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А new cloud center of parallel computing is to be created in the Laboratory of Information Technologies (LIT) of the Joint Institute for Nuclear Research JINR) what is expected to improve significantly the efficiency of numerical calculations and expedite the receipt of new physically meaningful results due to the more rational use of computing resources. To optimize a scheme of parallel computations at a cloud environment it is necessary to test this scheme for various combinations of equipment parameters (processor speed and numbers, throughput оf а communication network etc). As a test problem, the parallel MPI algorithm for calculations of the long Josephson junctions (LDJ) is chosen. Problems of evaluating the impact of abovementioned factors of computing mean on the computing speed of the test problem are solved by simulation with the simulation program SyMSim developed in LIT.

The simulation of the LDJ calculations in the cloud environment enable users without a series of test to find the optimal number of CPUs with a certain type of network run the calculations in a real computer environment. This can save significant computational time in countable resources. The main parameters of the model were obtained from the results of the computational experiment conducted on a special cloud-based testbed. Computational experiments showed that the pure computation time decreases in inverse proportion to the number of processors, but depends significantly on network bandwidth. Comparison of results obtained empirically with the results of simulation showed that the simulation model correctly simulates the parallel calculations performed using the MPI-technology. Besides it confirms our recommendation: for fast calculations of this type it is needed to increase both, — the number of CPUs and the network throughput at the same time. The simulation results allow also to invent an empirical analytical formula expressing the dependence of calculation time by the number of processors for a fixed system configuration. The obtained formula can be applied to other similar studies, but requires additional tests to determine the values of variables.

The repressor is the first genetic regulatory network in synthetic biology, which was artificially constructed in 2000. It is a closed network of three genetic elements — $lacI$, $\lambda cI$ and $tetR$, — which have a natural origin, but are not found in nature in such a combination. The promoter of each of the three genes controls the next cistron via the negative feedback, suppressing the expression of the neighboring gene. In this paper, the nonlinear dynamics of a modified repressilator, which has time delays in all parts of the regulatory network, has been studied for the first time. Delay can be both natural, i.e. arises during the transcription/translation of genes due to the multistage nature of these processes, and artificial, i.e. specially to be introduced into the work of the regulatory network using synthetic biology technologies. It is assumed that the regulation is carried out by proteins being in a dimeric form. The considered repressilator has two more important modifications: the location on the same plasmid of the gene $gfp$, which codes for the fluorescent protein, and also the presence in the system of a DNA sponge. In the paper, the nonlinear dynamics has been considered within the framework of the deterministic description. By applying the method of decomposition into fast and slow motions, the set of nonlinear differential equations with delay on a slow manifold has been obtained. It is shown that there exists a single equilibrium state which loses its stability in an oscillatory manner at certain values of the control parameters. For a symmetric repressilator, in which all three genes are identical, an analytical solution for the neutral Andronov–Hopf bifurcation curve has been obtained. For the general case of an asymmetric repressilator, neutral curves are found numerically. It is shown that the asymmetric repressor generally is more stable, since the system is oriented to the behavior of the most stable element in the network. Nonlinear dynamic regimes arising in a repressilator with increase of the parameters are studied in detail. It was found that there exists a limit cycle corresponding to relaxation oscillations of protein concentrations. In addition to the limit cycle, we found the slow manifold not associated with above cycle. This is the long-lived transitional regime, which reflects the process of long-term synchronization of pulsations in the work of individual genes. The obtained results are compared with the experimental data known from the literature. The place of the model proposed in the present work among other theoretical models of the repressilator is discussed.

The paper presents a review of recent developments of hybrid discrete-continuous models in cell population dynamics. Such models are widely used in the biological modelling. Cells are considered as individual objects which can divide, die by apoptosis, differentiate and move under external forces. In the simplest representation cells are considered as soft spheres, and their motion is described by Newton’s second law for their centers. In a more complete representation, cell geometry and structure can be taken into account. Cell fate is determined by concentrations of intra-cellular substances and by various substances in the extracellular matrix, such as nutrients, hormones, growth factors. Intra-cellular regulatory networks are described by ordinary differential equations while extracellular species by partial differential equations. We illustrate the application of this approach with some examples including bacteria filament and tumor growth. These examples are followed by more detailed studies of erythropoiesis and immune response. Erythrocytes are produced in the bone marrow in small cellular units called erythroblastic islands. Each island is formed by a central macrophage surrounded by erythroid progenitors in different stages of maturity. Their choice between self-renewal, differentiation and apoptosis is determined by the ERK/Fas regulation and by a growth factor produced by the macrophage. Normal functioning of erythropoiesis can be compromised by the development of multiple myeloma, a malignant blood disorder which leads to a destruction of erythroblastic islands and to sever anemia. The last part of the work is devoted to the applications of hybrid models to study immune response and the development of viral infection. A two-scale model describing processes in a lymph node and other organs including the blood compartment is presented.

The article deals with the problem of a distributed system operation reliability. The system core is an open integration platform that provides interaction of varied software for modeling gas transportation. Some of them provide an access through thin clients on the cloud technology “software as a service”. Mathematical models of operation, transmission and computing are to ensure the operation of an automated dispatching system for oil and gas transportation. The paper presents a system solution based on the theory of Markov random processes and considers the stable operation stage. The stationary operation mode of the Markov chain with continuous time and discrete states is described by a system of Chapman–Kolmogorov equations with respect to the average numbers (mathematical expectations) of the objects in certain states. The objects of research are both system elements that are present in a large number – thin clients and computing modules, and individual ones – a server, a network manager (message broker). Together, they are interacting Markov random processes. The interaction is determined by the fact that the transition probabilities in one group of elements depend on the average numbers of other elements groups.

The authors propose a multi-criteria dispersion model of risk assessment for such systems (both in the broad and narrow sense, in accordance with the IEC standard). The risk is the standard deviation of estimated object parameter from its average value. The dispersion risk model makes possible to define optimality criteria and whole system functioning risks. In particular, for a thin client, the following is calculated: the loss profit risk, the total risk of losses due to non-productive element states, and the total risk of all system states losses.

Finally the paper proposes compromise schemes for solving the multi-criteria problem of choosing the optimal operation strategy based on the selected set of compromise criteria.

Automated urban traffic monitoring systems are widely used to solve various tasks in intelligent transport systems of different regions. They include video enforcement, video surveillance, traffic management system, etc. Effective traffic management and rapid response to traffic incidents require continuous monitoring and analysis of information from these complexes, as well as time series forecasting for further anomaly detection in traffic flow. To increase the forecasting quality, data fusion from different sources is needed. It will reduce the forecasting error, related to possible incorrect values and data gaps. We implemented the approach for short-term and middle-term forecasting of traffic flow (5, 10, 15 min) based on data fusion from video enforcement and video surveillance systems. We made forecasting using different recurrent neural network architectures: LSTM, GRU, and bidirectional LSTM with one and two layers. We investigated the forecasting quality of bidirectional LSTM with 64 and 128 neurons in hidden layers. The input window size (1, 4, 12, 24, 48) was investigated. The RMSE value was used as a forecasting error. We got minimum RMSE = 0.032405 for basic LSTM with 64 neurons in the hidden layer and window size = 24.

As the population of cities grows, the need to plan for the development of transport infrastructure becomes more acute. For this purpose, transport modeling packages are created. These packages usually contain a set of convex optimization problems, the iterative solution of which leads to the desired equilibrium distribution of flows along the paths. One of the directions for the development of transport modeling is the construction of more accurate generalized models that take into account different types of passengers, their travel purposes, as well as the specifics of personal and public modes of transport that agents can use. Another important direction of transport models development is to improve the efficiency of the calculations performed. Since, due to the large dimension of modern transport networks, the search for a numerical solution to the problem of equilibrium distribution of flows along the paths is quite expensive. The iterative nature of the entire solution process only makes this worse. One of the approaches leading to a reduction in the number of calculations performed is the construction of consistent models that allow to combine the blocks of a 4-stage model into a single optimization problem. This makes it possible to eliminate the iterative running of blocks, moving from solving a separate optimization problem at each stage to some general problem. Early work has proven that such approaches provide equivalent solutions. However, it is worth considering the validity and interpretability of these methods. The purpose of this article is to substantiate a single problem, that combines both the calculation of the trip matrix and the modal choice, for the generalized case when there are different layers of demand, types of agents and classes of vehicles in the transport network. The article provides possible interpretations for the gauge parameters used in the problem, as well as for the dual factors associated with the balance constraints. The authors of the article also show the possibility of combining the considered problem with a block for determining network load into a single optimization problem.

Deep learning’s power stems from complex architectures; however, these can lead to overfitting, where models memorize training data and fail to generalize to unseen examples. This paper proposes a novel probabilistic approach to mitigate this issue. We introduce two key elements: Truncated Log-Uniform Prior and Truncated Log-Normal Variational Approximation, and Automatic Relevance Determination (ARD) with Bayesian Deep Neural Networks (BDNNs). Within the probabilistic framework, we employ a specially designed truncated log-uniform prior for noise. This prior acts as a regularizer, guiding the learning process towards simpler solutions and reducing overfitting. Additionally, a truncated log-normal variational approximation is used for efficient handling of the complex probability distributions inherent in deep learning models. ARD automatically identifies and removes irrelevant features or weights within a model. By integrating ARD with BDNNs, where weights have a probability distribution, we achieve a variational bound similar to the popular variational dropout technique. Dropout randomly drops neurons during training, encouraging the model not to rely heavily on any single feature. Our approach with ARD achieves similar benefits without the randomness of dropout, potentially leading to more stable training.

To evaluate our approach, we have tested the model on two datasets: the Canadian Institute For Advanced Research (CIFAR-10) for image classification and a dataset of Macroscopic Images of Wood, which is compiled from multiple macroscopic images of wood datasets. Our method is applied to established architectures like Visual Geometry Group (VGG) and Residual Network (ResNet). The results demonstrate significant improvements. The model reduced overfitting while maintaining, or even improving, the accuracy of the network’s predictions on classification tasks. This validates the effectiveness of our approach in enhancing the performance and generalization capabilities of deep learning models.

Considered the application of physics-informed neural networks using multi layer perceptrons to solve Cauchy initial value problems in which the right-hand sides of the equation are continuous monotonically increasing, decreasing or oscillating functions. With the use of the computational experiments the influence of the construction of the approximate neural network solution, neural network structure, optimization algorithm and software implementation means on the learning process and the accuracy of the obtained solution is studied. The analysis of the efficiency of the most frequently used machine learning frameworks in software development with the programming languages Python and C# is carried out. It is shown that the use of C# language allows to reduce the time of neural networks training by 20–40%. The choice of different activation functions affects the learning process and the accuracy of the approximate solution. The most effective functions in the considered problems are sigmoid and hyperbolic tangent. The minimum of the loss function is achieved at the certain number of neurons of the hidden layer of a single-layer neural network for a fixed training time of the neural network model. It’s also mentioned that the complication of the network structure increasing the number of neurons does not improve the training results. At the same time, the size of the grid step between the points of the training sample, providing a minimum of the loss function, is almost the same for the considered Cauchy problems. Training single-layer neural networks, the Adam method and its modifications are the most effective to solve the optimization problems. Additionally, the application of twoand three-layer neural networks is considered. It is shown that in these cases it is reasonable to use the LBFGS algorithm, which, in comparison with the Adam method, in some cases requires much shorter training time achieving the same solution accuracy. The specificity of neural network training for Cauchy problems in which the solution is an oscillating function with monotonically decreasing amplitude is also investigated. For these problems, it is necessary to construct a neural network solution with variable weight coefficient rather than with constant one, which improves the solution in the grid cells located near by the end point of the solution interval.

For cognitive radio systems, it is important to use efficient algorithms that search for free channels that can be provided to secondary users. Therefore, this paper is devoted to improving the accuracy of prediction frequency resource occupancy of a cellular communication system using spatiotemporal radio environment maps. The formation of a radio environment map is implemented for the fourthgeneration cellular communication system Long-Term Evolution. Taking this into account, a model structure has been developed that includes data generation and allows training and testing of an artificial neural network to predict the occupancy of frequency resources presented as the contents of radio environment map cells. A method for assessing prediction accuracy is described. The simulation model of the cellular communication system is implemented in the MatLab. The developed frequency resource occupancy prediction model is implemented in the Python. The complete file structure of the model is presented. The experiments were performed using artificial neural networks based on the Long Short-Term Memory and Kolmogorov – Arnold neural network architectures, taking into account its modification. It was found that with an equal number of parameters, the Kolmogorov –Arnold neural network learns faster for a given task. The obtained research results indicate an increase in the accuracy of prediction the occupancy of the frequency resource of the cellular communication system when using the Kolmogorov – Arnold neural network.

This paper considers the inverse problem of seismic exploration — determining the structure of the media based on the recorded wave response from it. Large cracks are considered as target objects, whose size and position are to be determined.

he direct problem is solved using the grid-characteristic method. The method allows using physically based algorithms for calculating outer boundaries of the region and contact boundaries inside the region. The crack is assumed to be thin, a special condition on the crack borders is used to describe the crack.

The inverse problem is solved using convolutional neural networks. The input data of the neural network are seismograms interpreted as images. The output data are masks describing the medium on a structured grid. Each element of such a grid belongs to one of two classes — either an element of a continuous geological massif, or an element through which a crack passes. This approach allows us to consider a medium with an unknown number of cracks.

The neural network is trained using only samples with one crack. The final testing of the trained network is performed using additional samples with several cracks. These samples are not involved in the training process. The purpose of testing under such conditions is to verify that the trained network has sufficient generality, recognizes signs of a crack in the signal, and does not suffer from overtraining on samples with a single crack in the media.

The paper shows that a convolutional network trained on samples with a single crack can be used to process data with multiple cracks. The networks detects fairly small cracks at great depths if they are sufficiently spatially separated from each other. In this case their wave responses are clearly distinguishable on the seismogram and can be interpreted by the neural network. If the cracks are close to each other, artifacts and interpretation errors may occur. This is due to the fact that on the seismogram the wave responses of close cracks merge. This cause the network to interpret several cracks located nearby as one. It should be noted that a similar error would most likely be made by a human during manual interpretation of the data. The paper provides examples of some such artifacts, distortions and recognition errors.