Multi-paradigm Modeling Research Articles

A Multi-Paradigm Computational Model of Materials Electrochemical Reactivity for Energy Conversion and Storage

In this paper we report a new multi-paradigm modeling approach devoted to the investigation of the electrochemical reactivity of materials in electrodes for energy conversion or storage applications. The approach couples an atomistically-resolved Kinetic Monte Carlo (KMC) modeling module describing the electrochemical kinetics in an active material, with continuum modeling modules describing reactants transport at the active material/electrolyte nanoscopic interface (electrochemical double layer region) and along the mesoscale electrode thickness. The KMC module is developed by extending the so-called Variable Step Size Method (VSSM) algorithm (called here Electrochemical-VSSM) and constitutes the first VSSM extension reported so far which allows calculating the electrode potential as function of the imposed current density. The KMC module can be parameterized with activation energies calculated from Density Functional Theory (DFT), and thanks to the coupling with the transport modules, it describes the materials reactivity in electrochemical conditions. This approach allows us to study how the surface morphology (e.g. distribution of inactive sites, size of the active material particle, etc.) impacts the performance of the electrode. As an application example, we report here a computational investigation of the Oxygen Reduction Reaction (ORR) kinetics in a Pt(111)-based Polymer Electrolyte Membrane Fuel Cell (PEMFC) cathode.

A multiparadigm intelligent tutoring system for robotic arm training

To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.

A multi-paradigm modeling framework to simulate dynamic reciprocity in a bioreactor.

Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications.

The effects of vehicle-to-grid systems on wind power integration

Renewable energy portfolio standards have created a large increase in the amount of renewable electricity production, and one technology that has benefited greatly from these standards is wind power. The uncertainty inherent in wind electricity production dictates that additional amounts of conventional generation resources be kept in reserve, should wind electricity output suddenly dip. The introduction of plug-in hybrid electric vehicles into the transportation fleet presents an possible solution to this problem through the concept of vehicle-to-grid power. The ability of vehicle-to-grid power systems to help solve the variability and uncertainty issuess in systems with large amounts of wind power capacity is examined through a multiparadigm simulation model. The problem is examined from the perspectives of three different stakeholders: policy makers, the electricity system operator and plug-in hybrid electric vehicle owners. Additionally, a preliminary economic analysis of the technology is performed, and a comparison made with generation technologies that perform similar functions. Copyright © 2011 John Wiley & Sons, Ltd.

Relational grounding facilitates development of scientifically useful multiscale models

We review grounding issues that influence the scientific usefulness of any biomedical multiscale model (MSM). Groundings are the collection of units, dimensions, and/or objects to which a variable or model constituent refers. To date, models that primarily use continuous mathematics rely heavily on absolute grounding, whereas those that primarily use discrete software paradigms (e.g., object-oriented, agent-based, actor) typically employ relational grounding. We review grounding issues and identify strategies to address them. We maintain that grounding issues should be addressed at the start of any MSM project and should be reevaluated throughout the model development process. We make the following points. Grounding decisions influence model flexibility, adaptability, and thus reusability. Grounding choices should be influenced by measures, uncertainty, system information, and the nature of available validation data. Absolute grounding complicates the process of combining models to form larger models unless all are grounded absolutely. Relational grounding facilitates referent knowledge embodiment within computational mechanisms but requires separate model-to-referent mappings. Absolute grounding can simplify integration by forcing common units and, hence, a common integration target, but context change may require model reengineering. Relational grounding enables synthesis of large, composite (multi-module) models that can be robust to context changes. Because biological components have varying degrees of autonomy, corresponding components in MSMs need to do the same. Relational grounding facilitates achieving such autonomy. Biomimetic analogues designed to facilitate translational research and development must have long lifecycles. Exploring mechanisms of normal-to-disease transition requires model components that are grounded relationally. Multi-paradigm modeling requires both hyperspatial and relational grounding.

A multi-paradigm modeling framework for energy systems simulation and analysis

The modern world energy system is highly complex and interconnected and the effects of energy policies may have unintended consequences. Modeling and analysis tools can therefore be crucial to gaining insight into the interactions between system components and formulating policies that will shape the future energy system. We present in this work a multi-paradigm modeling framework that allows for the continual adjustment and refinement of energy system models as the understanding of the system under study increases. This flexible and open framework allows for the consideration of different levels of model aggregation, timescales and geographic considerations within the same model through the use of different modeling formalisms. We also present a case study of the combined California natural gas and electricity systems that illustrates how the framework may be used to account for the significant uncertainty that exists within the system.

(P2-86) A Paradigm of Emergency Volunteering: The Case of a Remote Rural Area in Thailand

BackgroundChanges in gender roles, the nature of the family, marriage patterns, political shifts, the nature of work, and the increase in the number and severity of emergencies are influencing volunteering and changing it in accordance with these changes. This study in a remote rural area in the north of Thailand explores paradigms of emergency volunteering in such areas based on the concept of the multi-paradigm model of volunteering.MethodsA sample of twenty stakeholders: leaders, village health volunteers, the director of the local administrative organization and community heads provided data during in-depth interviews. Data were analyzed using content analysis.ResultsThe results showed that the paradigms of emergency volunteering existing in the community were the functionalist paradigm and the interpreted paradigm. The functionalist paradigm relies on the belief in universal truths, thus emergency volunteering should be controlled and ordered. The community also believe that as a collective entity they achieve positive outcomes and benefits from emergency volunteering. In contrast, the interpreted paradigm relies on the belief in multiple truths. Thus emergency volunteering should be understood in terms of the individual experiences that emerge for each person from emergency volunteering. Furthermore, the community also believe that emergency volunteering should be focused on extracted cues and the process of helping emergency patients.ConclusionThe conclusion of this study is that the management of emergency volunteering in the context of remote rural communities should be well ordered and be by consensus.

Multi-Paradigm Modeling of the Effects of PHEV Adoption on Electric Utility Usage Levels and Emissions

Plug-in hybrid electric vehicles (PHEVs) are a transportation technology on the cusp of introduction that has the potential to couple the transportation and electricity energy systems. A multiparadigm model of the electricity and transportation systems of Alexandria, Virginia has been developed in order to examine the effects of PHEV introduction. This model combines detailed subsector models of the transportation, household electricity demand, and electricity supply sectors. The effects of PHEV adoption on the electricity supply sector have been explored through an examination of the changes in both total electricity usage and peak electricity demand that could be expected. Additionally, the differences in emissions of carbon dioxide, nitrous oxides, and sulfur dioxide between conventional vehicles and PHEVs charged under varying electricity supply mixtures has been quantified. The results indicate that the charging pattern of the vehicles used can significantly alter the effects on the system as a whole.

Engineering the Dynamic Behavior of Metamodeled Languages

Language engineering is a key factor in Multi-Paradigm Modeling (MPM). Since MPM strongly builds on metamodeling, the applied language engineering methods must also be generic enough to support various metamodels. Besides the generic methods to build the abstract and concrete syntax of a visual language, only a few solutions are available to describe the dynamic behavior (‘animation’) of the models. The aim of this paper is to contribute (i) an event-based conceptual architecture to support animation, (ii) a set of visual languages to describe the animation of the models and their execution. These concepts were used to implement the animation support in our tool called Visual Modeling and Transformation System (VMTS). The VMTS animation framework introduces novel languages to describe certain aspects of animation, while integrating the benefits of the existing approaches. Our solution clearly separates the domain knowledge and the animation description both on a conceptual and implementation level. Thus, the VMTS offers a concise and systematic solution to provide a highly customizable animation framework for metamodeled languages with strong integration support to external systems such as simulation engines. The efficiency of the approach is illustrated with a rather complex animation case study implementing a model transformation debugger.

Guest Editorial: Special Issue on Multi-paradigm Modeling

For decades, computational modeling and simulation have been valuable tools in tackling specific problems in the design of engineered systems. More recently, however, computational methods have moved from being applied to solve specific problems such as algorithm design, to becoming essential at the system design level. As an exponent of this trend, Model-Based Design has become key to being successful and competitive in the design of computer-based systems. Model-Based Design promotes computational models to prime deliverables in design, which has allowed interand intra-departmental communication based on a shared representation. This has been critical in minimizing design misunderstandings, eliminating reimplementation errors, and reducing design iterations. The autocatalytic effect of using models at the system level and across organizations is putting forward an increasing set of demanding requirements on the theory, methodology, and technology of modeling and simulation. Modeling and simulation are indispensable for activities such as analysis, synthesis, design, understanding, testing, and optimization of systems, yet the complexity of modern systems is rapidly increasing. Though difficult to characterize, these systems share their heterogeneity of components, large number of individual interacting com-

Towards Formal Analysis of Multi-paradigm Model Transformations

The Multi-Paradigm Modeling (MPM) approach of model-based development emphasizes the specification of a system by multiple models. We use transformations to automatically transform, integrate and synchronize models. Verification and validation of model transformations are fundamental issues: we need to express what a valid model is and how a valid model transformation may transform the models; otherwise, we have to analyze each transformed model individually, which makes it difficult to automate the process of using models. We have formally analyzed various model transformations in several case studies and industrial projects. From this experience, we have distilled the frequently recurring techniques and solutions, referred to as Model Transformation Analysis (MTA) methods. These instances, similarly to design patterns in object-oriented programming, define special constructions as solutions for recurring problems that arise when one implements a model transformation. Moreover, MTA methods contain special techniques and language features that should be taken into account when one designs a model transformation framework or a model transformation language. We hope that MTA methods may be the basis of automated formal analysis techniques of model transformations. This paper contributes the concept and instances of MTA methods and provides a case study based on an industrial project of mobile application development. With this real-world example, we want to demonstrate the role and use of MTA methods. The case study is implemented in Visual Modeling and Transformation System (VMTS), which is a tool that realizes the MPM concept to provide a model and model transformation-based environment for software development.

Exploring Multi-Paradigm Modeling Techniques

Multi-Paradigm Modeling (MPM) addresses the necessity of using multiple modeling paradigms when designing complex systems. Because of its multidisciplinary nature, the MPM field involves research teams with technical backgrounds as different as control science, model checking, modeling language engineering or system-on-chip development. In this paper, we propose to explore the MPM domain through a survey of existing techniques from different horizons. Since the heterogeneity of models is at the heart of MPM, we first identify the sources of this heterogeneity and introduce the problems it raises. Then we show how the different existing techniques address these problems.

Model-Based Approaches for Railway Safety, Reliability and Security: The Experience of Ansaldo STS

In order to master the increasing complexity of modern railway control systems, novel model-based approaches are needed to allow engineers to evaluate such systems against strict system-level dependability requirements. In this talk, we provide an overview of the experience of Ansaldo STS in using model-based approaches for railway safety, reliability and security. Dependability requires assessment both at the software and at the hardware levels. At the software level, models have proven useful to support both static and dynamic functional analyses in order to discover systematic faults in the code. At the hardware level, we have experimented that compositional multi-formalism modeling approaches well suit the evaluation of system safety and reliability against random faults. The use of models has allowed engineers to improve both the effectiveness and the efficiency of system verification. Views of the Unified Modeling Language have been adopted to perform informal or semi-formal analyses, while Stochastic Petri Nets, (Repairable) Fault Trees, Continuous Time Markov Chains and Bayesian Networks have been employed for formal and quantitative analyses. Analytical risk and vulnerability models have also been experimented for security assessment with respect to intentional threats and natural hazards. Due to the wide range of possible applications, we are currently studying in depth both the theoretical and the technological issues related to the multi-paradigm dependability modeling using appropriate frameworks Francesco Flammini got with honours his laurea (July 2003) and doctorate (December 2006) degrees in Computer Engineering from the University Federico II of Naples. Since October 2003 to January 2007, he has worked in Ansaldo STS as a Software/RAMS Engineer in the Verification & Validation unit, specializing in model-based dependability evaluation and testing of real-time control systems. During these years, he has been involved in several ERTMS/ETCS (European Railway Traffic Management System / European Train Control System) projects. Since February 2007, he has worked on the protection of transportation infrastructures against external threats, specializing in risk assessment and design of security management systems. In 2006/2007 he has been an Adjunct Professor of Software Engineering at the Second University of Naples. He currently teaches Computer Science at the University “Federico II” of Naples and serves as the

Multiparadigm Modeling for Hybrid Dynamic Systems Using a Petri Net Framework

Recently, modeling and simulation of hybrid dynamic systems (HDSs) have attracted much attention. However, since simultaneously dealing with the discrete and continuous variables is very difficult, most of the models result in a unified but more complicated and unnatural format. Moreover, design engineers cannot be allowed to use their preferred domain models. Based on the multiparadigm modeling concept, this correspondence proposes a Petri net framework with associated state equations to model the HDSs. In the presented approach, modeling schemes of the hybrid systems are separated but combined in a hierarchical way through specified interfaces. Designers can still work in their familiar domain-specific modeling paradigms, and the heterogeneity is hidden when composing large systems. An application to a rapid thermal process in semiconductor manufacturing is provided to demonstrate the practicability of the developed approach.

Implementing a hybrid simulation model for a Kanban-based material handling system

Plant floor material handling is a loose loop in most assembly plants. Simulation offers a quick, controllable and tunable approach for prototyping complex material handling processes in manufacturing environments. This paper proposes a hybrid simulation approach, using both discrete event and agent-based technologies, to model complex material handling processes in an assembly line. A prototype system is implemented using a commercial multi-paradigm modeling tool. In this prototype, JIT principles are applied to both the production and the material handling processes. The system performance is evaluated and system optimization directions are suggested. The proposed hybrid modeling approach facilitates the implementation of a responsive and adaptive environment in that various “what-if” scenarios can be simulated under different simulation configurations and real-time situations.

Table of Contents

Abstract Volunteering Patterns A Look at The Multi‐paradigm Model of Volunteering Managing Volunteers in Changing Times Great News: VMR Is Going Online! Gear Up for ′08 Keep Former Chairpersons Involved National Recognition BR!CK Awards: The Oscars of Youth Volunteering Recruiting Within Your Ranks Regulations Surrounding Employees as Volunteers Family‐oriented Ideas Inexpensive Ideas for Volunteer‐run Children's Projects Large‐group Techniques Raffle Makes Group Volunteering Fun Volunteer Policy Tips Boost Attendance For Training Events Invest In The Next Generation Ambassador Program Introduces Students to Health Fields Reaching Ethnic Groups Volunteers Help Targeted Recruitment Efforts Merging New, Old Ways Make Tracking Hours Easy Appeal to Growing Number Of Young Adult Singles Advice When Asking For More Money Maximize Your Volunteer Potential Volunteers are Your Eyes and Ears for Public Event Networking Ideas Professional Memberships That Work for Others Publications That Get Results Brochure Helps Market New Position Five Great Website Ideas Expert Advice Tips to Take Nominations Seriously Volunteer Fulfillment Recognize What ‘Floats Your Boat’ Ways To Make Your Work More Fulfilling

Multiparadigm modeling by the SODAR-2 language

The SODAR-2 modeling language designed for specifying user commands to automate the integrity control of multi-language program projects is described. Using a model of the program project of the “double black box” type that is abstracted from the specific content of computing processes, we demonstrate the capabilities and benefits of interpretation metaprogramming in small projects utilizing program codes that are freely accessible in the Internet. The fragments of programs generated in the run of the processes of such metaprograms are presented.

A semantic framework and software design to enable the transparent integration, reorganization and discovery of natural systems knowledge

I present a conceptualization that attempts to unify diverse representations of natural knowledge while providing a workable computational framework, based on current semantic web theory, for developing, communicating, and running integrated simulation models. The approach is based on a long-standing principle of scientific investigation: the separation of the ontological character of the object of study from the semantics of the observation context, the latter including location in space and time and other observation-related aspects. I will show how current Knowledge Representation theories coupled with the object-oriented paradigm allow an efficient integration through the abstract model of a domain, which relates to the idea of aspect in software engineering. This conceptualization allows us to factor out two fundamental causes of complexity and awkwardness in the representation of knowledge about natural system: (a) the distinction between data and models, both seen here as generic knowledge sources; (b) the multiplicity of states in data sources, handled through the hierarchical composition of independently defined domain objects, each accounting for all states in one well-known observational dimension. This simplification leaves modelers free to work with the bare conceptual bones of the problem, encapsulating complexities connected to data format, and scale. I will then describe the design of a software system that implements the approach, referring to explicit ontologies to unambiguously characterize the semantics of the objects of study, and allowing the independent definition of a global observation context that can be redefined as required. I will briefly discuss applications to multi-scale, multi-paradigm modeling, intelligent database design, and web-based collaboration.

Special Issue on Critical Reliability Challenges and Practices [Guest Editorial

The six papers in this special issue address various research challenges in the reliability-related areas including optimization, software reliability and measurements, network reliability, software quality, semisupervised learning, Bayesian approach, statistical anomaly detection, flooding attacks, genetic algorithms, multistate systems, system redundancy, service reliability, multiparadigm modeling, heuristic algorithms, and grid and distributed computing. The selected papers are briefly summarized.

Language Support for Multi-Paradigm and Multi-Grain Parallelism on Smp-Cluster

The characteristics of large-scale parallel applications are multi-paradigm and multi-grain parallel in essence. The key factor in improving the performance of parallel application systems is to determine suitable parallel paradigms and grains according to the nature of the practical problem. Therefore, it is necessary to provide multi-paradigm and multi-grain parallel programming interface for development of large-scale parallel application systems. This paper proposes a multi-paradigm and multi-grain parallel execution model integrated coarse-grain parallelism (paralleled by macro tasks), mid-grain parallelism (paralleled by basic program blocks), and fine-grain parallelism (paralleled in repetition blocks). This model also supports the task parallel, data parallel, and sequential executing. In this paper we also discuss the programming mechanism of this model by extended OpenMP specification. The extensions include computing resource partition, defining different grain task groups, mapping from task groups to the respective processor groups, out-of-core computing, asynchronous parallel I/O , and definition of sequential relationship of tasks. We compare the performance of different implementations of benchmark, using the same numerical algorithm but employing different programming approaches, including MPI, MPI+OpenMP, and our extended OpenMP. We also discuss a case based on SMP-Cluster and network storage architecture.