Modeling Artifacts Research Articles

Attached eddy model revisited using a minimal quasi-linear approximation

Townsend’s model of attached eddies for boundary layers is revisited within a quasi-linear approximation. The velocity field is decomposed into a mean profile and fluctuations. While the mean is obtained from the nonlinear equations, the fluctuations are modelled by replacing the nonlinear self-interaction terms with an eddy-viscosity-based turbulent diffusion and stochastic forcing. Under this particular approximation, the resulting fluctuation equations remain linear, enabling solutions to be superposed, the same theoretical idea used in the original attached eddy model. By leveraging this feature, the stochastic forcing is determined self-consistently by solving an optimisation problem which minimises the difference between the Reynolds shear stresses from the mean and fluctuation equations, subject to a constraint that the averaged Reynolds shear-stress spectrum is sufficiently smooth in the spatial wavenumber space. The proposed quasi-linear approximation is subsequently applied to channel flow for Reynolds number ranging from 500 to 20 000. The best result is obtained when the Reynolds stress is calculated by retaining only the two leading proper orthogonal decomposition modes, which further filters out the modelling artefact caused by the unphysical stochastic forcing. In this case, the resulting turbulence intensity profile and energy spectra exhibit the same qualitative behaviour as direct numerical simulation (DNS) data throughout the entire wall-normal domain, while reproducing the early theoretical predictions of the original attached eddy model within a controlled approximation to the Navier–Stokes equations. Finally, the proposed quasi-linear approximation reveals that the peak streamwise and spanwise turbulence intensities may deviate slightly from the logarithmic scaling with the Reynolds number for , and the supporting evidence is presented using the existing DNS data.

Scalable model views over heterogeneous modeling technologies and resources

When engineering complex systems, models are typically used to represent various systems aspects. These models are often heterogeneous in terms of modeling languages, provenance, number or scale. As a result, the information actually relevant to engineers is usually split into different kinds of interrelated models. To be useful in practice, these models need to be properly integrated to provide global views over the system. This has to be made possible even when those models are serialized or stored in different formats adapted to their respective nature and scalability needs. Model view approaches have been proposed to tackle this issue. They provide unification mechanisms to combine and query various different models in a transparent way. These views usually target specific engineering tasks such as system design, monitoring and evolution. In an industrial context, there can be many large-scale use cases where model views can be beneficial, in order to trace runtime and design-time data, for example. However, existing model view solutions are generally designed to work on top of one single modeling technology (even though model import/export capabilities are sometimes provided). Moreover, they mostly rely on in-memory constructs and low-level modeling APIs that have not been designed to scale in the context of large models stored in different kinds of data sources. This paper presents a general solution to efficiently support scalable model views over heterogeneous modeling resources possibly handled via different modeling technologies. To this intent, it describes our integration approach between a model view framework and various modeling technologies providing access to multiple types of modeling resources (e.g., in XML/XMI, CSV, databases). It also presents how queries on such model views can be executed efficiently by benefiting from the optimization of the different model technologies and underlying persistence backends. Our solution has been evaluated on a practical large-scale use case provided by the industry-driven European MegaM@Rt2 project that aims at implementing a runtime $$\leftrightarrow $$ design time feedback loop. The corresponding EMF-based tooling support, modeling artifacts and reproducible benchmarks are all available online.

Um Modelo de Gamificação para Redes Sociais Educacionais

O uso de redes sociais associados a estratégias de gamificação podem auxiliar no processo de aprendizagem. Este estudo parte da hipótese de que uma rede social educacional gamificada potencializa o engajamento e a motivação dos sujeitos em práticas pedagógicas que envolvem a rede. Este artigo propõe um modelo de gamificação aplicada a redes sociais educacionais, com o intuito de potencializar a motivação e o engajamento dos alunos em práticas pedagógicas envolvendo os recursos da rede. O estudo faz uso do método Design Science Research (DSR), para construção do artefato proposto. A pesquisa tem caráter qualitativo, em que foram avaliadas as técnicas de gamificação propostas pelo modelo (artefato), tendo como estudo de caso sua aplicação na Rede Social Educacional Teia. Com base em dois experimentos realizados junto a um curso de graduação e a alunos de ensino médio participantes de um projeto junto a universidade, os resultados da análise da pesquisa evidenciaram que a rede Teia gamificada favoreceu a motivação e o engajamento dos alunos, melhorando a experiência destes durante as atividades realizadas. Palavras-chave: Gamificação. Redes sociais educacionais. Jogos digitais.

Nonparametric Estimation of DEM Error in Multitemporal InSAR

Isolating phase residuals due to inaccurate external digital elevation model (DEM) is important in retrieval and interpretation of deformation behavior from interferometric synthetic aperture radar (InSAR) observations. Multitemporal InSAR (MTInSAR), by taking DEM error as a parameter, can make the isolation possible. However, due to the presence of atmospheric artifacts in observations and improper deformation model employed in the observation system, accurate retrieval of DEM error cannot be guaranteed in current MTInSAR techniques. Considering that the DEM error has a fixed spatial pattern and its contribution to interferometric phase-only changes with spatial baselines, we propose here a nonparametric method that can estimate the DEM error in a more robust way. To retrieve signals having a fixed spatial pattern from unwrapped MTInSAR measurements, the independent component analysis (ICA) is used. Experiments with synthetic and real data sets indicate the proposed method is able to estimate DEM error with no $a$ priori information about deformation. Moreover, experiments also show that the method can provide a more robust estimation when the observed phase observations are affected by atmospheric delays and/or the number of interferograms used is limited.

New acoustic approximation for transversely isotropic media with a vertical symmetry axis

Seismic data processing in the elastic anisotropic model is complicated due to multiparameter dependency. Approximations to the P-wave kinematics are necessary for practical purposes. The acoustic approximation for P-waves in a transversely isotropic medium with a vertical symmetry axis (VTI) simplifies the description of wave propagation in elastic media, and as a result, it is widely adopted in seismic data processing and analysis. However, finite-difference implementations of that approximation are plagued with S-wave artifacts. Specifically, the resulting wavefield also includes artificial diamond-shaped S-waves resulting in a redundant signal for many applications that require pure P-wave data. To derive a totally S-wave-free acoustic approximation, we have developed a new acoustic approximation for pure P-waves that is totally free of S-wave artifacts in the homogeneous VTI model. To keep the S-wave velocity equal to zero, we formulate the vertical S-wave velocity to be a function of the model parameters, rather than setting it to zero. Then, the corresponding P-wave phase and group velocities for the new acoustic approximation are derived. For this new acoustic approximation, the kinematics is described by a new eikonal equation for pure P-wave propagation, which defines the new vertical slowness for the P-waves. The corresponding perturbation-based approximation for our new eikonal equation is used to compare the new equation with the original acoustic eikonal. The accuracy of our new P-wave acoustic approximation is tested on numerical examples for homogeneous and multilayered VTI models. We find that the accuracy of our new acoustic approximation is as good as the original one for the phase velocity, group velocity, and the kinematic parameters such as vertical slowness, traveltime, and relative geometric spreading. Therefore, the S-wave-free acoustic approximation could be further applied in seismic processing that requires pure P-wave data.

A Gaussian moment method for polydisperse multiphase flow modelling

The accurate prediction of multiphase flow when particles are differentiated by a set of “internal” variables, such as size or temperature, can pose modelling and numerical challenges. Although Lagrangian particle methods can provide predictive simulations of a wide spectrum of complex multiphase problems, they can become prohibitively expensive as the number of particles becomes large. Alternatively, Eulerian approaches have the potential to improve the computational efficiency of multiphase flows, but classical methods produce modelling artifacts or do not properly treat the local statistical dependence between the particle velocities and internal variables, or between the internal variables themselves. In this paper an extension is proposed of the classical Gaussian ten-moment model from gaskinetic theory to a model for the treatment of a dilute particle phase with an arbitrary number of internal variables based on an entropy-maximization argument. Unlike previous formulations, this new model provides a set of first-order robustly-hyperbolic balance laws that include a direct treatment for the local statistical variance of each variable, as well as the covariance between the internal variables or the internal variables and particle velocity. A study of the wave speeds of the general hyperbolic system is presented. To demonstrate an example application, the model is then specialized for polydisperse flows that are subject to viscous fluid drag as well as gravitational and buoyancy forces. The complete eigenstructure of this fifteen-moment polydisperse Gaussian model (PGM) is presented and the PGM is shown to maintain a physically realizable distribution function for all admissible initial conditions. Finally, several illustrative low-dimensional problems are studied to demonstrate the predictive capabilities of the new model.

Explaining the leopards’ spots: Responsibility-embedding in business model artefacts across spaces of institutional complexity

This article explores the phenomenon that corporations' commercial-logic business models increasingly embed responsibility-logic patterns: The proverbial leopards' spots. We have conducted a thematic analysis of responsibility patterns in annual report business model descriptions (artefacts) and a qualitative comparative analysis of corporations' strategic environment. We find distinct responsibility patterns, uniquely combined in four distinct types of responsibility business model artefacts. We explain these types through responses to institutional complexity between commercial and responsibility logics. Strategic responses were enabled or constrained by the characteristics of the varying spaces of institutional complexity of particular corporations. We contribute to an emerging heterogeneous business model literature by providing empirical evidence. We contribute to the institutional discussion of corporate responsibility by providing a typology of responsibility business models and by explaining why each type emerged in their respective institutional space. We also discuss how practitioners’ efforts to embed responsibility into their business models may benefit from our findings.

A tool-supported approach for assessing the quality of modeling artifacts

Context: Software quality engineering is increasingly gaining interests also in the Model-Driven Engineering community as testified by a large corpus of research that has been produced over the last few years. Quality models are presented as convenient artifacts to specify and organize quality attributes that are of interest for considered stakeholders. Motivation: Existing approaches enabling the specification of quality models are affected by relevant limitations including limited extensibility, artifact specificity, and manual assessment, which might lead to informal, subjective, and non-reproducible assessment processes. Goal: This paper presents an approach and related tools supporting the definition of quality models underpinning the quality assessment of modeling artifacts. Quality models are defined in terms of sets of high-level quality attributes, which are top-down decomposed into sets of subordinate attributes. An operative environment is also provided to apply the defined quality models on actual modeling artifacts enabling automated quality assessment. A set of dedicated experiments is conducted to validate the approach. The experimental results show that the proposed techniques permit modelers to define quality models taken from the literature, and apply them to assess the quality of metamodels and transformations retrieved from public repositories. The validation permitted also to analyse the performance in terms of various population structures and size.

A Bayesian iterative geomagnetic model with universal data input: Self-consistent spherical harmonic evolution for the geomagnetic field over the last 4000 years

Changes of the Earth's magnetic field have been observed with varying spatial and temporal resolution since the middle ages by means of compasses, and later by refined mechanical inclination and intensity instruments, until the modern use of quantum mechanical devices and satellite observations. Ancient records, on the other hand, rely on the natural magnetization of archaeological artifacts and other materials, such as rocks and sediments, being therefore indirect, less accurate and sparser. The combination of such heterogeneous records into a single, self-consistent, global model of the geomagnetic field is very challenging: the highly uneven data coverage, both in space and time, requires a careful handling of data uncertainties and error correlations. Previous models dealt with this problem using separated treatments of instrumental data and indirect records, respectively, as well as a different handling of the dipole field component, with respect to non-dipole terms. Here we present a global geomagnetic field model based, for the first time, on the simultaneous inversion of historical, archaeomagnetic, and volcanic records, using a Bayesian approach with minimal-committing time regularization that minimizes the energy of secular variation. A detailed assessment of data uncertainty and error correlation is used to minimize artifacts generated by the appearance of an overwhelming number of incomplete, mostly declination-only records, associated with shipboard measurements in the early colonial period. Our model yields lower dipole energies, which better match modern values, as well as higher non-dipole energies and a stronger secular variation of the dipole moment. Some model artifacts associated with extremely heterogeneous distributions of records in time and space are not completely eliminable and might be caused by incorrect a-priori uncertainty assessments. Alternatively, they might represent an intrinsic limit that can be overcome only by adding new records at critical locations and times.

Integrated revision and variation control for evolving model-driven software product lines

Software engineering projects are faced with abstraction, which is achieved by software models, historical evolution, which is addressed by revision control, and variability, which is managed with the help of software product line engineering. Addressing these phenomena by separate tools ignores obvious overlaps and therefore fails at exploiting synergies between revision and variation control for models. In this article, we present a conceptual framework for integrated revision and variation control of model-driven software projects. The framework reuses the abstractions of revision graphs and feature models and follows an iterative, revision-control-like approach to software product line engineering called product-based product line development. A single version (i.e., a variant of a selected revision) is made available in a workspace, where the user may apply arbitrary modifications. Based on a user-provided specification of the affected variants, the changes are automatically written back to a transparent repository that relies on an internal multi-version storage. The uniform handling of revisions and variants of models is achieved by transparently mapping version concepts to a semantic base layer, which is defined upon propositional logic. At the heart of the conceptual framework is a dynamic filtered editing model, which allows that the versioned artifacts and the feature model co-evolve. We contribute algorithms for checkout and commit, which satisfy a set of consistency constraints referring to variant specifications in an evolving feature model. This article furthermore addresses the orchestration of collaborative development by distributed replication and the well formedness of text and model artifacts to be checked out into the workspace. The Eclipse-based tool SuperMod demonstrates the feasibility of the conceptual framework. It allows the user to reuse arbitrary editing tools for text-based programming and/or Ecore-based modeling languages. An evaluation based on three case studies investigates the properties of SuperMod with a specific focus on filtered editing. The evaluation demonstrates that the dynamic filtered editing model reduces the cognitive complexity and the amount of user interaction necessary for variation control when compared to unfiltered model-driven approaches to software product line engineering.

Usability Profiling of Enterprise Validation Matrices, Metaphors and Methodologies

The evolution of Enterprise Architecture modeling techniques need for integration of models with other Information Systems collaborations require a methodology that ensures that the heterogeneous models can be validated for their coherence andcongruity with common business motivation. However, a major challenge that impedes this requirement is that the more EAFmodeling tools and methodologies are broadened, the more complex it tends to become as the tools strive to satisfy all necessaryaspects of the enterprise identified collectively. Many enterprises that have adopted a mix and match methodologies are facedwith the issue of models consolidation resulting from inconsistent and heterogeneous modeling artifacts over a spuriousframework that needs validation. This article seeks to review the composition and capabilities of existing EA validation techniquesand to recommend a surrogate innovative approach that addresses specifically model validation. An approach that ensuresharmonization of EA models within similar abstractions irrespective of the impediments occasioned by the multi-variant EAF modeling methodologies is proposed

Schematization of Enterprise Architecture Models for Ontology Validation

In order to maintain agility and flexibility within competitive markets, organizations have been exploring ways of adjustingproactively to innovations, changes and complex events by use of EA concepts as a means to model their business processes andstrategies. Consequently, the need to ensure appropriate validation of EA taxonomies has been considered as an essential part ofthese processes as the requirement ensures that business motivation relates to information systems and technological infrastructure. In this research, an approach referred to as Model Driven Validation Approach (MDVA) is introduced. MDVAallows modeling of EA with validation attributes, formalization of the validation concepts and transformation of model artifacts toontologies. The transformation simplifies querying based on motivation and constraints. The major contributions of this work arethe extension of a metamodel of Business Layer of an EAF with Validation Element and the development of EAF model toontology transformation Approach. With this innovation, domain-driven design and object-oriented analysis concepts are appliedto achieve the EAF model’s validation using ontology querying methodology. Two case studies were deployed in the researchusing the Design Science Research approach. The produced artifacts were evaluated based on the criteria specified in therequirements of the design context with emphasis on functionality, completeness, consistency, accuracy, performance, reliabilityand usability.

How do Individuals Interpret Multiple Conceptual Models? A Theory of Combined Ontological Completeness and Overlap

When analyzing or designing information systems, users often work with multiple conceptual models because each model articulates a different, partial aspect of a real-world domain. However, the available research in this area has largely studied the use of single modeling artifacts only. We develop a new theory about interpreting multiple conceptual models that details propositions for evaluating how individuals select, understand, and perceive the usefulness of multiple conceptual models. We detail implications of our theory development for empirical research on conceptual modeling. We also outline practical contributions for the design of conceptual models and for choosing models for systems analysis and design tasks. Finally, to stimulate research that builds on our theory, we illustrate procedures for enacting our theory and discuss a range of empirically relevant boundary conditions.

The Opportunities of Using Business Models in Service Management - A Review and Classification of Business Model Artefacts

Through increased globalization and fast changing customer demands companies need to adapt their service management constantly. Business model approaches can provide a suitable base to comprehend the service management of a company. However, existing knowledge about the business model artefacts and the possibilities in service management is very diffuse. With this work, we want to shed light on the state-of-the-art of business model research with a specific focus on the different possibilities for a use in service management. We perform a systematic literature review and present a comprehensive overview of different existing business model artefacts including constructs, models, methods and instantiations. This can be possibly used to support service management and for future research this field. This demands requirements of suitable tool support from theory and practice both. It furthermore strengthens the relation between business modeling and service management. Overall, this research should strengthen the awareness of using the available business model capabilities for service management in order to create powerful management tools.

PLS path modeling – a confirmatory approach to study tourism technology and tourist behavior

研究目的因为旅游酒店科技（TTH）根据游客需求而定制科技产品, TTH必须在结构方程模型（SEM）下结合游客行为和设计等变量。一般行为变量在模型中是常见因子, 本研究将这些变量编入TTH结构成为模块。本研究采用PLS-PM方法来预估含有隐性变量和模块的模型。.研究设计/方法/途径本研究设计PLS-PM模式, 即确定性全变量方法。TTH学者们通过引进结构形成模型模块, 使用PLS-PM研究方法, 以回答研究问题“模块xyz有用吗？”, 因此对TTH进一步理解。为了展示复合模型, 本论文采用实际验证。.研究结果PLS-PM在面对模块内存在常见因子和复合模块的结构时是有力方法。实际验证结果通过整体最佳模型参数, 得到隐性变量和模块。为此, 研究者们能够在统计方法上测量是否开发的模型模块是否有用。.研究原创性/研究价值据作者所知, 本论文是首个研究在TTH领域上应用模块和常见因子模型。本研究引进显性变量在模型模块中, 以指导学者评估PLS-PM结果报告。.

A Higher-Order Polynomial Method for SPECT Reconstruction.

Existing single-photon emission computed tomography (SPECT) reconstruction methods are mostly based on discrete models that may be viewed as piecewise constant approximations of a continuous data acquisition process. Due to low accuracy order of piecewise constant approximations, a traditional discrete model introduces irreducible model errors which are a bottleneck of the quality improvement of reconstructed images in clinical applications. To overcome this drawback, we develop a higher-order polynomial method for SPECT reconstruction. Specifically, we represent the data acquisition of SPECT imaging by using an integral equation model, approximate the solution of the underlying integral equation by higher-order piecewise polynomials leading to a new discrete system and introduce two novel regularizers for the system, by exploring the a priori knowledge of the radiotracer distribution, suitable for the approximation. The proposed higher-order polynomial method outperforms significantly the cutting edge reconstruction method based on a traditional discrete model in terms of model error reduction, noise suppression, and artifact reduction. In particular, the coefficient of variation of images reconstructed by the piecewise linear polynomial method is reduced by a factor of 10 in comparison to that of a traditional discrete model-based method.

Influence of the Pore Shape and Size Distribution in Hierarchically Porous Electrodes on Energy and Power Densities of Electrochemical Devices

The microporous-mesoporous hierarchical structure of nanostructural carbon and modified carbon materials has a strong influence on the electrochemical characteristics of the electrical energy conversion and storage devices [1-4]. To improve the mass transfer rate, the understanding of the accumulation mechanisms of the ions in microporous-mesoporous materials is inevitable having huge impact on the power and energy density characteristics of supercapacitors, Li- and Na-ion batteries as well as fuel cells. Therefore, it is crucial to have a good understanding of the graphitization level, porosity and hierarchical structure of carbon and modified carbon materials used [1-5]. Therefore, systematical analysis of the gas adsorption, Raman, X-ray diffraction, scanning electron microscopy, photoelectron spectroscopy (including synchrotron beam based method), high-resolution transmission electron microscopy (with EELS and SAED) and small-angle neutron scattering data for various carbon materials prepared by high-temperature chlorination, hydrothermal carbonization, etc. methods will be given. Comparison with small-angle X-ray scattering data [6] will be given. The data obtained by gas-phase analysis methods will be compared with cyclic voltammetry, constant current charge/discharge, electrochemical impedance and constant power discharge data. Influence of electrolyte chemical composition, solvent characteristics (aqueous and non-aqueous) and ionic liquids with and without non-aqueous solvent additions will be discussed in combination with hierarchical porosity, graphitization level and particle size analysis data. All carbon samples were very carefully reduced with hydrogen at 800 оC, thereafter dried under vacuum at 350 оC at least for 24 h priori each measurement. N2 sorption analysis at -196 оC, Ar at -186 oC and CO2 sorption analysis at 0 оC were carried out on an ASAP 2020 instrument (Micromeritics, USA). The experimental data were treated with classical models (BET and t-plot) as well as different NLDFT models available through 3Flex and SAIEUS (Micromeritics, USA) software [7,8]. The results for pore size distribution (PSD) calculations from N2 isotherms using a slit shape pore model and model “Carbon-N2, 2D-NLDFT Heterogeneous Surface” [9,10] (SAIEUS, Micromeritics, USA) will be reported. The pore size distribution data calculated from CO2 sorption isotherms using a slit shaped pore model and model “CO2-DFT Model” (software 3Flex, Micromeritics, USA) will be discussed. Although, the pore size distributions calculated from CO2 have high level of roughness and several model artefacts (e.g. around 0.7 nm) [11,12], the results generally support the findings from N2 sorption isotherms. In addition, Raman spectra analysis, small-angle neutrons scattering and small angle X-ray scattering combined with the FIB-SEM and HR-TEM data will be discussed. The results obtained will be compared to the behavior of these materials in different energy storage/conversion related devices like supercapacitors [1-6] and Li- and Na-ion battery negatively charged electrodes [4]. Thus, the importance of graphitization level, hierarchical porous structure and pore shape along with pore size distribution data for carbon materials energy and power densities will be addressed and demonstrated. Acknowledgments Authors would like to thank HZB and PSI for the allocation of neutron radiation beamtime on instruments V16 and FOCUS, respectively; and The Estonian Ministry of Education and Research (institutional research project IUT20-13, personal research grant PUT55) and European Regional Development Fund (The Centres of Excellence 3.2.0101–0030 and 2014-2020.4.01.15-0011) for financial support.

Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts’ annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.

Dependability-enhanced unified modeling and simulation methodology for Critical Infrastructures

ContextCritical infrastructures (CIs) are mission-critical, large-scale systems that provide essential products and services for everyday life. Modeling and simulation (M&S) technique is one of useful methods for understanding the complex and emergent behavior of CIs. However, the several characteristics of CIs, such as interdependence, adaptability, dependability, etc., can disturb modelers’ activities to develop the quality models for CIs. The quality of these models can affect the reliability of simulation results as well as the reusability of the models in further simulations. ObjectiveIn this paper, we propose a M&S methodology that aims to improve the quality of CI models by means of focusing on dependability, which is one of important characteristics of CIs. MethodFirst, we propose a system to model and simulate CIs based on discrete event system specification, a belief-desire-intention model, and a role-oriented command hierarchy model. Next, we integrate several modeling methods, such as goal modeling, agent-based modeling, and object-oriented modeling, into a unified M&S methodology (UMAS) to seamlessly develop CI models from initial requirements to final modeling artifacts. Finally, we propose a dependability-enhanced UMAS (DUMAS) that can deal with the dependability of CI models. ResultsIn our case study of applying the DUMAS into the M&S of a smart grid, we show how the DUMAS reflects the characteristics of CIs on the overall modeling artifacts. ConclusionThe DUMAS provides a novel method that can improve the quality of CI models through elaborating on the activities of requirements engineering with regard to the dependability of CIs. Therefore, modelers can systematically develop quality models from requirements analysis to implementation in compliance with the DUMAS.

Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation

Retrieved soil moisture estimates from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) radiometer are assimilated into the Noah land surface model (LSM) within the NASA Land Information System. The experimental testbed is based on a real-time LSM system produced by the NASA Short-Term Prediction Research and Transition Center. A nonlocalized cumulative distribution function-matching bias correction (BC) is applied to the SMAP retrievals, with separate correction curves calculated based on soil texture categories. We show that the assimilation of SMAP soil moisture retrievals with nonlocalized BC can mitigate two types of artifacts due to spatially varying errors in the forcing data from: 1) bad point (rain gauge) data and 2) strong gradients along the eastern U.S.–Canada border, resulting from blending different observing systems.