896 resultados para Pattern-based interaction models
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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
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This paper presents a way to describe design patterns rigorously based on role concepts. Rigorous pattern descriptions are a key aspect for patterns to be used as rules for model evolution in the MDA context, for example. We formalize the role concepts commonly used in defining design patterns as a role metamodel using Object-Z. Given this role metamodel, individual design patterns are specified generically as a formal pattern role model using Object-Z. We also formalize the properties that must be captured in a class model when a design pattern is deployed. These properties are defined generically in terms of role bindings from a pattern role model to a class model. Our work provides a precise but abstract approach for pattern definition and also provides a precise basis for checking the validity of pattern usage in designs.
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The advent of distributed and heterogeneous systems has laid the foundation for the birth of new architectural paradigms, in which many separated and autonomous entities collaborate and interact to the aim of achieving complex strategic goals, impossible to be accomplished on their own. A non exhaustive list of systems targeted by such paradigms includes Business Process Management, Clinical Guidelines and Careflow Protocols, Service-Oriented and Multi-Agent Systems. It is largely recognized that engineering these systems requires novel modeling techniques. In particular, many authors are claiming that an open, declarative perspective is needed to complement the closed, procedural nature of the state of the art specification languages. For example, the ConDec language has been recently proposed to target the declarative and open specification of Business Processes, overcoming the over-specification and over-constraining issues of classical procedural approaches. On the one hand, the success of such novel modeling languages strongly depends on their usability by non-IT savvy: they must provide an appealing, intuitive graphical front-end. On the other hand, they must be prone to verification, in order to guarantee the trustworthiness and reliability of the developed model, as well as to ensure that the actual executions of the system effectively comply with it. In this dissertation, we claim that Computational Logic is a suitable framework for dealing with the specification, verification, execution, monitoring and analysis of these systems. We propose to adopt an extended version of the ConDec language for specifying interaction models with a declarative, open flavor. We show how all the (extended) ConDec constructs can be automatically translated to the CLIMB Computational Logic-based language, and illustrate how its corresponding reasoning techniques can be successfully exploited to provide support and verification capabilities along the whole life cycle of the targeted systems.
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Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models.
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An updated flow pattern map was developed for CO2 on the basis of the previous Cheng-Ribatski-Wojtan-Thome CO2 flow pattern map [1,2] to extend the flow pattern map to a wider range of conditions. A new annular flow to dryout transition (A-D) and a new dryout to mist flow transition (D-M) were proposed here. In addition, a bubbly flow region which generally occurs at high mass velocities and low vapor qualities was added to the updated flow pattern map. The updated flow pattern map is applicable to a much wider range of conditions: tube diameters from 0.6 to 10 mm, mass velocities from 50 to 1500 kg/m(2) s, heat fluxes from 1.8 to 46 kW/m(2) and saturation temperatures from -28 to +25 degrees C (reduced pressures from 0.21 to 0.87). The updated flow pattern map was compared to independent experimental data of flow patterns for CO2 in the literature and it predicts the flow patterns well. Then, a database of CO2 two-phase flow pressure drop results from the literature was set up and the database was compared to the leading empirical pressure drop models: the correlations by Chisholm [3], Friedel [4], Gronnerud [5] and Muller-Steinhagen and Heck [6], a modified Chisholm correlation by Yoon et al. [7] and the flow pattern based model of Moreno Quiben and Thome [8-10]. None of these models was able to predict the CO2 pressure drop data well. Therefore, a new flow pattern based phenomenological model of two-phase flow frictional pressure drop for CO2 was developed by modifying the model of Moreno Quiben and Thome using the updated flow pattern map in this study and it predicts the CO2 pressure drop database quite well overall. (C) 2007 Elsevier Ltd. All rights reserved.
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Sensory neuronopathies (SNNs) encompass paraneoplastic, infectious, dysimmune, toxic, inherited, and idiopathic disorders. Recently described diagnostic criteria allow SNN to be differentiated from other forms of sensory neuropathy, but there is no validated strategy based on routine clinical investigations for the etiological diagnosis of SNN. In a multicenter study, the clinical, biological, and electrophysiological characteristics of 148 patients with SNN were analyzed. Multiple correspondence analysis and logistic regression were used to identify patterns differentiating between forms of SNNs with different etiologies. Models were constructed using a study population of 88 patients and checked using a test population of 60 cases. Four patterns were identified. Pattern A, with an acute or subacute onset in the four limbs or arms, early pain, and frequently affecting males over 60 years of age, identified mainly paraneoplastic, toxic, and infectious SNN. Pattern B identified patients with progressive SNN and was divided into patterns C and D, the former corresponding to patients with inherited or slowly progressive idiopathic SNN with severe ataxia and electrophysiological abnormalities and the latter to patients with idiopathic, dysimmune, and sometimes paraneoplastic SNN with a more rapid course than in pattern C. The diagnostic strategy based on these patterns correctly identified 84/88 and 58/60 patients in the study and test populations, respectively.
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Cold-formed steel (CFS) combined with wood sheathing, such as oriented strand board (OSB), forms shear walls that can provide lateral resistance to seismic forces. The ability to accurately predict building deformations in damaged states under seismic excitations is a must for modern performance-based seismic design. However, few static or dynamic tests have been conducted on the non-linear behavior of CFS shear walls. Thus, the purpose of this research work is to provide and demonstrate a fastener-based computational model of CFS wall models that incorporates essential nonlinearities that may eventually lead to improvement of the current seismic design requirements. The approach is based on the understanding that complex interaction of the fasteners with the sheathing is an important factor in the non-linear behavior of the shear wall. The computational model consists of beam-column elements for the CFS framing and a rigid diaphragm for the sheathing. The framing and sheathing are connected with non-linear zero-length fastener elements to capture the OSB sheathing damage surrounding the fastener area. Employing computational programs such as OpenSees and MATLAB, 4 ft. x 9 ft., 8 ft. x 9 ft. and 12 ft. x 9 ft. shear wall models are created, and monotonic lateral forces are applied to the computer models. The output data are then compared and analyzed with the available results of physical testing. The results indicate that the OpenSees model can accurately capture the initial stiffness, strength and non-linear behavior of the shear walls.
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Imitation learning is a promising approach for generating life-like behaviors of virtual humans and humanoid robots. So far, however, imitation learning has been mostly restricted to single agent settings where observed motions are adapted to new environment conditions but not to the dynamic behavior of interaction partners. In this paper, we introduce a new imitation learning approach that is based on the simultaneous motion capture of two human interaction partners. From the observed interactions, low-dimensional motion models are extracted and a mapping between these motion models is learned. This interaction model allows the real-time generation of agent behaviors that are responsive to the body movements of an interaction partner. The interaction model can be applied both to the animation of virtual characters as well as to the behavior generation for humanoid robots.
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Conceptual database design is an unusually difficult and error-prone task for novice designers. This study examined how two training approaches---rule-based and pattern-based---might improve performance on database design tasks. A rule-based approach prescribes a sequence of rules for modeling conceptual constructs, and the action to be taken at various stages while developing a conceptual model. A pattern-based approach presents data modeling structures that occur frequently in practice, and prescribes guidelines on how to recognize and use these structures. This study describes the conceptual framework, experimental design, and results of a laboratory experiment that employed novice designers to compare the effectiveness of the two training approaches (between-subjects) at three levels of task complexity (within subjects). Results indicate an interaction effect between treatment and task complexity. The rule-based approach was significantly better in the low-complexity and the high-complexity cases; there was no statistical difference in the medium-complexity case. Designer performance fell significantly as complexity increased. Overall, though the rule-based approach was not significantly superior to the pattern-based approach in all instances, it out-performed the pattern-based approach at two out of three complexity levels. The primary contributions of the study are (1) the operationalization of the complexity construct to a degree not addressed in previous studies; (2) the development of a pattern-based instructional approach to database design; and (3) the finding that the effectiveness of a particular training approach may depend on the complexity of the task.
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The enzyme purine nucleoside phosphorylase from Schistosoma mansoni (SmPNP) is an attractive molecular target for the treatment of major parasitic infectious diseases, with special emphasis on its role in the discovery of new drugs against schistosomiasis, a tropical disease that affects millions of people worldwide. In the present work, we have determined the inhibitory potency and developed descriptor- and fragment-based quantitative structure-activity relationships (QSAR) for a series of 9-deazaguanine analogs as inhibitors of SmPNP. Significant statistical parameters (descriptor-based model: r² = 0.79, q² = 0.62, r²pred = 0.52; and fragment-based model: r² = 0.95, q² = 0.81, r²pred = 0.80) were obtained, indicating the potential of the models for untested compounds. The fragment-based model was then used to predict the inhibitory potency of a test set of compounds, and the predicted values are in good agreement with the experimental results
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This trial compared the cost of an integrated home-based care model with traditional inpatient care for acute chronic obstructive pulmonary disease (COPD). 25 patients with acute COPD were randomised to either home or hospital management following request for hospital admission. The acute care at home group costs per separation ($745, CI95% $595-$895, n = 13) were significantly lower (p < 0.01) than the hospital group ($2543, CI95% $1766-$3321, n = 12). There was an improvement in lung function in the hospital-managed group at the Outpatient Department review, decreased anxiety in the Emergency Department in the home-managed group and equal patient satisfaction with care delivery. Acute care at home schemes can substitute for usual hospital care for some patients without adverse effects, and potentially release resources. A funding model that allows adequate resource delivery to the community will be needed if there is a move to devolve acute care to community providers.
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Sandwich structures with soft cores are widely used in applications where a high bending stiffness is required without compromising the global weight of the structure, as well as in situations where good thermal and damping properties are important parameters to observe. As equivalent single layer approaches are not the more adequate to describe realistically the kinematics and the stresses distributions as well as the dynamic behaviour of this type of sandwiches, where shear deformations and the extensibility of the core can be very significant, layerwise models may provide better solutions. Additionally and in connection with this multilayer approach, the selection of different shear deformation theories according to the nature of the material that constitutes the core and the outer skins can predict more accurately the sandwich behaviour. In the present work the authors consider the use of different shear deformation theories to formulate different layerwise models, implemented through kriging-based finite elements. The viscoelastic material behaviour, associated to the sandwich core, is modelled using the complex approach and the dynamic problem is solved in the frequency domain. The outer elastic layers considered in this work may also be made from different nanocomposites. The performance of the models developed is illustrated through a set of test cases. (C) 2015 Elsevier Ltd. All rights reserved.
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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
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A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.
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Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression