982 resultados para Spatial dynamic
Resumo:
A novel test of spatial independence of the distribution of crystals or phases in rocksbased on compositional statistics is introduced. It improves and generalizes the commonjoins-count statistics known from map analysis in geographic information systems.Assigning phases independently to objects in RD is modelled by a single-trial multinomialrandom function Z(x), where the probabilities of phases add to one and areexplicitly modelled as compositions in the K-part simplex SK. Thus, apparent inconsistenciesof the tests based on the conventional joins{count statistics and their possiblycontradictory interpretations are avoided. In practical applications we assume that theprobabilities of phases do not depend on the location but are identical everywhere inthe domain of de nition. Thus, the model involves the sum of r independent identicalmultinomial distributed 1-trial random variables which is an r-trial multinomialdistributed random variable. The probabilities of the distribution of the r counts canbe considered as a composition in the Q-part simplex SQ. They span the so calledHardy-Weinberg manifold H that is proved to be a K-1-affine subspace of SQ. This isa generalisation of the well-known Hardy-Weinberg law of genetics. If the assignmentof phases accounts for some kind of spatial dependence, then the r-trial probabilitiesdo not remain on H. This suggests the use of the Aitchison distance between observedprobabilities to H to test dependence. Moreover, when there is a spatial uctuation ofthe multinomial probabilities, the observed r-trial probabilities move on H. This shiftcan be used as to check for these uctuations. A practical procedure and an algorithmto perform the test have been developed. Some cases applied to simulated and realdata are presented.Key words: Spatial distribution of crystals in rocks, spatial distribution of phases,joins-count statistics, multinomial distribution, Hardy-Weinberg law, Hardy-Weinbergmanifold, Aitchison geometry
Resumo:
BACKGROUND: Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether BMI clusters among children and how age-specific BMI clusters are related remains unknown. We aimed to identify and compare the spatial dependence of BMI in adults and children in a Swiss general population, taking into account the area's income level. METHODS: Geo-referenced data from the Bus Santé study (adults, n=6663) and Geneva School Health Service (children, n=3601) were used. We implemented global (Moran's I) and local (local indicators of spatial association (LISA)) indices of spatial autocorrelation to investigate the spatial dependence of BMI in adults (35-74 years) and children (6-7 years). Weight and height were measured using standardized procedures. Five spatial autocorrelation classes (LISA clusters) were defined including the high-high BMI class (high BMI participant's BMI value correlated with high BMI-neighbors' mean BMI values). The spatial distributions of clusters were compared between adults and children with and without adjustment for area's income level. RESULTS: In both adults and children, BMI was clearly not distributed at random across the State of Geneva. Both adults' and children's BMIs were associated with the mean BMI of their neighborhood. We found that the clusters of higher BMI in adults and children are located in close, yet different, areas of the state. Significant clusters of high versus low BMIs were clearly identified in both adults and children. Area's income level was associated with children's BMI clusters. CONCLUSIONS: BMI clusters show a specific spatial dependence in adults and children from the general population. Using a fine-scale spatial analytic approach, we identified life course-specific clusters that could guide tailored interventions.
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Nessie is an Autonomous Underwater Vehicle (AUV) created by a team of students in the Heriot Watt University to compete in the Student Autonomous Underwater Competition, Europe (SAUC-E) in August 2006. The main objective of the project is to find the dynamic equation of the robot, dynamic model. With it, the behaviour of the robot will be easier to understand and movement tests will be available by computer without the need of the robot, what is a way to save time, batteries, money and the robot from water inside itself. The object of the second part in this project is setting a control system for Nessie by using the model
Resumo:
An adaptation technique based on the synoptic atmospheric circulation to forecast local precipitation, namely the analogue method, has been implemented for the western Swiss Alps. During the calibration procedure, relevance maps were established for the geopotential height data. These maps highlight the locations were the synoptic circulation was found of interest for the precipitation forecasting at two rain gauge stations (Binn and Les Marécottes) that are located both in the alpine Rhône catchment, at a distance of about 100 km from each other. These two stations are sensitive to different atmospheric circulations. We have observed that the most relevant data for the analogue method can be found where specific atmospheric circulation patterns appear concomitantly with heavy precipitation events. Those skilled regions are coherent with the atmospheric flows illustrated, for example, by means of the back trajectories of air masses. Indeed, the circulation recurrently diverges from the climatology during days with strong precipitation on the southern part of the alpine Rhône catchment. We have found that for over 152 days with precipitation amount above 50 mm at the Binn station, only 3 did not show a trajectory of a southerly flow, meaning that such a circulation was present for 98% of the events. Time evolution of the relevance maps confirms that the atmospheric circulation variables have significantly better forecasting skills close to the precipitation period, and that it seems pointless for the analogue method to consider circulation information days before a precipitation event as a primary predictor. Even though the occurrence of some critical circulation patterns leading to heavy precipitation events can be detected by precursors at remote locations and 1 week ahead (Grazzini, 2007; Martius et al., 2008), time extrapolation by the analogue method seems to be rather poor. This would suggest, in accordance with previous studies (Obled et al., 2002; Bontron and Obled, 2005), that time extrapolation should be done by the Global Circulation Model, which can process atmospheric variables that can be used by the adaptation method.
Resumo:
Different spatial representations are not stored as a single multipurpose map in the brain. Right brain-damaged patients can show a distortion, a compression of peripersonal and extrapersonal space. Here we report the case of a patient with a right insulo-thalamic disconnection without spatial neglect. The patient, compared with 10 healthy control subjects, showed a constant and reliable increase of her peripersonal and extrapersonal egocentric space representations - that we named spatial hyperschematia - yet left her allocentric space representations intact. This striking dissociation shows that our interactions with the surrounding world are represented and processed modularly in the human brain, depending on their frame of reference.
The role of energetic value in dynamic brain response adaptation during repeated food image viewing.
Resumo:
The repeated presentation of simple objects as well as biologically salient objects can cause the adaptation of behavioral and neural responses during the visual categorization of these objects. Mechanisms of response adaptation during repeated food viewing are of particular interest for better understanding food intake beyond energetic needs. Here, we measured visual evoked potentials (VEPs) and conducted neural source estimations to initial and repeated presentations of high-energy and low-energy foods as well as non-food images. The results of our study show that the behavioral and neural responses to food and food-related objects are not uniformly affected by repetition. While the repetition of images displaying low-energy foods and non-food modulated VEPs as well as their underlying neural sources and increased behavioral categorization accuracy, the responses to high-energy images remained largely invariant between initial and repeated encounters. Brain mechanisms when viewing images of high-energy foods thus appear less susceptible to repetition effects than responses to low-energy and non-food images. This finding is likely related to the superior reward value of high-energy foods and might be one reason why in particular high-energetic foods are indulged although potentially leading to detrimental health consequences.
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Short-term dynamic psychotherapy (STDP) has rarely been investigated with regard to its underlying mechanisms of change, even if psychoanalytic theory informs us about several potential putative mechanisms of change in patients. Change in overall defensive functioning is one. In this study, we explored the role of overall defensive functioning, by comparing it on the process level with the neighbouring concept of overall coping functioning. A total of N=32 patients, mainly presenting adjustment disorder, were included in the study. The patients underwent STDP up to 40 sessions; three sessions per psychotherapy were transcribed and analyzed by using two observer-rating scales: Defense Mechanism Rating Scales (Perry, 1990) and Coping Action Patterns (Perry, Drapeau, Dunkley, & Blake, 2005). Hierarchical linear modeling was applied to model the change over the course of therapy and relate it to outcome. Results suggest that STDP has an effect on the target variable of overall defensive functioning, which was absent for overall coping functioning. Links with outcome confirm the importance of the effect. These results are discussed from methodological and clinical viewpoints.
Resumo:
This paper deals with fault detection and isolation problems for nonlinear dynamic systems. Both problems are stated as constraint satisfaction problems (CSP) and solved using consistency techniques. The main contribution is the isolation method based on consistency techniques and uncertainty space refining of interval parameters. The major advantage of this method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements, and model errors. Interval calculations bring independence from the assumption of monotony considered by several approaches for fault isolation which are based on observers. An application to a well known alcoholic fermentation process model is presented
Resumo:
A model-based approach for fault diagnosis is proposed, where the fault detection is based on checking the consistencyof the Analytical Redundancy Relations (ARRs) using an interval tool. The tool takes into account the uncertainty in theparameters and the measurements using intervals. Faults are explicitly included in the model, which allows for the exploitation of additional information. This information is obtained from partial derivatives computed from the ARRs. The signs in the residuals are used to prune the candidate space when performing the fault diagnosis task. The method is illustrated using a two-tank example, in which these aspects are shown to have an impact on the diagnosis and fault discrimination, since the proposed method goes beyond the structural methods
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The speed of fault isolation is crucial for the design and reconfiguration of fault tolerant control (FTC). In this paper the fault isolation problem is stated as a constraint satisfaction problem (CSP) and solved using constraint propagation techniques. The proposed method is based on constraint satisfaction techniques and uncertainty space refining of interval parameters. In comparison with other approaches based on adaptive observers, the major advantage of the presented method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements and model errors and without the monotonicity assumption. In order to illustrate the proposed approach, a case study of a nonlinear dynamic system is presented
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We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.
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Compact expressions, complete through second order in electrical and/or mechanical anharmonicity, are given for the dynamic dipole vibrational polarizability and dynamic first and second vibrational hyperpolarizabilities. Certain contributions not previously formulated are now included
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In the static field limit, the vibrational hyperpolarizability consists of two contributions due to: (1) the shift in the equilibrium geometry (known as nuclear relaxation), and (2) the change in the shape of the potential energy surface (known as curvature). Simple finite field methods have previously been developed for evaluating these static field contributions and also for determining the effect of nuclear relaxation on dynamic vibrational hyperpolarizabilities in the infinite frequency approximation. In this paper the finite field approach is extended to include, within the infinite frequency approximation, the effect of curvature on the major dynamic nonlinear optical processes