926 resultados para dynamic analysis


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Thesis (Ph.D.)--University of Washington, 2016-06

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This research used resource allocation theory to generate predictions regarding dynamic relationships between self-efficacy and task performance from 2 levels of analysis and specificity. Participants were given multiple trials of practice on an air traffic control task. Measures of task-specific self-efficacy and performance were taken at repeated intervals. The authors used multilevel analysis to demonstrate differential and dynamic effects. As predicted, task-specific self-efficacy was negatively associated with task performance at the within-person level. On the other hand, average levels of task-specific self-efficacy were positively related to performance at the between-persons level and mediated the effect of general self-efficacy. The key findings from this research relate to dynamic effects - these results show that self-efficacy effects can change over time, but it depends on the level of analysis and specificity at which self-efficacy is conceptualized. These novel findings emphasize the importance of conceptualizing self-efficacy within a multilevel and multispecificity framework and make a significant contribution to understanding the way this construct relates to task performance.

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Tropical deforestation is the major contemporary threat to global biodiversity, because a diminishing extent of tropical forests supports the majority of the Earth's biodiversity. Forest clearing is often spatially concentrated in regions where human land use pressures, either planned or unplanned, increase the likelihood of deforestation. However, it is not a random process, but often moves in waves originating from settled areas. We investigate the spatial dynamics of land cover change in a tropical deforestation hotspot in the Colombian Amazon. We apply a forest cover zoning approach which permitted: calculation of colonization speed; comparative spatial analysis of patterns of deforestation and regeneration; analysis of spatial patterns of mature and recently regenerated forests; and the identification of local-level hotspots experiencing the fastest deforestation or regeneration. The colonization frontline moved at an average of 0.84 km yr(-1) from 1989 to 2002, resulting in the clearing of 3400 ha yr(-1) of forests beyond the 90% forest cover line. The dynamics of forest clearing varied across the colonization front according to the amount of forest in the landscape, but was spatially concentrated in well-defined 'local hotspots' of deforestation and forest regeneration. Behind the deforestation front, the transformed landscape mosaic is composed of cropping and grazing lands interspersed with mature forest fragments and patches of recently regenerated forests. We discuss the implications of the patterns of forest loss and fragmentation for biodiversity conservation within a framework of dynamic conservation planning.

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Bifurcation analysis is a very useful tool for power system stability assessment. In this paper, detailed investigation of power system bifurcation behaviour is presented. One and two parameter bifurcation analysis are conducted on a 3-bus power system. We also examined the impact of FACTS devices on power system stability through Hopf bifurcation analysis by taking static Var compensator (SVC) as an example. A simplified first-order model of the SVC device is included in the 3-bus sample system. Real and reactive powers are used as bifurcation parameter in the analysis to compare the system oscillatory properties with and without SVC. The simulation results indicate that the linearized system model with SVC enlarge the voltage stability boundary by moving Hopf bifurcation point to higher level of loading conditions. The installation of SVC increases the dynamic stability range of the system, however complicates the Hopf bifurcation behavior of the system

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Very little is known about the neural structures involved in the perception of realistic dynamic facial expressions. In the present study, a unique set of naturalistic dynamic facial emotional expressions was created. Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend Haxby et al.'s [Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. The distributed human neural system for face perception. Trends in Cognitive Science, 4, 223–233, 2000] distributed neural system for face perception. This network includes early visual regions, such as the inferior occipital gyrus, which is identified as insensitive to motion or affect but sensitive to the visual stimulus, the STS, identified as specifically sensitive to motion, and the amygdala, recruited to process affect. Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as the inferior occipital gyrus and the STS, along with coupling between the STS and the amygdala, as well as the inferior frontal gyrus. These findings support the presence of a distributed network of cortical regions that mediate the perception of different dynamic facial expressions.

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In this paper we propose a data envelopment analysis (DEA) based method for assessing the comparative efficiencies of units operating production processes where input-output levels are inter-temporally dependent. One cause of inter-temporal dependence between input and output levels is capital stock which influences output levels over many production periods. Such units cannot be assessed by traditional or 'static' DEA which assumes input-output correspondences are contemporaneous in the sense that the output levels observed in a time period are the product solely of the input levels observed during that same period. The method developed in the paper overcomes the problem of inter-temporal input-output dependence by using input-output 'paths' mapped out by operating units over time as the basis of assessing them. As an application we compare the results of the dynamic and static model for a set of UK universities. The paper is suggested that dynamic model capture the efficiency better than static model. © 2003 Elsevier Inc. All rights reserved.

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Recent functional magnetic resonance imaging (fMRI) investigations of the interaction between cognition and reward processing have found that the lateral prefrontal cortex (PFC) areas are preferentially activated to both increasing cognitive demand and reward level. Conversely, ventromedial PFC (VMPFC) areas show decreased activation to the same conditions, indicating a possible reciprocal relationship between cognitive and emotional processing regions. We report an fMRI study of a rewarded working memory task, in which we further explore how the relationship between reward and cognitive processing is mediated. We not only assess the integrity of reciprocal neural connections between the lateral PFC and VMPFC brain regions in different experimental contexts but also test whether additional cortical and subcortical regions influence this relationship. Psychophysiological interaction analyses were used as a measure of functional connectivity in order to characterize the influence of both cognitive and motivational variables on connectivity between the lateral PFC and the VMPFC. Psychophysiological interactions revealed negative functional connectivity between the lateral PFC and the VMPFC in the context of high memory load, and high memory load in tandem with a highly motivating context, but not in the context of reward alone. Physiophysiological interactions further indicated that the dorsal anterior cingulate and the caudate nucleus modulate this pathway. These findings provide evidence for a dynamic interplay between lateral PFC and VMPFC regions and are consistent with an emotional gating role for the VMPFC during cognitively demanding tasks. Our findings also support neuropsychological theories of mood disorders, which have long emphasized a dysfunctional relationship between emotion/motivational and cognitive processes in depression.

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The rapid developments in computer technology have resulted in a widespread use of discrete event dynamic systems (DEDSs). This type of system is complex because it exhibits properties such as concurrency, conflict and non-determinism. It is therefore important to model and analyse such systems before implementation to ensure safe, deadlock free and optimal operation. This thesis investigates current modelling techniques and describes Petri net theory in more detail. It reviews top down, bottom up and hybrid Petri net synthesis techniques that are used to model large systems and introduces on object oriented methodology to enable modelling of larger and more complex systems. Designs obtained by this methodology are modular, easy to understand and allow re-use of designs. Control is the next logical step in the design process. This thesis reviews recent developments in control DEDSs and investigates the use of Petri nets in the design of supervisory controllers. The scheduling of exclusive use of resources is investigated and an efficient Petri net based scheduling algorithm is designed and a re-configurable controller is proposed. To enable the analysis and control of large and complex DEDSs, an object oriented C++ software tool kit was developed and used to implement a Petri net analysis tool, Petri net scheduling and control algorithms. Finally, the methodology was applied to two industrial DEDSs: a prototype can sorting machine developed by Eurotherm Controls Ltd., and a semiconductor testing plant belonging to SGS Thomson Microelectronics Ltd.

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Structural analysis in handwritten mathematical expressions focuses on interpreting the recognized symbols using geometrical information such as relative sizes and positions of the symbols. Most existing approaches rely on hand-crafted grammar rules to identify semantic relationships among the recognized mathematical symbols. They could easily fail when writing errors occurred. Moreover, they assume the availability of the whole mathematical expression before being able to analyze the semantic information of the expression. To tackle these problems, we propose a progressive structural analysis (PSA) approach for dynamic recognition of handwritten mathematical expressions. The proposed PSA approach is able to provide analysis result immediately after each written input symbol. This has an advantage that users are able to detect any recognition errors immediately and correct only the mis-recognized symbols rather than the whole expression. Experiments conducted on 57 most commonly used mathematical expressions have shown that the PSA approach is able to achieve very good performance results.

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Our aim was to approach an important and well-investigable phenomenon – connected to a relatively simple but real field situation – in such a way, that the results of field observations could be directly comparable with the predictions of a simulation model-system which uses a simple mathematical apparatus and to simultaneously gain such a hypothesis-system, which creates the theoretical opportunity for a later experimental series of studies. As a phenomenon of the study, we chose the seasonal coenological changes of aquatic and semiaquatic Heteroptera community. Based on the observed data, we developed such an ecological model-system, which is suitable for generating realistic patterns highly resembling to the observed temporal patterns, and by the help of which predictions can be given to alternative situations of climatic circumstances not experienced before (e.g. climate changes), and furthermore; which can simulate experimental circumstances. The stable coenological state-plane, which was constructed based on the principle of indirect ordination is suitable for unified handling of data series of monitoring and simulation, and also fits for their comparison. On the state-plane, such deviations of empirical and model-generated data can be observed and analysed, which could otherwise remain hidden.