis is aimed at forecasting of experimental results in a lab environment, investigating often discussed external validity of laboratory experiments. We run a novel laboratory experiment in which the subject pool is asked to make predictions on results of a certain field experiment. The collected data is ana­ lyzed using different accuracy measures, arriving at several interesting results. First, the forecast among the 94 subjects is quite informative about the actual treatment effects although its accuracy substantially varies based on a type of accuracy measure and a particular treatment. Second, the average forecast is either more accurate or at least comparable to the mean individual forecast, proving the presence of "wisdom-of-crowds" effect.

O alcance de altas produtividades em milho (Zea mays L.) depende do desenvolvimento de híbridos, o principal produto explorado nos programas de melhoramento. O sucesso na obtenção deste tipo de cultivar é conseguido com extensivo cruzamento de linhagens, seguido de avaliações para identificação das combinações de maior potencial. Geralmente, o melhorista tem à sua disponibilidade grande número de linhagens, possibilitando a realização de centenas a milhares de cruzamentos distintos, dos quais apenas uma pequena quantidade pode ser avaliada experimentalmente devido a limitação de tempo e recursos. Com o advento da Seleção Genômica (GS) tornou-se possível predizer o comportamento destes indivíduos não avaliados com base em seu genoma. No decorrer do processo de consolidação da GS várias metodologias foram propostas. A aptidão destas em predizer desempenhos fenotípicos é dependente da sua capacidade de acomodar a arquitetura genética das características e lidar com a multicolinearidade das matrizes genômicas. Neste sentido, métodos baseados em modelos mistos podem apresentar menor eficiência na predição de características oligogênicas devido à não capacidade de representar a distribuição real do efeito dos QTL. Além disso, a regularização das predições na presença de multicolinearidade é realizada por meio de um parâmetro de penalização (λ), o qual pode ser estimado de várias formas e consequentemente modificar a acurácia dos modelos. Além do aprimoramento dos métodos, outro aspecto importante é o procedimento de correção dos dados fenotípicos previamente à GS, o qual não é consenso na comunidade científica. Diante do exposto, este trabalho objetivou: verificar o efeito das formas de obtenção do λ (via REML na GS e pela herdabilidade da característica) e da correção do fenótipo (valor genotípico e média ajustada) na GS e avaliar a eficiência da modelagem diferencial de QTL de maior efeito na capacidade preditiva da metodologia G-BLUP, comparando-a ao LASSO Bayesiano, BayesB e G-BLUP convencional. Para isso foram utilizadas informações de híbridos simples de milho tropical avaliados em cinco locais para produtividade de grãos, altura de planta e espiga no ano de 2015. Os dados genômicos foram obtidos com a plataforma Affymetrix® Axiom® Maize Genotyping Array de 616.201 SNPs. Foram estudados diferentes cenários de GS considerando os fatores supracitados, sendo estes comparados entre si por suas capacidades preditivas e seletivas. Os resultados obtidos indicam que a correção do fenótipo e a forma de estimação de λ afetam a capacidade preditiva. O uso de valores genotípicos como correção dos fenótipos e estimação de λ via REML apresentaram os melhores resultados. Foi também observado que a modelagem de SNPs de maior efeito como fator fixo aumenta discretamente a capacidade preditiva da metodologia G-BLUP para as características oligogênicas avaliadas (altura de planta e espiga), sendo indicado o uso do G-BLUP convencional. Complementarmente, observou-se que a GS apresentou modesta eficiência na seleção de híbridos superiores sob intensidades moderadas. Entretanto, a sua alta capacidade de selecionar sob baixa intensidade pode ser amplamente explorada nos programas de melhoramento de milho visando a seleção precoce direta. / The achievement of high yield in maize (Zea mays L.) relies on the development of hybrids, which is the main product of breeding programs. The success in obtaining this kind of cultivar is achieved through extensive crossing of inbred lines followed by field trials to identify the combinations with greatest potential. Generally, breeders have a large number of inbred lines on their hands, being able to perform hundreds to thousands of different crosses, of which only a small portion can be experimentally evaluated due to time and resource limitations. Genomic Selection (GS) has made it possible to predict phenotypes of unevaluated individuals based on their genome. Throughout the establishment process of GS many approaches have been proposed. The ability of these approaches at predicting phenotypic performance depends on their capacity of accommodating the genetic architecture of the traits and dealing with the multicollinearity of the genomic matrices. Hence, methods based on mixed model equations may present lower prediction efficiency for oligogenic traits due to their inability of depicting the real distribution of the QTL effects. Moreover, the prediction regularization in the presence of multicollinearity is done by a shrinkage factor (λ), which can be estimated in a number of ways and may affect the accuracy of the models. In addition to the improvement of the models, the correction of the phenotype utilized in the predictions is also important, which is not a consensus among researchers. Based on these facts, this study aimed to assess the effect of estimation of λ (by REML in the GS model and by the heritability of the traits) and the correction of the phenotype (genotypic value and adjusted mean) on the GS. It also targeted to evaluate the effect of differential modeling of major makers on the prediction accuracy of G-BLUP, comparing it to Bayesian LASSO, BayesB and ordinary G-BLUP. To those ends, tropical maize single-crosses evaluated at five sites for grain yield, plant and ear height in 2015 were utilized. The genomic data was obtained with the Affymetrix® Axiom® Maize Genotyping Array of 616,201 SNPs. Distinct GS scenarios were studied considering the aforementioned factors which were compared by their prediction and selection accuracy. The results suggest that the correction of the phenotype and the way of estimation of λ do affect prediction accuracies. The use of genotypic values as the correction of phenotypes and the estimation of λ by REML showed best results. It was also observed that modeling major SNPs as fixed effect factors had little improvement on the prediction accuracy of G-BLUP for the oligogenic traits evaluated (plant and ear height). Thereby, ordinary G-BLUP should be the method of choice to predict these traits. Additionally, it was observed that GS presented modest efficiency for selecting superior hybrids under moderate intensities. However, its high effectiveness at selecting under low intensities might be exploited on maize breeding programs for early direct selection.

Bovine Tuberculosis (bTB) is a disease of significant economic importance, being one of the most persistent animal health problems in the UK and the Republic of Ireland and increasingly constituting a public health concern especially for the developing world. Limitations of the currently available diagnostic and control methods, along with our incomplete understanding of bTB transmission, prevent successful eradication. This Thesis addresses the development of a complementary control strategy which will be based on animal genetics and will allow us to identify animals genetically predisposed to be more resistant to disease. Specifically, the aim of my PhD project is to investigate the genetic architecture of resistance to bTB and demonstrate the feasibility of whole genome prediction for the control of bTB in cattle. Genomic selection for disease resistance in livestock populations will assist with the reduction of the in herd-level incidence and the severity of potential outbreaks. The first objective was to explore the estimation of breeding values for bTB resistance in UK dairy cattle, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates. Through using dense SNP chip data the results of Chapter 2 demonstrate that genomic selection for bTB resistance is feasible (h2 = 0.23(SE = 0.06)) and bTB resistance can be predicted using genetic markers with an estimate of prediction accuracy of r(g, ĝ) = 0.33 in this data. It was shown that genotypes help to predict disease state (AUC ≈ 0.58) and animals lacking bTB phenotypes can be selected based on their genotypes. In Chapter 3, a novel approach is presented to identify loci displaying heterozygote (dis)advantage associated with resistance to M. bovis, hypothesising underlying non-additive genetic variation, and these results are compared with those obtained from standard genome scans. A marker was identified suggesting an association between locus heterozygosity and increased susceptibility to bTB i.e. a heterozygote disadvantage, with the heterozygotes being significantly more in the cases than in the controls (x2 = 11.50, p < 0.001). Secondly, this thesis focused on conducting a meta-analysis on two dairy cattle populations with bTB phenotypes and SNP chip genotypes, identifying genomic regions underlying bTB resistance and testing genomic predictions by means of cross-validation. In Chapter 4, exploration of the genetic architecture of the trait revealed that bTB resistance is a moderately polygenic, complex trait with clusters of causal variants spread across a few major chromosomes collectively controlling the trait. A region was identified on chromosome 6, putatively associated with bTB resistance and this chromosome as a whole was shown to contribute a major proportion (hc 2= 0.051) of the observed variation in this dataset. Genomic prediction for bTB was shown to be feasible even when only distantly related populations are combined (r(g,ĝ)=0.33 (SE = 0.05)), with the chromosomal heritability results suggesting that the accuracy arises from the SNPs capturing linkage disequilibrium between markers and QTL, as well as additive relationships between animals (~80% of estimated genomic h2 is due to relatedness). To extend the analysis, in Chapter 5, high density genotypes were inferred by means of genotype imputation, anticipating that these analyses will allow the identification of genomic regions associated with bTB resistance more closely, and that would increase the prediction accuracy. Genotype imputation was successful, however, using all imputed genotypes added little information. The limiting factor was found to be the number of animals and the trait definitions rather than the density of genotypes. Thirdly, a quantitative genetic analysis of actual Single Intradermal Comparative Cervical Test (SICCT) values collected during bTB herd testing was conducted aiming to investigate if selection for bTB resistance is likely to have an impact on the SICCT diagnostic test. This analysis demonstrated that the SICCT has a negligibly low heritability (h2=0.0104 (SE = 0.0032)) and any effect on the responsiveness to the test is likely to be small. In conclusion, breeding for disease resistance in livestock is feasible and we can predict the risk of bTB in cattle using genomic information. Further, putative QTLs associated with bTB resistance were identified, and exploration of the genetic architecture of bTB resistance revealed a moderately polygenic trait. These results suggest that given that larger datasets with more phenotyped and genotyped animals will be available, we can breed for bTB resistance and implement the genomic selection technology in breeding programmes aiming to improve the disease status and overall health of the livestock population. Using the genomics this can be continued as the epidemic declines.

FundaÃÃo de Amparo a Pesquisa do Estado do Piauà / A prediÃÃo de dados nÃo enviados ao sorvedouro à uma tÃcnica usada para economizar energia em RSSF atravÃs da reduÃÃo da quantidade de dados trafegados. PorÃm, os dispositivos devem rodar mecanismos simples devido as suas limitaÃÃes de recursos, os quais podem gerar erros indesejÃveis e isto pode nÃo ser muito preciso. Este trabalho propÃe um mÃtodo baseado na correlaÃÃo espacial e temporal multivariada para melhorar a precisÃo da prediÃÃo na reduÃÃo de dados de Redes de Sensores Sem Fio (RSSF). SimulaÃÃes foram feitas envolvendo funÃÃes de regressÃo linear simples e regressÃo linear mÃltipla para verificar o desempenho do mÃtodo proposto. Os resultados mostram um maior grau de correlaÃÃo entre as variÃveis coletadas em campo, quando comparadas com a variÃvel tempo, a qual à uma variÃvel independente usada para prediÃÃo. A precisÃo da prediÃÃo à menor quando a regressÃo linear simples à usada, enquanto a regressÃo linear mÃltipla à mais precisa. AlÃm disto, a soluÃÃo proposta supera algumas soluÃÃes atuais em cerca de 50% na prediÃÃo da variÃvel umidade e em cerca de 21% na prediÃÃo da variÃvel luminosidade. / Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, sensor devices must run simple mechanisms due to its constrained resources, which may cause unwanted errors and this may not be very accurate. This work proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to variable time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, the proposed solution outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction.

O alcance de altas produtividades em milho (Zea mays L.) depende do desenvolvimento de híbridos, o principal produto explorado nos programas de melhoramento. O sucesso na obtenção deste tipo de cultivar é conseguido com extensivo cruzamento de linhagens, seguido de avaliações para identificação das combinações de maior potencial. Geralmente, o melhorista tem à sua disponibilidade grande número de linhagens, possibilitando a realização de centenas a milhares de cruzamentos distintos, dos quais apenas uma pequena quantidade pode ser avaliada experimentalmente devido a limitação de tempo e recursos. Com o advento da Seleção Genômica (GS) tornou-se possível predizer o comportamento destes indivíduos não avaliados com base em seu genoma. No decorrer do processo de consolidação da GS várias metodologias foram propostas. A aptidão destas em predizer desempenhos fenotípicos é dependente da sua capacidade de acomodar a arquitetura genética das características e lidar com a multicolinearidade das matrizes genômicas. Neste sentido, métodos baseados em modelos mistos podem apresentar menor eficiência na predição de características oligogênicas devido à não capacidade de representar a distribuição real do efeito dos QTL. Além disso, a regularização das predições na presença de multicolinearidade é realizada por meio de um parâmetro de penalização (&lambda;), o qual pode ser estimado de várias formas e consequentemente modificar a acurácia dos modelos. Além do aprimoramento dos métodos, outro aspecto importante é o procedimento de correção dos dados fenotípicos previamente à GS, o qual não é consenso na comunidade científica. Diante do exposto, este trabalho objetivou: verificar o efeito das formas de obtenção do &lambda; (via REML na GS e pela herdabilidade da característica) e da correção do fenótipo (valor genotípico e média ajustada) na GS e avaliar a eficiência da modelagem diferencial de QTL de maior efeito na capacidade preditiva da metodologia G-BLUP, comparando-a ao LASSO Bayesiano, BayesB e G-BLUP convencional. Para isso foram utilizadas informações de híbridos simples de milho tropical avaliados em cinco locais para produtividade de grãos, altura de planta e espiga no ano de 2015. Os dados genômicos foram obtidos com a plataforma Affymetrix&reg; Axiom&reg; Maize Genotyping Array de 616.201 SNPs. Foram estudados diferentes cenários de GS considerando os fatores supracitados, sendo estes comparados entre si por suas capacidades preditivas e seletivas. Os resultados obtidos indicam que a correção do fenótipo e a forma de estimação de &lambda; afetam a capacidade preditiva. O uso de valores genotípicos como correção dos fenótipos e estimação de &lambda; via REML apresentaram os melhores resultados. Foi também observado que a modelagem de SNPs de maior efeito como fator fixo aumenta discretamente a capacidade preditiva da metodologia G-BLUP para as características oligogênicas avaliadas (altura de planta e espiga), sendo indicado o uso do G-BLUP convencional. Complementarmente, observou-se que a GS apresentou modesta eficiência na seleção de híbridos superiores sob intensidades moderadas. Entretanto, a sua alta capacidade de selecionar sob baixa intensidade pode ser amplamente explorada nos programas de melhoramento de milho visando a seleção precoce direta. / The achievement of high yield in maize (Zea mays L.) relies on the development of hybrids, which is the main product of breeding programs. The success in obtaining this kind of cultivar is achieved through extensive crossing of inbred lines followed by field trials to identify the combinations with greatest potential. Generally, breeders have a large number of inbred lines on their hands, being able to perform hundreds to thousands of different crosses, of which only a small portion can be experimentally evaluated due to time and resource limitations. Genomic Selection (GS) has made it possible to predict phenotypes of unevaluated individuals based on their genome. Throughout the establishment process of GS many approaches have been proposed. The ability of these approaches at predicting phenotypic performance depends on their capacity of accommodating the genetic architecture of the traits and dealing with the multicollinearity of the genomic matrices. Hence, methods based on mixed model equations may present lower prediction efficiency for oligogenic traits due to their inability of depicting the real distribution of the QTL effects. Moreover, the prediction regularization in the presence of multicollinearity is done by a shrinkage factor (&lambda;), which can be estimated in a number of ways and may affect the accuracy of the models. In addition to the improvement of the models, the correction of the phenotype utilized in the predictions is also important, which is not a consensus among researchers. Based on these facts, this study aimed to assess the effect of estimation of &lambda; (by REML in the GS model and by the heritability of the traits) and the correction of the phenotype (genotypic value and adjusted mean) on the GS. It also targeted to evaluate the effect of differential modeling of major makers on the prediction accuracy of G-BLUP, comparing it to Bayesian LASSO, BayesB and ordinary G-BLUP. To those ends, tropical maize single-crosses evaluated at five sites for grain yield, plant and ear height in 2015 were utilized. The genomic data was obtained with the Affymetrix&reg; Axiom&reg; Maize Genotyping Array of 616,201 SNPs. Distinct GS scenarios were studied considering the aforementioned factors which were compared by their prediction and selection accuracy. The results suggest that the correction of the phenotype and the way of estimation of &lambda; do affect prediction accuracies. The use of genotypic values as the correction of phenotypes and the estimation of &lambda; by REML showed best results. It was also observed that modeling major SNPs as fixed effect factors had little improvement on the prediction accuracy of G-BLUP for the oligogenic traits evaluated (plant and ear height). Thereby, ordinary G-BLUP should be the method of choice to predict these traits. Additionally, it was observed that GS presented modest efficiency for selecting superior hybrids under moderate intensities. However, its high effectiveness at selecting under low intensities might be exploited on maize breeding programs for early direct selection.

Predicting stock market trends is a complex task due to the inherent volatility and unpredictability of financial markets. Nevertheless, accurate forecasts are of critical importance to investors, financial analysts, and stakeholders, as they directly inform decision-making processes and risk management strategies associated with financial investments. Inaccurate forecasts can lead to notable financial consequences, emphasizing the crucial and demanding task of developing models that provide accurate and trustworthy predictions. This article addresses this challenging problem by utilizing a long-short term memory (LSTM) model to predict stock market developments. The study undertakes a thorough analysis of the LSTM model's performance across multiple datasets, critically examining the impact of different timespans and epochs on the accuracy of its predictions. Additionally, a comparison is made with a support vector regression (SVR) model using the same datasets and timespans, which allows for a comprehensive evaluation of the relative strengths of the two techniques. The findings offer insights into the capabilities and limitations of both models, thus paving the way for future research in stock market prediction methodologies. Crucially, the study reveals that larger datasets and an increased number of epochs can significantly enhance the LSTM model's performance. Conversely, the SVR model exhibits significant challenges with overfitting. Overall, this research contributes to ongoing efforts to improve financial prediction models and provides potential solutions for individuals and organizations seeking to make accurate and reliable forecasts of stock market trends.

This thesis presents performance evaluation and a field validation study of a time and temperature indexed autoregressive with exogenous (4-3-5 ARX) building thermal load prediction model with an aim to integrate the model with actual predictive control systems. The 4-3-5 ARX model is very simple and computationally efficient with relatively high prediction accuracy compared to the existing sophisticated prediction models, such as artificial neural network prediction models. However, performance evaluation and field validation of the model are essential steps before implementing the model in actual practice. The performance of the model was evaluated under different climate conditions as well as under modeling uncertainty. A field validation study was carried out for three buildings at Mississippi State University. The results demonstrate that the 4-3-5 ARX model can predict building thermal loads in an accurate manner most of the times, indicating that the model can be readily implemented in predictive control systems.

No description available.

The federal communications commission (FCC) has predicted that, under the current regulatory environment, a spectrum shortage may be faced in the near future. This impending spectrum shortage is in part due to a rapidly increasing demand for wireless services and in part due to inefficient usage of currently licensed bands. A new paradigm pertaining to wireless spectrum allocation, known as cognitive radio (CR), has been proposed as a potential solution to this problem. This dissertation seeks to contribute to research in the field of CR through an investigation into the effect that a primary user (PU) channel occupancy model will have on the performance of a secondary user (SU) in a CR network. The model assumes that PU channel occupancy can be described as a binary process and a two state Hidden Markov Model (HMM) was thus chosen for this investigation. Traditional algorithms for training the model were compared with certain evolutionary-based training algorithms in terms of their resulting prediction accuracy and computational complexity. The performance of this model is important since it provides SUs with a basis for channel switching and future channel allocations. A CR simulation platform was developed and the results gained illustrated the effect that the model had on channel switching and the subsequently achievable performance of a SU operating within a CR network. Performance with regard to achievable SU data throughput, PU disruption rate and SU power consumption, were examined for both theoretical test data as well as data obtained from real world spectrum measurements (taken in Pretoria, South Africa). The results show that a trade-off exists between the achievable SU throughput and the average PU disruption rate. Significant SU performance improvements were observed when prediction modelling was employed and it was found that the performance and complexity of the model were influenced by the algorithm employed to train it. SU performance was also affected by the length of the quick sensing interval employed. Results obtained from measured occupancy data were comparable with those obtained from theoretical occupancy data with an average percentage similarity score of 96% for prediction accuracy (using the Viterbi training algorithm), 90% for SU throughput, 83% for SU power consumption and 71% for PU disruption rate. / Dissertation (MEng)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted