In the last decade, (non)prescription opioid abuse, opioid use disorder (OUD) diagnoses, and opioid-related overdoses have risen and represent a significant public health concern. One method of understanding OUD is as a disorder of choice that requires choosing opioid rewards at the expense of other nondrug rewards. The characterization of OUD as a disorder of choice is important as it implicates decision- making processes as therapeutic targets, such as the valuation of opioid rewards. However, reward-value measurement and interpretation are traditionally different in substance abuse research compared to related fields such as economics, animal behavior, and neuroeconomics and may be less effective for understanding how opioid rewards are valued. The present research therefore used choice procedures in line with behavioral/neuroeconomic studies to determine if drug-associated decision making could be predicted from economic choice theories. In Experiment 1, rats completed an isomorphic food-food probabilistic choice task with dynamic, unpredictable changes in reward probability that required constant updating of reward values. After initial training, the reward magnitude of one choice subsequently increased from one to two to three pellets. Additionally, rats were split between the Signaled and Unsignaled groups to understand how cues modulate reward value. After each choice, the Unsignaled group received distinct choice-dependent cues that were uninformative of the choice outcome. The Signaled group also received uninformative cues on one option, but the alternative choice produced reward-predictive cues that informed the trial outcome as a win or loss. Choice data were analyzed at a molar level using matching equations and molecular level using reinforcement learning (RL) models to determine how probability, reward magnitude, and reward-associated cues affected choice. Experiment 2 used an allomorphic drug versus food procedure where the food reward for one option was replaced by a self-administered remifentanil (REMI) infusion at doses of 1, 3 and 10 μg/kg. Finally, Experiment 3 assessed the potential for both REMI and food reward value to be commonly scaled within the brain by examining changes in nucleus accumbens (NAc) Oxygen (O2) dynamics. Results showed that increasing reward probability, magnitude, and the presence of reward-associated cues all independently increased the propensity of choosing the associated choice alternative, including REMI drug choices. Additionally, both molar matching and molecular RL models successfully parameterized rats’ decision dynamics. O2 dynamics were generally commensurate with the idea of a common value signal for REMI and food with changes in O2 signaling scaling with the reward magnitude of REMI rewards. Finally, RL model-derived reward prediction errors significantly correlated with peak O2 activity for reward delivery, suggesting a possible neurological mechanism of value updating. Results are discussed in terms of their implications for current conceptualizations of substance use disorders including a potential need to change the discourse surrounding how substance use disorders are modeled experimentally. Overall, the present research provides evidence that a choice model of substance use disorders may be a viable alternative to the disease model and could facilitate future treatment options centered around economic principles.

In recent years, there has been a surge of research interest in the analysis of time series and spatial data. While on one hand more and more sophisticated models are being developed, on the other hand the resulting theory and estimation process has become more and more involved. This dissertation addresses the development of statistical inference procedures for data exhibiting dependencies of varied form and structure. In the first work, we consider estimation of the mean squared prediction error (MSPE) of the best linear predictor of (possibly) nonlinear functions of finitely many future observations in a stationary time series. We develop a resampling methodology for estimating the MSPE when the unknown parameters in the best linear predictor are estimated. Further, we propose a bias corrected MSPE estimator based on the bootstrap and establish its second order accuracy. Finite sample properties of the method are investigated through a simulation study. The next work considers nonparametric inference on spatial data. In this work the asymptotic distribution of the Discrete Fourier Transformation (DFT) of spatial data under pure and mixed increasing domain spatial asymptotic structures are studied under both deterministic and stochastic spatial sampling designs. The deterministic design is specified by a scaled version of the integer lattice in IRd while the data-sites under the stochastic spatial design are generated by a sequence of independent random vectors, with a possibly nonuniform density. A detailed account of the asymptotic joint distribution of the DFTs of the spatial data is given which, among other things, highlights the effects of the geometry of the sampling region and the spatial sampling density on the limit distribution. Further, it is shown that in both deterministic and stochastic design cases, for "asymptotically distant" frequencies, the DFTs are asymptotically independent, but this property may be destroyed if the frequencies are "asymptotically close". Some important implications of the main results are also given.

This thesis reports the identification of linear and nonlinear handling models for road vehicles starting from structural identifiability analysis, continuing with the experiments to acquire data on a vehicle equipped with a sensor set and data acquisition system and ending with the estimation of parameters using the collected data. The 2 degrees of freedom (dof) linear model structure originates from the well known linear bicycle model that is frequently used in handling analysis of road vehicles. Physical parameters of the bicycle model structure are selected as the unknown parameter set that is to be identified. Global identifiability of the model structure is analysed, in detail, and concluded according to various available sensor sets. Physical parameters of the bicycle model structure are estimated using prediction error estimation method. Genetic algorithms are used in the optimization phase of the identification algorithm to overcome the difficulty in the selection of initial values for parameter estimates. Validation analysis of the identified model is also presented. Identified model is shown to track the system response successfully. Following the linear model identification, identification of 3 dof nonlinear models are studied. Local identifiability analysis is done and optimal input is designed using the same procedure for linear model structure identification. Practical identifiability analysis is performed using Fisher Information Matrix. Physical parameters are estimated using the data from simulated experiments. High accuracy estimates are obtained. Methodology for nonlinear handling model identification is presented.

Attention-Deficit/Hyperactive Disorder is a well studied but poorly understood disorder. Given that the underlying neurological mechanisms involved in the disorder have yet to be established, diagnosis is dependent upon behavioural markers. However, recent research has begun to associate a dopamine system dysfunction with ADHD; though, consensus on the nature of dopamine’s role in ADHD has yet to be established. Here, I use a computational modelling approach to investigate two opposing theories of the dopaminergic dysfunction in ADHD. The hyper-active dopamine theory posits that ADHD is associated with a midbrain dopamine system that produces abnormally large prediction errors signals; whereas the dynamic developmental theory argues that abnormally small prediction errors give rise to ADHD. Given that these two theories center on the size of prediction errors encoded by the midbrain dopamine system, I have formally investigated the implications of each theory within the framework of temporal-difference learning, a reinforcement learning algorithm demonstrated to model midbrain dopamine activity. The results presented in this thesis suggest that neither theory provides a good account for the behaviour of children and animal models of ADHD. Instead, my results suggest ADHD is the result of asymmetrically effective reinforcement learning signals encoded by the midbrain dopamine system. More specifically, the model presented here reproduced behaviours associated with ADHD when positive prediction errors were more effective than negative prediction errors. The biological sources of this asymmetry are considered, as are other computational models of ADHD.

Atualmente o Brasil é o quinto maior produtor de cacau no mundo e a sua produção está diretamente ligada ao ponto certo da colheita, colher frutos verdes ou verdoengos faz com que suas sementes tenham menor peso, em 1000 frutos maduros as amêndoas secas pesam em média 40 kg, colher frutos verdoengos, ou seja, frutos que estão parcialmente maduros, faz com que esse peso caia para 36 kg em média uma perda verificada de 10%, já quando os frutos estão verdes o peso passa a ser em média de 32 kg, possuindo uma perda de 20%, por isso é importante conhecer as fases de crescimento, que permite estabelecer formas adequadas de manejo, adubação e irrigação. Dentre as características biométricas do fruto do cacaueiro as que tem maior relevância econômica são o comprimento, o diâmetro e o volume. Uma forma de explicar relações de crescimento e produtividade de plantas, árvores, frutos ou animais é por meio da utilização de modelos de crescimento, pois possuem parâmetros com interpretação biológica. Os mais utilizados nestas áreas são os modelos: Logístico, Gompertz, Von Bertalanffy, Richards e Brody, sendo os dois últimos mais utilizados para descrever o crescimento animal. O objetivo do trabalho é avaliar a qualidade de ajuste e de predição dos modelos não lineares, Logístico, Gompertz e Von Bertalanffy, para medidas de comprimento e diâmetro do fruto do cacau, com a finalidade de predizer o seu volume. Para predizer o volume do fruto foi utilizado a fórmula de volume do esferoide prolato. Os critérios AIC e BIC foram utilizados para verificar qual dos modelos se ajusta melhor à essas medidas, já para verificar qual modelo se ajusta melhor na predição do volume do fruto, foram relacionadas as estatísticas: viés médio, índice de concordância, eficiência da modelagem e o desdobramento do quadrado médio do erro de predição. / Currently Brazil is the fifth largest producer of cocoa in the world and its production is directly linked to the right point of harvest, harvesting green or green fruits makes their seeds have less weight, in 1000 mature fruits dry almonds weight on average 40 kg, harvested green fruits, that is, fruits that are partially ripe, causes this weight to fall to 36 kg on average a verified loss of 10%, when the fruits are green the weight becomes an average of 32 kg, with a loss of 20%, so it is important to know the growth phases, which allows to establish appropriate forms of management, fertilization and irrigation. Among the biometric characteristics of the cacao fruit, the most economically important are length, diameter and volume. One way to explain growth and productivity relationships of plants, trees, fruits or animals is through the use of growth models, since they have parameters that with biological interpretation are the most used in these areas: Logistics, Gompertz, Von Bertalanffy, Richards and Brody, the last two being most used to describe animal growth. The objective of this work is to evaluate the quality of fit and prediction of the non linear models, Logistic, Gompertz and Von Bertalanffy, for measures of length and diameter of the fruit of the cocoa, in order to predict its volume. To predict the volume of the fruit, the volume formula of the prolate spheroid was used. The AIC and BIC criteria were used to verify which model best fits these measures, and to verify which model best fits the fruit volume prediction, the statistics were related: mean bias, concordance index, modeling efficiency, and the unfolding of the mean square of the prediction error.

Made available in DSpace on 2014-06-11T19:27:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-02-29Bitstream added on 2014-06-13T19:35:06Z : No. of bitstreams: 1 mesquitaneto_c_me_ilha.pdf: 1673109 bytes, checksum: 0f8131abf5fc45715c92abece81e6a7a (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho é apresentado uma proposta de absorvedor de vibrações ajustável tipo viga sanduíche utilizando fluido Magnetoreológico no centro. Para o desenvolvimento deste projeto foi realizada uma revisão sobre os vários tipos de absorvedores e algumas aplicações. Em seguida foi realizado um estudo sobre o comportamento do fluido magnetoreológico, mostrando como este material inteligente varia suas propriedades quando submetido a um campo magnético. O objetivo do estudo foi verificar as propriedades do sistema para realização de um futuro controle, que é realizado através da variação do campo magnético. Avaliou-se, também, a relação com a corrente elétrica, quais os parâmetros que o influenciam e como podemos produzir um campo magnético com a intensidade desejada. Para avaliar as características do sistema foi utilizado o modelo no programa Ansys, com o objetivo de se verificar o comportamento do sistema. Para encontrar as características reais do sistema foi utilizado o modelo na forma de espaço de estados modais, identificado através do método PEM, Método de Predição de Erros (do inglês Prediction Error Methods PEM). Os testes experimentais foram realizados para se adquirir conhecimento do comportamento dinâmico deste tipo de fluido e, verificar se há repetibilidade nas medidas / This work presents a proposal of a tunable vibrations absorber type sandwich beam, using the Magnetorheologic fluid in the intermediate layer. For the development of this study a revision of some types of absorber with some applications was carried out. After that, a study of the behavior of the magnetorheologic fluid was carried through, showing as this intelligent material tunable its properties when submitted to a magnetic field. The objective of this analysis was to verify the properties of the system for implementation of a future control, which is based on the variation of the magnetic field. It was realized an analysis of the relation of the electric current and the parameters that influence it, in order to produce a magnetic field with the desired intensity. The characteristics of the system were verified through a mathematical model obtained with the software Ansys. The real characteristics of the system were found through the identification method PEM, Prediction Error Methods, using modal space states formulation. Experimental tests were carried out in order to obtain know how of the dynamic behavior of this type of material

This thesis deals with a description of three claims reserving methods - with stochastic models for Chain ladder, Bornhuetter/Ferguson and multiplicative method. There are mentioned their assumptions, parameter estimates, their properties and formulas for loss reserves in the first part. The second part of the text is devoted to formulas for the mean squared error of prediction and its estimate. Finally, a numerical example shows comparison of these methods.

One of the most challenging tasks of our visual systems is to structure and integrate the enormous amount of incoming information into distinct coherent objects. It is an ongoing debate whether or not the formation of visual objects requires attention. Implicit behavioral measures suggest that object formation can occur for task-irrelevant and unattended visual stimuli. The present study investigated pre-attentive visual object formation by combining implicit behavioral measures and an electrophysiological indicator of pre-attentive visual irregularity detection, the visual mismatch negativity (vMMN) of the event-related potential. Our displays consisted of two symmetrically arranged, task-irrelevant ellipses, the objects. In addition, there were two discs of either high or low luminance presented on the objects, which served as targets. Participants had to indicate whether the targets were of the same or different luminance. In separate conditions, the targets either usually were enclosed in the same object or in two different objects (standards). Occasionally, the regular target-to-object assignment was changed (deviants). That is, standards and deviants were exclusively defined on the basis of the task-irrelevant target-to-object assignment but not on the basis of some feature regularity. Although participants did not notice the regularity nor the occurrence of the deviation in the sequences, task-irrelevant deviations resulted in increased reaction times. Moreover, compared with physically identical standard displays deviating target-to-object assignments elicited a negative potential in the 246–280 ms time window over posterio-temporal electrode positions which was identified as vMMN. With variable resolution electromagnetic tomography (VARETA) object-related vMMN was localized to the inferior temporal gyrus. Our results support the notion that the visual system automatically structures even task-irrelevant aspects of the incoming information into objects.

Expectations are probabilistic beliefs about the future that shape and influence our perception, affect, cognition, and behavior in many contexts. This makes expectations a highly relevant concept across basic and applied psychological disciplines. When expectations are confirmed or violated, individuals can respond by either updating or maintaining their prior expectations in light of the new evidence. Moreover, proactive and reactive behavior can change the probability with which individuals encounter expectation confirmations or violations. The investigation of predictors and mechanisms underlying expectation update and maintenance has been approached from many research perspectives. However, in many instances there has been little exchange between different research fields. To further advance research on expectations and expectation violations, collaborative efforts across different disciplines in psychology, cognitive (neuro)science, and other life sciences are warranted. For fostering and facilitating such efforts, we introduce the ViolEx 2.0 model, a revised framework for interdisciplinary research on cognitive and behavioral mechanisms of expectation update and maintenance in the context of expectation violations. To support different goals and stages in interdisciplinary exchange, the ViolEx 2.0 model features three model levels with varying degrees of specificity in order to address questions about the research synopsis, central concepts, or functional processes and relationships, respectively. The framework can be applied to different research fields and has high potential for guiding collaborative research efforts in expectation research.

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system. / Applied Science University (ASU) of Jordan