908 resultados para probability and reinforcement proportion
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Dans cette thèse l’ancienne question philosophique “tout événement a-t-il une cause ?” sera examinée à la lumière de la mécanique quantique et de la théorie des probabilités. Aussi bien en physique qu’en philosophie des sciences la position orthodoxe maintient que le monde physique est indéterministe. Au niveau fondamental de la réalité physique – au niveau quantique – les événements se passeraient sans causes, mais par chance, par hasard ‘irréductible’. Le théorème physique le plus précis qui mène à cette conclusion est le théorème de Bell. Ici les prémisses de ce théorème seront réexaminées. Il sera rappelé que d’autres solutions au théorème que l’indéterminisme sont envisageables, dont certaines sont connues mais négligées, comme le ‘superdéterminisme’. Mais il sera argué que d’autres solutions compatibles avec le déterminisme existent, notamment en étudiant des systèmes physiques modèles. Une des conclusions générales de cette thèse est que l’interprétation du théorème de Bell et de la mécanique quantique dépend crucialement des prémisses philosophiques desquelles on part. Par exemple, au sein de la vision d’un Spinoza, le monde quantique peut bien être compris comme étant déterministe. Mais il est argué qu’aussi un déterminisme nettement moins radical que celui de Spinoza n’est pas éliminé par les expériences physiques. Si cela est vrai, le débat ‘déterminisme – indéterminisme’ n’est pas décidé au laboratoire : il reste philosophique et ouvert – contrairement à ce que l’on pense souvent. Dans la deuxième partie de cette thèse un modèle pour l’interprétation de la probabilité sera proposé. Une étude conceptuelle de la notion de probabilité indique que l’hypothèse du déterminisme aide à mieux comprendre ce que c’est qu’un ‘système probabiliste’. Il semble que le déterminisme peut répondre à certaines questions pour lesquelles l’indéterminisme n’a pas de réponses. Pour cette raison nous conclurons que la conjecture de Laplace – à savoir que la théorie des probabilités présuppose une réalité déterministe sous-jacente – garde toute sa légitimité. Dans cette thèse aussi bien les méthodes de la philosophie que de la physique seront utilisées. Il apparaît que les deux domaines sont ici solidement reliés, et qu’ils offrent un vaste potentiel de fertilisation croisée – donc bidirectionnelle.
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The current research investigates the possibility of using single walled carbon nanotubes (SWNTs) as filler in polymers to impart several properties to the matrix polymer. SWNTs in a polymer matrix like poly(ethylene terephthalate) induce nucleation in its melt crystallization, provide effective reinforcement and impart electrical conductivity. We adopt a simple melt compounding technique for incorporating the nanotubes into the polymer matrix. For attaining a better dispersion of the filler, an ultrasound assisted dissolution-evaporation method has also been tried. The resulting enhancement in the materials properties indicates an improved disentanglement of the nanotube ropes, which in turn provides effective matrix-filler interaction. PET-SWNT nanocomposite fibers prepared through melt spinning followed by subsequent drawing are also found to have significantly higher mechanical propertiesas compared to pristine PET fiber.SWNTs also find applications in composites based on elastomers such as natural rubber as they can impart electrical conductivity with simultaneous improvement in the mechanical properties.
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Department of Statistics, Cochin University of Science and Technology
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In this thesis we attempt to make a probabilistic analysis of some physically realizable, though complex, storage and queueing models. It is essentially a mathematical study of the stochastic processes underlying these models. Our aim is to have an improved understanding of the behaviour of such models, that may widen their applicability. Different inventory systems with randon1 lead times, vacation to the server, bulk demands, varying ordering levels, etc. are considered. Also we study some finite and infinite capacity queueing systems with bulk service and vacation to the server and obtain the transient solution in certain cases. Each chapter in the thesis is provided with self introduction and some important references
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Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems
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The reinforcement omission effects have been traditionally interpreted in terms of: behavioral facilitation after reinforcement omission induced by primary frustration or behavioral suppression after reinforcement delivery induced by postconsummatory states. The studies reviewed here indicate that amygdala is involved in modulation of these effects. However, the fact that amygdala lesions, extensive or selective, can eliminate, reduce and enhance the omission effects makes it difficult to understand how it is the exact nature of their involvement. The amygdala is related to several functions that depend on its connections with other brain systems. Thus, it is necessary to consider the involvement of a more complex neural network in the modulation of the reinforcement omission effects. The connection of amygdala subareas to cortical and subcortical structures may be involved in this modulation since they also are linked to processes related to reward and expectancy.
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In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.
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Resumen basado en el de la publicación
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El uso de barras de materiales compuestos (FRP) se propone como una alternativa efectiva para las tradicionales estructuras de hormigón armadas con acero que sufren corrosión en ambientes agresivos. La aceptación de estos materiales en el mundo de la construcción está condicionada a la compresión de su comportamiento estructural. Este trabajo estudia el comportamiento adherente entre barras de FRP y hormigón mediante dos programas experimentales. El primero incluye la caracterización de la adherencia entre barras de FRP y hormigón mediante ensayos de pull-out y el segundo estudia el proceso de fisuración de tirantes de hormigón reforzados con barras de GFRP mediante ensayo a tracción directa. El trabajo se concluye con el desarrollo de un modelo numérico para la simulación del comportamiento de elementos de hormigón reforzado bajo cargas de tracción. La flexibilidad del modelo lo convierte en una herramienta flexible para la realización de un estudio paramétrico sobre las variables que influyen en el proceso de fisuración.
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The purpose of Research Theme 4 (RT4) was to advance understanding of the basic science issues at the heart of the ENSEMBLES project, focusing on the key processes that govern climate variability and change, and that determine the predictability of climate. Particular attention was given to understanding linear and non-linear feedbacks that may lead to climate surprises,and to understanding the factors that govern the probability of extreme events. Improved understanding of these issues will contribute significantly to the quantification and reduction of uncertainty in seasonal to decadal predictions and projections of climate change. RT4 exploited the ENSEMBLES integrations (stream 1) performed in RT2A as well as undertaking its own experimentation to explore key processes within the climate system. It was working at the cutting edge of problems related to climate feedbacks, the interaction between climate variability and climate change � especially how climate change pertains to extreme events, and the predictability of the climate system on a range of time-scales. The statisticalmethodologies developed for extreme event analysis are new and state-of-the-art. The RT4-coordinated experiments, which have been conducted with six different atmospheric GCMs forced by common timeinvariant sea surface temperature (SST) and sea-ice fields (removing some sources of inter-model variability), are designed to help to understand model uncertainty (rather than scenario or initial condition uncertainty) in predictions of the response to greenhouse-gas-induced warming. RT4 links strongly with RT5 on the evaluation of the ENSEMBLES prediction system and feeds back its results to RT1 to guide improvements in the Earth system models and, through its research on predictability, to steer the development of methods for initialising the ensembles
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Individual identification via DNA profiling is important in molecular ecology, particularly in the case of noninvasive sampling. A key quantity in determining the number of loci required is the probability of identity (PIave), the probability of observing two copies of any profile in the population. Previously this has been calculated assuming no inbreeding or population structure. Here we introduce formulae that account for these factors, whilst also accounting for relatedness structure in the population. These formulae are implemented in API-CALC 1.0, which calculates PIave for either a specified value, or a range of values, for F-IS and F-ST.
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Based on the potential benefits of cis-9, trans- 11 conjugated linoleic acid (CLA) for human health there is a need to develop effective strategies for enhancing milk fat CLA concentrations. In this experiment, the effect of forage type and level of concentrate in the diet on milk fatty acid composition was examined in cows given a mixture of fish oil and sunflower oil. Four late lactation Holstein-British Friesian cows were used in a 4 x 4 Latin-square experiment with a 2 x 2 factorial arrangement of treatments and 21-day experimental periods. Treatments consisted of grass (G) or maize (M) silage supplemented with low (L) or high (H) levels of concentrates (65: 35 and 35: 65; forage: concentrate ratio, on a dry matter (DM) basis, respectively) offered as a total mixed ration at a restricted level of intake (20 kg DM per day). Lipid supplements (30 g/kg DM) containing fish oil and sunflower oil (2: 3 w/w) were offered during the last 14 days of each experimental period. Treatments had no effect on total DM intake, milk yield, milk constituent output or milk fat content, but milk protein concentrations were lower (P<0.05) for G than M diets (mean 43.0 and 47.3 g/kg, respectively). Compared with grass silage, milk fat contained higher (P<0.05) amounts Of C-12: 0, C-14: 0, trans C-18:1 and long chain >= C20 (n-3) polyunsaturated fatty acids (PUFA) and lower (P<0.05) levels Of C-18:0 and trans C-18:2 when maize silage was offered. Increases in the proportion of concentrate in the diet elevated (P<0.05) C-18:2 (n-6) and long chain >= C20 (n-3) PUFA content, but reduced (P<0.05) the amount Of C-18:3 (n-3). Concentrations of trans-11 C-18:1 in milk were independent of forage type, but tended (P<0.10) to be lower for high concentrate diets (mean 7.2 and 4.0 g/100 g fatty acids, for L and H respectively). Concentrations of trans-10 C-18:1 were higher (P<0.05) in milk from maize compared with grass silage (mean 10.3 and 4.1 g/100 g fatty acids, respectively) and increased in response to high levels of concentrates in the diet (mean 4.1 and 10.3 g/100 g fatty acids, for L and H, respectively). Forage type had no effect (P>0.05) on total milk conjugated linoleic acid (CLA) (2.7 and 2.8 g/100 g fatty acids, for M and G, respectively) or cis-9, trans-11 CLA content (2.2 and 2.4 g/100 g fatty acids). Feeding high concentrate diets tended (P<0.10) to decrease total CLA (3.3 and 2.2 g/100 g fatty acids, for L and H, respectively) and cis-9, trans-11 CLA (2.9 and 1/7 g/100 g fatty acids) concentrations and increase milk trans-9, cis-11 CLA and trans-10, cis-12 CLA content. In conclusion, the basal diet is an important determinant of milk fatty acid composition when a supplement of fish oil and sunflower oil is given.