Dynamic Renormalization Group and Noise Induced Transitions in a Reaction Diffusion Model

We investigate how additive weak noise (correlated as well as uncorrelated) modifies the parameters of the Gray-Scott (GS) reaction diffusion system by performing numerical simulations and applying a Renormalization Group (RG) analysis in the neighborhood of the spatial scale where biochemical reactions take place. One can obtain the same sequence of spatial-temporal patterns by means of two equivalent routes: (i) by increasing only the noise intensity and keeping all other model parameters fixed, or (ii) keeping the noise fixed, and adjusting certain model parameters to their running scale-dependent values as predicted by the RG. This explicit demonstration validates the dynamic RG transformation for finite scales in a two-dimensional stochastic model and provides further physical insight into the coarse-graining analysis proposed by this scheme. Through several study cases we explore the role of noise and its temporal correlation in self-organization and propose a way to drive the system into a new desired state in a controlled way.Comment: 8 pages, 21 figure

Particle In Cell Simulation of Combustion Synthesis of TiC Nanoparticles

A coupled continuum-discrete numerical model is presented to study the synthesis of TiC nanosized aggregates during a self-propagating combustion synthesis (SHS) process. The overall model describes the transient of the basic mechanisms governing the SHS process in a two-dimensional micrometer size geometry system. At each time step, the continuum (micrometer scale) model computes the current temperature field according to the prescribed boundary conditions. The overall system domain is discretized with a desired number of uniform computational cells. Each cell contains a convenient number of computation particles which represent the actual particles mixture. The particle-in-cell (discrete) model maps the temperature field from the (continuum) cells to the respective internal particles. Depending on the temperature reached by the cell, the titanium particles may undergo a solid-liquid transformation. If the distance between the carbon particle and the liquid titanium particles is within a certain tolerance they will react and a TiC particle will be formed in the cell. Accordingly, the molecular dynamic method will update the location of all particles in the cell and the amount of transformation heat accounted by the cell will be entered into the source term of the (continuum) heat conduction equation. The new temperature distribution will progress depending on the cells which will time-by-time undergo the chemical reaction. As a demonstration of the effectiveness of the overall model some paradigmatic examples are shown.Comment: submitted to Computer Physics Communication

An integrable SIS model

AbstractWe provide a demonstration of the integrability of a classical model of an infectious disease which neither kills nor induces autoimmunity by means of the Painlevé analysis and use the Lie theory of transformation groups to present an explicit solution

A demonstration case on the transformation of software architectures for service specification

This paper presents a demonstration case on the successive application of a model-based technique to assist on the refinement of software logical architectures. The technique is essentially based on the transformation of use cases into object diagrams. The applicability of the technique is illustrated by presenting some results from a mobile application. For mobile software, the definition of the underlying service-oriented architecture must consider as user requirements the services themselves, the mobile operators entry points and the final clients interfaces, and use them to characterize the platform. Within the presented demonstration case, the specification of one service of the mobile application was obtained by successively applying the technique.Research funded by FCT and FEDER under project STACOS (POSI/CHS/48875/2002)

Toward the adaptation of component-based architectures by model transformation: behind smart user interfaces

Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component-based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run-time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component-based architectures by using model transformation. Our aim is to generate at run-time a dynamic model transformation, because the rules describing their behavior are not pre set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run-time a rule subset from a repository. Selected rules are used to generate, through a higher-order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre-set transformation, which enables the dynamic adaptation of component-based architectures

Toward the adaptation of component-based architectures by model transformation: behind smart user interfaces

Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component-based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run-time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component-based architectures by using model transformation. Our aim is to generate at run-time a dynamic model transformation, because the rules describing their behavior are not pre set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run-time a rule subset from a repository. Selected rules are used to generate, through a higher-order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre-set transformation, which enables the dynamic adaptation of component-based architectures

Adding Rule-Based Model Transformation to Modelling Languages in MetaEdit+

MetaEdit+ is a commercial tool by MetaCase for creating domain-specific, syntax-directed visual modelling environments. MetaEdit+ synthesizes such environments from user-provided metamodels and contains a Generator Editor for code/report generation. An API is provided to allow external manipulation of models through SOAP. Currently, the MetaEdit+ tool does not natively support rule-based model-to-model transformation. Such transformations are useful as they allow domain experts to intuitively (using domain-specific notations) model either operational semantics (a simulator) or denotational semantics (through model-to-model transformation onto a model in a known formalism) of a modelling language. We will demonstrate how to add rule-based operational semantics to modelling languages in MetaEdit+. In our approach, transformation rules are visually created in MetaEdit+. The rule editor is synthesized using modified versions of the original language's metamodel. This modification is performed in a structured fashion using a process called RAMification. Both the model and the rules are exported from MetaEdit+ to Python code. This code is combined with Py-T-Core, our library of transformation language primitives, to apply the rules on the model. Our demonstration has a client-server architecture, with the MetaEdit+ visual modelling environment as the client and the transformation engine as the server. After each transformation step, in-place changes to the model are propagated to MetaEdit+ for visualization using the SOAP API. A simple (manufacturing) Production System modelling language is used as an example

Evaluating Modeled Intra- to Multidecadal Climate Variability Using Running Mann–Whitney \u3cem\u3eZ\u3c/em\u3e Statistics

An analysis method previously used to detect observed intra- to multidecadal (IMD) climate regimes was adapted to compare observed and modeled IMD climate variations. Pending the availability of the more appropriate phase 5 Coupled Model Intercomparison Project (CMIP-5) simulations, the method is demonstrated using CMIP-3 model simulations. Although the CMIP-3 experimental design will almost certainly prevent these model runs from reproducing features of historical IMD climate variability, these simulations allow for the demonstration of the method and illustrate how the models and observations disagree. This method samples a time series’s data rankings over moving time windows, converts those ranking sets to a Mann–Whitney U statistic, and then normalizes the U statistic into a Z statistic. By detecting optimally significant IMD ranking regimes of arbitrary onset and varying duration, this process generates time series of Z values that are an adaptively low-passed and normalized transformation of the original time series. Principal component (PC) analysis of the Z series derived from observed annual temperatures at 92 U.S. grid locations during 1919–2008 shows two dominant modes: a PC1 mode with cool temperatures before the late 1960s and warm temperatures after the mid-1980s, and a PC2 mode indicating a multidecadal temperature cycle over the Southeast. Using a graphic analysis of a Z error metric that compares modeled and observed Z series, the three CMIP-3 model simulations tested here are shown to reproduce the PC1 mode but not the PC2 mode. By providing a way to compare grid-level IMD climate response patterns in observed and modeled data, this method can play a useful diagnostic role in future model development and decadal climate forecasting