947 resultados para Pattern Taxonomy Model
Resumo:
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.
Model-View-Controller architectural pattern and its evolution in graphical user interface frameworks
Resumo:
Model-View-Controller (MVC) is an architectural pattern used in software development for graphical user interfaces. It was one of the first proposed solutions in the late 1970s to the Smart UI anti-pattern, which refers to the act of writing all domain logic into a user interface. The original MVC pattern has since evolved in multiple directions, with various names and may confuse many. The goal of this thesis is to present the origin of the MVC pattern and how it has changed over time. Software architecture in general and the MVC’s evolution within web applications are not the primary focus. Fundamen- tal designs are abstracted, and then used to examine the more recent versions. Prob- lems with the subject and its terminology are also presented.
Resumo:
The reported productivity gains while using models and model transformations to develop entire systems, after almost a decade of experience applying model-driven approaches for system development, are already undeniable benefits of this approach. However, the slowness of higher-level, rule based model transformation languages hinders the applicability of this approach to industrial scales. Lower-level, and efficient, languages can be used but productivity and easy maintenance seize to exist. The abstraction penalty problem is not new, it also exists for high-level, object oriented languages but everyone is using them now. Why is not everyone using rule based model transformation languages then? In this thesis, we propose a framework, comprised of a language and its respective environment, designed to tackle the most performance critical operation of high-level model transformation languages: the pattern matching. This framework shows that it is possible to mitigate the performance penalty while still using high-level model transformation languages.
Resumo:
Models of the dynamics of nitrogen in soil (soil-N) can be used to aid the fertilizer management of a crop. The predictions of soil-N models can be validated by comparison with observed data. Validation generally involves calculating non-spatial statistics of the observations and predictions, such as their means, their mean squared-difference, and their correlation. However, when the model predictions are spatially distributed across a landscape the model requires validation with spatial statistics. There are three reasons for this: (i) the model may be more or less successful at reproducing the variance of the observations at different spatial scales; (ii) the correlation of the predictions with the observations may be different at different spatial scales; (iii) the spatial pattern of model error may be informative. In this study we used a model, parameterized with spatially variable input information about the soil, to predict the mineral-N content of soil in an arable field, and compared the results with observed data. We validated the performance of the N model spatially with a linear mixed model of the observations and model predictions, estimated by residual maximum likelihood. This novel approach allowed us to describe the joint variation of the observations and predictions as: (i) independent random variation that occurred at a fine spatial scale; (ii) correlated random variation that occurred at a coarse spatial scale; (iii) systematic variation associated with a spatial trend. The linear mixed model revealed that, in general, the performance of the N model changed depending on the spatial scale of interest. At the scales associated with random variation, the N model underestimated the variance of the observations, and the predictions were correlated poorly with the observations. At the scale of the trend, the predictions and observations shared a common surface. The spatial pattern of the error of the N model suggested that the observations were affected by the local soil condition, but this was not accounted for by the N model. In summary, the N model would be well-suited to field-scale management of soil nitrogen, but suited poorly to management at finer spatial scales. This information was not apparent with a non-spatial validation. (c),2007 Elsevier B.V. All rights reserved.
Resumo:
Purpose: The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems. Design/methodology/approach: The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation. Findings: A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment. Originality/value: This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel.
Resumo:
The work reported in this paper is motivated towards the development of a mathematical model for swarm systems based on macroscopic primitives. A pattern formation and transformation model is proposed. The pattern transformation model comprises two general methods for pattern transformation, namely a macroscopic transformation method and a mathematical transformation method. The problem of transformation is formally expressed and four special cases of transformation are considered. Simulations to confirm the feasibility of the proposed models and transformation methods are presented. Comparison between the two transformation methods is also reported.
Resumo:
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.
Resumo:
In this paper, we present a framework for pattern-based model evolution approaches in the MDA context. In the framework, users define patterns using a pattern modeling language that is designed to describe software design patterns, and they can use the patterns as rules to evolve their model. In the framework, design model evolution takes place via two steps. The first step is a binding process of selecting a pattern and defining where and how to apply the pattern in the model. The second step is an automatic model transformation that actually evolves the model according to the binding information and the pattern rule. The pattern modeling language is defined in terms of a MOF-based role metamodel, and implemented using an existing modeling framework, EMF, and incorporated as a plugin to the Eclipse modeling environment. The model evolution process is also implemented as an Eclipse plugin. With these two plugins, we provide an integrated framework where defining and validating patterns, and model evolution based on patterns can take place in a single modeling environment.
Resumo:
Intermediate-complexity general circulation models are a fundamental tool to investigate the role of internal and external variability within the general circulation of the atmosphere and ocean. The model used in this thesis is an intermediate complexity atmospheric general circulation model (SPEEDY) coupled to a state-of-the-art modelling framework for the ocean (NEMO). We assess to which extent the model allows a realistic simulation of the most prominent natural mode of variability at interannual time scales: El-Niño Southern Oscillation (ENSO). To a good approximation, the model represents the ENSO-induced Sea Surface Temperature (SST) pattern in the equatorial Pacific, despite a cold tongue-like bias. The model underestimates (overestimates) the typical ENSO spatial variability during the winter (summer) seasons. The mid-latitude response to ENSO reveals that the typical poleward stationary Rossby wave train is reasonably well represented. The spectral decomposition of ENSO features a spectrum that lacks periodicity at high frequencies and is overly periodic at interannual timescales. We then implemented an idealised transient mean state change in the SPEEDY model. A warmer climate is simulated by an alteration of the parametrized radiative fluxes that corresponds to doubled carbon dioxide absorptivity. Results indicate that the globally averaged surface air temperature increases of 0.76 K. Regionally, the induced signal on the SST field features a significant warming over the central-western Pacific and an El-Niño-like warming in the subtropics. In general, the model features a weakening of the tropical Walker circulation and a poleward expansion of the local Hadley cell. This response is also detected in a poleward rearrangement of the tropical convective rainfall pattern. The model setting that has been here implemented provides a valid theoretical support for future studies on climate sensitivity and forced modes of variability under mean state changes.
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Desde o começo da ocupação humana no litoral centro-sul de Santa Catarina, Brasil, a articulação entre processos naturais e antrópicos modelou uma paisagem fortemente domesticada, marcada pela construção massiva de concheiros de dimensões monumentais e pela permanência milenar. Na planície costeira entre Passagem da Barra (município de Laguna) e lago Figueirinha (município de Jaguaruna), 76 sambaquis foram mapeados, dos quais 48 possuem datação. O levantamento sistemático de sítios e datações permitiu identificar padrões de distribuição espacial nos sambaquis da região, quanto a contexto sedimentar da época de construção, estratigrafia e idade. Desse modo, reconheceram-se nos sítios da região: cinco contextos geológico-geomorfológicos de localização; três padrões estratigráficos; e quatro fases de ocupação sambaquieira baseadas na quantidade de sítios e no tipo de padrão construtivo dominante. O modelo integrado de evolução sedimentar e distribuição tempo-espacial de sambaquis indica que estes sítios eram construídos em áreas já emersas e pouco alagáveis, e que sítios interiores, afastados dos corpos lagunares, podem não se ter preservado ou não estarem expostos devido ao processo de assoreamento contínuo que caracterizou a região após a máxima transgressão holocênica. O cruzamento de dados aqui proposto evidencia a importância de abordagens integradas entre arqueologia e geociências no estudo da evolução das paisagens.
Resumo:
The application of Discriminant function analysis (DFA) is not a new idea in the studyof tephrochrology. In this paper, DFA is applied to compositional datasets of twodifferent types of tephras from Mountain Ruapehu in New Zealand and MountainRainier in USA. The canonical variables from the analysis are further investigated witha statistical methodology of change-point problems in order to gain a betterunderstanding of the change in compositional pattern over time. Finally, a special caseof segmented regression has been proposed to model both the time of change and thechange in pattern. This model can be used to estimate the age for the unknown tephrasusing Bayesian statistical calibration
Resumo:
About sixty small water bodies (coastal lagoons, marshes, salt pans, channels, springs, etc.) of the Spanish Mediterranean coast were sampled seasonally for one year (1979-1980), in order to study different aspects of their chemical composition. The concentrations of major ions (alkalinity, Cl-, Ca2+, Mg2+, Na+, and K+), nutrients (N.NO-3, N.NO2-, TRP and Si), oxygen and pH were determined for this purpose. The salt concentrations measured range between 0.4 and 361.3 g l-1. The samples have been divided into four classes of salinity (in g l-1): Cl, S < 5; C2, 5 40. Within these classes, the pattern of ionic dominance recorded is remarkably constant and similar to that found in most coastal lagoons (Cl- > So42- > Alk., for the anions, and Na+ > Mg2+ > Ca2+ > K+, for the cations), although other models occur especially in the first class. The dominance of Na+ and Cl-, as well as the molar ratios Mg2+/Ca2+ and Cl- / SO42- ,clearly increase from class Cl to class C4. The hyperhaline waters include different subtypes of the major brine type"c",, of EUGSTER & HARDIE (1978), the Na+ - (Mg2+) - Cl- - (SO42-) being the most frequent. Nutrient concentrations fall within a wide range (N.NO3 from 0.1 to 1100 mg-at 1-1; PRT from 0.01 to 23.56 mg-at l-1 and Si from 1.0 to 502.0 mg-at l-1). The oxygen values are very variable too, ranging between 0 and 14.4 ml l-1. Four different patterns of nutrient distribution have been distinguished based on the mean concentrations of N.NO3-, and TRP (mean values in mg-at l-1): A, N.NO3- < 10, TRP > l ; B, N.NO3- > 100, TRP < 1; C, 10 < N.NO3- < 100, TRP < 1; C, D, N.NO3- < 10, TRP < 1. As a rule, lagoons of low salinity (C1 and C2 classes) display the nutrient pattern C, and lagoons of high salinity (C3 and C4) show the nutrient pattern D. Model A only appears in waters of very low salinity, whereas model B does not seem to be related to salinity.
Resumo:
The application of Discriminant function analysis (DFA) is not a new idea in the study of tephrochrology. In this paper, DFA is applied to compositional datasets of two different types of tephras from Mountain Ruapehu in New Zealand and Mountain Rainier in USA. The canonical variables from the analysis are further investigated with a statistical methodology of change-point problems in order to gain a better understanding of the change in compositional pattern over time. Finally, a special case of segmented regression has been proposed to model both the time of change and the change in pattern. This model can be used to estimate the age for the unknown tephras using Bayesian statistical calibration
Resumo:
Este proyecto aspira comprender el proceder que le ha permitido a Crepes & Waffles generar perdurabilidad empresarial, motivo por el cual fue galardonada con el Premio Empresario Colombiano Mariposa de Lorenz (2008). La práctica empresarial de Crepes & Waffles permite resaltar su gestión de calidad, identidad organizacional y estrategia, factores que contrastaremos con las herramientas de medición de perdurabilidad empresarial planteados por la Universidad del Rosario y generar un diagnostico sobre la efectividad de estos en la empresa.
Resumo:
P>In the context of either Bayesian or classical sensitivity analyses of over-parametrized models for incomplete categorical data, it is well known that prior-dependence on posterior inferences of nonidentifiable parameters or that too parsimonious over-parametrized models may lead to erroneous conclusions. Nevertheless, some authors either pay no attention to which parameters are nonidentifiable or do not appropriately account for possible prior-dependence. We review the literature on this topic and consider simple examples to emphasize that in both inferential frameworks, the subjective components can influence results in nontrivial ways, irrespectively of the sample size. Specifically, we show that prior distributions commonly regarded as slightly informative or noninformative may actually be too informative for nonidentifiable parameters, and that the choice of over-parametrized models may drastically impact the results, suggesting that a careful examination of their effects should be considered before drawing conclusions.Resume Que ce soit dans un cadre Bayesien ou classique, il est bien connu que la surparametrisation, dans les modeles pour donnees categorielles incompletes, peut conduire a des conclusions erronees. Cependant, certains auteurs persistent a negliger les problemes lies a la presence de parametres non identifies. Nous passons en revue la litterature dans ce domaine, et considerons quelques exemples surparametres simples dans lesquels les elements subjectifs influencent de facon non negligeable les resultats, independamment de la taille des echantillons. Plus precisement, nous montrons comment des a priori consideres comme peu ou non-informatifs peuvent se reveler extremement informatifs en ce qui concerne les parametres non identifies, et que le recours a des modeles surparametres peut avoir sur les conclusions finales un impact considerable. Ceci suggere un examen tres attentif de l`impact potentiel des a priori.