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This paper addresses the translation of Systems Biology Mark-Up Language (SBML) Level 2 models of network of biochemical reactions into the Biochemical Stochastic π-calculus (SPI). SBML is XML-based formalism for systems biology, while SPI can describe the concurrency of the different interactions occurring in a network of biochemical stochastic reactions. SPI models can be used for simulation by available computer packages. We present the approach followed in designing a software tool for working biologists that parses an SBML model and performs the unsupervised translation into the process algebra model. To test the correctness of the translation process we present the results obtained by performing simulations of a translated simplified circadian clock model, comparing our results with that obtained with the original differential equation model.

The material parameters of a constitutive model have to be identified by minimization of the distance between the model response and the experimental data using an appropriate optimization algorithm. However, measurement failures and differences in the specimens lead to uncertainties in the determined parameters. Since the amount of test data is not sufficient for a statistical analysis, a method of stochastic simulation is introduced in order to generate artificial data with the same behaviour as the experimental data. The stochastic simulations are validated by comparing the results of the parameter fits from artificial data with the results from experimental data. Furthermore, the results of the parameter identification for different inelastic constitutive models are presented and a principal component analysis is performed to study the correlation structure of the estimated material parameters. The presented simulation method is a suitable tool for various kinds of analysis purposes since the artificial data presents an alternative to a high amount of experimental data.

The paper discusses the high-performance parallel algorithms for stochastic simulation of metocean processes and fields. The approaches for parallel representation of sample estimation procedures, linear stochastic systems, Markov chains, periodically correlated processes and inhomogeneous fields are proposed. The speedup of the proposing algorithms is studied in respect to parameters of the models.

We extend the recently considered toy model of Weierstrass (or Levy) walks with varying velocity of the walker [1,2] by introducing a more realistic possibility that the walk can be occasionally intermitted by its momentary localization; the localizations themselves are again described by the Weierstrass (or Levy) process. The direct empirical motivation for developing this combined model is, for example, the dynamics of financial high-frequency time series or meteorological ones. This approach makes it possible to study by efficient stochastic simulations the whole spatial-temporal range. To describe empirical data, which are collected at discrete time-steps, we used in the continuous-time series produced by the model a discretization procedure. We observed that such a procedure constitutes a basis for long-time autocorrelations (of the variation of the walker single-step displacements) which appear to be similar to those observed, e.g., in financial time series [3,4,5,6,7,8], although single steps of the walker within the continuous time are uncorrelated.

We are interested in a host-parasite system occuring in fish farms, i.e. the sea bass - Diplectanum aequans system. A discrete mathematical model is used to describe the dynamics of both populations. A deterministic numerical simulator and, lately, a stochastic simulator were developed to study this biological system. Parallelization is required because execution times are too long. The Monte Carlo algorithm of the stochastic simulator and its three levels of parallelism are described. Analysis and performances, up to 256 processors, of a hybrid MPI/OpenMP code are then presented for a cluster of SMP nodes. Qualitative results are given for the host-parasite system.

The creep-fatigue failure of polycrystalline steels and alloys is caused by multiple intergranular cracks which begin to initiate from the early stage of life. The initiation and the growth of small cracks show random behavior due to the local inhomogeneity of materials. In order to investigate the mechanics hidden behind the stochastic nature, a method of inverse analysis based on a numerical simulation is proposed. As a result, the characteristics of the failure process is successfully drawn out in terms of the cracking behavior by the analyses.

Monte Carlo Method as a stochastic simulation method is used to evaluate many financial derivatives by financial engineers. Monte Carlo simulation is harder and more difficult to implement and analyse in many fields than other numerical methods. In this paper, we derive term structure models with jump and perform Monte Carlo simulations for them. We also make a comparison between the term structure models of interest rates with jump and HJM models based on jump. Bond pricing with Monte Carlo simulation is investigated for the term structure models with jump.

The aim of this research is to assess the role of a hypothetical inherited feature (gene) promoting altruism between relatives as a factor for survival in the context of a multi-agent system simulating natural selection. Classical Darwinism and Neo-Darwinism are compared, and the principles of the latter are implemented in the system. The experiments study the factors that influence the successful propagation of altruistic behaviour in the population. The results show that the natural phenomenon of kinship-driven altruism has been successfully replicated in a multi-agent system, which implements a model of natural selection different from the one commonly used in genetic algorithms and multiagent systems, and closer to nature.

Successful trade in the newly deregulated European electricity market requires both maximising the net profits and maintaining an optimal portfolio of energy sales and purchase contracts. Energy is acquired from multiple multi-tariff contracts, thermal and hydro power plants, cogeneration facilities and the spot market. Minimisation of the procurement costs results in a time dependent resource allocation problem which can be solved efficiently using decomposition techniques. To maximise the net profits in trade, the feasibility and profitability of different types of contracts can be analysed using for instance marginal cost analysis and scenarios.

A stochastic simulation of elementary hypercycles of dimensions n = 3, 4, 5 and 8 is presented. In populations of sizes which can be realized in nature and in laboratory experiments, hypercycles with dimensions n ≥ 5 die out after a short time. This instability of hypercycles of higher dimensions is to be distinguished from a general instability of the model system presented: all model systems considered here are metastable in the sense that the system dies out with probability one for infinite time.