Formulation Of Dynamic Model Research Articles

Parameter Heterogeneity in the Market-Accounting Relation

In this paper firm parameter heterogeneity in cross section regression analysis in capital market research (CMR) is investigated. Using panel data for 30 large US firms over the period 1955 to 2004, a well-specified common form of dynamic model for each firm is identified. Average parameter estimates from these models are compared to average parameter estimates from 50 annual cross section models having the same functional form. The dynamic parameters are mostly stable over time but variation in individual firm parameters is apparent. Analysis shows that even well-specified annual cross section models using large samples of data cannot guarantee valid and reliable estimates of the parameters of interest. Firm-level dynamic analysis is necessary to avoid this problem. We show how a fixed effects panel analysis of the sample data can be used to approximate the average data generating process of the firms in the sample. Although the impact of accounting variables is slight, compared to the autoregressive component in market value, it is systematic. There is weak evidence of cointegration between market and accounting data in most firms in the sample. Consequently, it is possible to construct the cross section analogue of the dynamic error correction model. Book value of net assets is used to illustrate the role of accounting variables. Other variables could be used, but single variable, multiplicative noise models perform best when judged by joint explanatory power and forecast ability criteria.

Realizable versus non-realizable dynamic subgrid-scale stress models

The existence of many different dynamic large eddy simulation (LES) methods leads to questions about the theoretical foundation of dynamic LES methods. It was shown recently that the use of stochastic analysis enables a theoretically well based systematic derivation of a realizable linear dynamic model (LDM) and a realizable nonlinear dynamic model (NDM). A priori and a posteriori analyses of turbulent channel flow are used here to study the characteristic properties of these dynamic models. The LDM and NDM are compared with other dynamic models: the non-stabilized and stabilized dynamic Smagorinsky model (DSM), which is used in many applications of LES, and Wang-Bergstrom's dynamic model (WBDM), which represents an extension of the DSM. The DSM and WBDM do not represent realizable models because they are not derived as consequences of a realizable stochastic process. The comparisons reported here show that the LDM and NDM are based on a dynamic model formulation that avoids shortcomings of existing concepts. The LDM and NDM account for backscatter, and they are computationally stable without any modification. The LDM and NDM represent the instantaneous small scale structure of turbulence very well. Compared to the DSM and WBDM, respectively, the LDM and NDM are computationally more efficient.

Internalization of the Risk of Environmental Catastrophes as an Incentive for IEA

Relying on a standard formulation of dynamic models of pollution abatement, we propose a competition among countries committed to maximize their respective welfare without any form of agreement. When there exists a threshold level for the stock of pollution above which a catastrophe occurs, it gets involved in the well-known 'tragedy of the commons' effect: non-cooperative policies allow to attain a higher level of social welfare, but, given their polluting emissions, are risky to lead to catastrophic consequences. We investigate how the internalization of the benchmark for the aggregate stock of pollution in the countries' optimal emissions strategies may trigger a cooperative behavior aimed at avoiding environmental disasters.

Econometric and stochastic general equilibrium models for evaluation of economic policies

Comparative analysis of economic structure and forecasts generated from simultaneous equation, VAR and autoregressive models based on quarterly series from 1966:1 to 2007:3 of UK to those from the stochastic general equilibrium models has provided insights into objective and subjective evaluation of macro economic impacts of public policies. Econometric estimates are used in formulation of stochastic dynamic general equilibrium models to generate time series of macro variables from stochastic general equilibrium models. Calibrating to ratios, variances, covariance and correlations econometric analyses and general equilibrium models are integrated and shown to be complementary not competitive techniques.

Retailer’s optimal sourcing strategy by using one major supplier and one emergent supplier

Fast-changing market demand, short product life cycle, and unpredictable events have been enforcing companies in a supply chain to respond to customers’ needs as quickly as possible. While many firms are still seeking a better inventory management within the general business environment, this paper develops an ordering decision support model within a business environment with more unpredictable events. To reduce the uncertainty, a retailer adopts a double sourcing policy with one major supplier and one emergent supplier. Through a two-period dynamic programming model formulation and the simulations based on design of experiments, we analyzed the effects of factors such as penalty cost, backup ratio, purchasing cost ratio, and demand correlation coefficient on retailer’s ordering policy and expected profit. Detailed sensitivity analysis is further conducted based on combinations of penalty cost and backup ratio. Results show that the use of an emergent supplier help increase the retailer’s expected profits. It not only increases the retailer’s upstream sourcing flexibility, but also increases the retailer’s service level with a lower inventory level. As penalty cost or backup ratio increases, the contribution of emergent supplier increases.

A Self-Deployment Hexapod Model for a Space Application

This paper presents a simulation tool for predicting the self-deployment of an on-board deployable hexapod based on the release of strain energy stored in six tape-spring actuators. Their hysteretic behavior is described by six restoring force models, and a formulation of a direct dynamic model developed with a Lagrangian approach is performed. Furthermore, tensor representation is used to condense and simplify the calculation of Lagrangian partial derivatives. The results are compared with a numerical model that implements the recursive Newton–Euler technique. Finally, the impact of base excitations on the hexapod deployment performances is evaluated by using the proposed restoring force models.

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Flux balance analysis (FBA) in combination with the decomposition of metabolic networks into elementary modes has provided a route to modeling cellular metabolism. It is dependent, however, on the availability of external fluxes such as substrate uptake or growth rate before estimates can become available of intracellular fluxes. The framework classically does not allow modeling of metabolic regulation or the formulation of dynamic models except through dynamic measurement of external fluxes. The cybernetic modeling approach of Ramkrishna and coworkers provides a dynamic framework for modeling metabolic systems because of its focus on describing regulatory processes based on cybernetic arguments and hence has the capacity to describe both external and internal fluxes. In this article, we explore the alternative of developing hybrid models combining cybernetic models for the external fluxes with the flux balance approach for estimation of the internal fluxes. The approach has the merit of the simplicity of the early cybernetic models and hence computationally facile while also providing detailed information on intracellular fluxes. The hybrid model of this article is based on elementary mode decomposition of the metabolic network. The uptake rates for the various elementary modes are combined using global cybernetic variables based on maximizing substrate uptake rates. Estimation of intracellular metabolism is based on its stoichiometric coupling with the external fluxes under the assumption of (pseudo-) steady state conditions. The set of parameters of the hybrid model was estimated with the aid of nonlinear optimization routine, by fitting simulations with dynamic experimental data on concentrations of biomass, substrate, and fermentation products. The hybrid model estimations were tested with FBA (based on measured substrate uptake rate) for two different metabolic networks (one is a reduced network which fixes ATP contribution to the biomass and maintenance requirement of ATP, and the other network is a more complex network which has a separate reaction for maintenance.) for the same experiment involving anaerobic growth of E. coli GJT001. The hybrid model estimated glucose consumption and all fermentation byproducts to better than 10%. The FBA makes similar estimations of fermentation products, however, with the exception of succinate. The simulation results show that the global cybernetic variables alone can regulate the metabolic reactions obtaining a very satisfactory fit to the measured fermentation byproducts. In view of the hybrid model's ability to predict biomass growth and fermentation byproducts of anaerobic E. coli GJT001, this reduced order model offers a computationally efficient alternative to more detailed models of metabolism and hence useful for the simulation of bioreactors.

Realizability of dynamic subgrid-scale stress models via stochastic analysis

Large eddy simulations involving dynamic subgrid-scale stress models reveal questions regarding the formulation of dynamic stress models. The dynamic Smagorinsky model, for example, yields large fluctuations and can easily become unstable. An analysis explains the reasons for these problems: it is shown that the dynamic Smagorinsky model involves an incorrect scale dependence which may produce significant errors leading to instabilities. These problems are addressed by analyzing the implications of the realizability constraint, this means the constraint that an acceptable turbulence closure model be based on the statistics of a velocity field that is physically achievable or realizable. The realizable dynamic stress models obtained have strong theoretical support: these models are the result of a well based systematic development of stress models. From a practical point of view, the realizable dynamic stress models obtained have the advantage that they do not support the development of instabilities due to possibly huge model errors. It is also shown that the consideration of nonlinear dynamic stress models further improves the accuracy of simulations.

Systems-level insights into cellular regulation: inferring, analysing, and modelling intracellular networks

Genes and gene products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic- and signal transduction networks. Genetic, biochemical and molecular biology techniques have been used for decades to identify biological interactions; newly developed high-throughput methods now allow for the construction of genome-level interaction maps. In parallel, high-throughput expression data paired with computational algorithms can be used to infer networks of interactions and causal relationships capable of producing the observed experimental data. Graph-theoretical measures and network models are more and more frequently used to discern functional and evolutionary constraints in the organisation of biological networks. Perhaps most importantly, the combination of interaction and expression information allows the formulation of quantitative and predictive dynamic models. Some of the dominant experimental and computational methods used for the reconstruction or inference of cellular networks are reviewed, also the biological insights that have been obtained from graph-theoretical analysis of these networks, and the extension of static networks into various dynamic models capable of providing a new layer of insight into the functioning of cellular systems is discussed.

Estimation of parameterized spatio-temporal dynamic models

Spatio-temporal processes are often high-dimensional, exhibiting complicated variability across space and time. Traditional state-space model approaches to such processes in the presence of uncertain data have been shown to be useful. However, estimation of state-space models in this context is often problematic since parameter vectors and matrices are of high dimension and can have complicated dependence structures. We propose a spatio-temporal dynamic model formulation with parameter matrices restricted based on prior scientific knowledge and/or common spatial models. Estimation is carried out via the expectation–maximization (EM) algorithm or general EM algorithm. Several parameterization strategies are proposed and analytical or computational closed form EM update equations are derived for each. We apply the methodology to a model based on an advection–diffusion partial differential equation in a simulation study and also to a dimension-reduced model for a Palmer Drought Severity Index (PDSI) data set.

506 粘弾性体の力学モデル構築に関する基礎研究(機械力学・振動)

506 粘弾性体の力学モデル構築に関する基礎研究(機械力学・振動)

Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps

This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically, use notions borrowed from artificial intelligence and combine characteristics of both fuzzy logic and neural networks, in the form of dynamic models that describe a given political setting. The present work proposes the use of the genetically evolved certainty neuron fuzzy cognitive map (GECNFCM) as an extension of certainty neuron fuzzy cognitive maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with genetic algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. Using a multiple scenario analysis we demonstrate the value of such a hybrid technique in the context of a model that reflects the political and strategic complexity of the Cyprus issue, as well as the uncertainties involved in it. The issue has been treated on a purely technical level, with distances carefully kept concerning all sides involved in it.

Non-linear dynamics in marine-phytoplankton population systems

Although non-linear density-dependence has been widely emphasized in population dynamics studies, the existence of non-linear exogenous forces have been less explored. In particu- lar, we propose that the formulation of population dynamics models that include both non-linear endogenous (i.e. feedback structure) and exogenous (e.g. climatic) responses is relevant for under- standing how climate variability affects natural systems. Here we show that single-species non-linear logistic models for marine phytoplankton in combination with non-linear exogenous forces capture observed population oscillations very well. Our results suggest that general population dynamic theory represents a useful tool for understanding the natural fluctuations of phytoplankton popula- tions. Altogether we document what might be called a switch-on/switch-off dynamics in the phyto- plankton population dynamics in relation to climate variability. We suggest the importance of, and the need for, linking population dynamics processes at local and regional scales to processes at the ecosystem level, thus reflecting a macroecological perspective of marine pelagic systems.

Group method of data handling (GMDH) in modelling of growth dynamics of trees irrigated with wastewater

This paper describes the formulation and analysis of growth dynamics models for trees irrigated with wastewater. The models can be used to obtain the characteristics of a species, depending on treatment conditions and climate factors, at all stages of growth. The experiments were carried out at the University of Patras to identify the characteristics (height rate and mortality) of the forest tree Pinus brutia cultivated under different treatment conditions. The growth dynamics models are designed on the basis of the group method of data handling. This principle generates sets of estimation models with different complexity and accuracy. By analysing their structures, qualitative features of the models may be assessed, and general linear models for different treatment cases compiled.

New Rigorous Model for Rx ISI Effects in WDM Systems

We present a model for Rx ISI effects using a new and completely general and rigorous dynamic Rx model formulation aiming at 10-40 Gbit/s WDM system design applications. The signal shaping part of an electrical filter is modelled in the optical domain using the filter impulse response to modify the signal vector and the covariance matrix. In this way inter-symbol-interference (ISI) effects from any type of electrical filter - specified by the filter impulse response - can be described. The formulation leads to a Hermitian form of the covariance matrix, thus allowing a straightforward analytical formulation of the moment generating function (MGF) which in turn specifies the bit-error-ratio (BER). Numerical examples for a 10 Gbit/s pre-amplified Rx shows that the ISI leads to (up to) several dB's degradation in sensitivity. Thus it is imperative to account for the Rx ISI effect in rigorous and accurate form in any general WDM transmission model.

A Unified Dynamic Model Formulation for Robotic Manipulator Systems

AbstractThis paper addresses the problem of the formulation of a unified dynamic model for sundry robotic manipulator systems derived from the first principle of mechanics instead of the existing formulation based on linear separability principle. It provides a systematic derivation, evaluation, and subsequent conceptual interpretation of manipulator dynamics model. Further, it analyzes the generality of the unified model over a wide range of manipulator configurations. In addition, it describes the implementation aspects of the unified model. © 2003 Wiley Periodicals, Inc.

Relating arctic pack ice stress and deformation under winter conditions

Together, thermodynamic and dynamic processes determine the thickness distribution of the ice cover on polar oceans, which governs the exchange of energy between the atmosphere and the ocean. Key to the dynamic processes is the mechanical behavior of the ice cover. During the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment, we deployed sensors to measure the internal ice stress at several locations within a 15 × 15 km area. These measurements are combined with satellite‐derived ice motion and imagery products. The objective is to make a first step toward using these data sources for evaluating sea ice dynamics models by assessing whether the stress signal can be qualitatively linked to the regional‐scale (10–100 km) deformation activity. Four case studies are presented, each with distinguishing characteristics: consolidation of the seasonal ice zone against the Alaskan coast (5–7 December 1997); advancement of the consolidation zone into the perennial ice pack (11–13 December); extreme divergence (14–17 January); and consolidation of the pack against Wrangel Island and the Siberian coast (20–23 February). The results of this analysis (1) demonstrate that stress measurements are related to the regional deformation behavior of the ice cover, (2) confirm that regional‐scale ice dynamics is primarily a function of coastal geometry and sustained, large‐scale wind direction and magnitude, (3) provide continued evidence that the ice pack behaves as a granular hardening plastic, and (4) encourage pursuit of efforts to use direct measurements of ice stress and deformation in the formulation and development of sea ice dynamics models.

Dynamic hierarchical models: an extension to matrix-variate observations

In this paper we propose a dynamic hierarchical model formulation in an environment where the observations are matrix normal random variables. First, we present the model assuming known variance–covariance matrices, except for a common scale factor matrix. With such an assumption one can perform conjugate analysis of the model by specifying a Normal-inverted-Wishart prior distribution. We proceed by extending the model to the case where all variance–covariance matrices are unknown and propose an approach based on the Gibbs sampler to estimate all model parameters. A sampling-based approach to obtain forecasts is also presented. Examples with artificial and real data are presented.

Dynamic Modeling and Controller Design for a Piezoelectric Actuation System Used for Machine Tool Control

The dynamic model of a commercially available piezoelectric actuation system intended for use in a machine tool position control system is presented, and its fidelity to observed behavior evaluated. The components of the actuation system are presented and an available nonlinear dynamic model form for piezoelectric actuators, capable of representing actuator hysteresis, used as the basis for the system dynamic model. Parameters in the dynamic model are obtained from manufacturer’s specifications. The resulting actuator model is combined with a nonlinear model of the amplifier, the resulting system model forming the basis of a SIMULINK simulation. System responses based on the simulation are compared with experimentally measured responses. The agreement between simulation and experimental responses is reasonably good at low frequencies, and the model is used for preliminary closed loop position control system evaluations. Specific recommendations for improvement in model accuracy are also made.

On Controlling Chaos in Cournot-Games with Two and Three Competitors

Controlling chaotic economic systems is an important problem. The Ott, Grebogi and Yorke (1990) method is a way to control chaos gradually. However, in order to apply this method the control parameter has to be close to a prescribed value. To achieve this, the targeting method of Shinbrot Ott, Grebogi and Yorke can be applied first to speed up convergence. In this paper the Ott et al method is modified so that the system is guided gradually out of the chaotic region and in the stability zone. The control parameter does not have to be in a prescribed region. The method is applied to the Puu formulation of Cournot dynamic duopoly and oligopoly models.