Modeling of Magnetite Nanoparticles Behavior under Conditions of Microcirculation and Analysis of In Vivo Toxicity
It is difficult to imagine an enterprise, company, firm, an education organizations or organizations of health which does not deal with information systems. The openness and flexibility of the information systems provide a flexible and effective management. So it is necessary to adapt information system to new conditions being changed and to team up with other systems, with simulation system, for example. So it is possible change business processes, to execute their reengineering and to anticipate the conse-quences of any event and to take into account the different risks.
Nested Petri nets (NP-nets) are Petri nets with net tokens - an extension of high-level Petri nets for modeling active objects, mobility and dynamics in distributed systems. In this paper we present an algorithm for translating two-level NP-nets into behaviorally equivalent Colored Petri nets with the view of applying CPN methods and tools for nested Petri nets analysis. We prove, that the proposed translation preserves dynamic semantics in terms of bisimulation equivalence.
The monograph presents results by professor Dr. A. Shalumov’s Research School of Modeling, Information Technology and Automated Systems (Russia). The program, ASONIKA, developed by the school is reviewed here regarding reliability and quality of devices for simulation of electronics and chips during harmonic and random vibration, single and multiple impacts, linear acceleration and acoustic noise, and steady-state and transient thermal effects. Calculations are done for thermal stress during changes in temperature and power in time. Calculations are done for number of cycles to fatigue failure under mechanical loads as well as under cyclic thermal effects. Simulation results for reliability analysis are taken into account. Models, software interface, and simulation examples are presented.
For engineers and scientists involved in design automation of electronics.
Financial markets have always been attractive as a means of increasing one's wealth, and those who make accurate predictions take the prize. Forecasting models such as linear ones are simple to compute, however, they give rough approximations of the underlying relationships in the data, thus, producing poor forecasts. The solution to this issue could be the nonlinear models which try to fit the data and display the relationships with higher accuracy. Previous research seems to prove this statement from the statistician's point of view which might be of little use for an investor. Therefore, the focus of this paper is on the comparison of three types of models (nonlinear: ANN, STAR, and linear: AR) in terms of financial performance. Our research is based on the initial code for GAUSS and papers by Dick van Dijk. The data used is the monthly S&P 500 Index values from 1970 to 2012 provided by the Robert Shiller's website. Forecasting index changes begins at 1995 and ends in 2012 providing up-to-date results for 14 model specifications. The best model proves to be the flexible ANN, beating the linear AR in the majority of cases, leaving the underperforming heavy-parameterized STAR model behind. Thus, it is evident that the more flexible nonlinear models outperform the heavily parameterized ones as well as linear models for the S&P 500 Index. The introduced type of performance evaluation has a more comprehensible application to the financial market analysis.
In the paper integrated information systems for corporate planning and budgeting are considered. Four groups of practical tasks exceeding the bounds of typical functionality of special-purpose planning and budgeting information systems are allocated. Several classes of information systems (simulation, statistical analysis, financial analysis and modeling, group decision making, business intelligence), which may provide the completeness of corporate planning and budgeting are denoted as solutions complementary to special-purpose planning and budgeting systems.