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The creation of a virtual laboratory stand that allows one to obtain reliable characteristics that can be proven as actual, taking into account errors and noises (which is the main distinguishing feature of a computational experiment from model studies) is one of the main problems of this work. It considers the following task: there is a rectangular waveguide in the single operating mode, on the wide wall of which a technological hole is cut, through which a sample for research is placed into the cavity of the transmission line. The recovery algorithm is as follows: the laboratory measures the network parameters (S₁₁ and/or S₂₁) in the transmission line with the sample. In the computer model of the laboratory stand, the sample geometry is reconstructed and an iterative process of optimization (or sweeping) of the electrophysical parameters is started, the mask of this process is the experimental data, and the stop criterion is the interpretive estimate of proximity (or residual). It is important to note that the developed computer model, along with its apparent simplicity, is initially ill-conditioned. To set up a computational experiment, the Comsol modeling environment is used. The results of the computational experiment with a good degree of accuracy coincided with the results of laboratory studies. Thus, experimental verification was carried out for several significant components, both the computer model in particular and the algorithm for restoring the target parameters in general. It is important to note that the computer model developed and described in this work may be effectively used for a computational experiment to restore the full dielectric parameters of a complex geometry target. Weak bianisotropy effects can also be detected, including chirality, gyrotropy, and material nonreciprocity. The resulting model is, by definition, incomplete, but its completeness is the highest of the considered options, while at the same time, the resulting model is well conditioned. Particular attention in this work is paid to the modeling of a coaxial-waveguide transition, it is shown that the use of a discrete-element approach is preferable to the direct modeling of the geometry of a microwave device.

This work deals with numerical modeling of motion of the multibody systems consisting of rigid bodies with arbitrary masses and inertial properties. We consider both planar and spatial systems which may contain kinematic loops.

The numerical modeling is fully automatic and its computational algorithm contains three principal steps. On step one a graph of the considered mechanical system is formed from the userinput data. This graph represents the hierarchical structure of the mechanical system. On step two the differential-algebraic equations of motion of the system are derived using the so-called Joint Coordinate Method. This method allows to minimize the redundancy and lower the number of the equations of motion and thus optimize the calculations. On step three the equations of motion are integrated numerically and the resulting laws of motion are presented via user interface or files.

The aforementioned algorithm is implemented in the software complex that contains a computer algebra system, a graph library, a mechanical solver, a library of numerical methods and a user interface.

Storage is the essential and expensive part of cloud computation both from the point of view of network requirements and data access organization. So the choice of storage architecture can be crucial for any application. In this article we can look at the types of cloud architectures for data processing and data storage based on the proven technology of enterprise storage. The advantage of cloud computing is the ability to virtualize and share resources among different applications for better server utilization. We are discussing and evaluating distributed data processing, database architectures for cloud computing and database query in the local network and for real time conditions.

In this paper, we illustrates the ways to improve abilities of the computing environments by using virtualization, single system image (SSI) and hypervisor technologies’ collaboration for goal to improve computational abilities. Recently cloud computing as a new service concept has become popular to provide various services to user such as multi-media sharing, online office software, game and online storage. The cloud computing is bringing together multiple computers and servers in a single environment designed to address certain types of tasks, such as scientific problems or complex calculations. By using virtualization technologies, cloud computing environment is able to virtualize and share resources among different applications with the objective for better server utilization, better load balancing and effectiveness.

Virtual machines are usually associated with an ability to create them on demand by calling web services, then these machines are used to deliver resident services to their clients; however, providing clients with an ability to run an arbitrary programme on the newly created machines is beyond their power. Such kind of usage is useful in a high performance computing environment where most of the resources are consumed by batch programmes and not by daemons or services. In this case a cluster of virtual machines is created on demand to run a distributed or parallel programme and to save its output to a network attached storage. Upon completion this cluster is destroyed and resources are released. With certain modifications this approach can be extended to interactively deliver computational resources to the user thus providing virtual desktop as a service. Experiments show that the process of creating virtual clusters on demand can be made efficient in both cases.

Most biomechanical tasks of interest to clinicians can be solved only using personalized mathematical models. Such models allow to formalize and relate key pathophysiological processes, basing on clinically available data evaluate non-measurable parameters that are important for the diagnosis of diseases, predict the result of a therapeutic or surgical intervention. The use of models in clinical practice imposes additional restrictions: clinicians require model validation on clinical cases, the speed and automation of the entire calculated technological chain, from processing input data to obtaining a result. Limitations on the simulation time, determined by the time of making a medical decision (of the order of several minutes), imply the use of reduction methods that correctly describe the processes under study within the framework of reduced models or machine learning tools.

Personalization of models requires patient-oriented parameters, personalized geometry of a computational domain and generation of a computational mesh. Model parameters are estimated by direct measurements, or methods of solving inverse problems, or methods of machine learning. The requirement of personalization imposes severe restrictions on the number of fitted parameters that can be measured under standard clinical conditions. In addition to parameters, the model operates with boundary conditions that must take into account the patient’s characteristics. Methods for setting personalized boundary conditions significantly depend on the clinical setting of the problem and clinical data. Building a personalized computational domain through segmentation of medical images and generation of the computational grid, as a rule, takes a lot of time and effort due to manual or semi-automatic operations. Development of automated methods for setting personalized boundary conditions and segmentation of medical images with the subsequent construction of a computational grid is the key to the widespread use of mathematical modeling in clinical practice.

The aim of this work is to review our solutions for personalization of mathematical models within the framework of three tasks of clinical cardiology: virtual assessment of hemodynamic significance of coronary artery stenosis, calculation of global blood flow after hemodynamic correction of complex heart defects, calculating characteristics of coaptation of reconstructed aortic valve.

We'd like to present a specific grid infrastructure and web application development and deployment. The purpose of infrastructure and web application is to solve particular geophysical problems that require heavy computational resources. Here we cover technology overview and connector framework internals. The connector framework links problem-specific routines with middleware in a manner that developer of application doesn't have to be aware of any particular grid software. That is, the web application built with this framework acts as an interface between the user 's web browser and Grid's (often very) own middleware.

Our distributed computing system is built around Gridway metascheduler. The metascheduler is connected to TORQUE resource managers of virtual compute nodes that are being run atop of compute cluster utilizing the virtualization technology. Such approach offers several notable features that are unavailable to bare-metal compute clusters.

The first application we've integrated with our framework is seismic anisotropic parameters determination by inversion of SKS and converted phases. We've used probabilistic approach to inverse problem solution based on a posteriory probability distribution function (APDF) formalism. To get the exact solution of the problem we have to compute the values of multidimensional function. Within our implementation we used brute-force APDF calculation on rectangular grid across parameter space.

The result of computation is stored in relational DBMS and then represented in familiar human-readable form. Application provides several instruments to allow analysis of function's shape by computational results: maximum value distribution, 2D cross-sections of APDF, 2D marginals and a few other tools. During the tests we've run the application against both synthetic and observed data.

The article analyses the activity statistics of the Russian participants of volunteer computing (VC) using platform BOINC obtained by the authors. The data has been received with API BOINC and site www.boincstats.com. The script for the database was written in PHP, for data storing was used MySQL.

The database indicators were accumulated across all Russian projects, which allowed the calculation of the indicators characterizing the behavior of the Russian participants in all projects and teams BOINC — absolute and relative number of Russian participants, their activity, the number of introduced points system, the number of participants in each of the Russian project participants, interest in the concept of the VC.

It is shown that the position of Russia in the countries ranking is very low and is retained at the same level for 4 years. According to the authors, low activity of the Russian participants of the VC, due to individualism and the closure of Russian Internet users, as well as to a small interest in the development of fundamental science, scientific research. This, possibly due to the low-prestige as a science as a whole, as well as civil science, crowdsourcing, in particular. And, therefore, we can see insufficient dissemination of the ideas of using the mechanism of VC for research projects.

Nowadays cloud computing is an important topic in the field of information technology and computer system. Several companies and educational institutes have deployed cloud infrastructures to overcome their problems such as easy data access, software updates with minimal cost, large or unlimited storage, efficient cost factor, backup storage and disaster recovery, and some other benefits if compare with the traditional network infrastructures. The paper present the study of cloud computing technology for marine environmental data and processing. Cloud computing of marine environment information is proposed for the integration and sharing of marine information resources. It is highly desirable to perform empirical requiring numerous interactions with web servers and transfers of very large archival data files without affecting operational information system infrastructure. In this paper, we consider the cloud computing for virtual testbed to minimize the cost. That is related to real time infrastructure.