We present detailed analysis of the T-even lepton angular distribution in the Drell-Yan process including γ/Z0 gauge boson exchange and using perturbative QCD based on the collinear factorization scheme at leading or...
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The effect of axial meson exchange on extraction of proton electromagnetic form factors in ep scattering is calculated. Taking as an example f1(1285) meson exchange, it is quantitatively shown how it affects Rosenblut...
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Nowadays, the study of the behavior of social, economic, and technical queuing systems at different stages of their design and operation is a challenge for simulation modeling. The complexity of the dynamic structure ...
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Model-based control of biotechnological processes is, in general, challenging. Often the processes are complex, nonlinear, and uncertain. Hence modeling tends to be complex and is often inaccurate. For this reason, no...
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Model-based control of biotechnological processes is, in general, challenging. Often the processes are complex, nonlinear, and uncertain. Hence modeling tends to be complex and is often inaccurate. For this reason, non-model-based control strategies developed via fask, bench-scale, or pilot plant experiments are often applied in the biotechnology industry. Model-based control and optimization techniques can increase processes’ performance and automation level, thereby decreasing costs and guaranteeing the desired specifications. These rely on a model of the process to make predictions and optimize the inputs to the plant. To improve the quality of the models, it is often helpful to use combined first principle and data-driven models together in a hybrid modeling approach which increases the model prediction capabilities. The residual uncertainty of the hybrid model should be taken into account in the control level to satisfy the process specifications and constraints. This paper proposes to use a stochastic model predictive control scheme that exploits a hybrid, Gaussian processes-based model. We outline the effectiveness of the stochastic model-based approach in combination with a suitable Kalman filter for state estimation considering an example biotechnological process. Furthermore, we underline that appropriate tools exist that allow the simple application of such methods even for the novice user. To do so, we use an open-source Python package — HILO-MPC, which allows the simple yet efficient formulation and solution of machine learning-supported optimal control and estimation problems.
In this paper, we presented a Monte Carlo-based approach for vulnerability analyse of energy systems. For high-performance Monte Carlo simulation the PARMONC software library was used. The PARMONC is implemented on hi...
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This article is aimed at solving an urgent problem for modern transport science – development of tools to support the modeling of railway transport objects. Since railway transport belongs to the category of so-call...
This article is aimed at solving an urgent problem for modern transport science – development of tools to support the modeling of railway transport objects. Since railway transport belongs to the category of so-called “complex systems”, the analytical approach is of little use for carrying out system analysis using methods of mathematical modeling, and it is necessary to use the simulation modeling methods. During the analysis of the subject area and the known approaches used for simulation modeling of complex technical systems, it was found that, firstly, the most convenient in this case is the method of agent-based modeling; secondly, AnyLogic that has a well-developed railway library is the most suitable as a modeling environment. The railway marshalling yard is considered as an object of modeling. The main technological processes are built in the form of blocks of the AnyLogic railway library. The simulation process of the model is visualized and presented in 3D. The offered method is suitable for describing a wide range of systems for freight and passenger transport that is expected to be implemented in the future.
We consider a linear nonstationary system of ordinary differential equations with interval coefficients which is not solvable with respect to the derivative of the unknown vector-valued function for any matrix coeffic...
The paper addresses the problem of supporting research of the Vietnam energy sector vulnerability. The vulnerability study is understood as search for system weak points. A mathematical model has been developed to des...
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We consider a semilinear differential-algebraic system of partial differential equations of index (k, 0). We numerically solve this system by applying the spline-collocation method based on splitting the matrix pencil...
The method for controlling a DC-DC converter is proposed to ensures the high quality control at large fluctuations in load currents by using differential gain control coefficients and second derivative control. Variou...
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