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Selection and correct applying of the geosensor — the instrument of mineral geothermobarometry is challenging because of the wide variety of existing geosensors on the one hand and the availability of specific requirements for their use on the other. In this paper, organization of the geosensors within the computer system called interactive registry was proposed for reducing the labor intensity of the geosensors usage and providing information support for them. The article provides a formal description of the thermodynamic geosensor, as a function of the minerals composition and independent parameters, as well as the basic steps of pressure and temperature estimation which are common for all geosensors: conversion to the formula units, calculation of the additional parameters and the calculation of the required values. Existing collections of geosensors made as standalone applications, or as spreadsheets was examined for advantages and disadvantages of these approaches. Additional information necessary to use the geosensor was described: paragenesis, accuracy and range of parameter values, reference and others. Implementation of the geosensors registry as the webbased application which uses wiki technology was proposed. Usage of the wiki technology allows to effectively organize not so well formalized additional information about the geosensor and it’s algorithm which had written in a programming language into a single information system. For information organization links, namespaces and wiki markup was used. The article discusses the implementation of the applications on the top of DokuWiki system with specially designed RESTful server, allowing users to apply the geosensors from the registry to their own data. Programming language R uses as a geosensors description language. RServe server uses for calculations. The unittest for each geosensor allows to check the correctness of it’s implementation. The user interface of the application was developed as DokuWiki plug-in. The example of usage was given. In the article conclusion, the questions of the application security, performance and scaling was discussed.

Recently, saddle point problems have received much attention due to their powerful modeling capability for a lot of problems from diverse domains. Applications of these problems occur in many applied areas, such as robust optimization, distributed optimization, game theory, and many applications in machine learning such as empirical risk minimization and generative adversarial networks training. Therefore, many researchers have actively worked on developing numerical methods for solving saddle point problems in many different settings. This paper is devoted to developing a numerical method for solving saddle point problems in the nonconvex uniformly-concave setting. We study a general class of saddle point problems with composite structure and H\"older-continuous higher-order derivatives. To solve the problem under consideration, we propose an approach in which we reduce the problem to a combination of two auxiliary optimization problems separately for each group of variables, the outer minimization problem w.r.t. primal variables, and the inner maximization problem w.r.t the dual variables. For solving the outer minimization problem, we use the Adaptive Gradient Method, which is applicable for nonconvex problems and also works with an inexact oracle that is generated by approximately solving the inner problem. For solving the inner maximization problem, we use the Restarted Unified Acceleration Framework, which is a framework that unifies the high-order acceleration methods for minimizing a convex function that has H\"older-continuous higher-order derivatives. Separate complexity bounds are provided for the number of calls to the first-order oracles for the outer minimization problem and higher-order oracles for the inner maximization problem. Moreover, the complexity of the whole proposed approach is then estimated.

Flexible woven composites are classified as high-tech innovative materials. Due to the combination of various components of the filler and reinforcement elements, such materials are used in construction, in the defense industry, in shipbuilding and aircraft construction, etc. In the domestic literature, insufficient attention is paid to woven composites that change their geometric structure of the reinforcing layer during deformation. This paper presents an analysis of the previously proposed complex approach to modeling the behavior of flexible woven composites under static uniaxial tension for further generalization of the approach to biaxial tension. The work is aimed at qualitative and quantitative description of mechanical deformation processes occurring in the structure of the studied materials under tension, which include straightening the strands of the reinforcing layer and increasing the value of mutual pressure of the cross-lying reinforcement strands. At the beginning of the deformation process, the straightening of the threads and the increase in mutual pressure of the threads are most intense. With the increase in the level of load, the change of these parameters slows down. For example, the bending of the reinforcement strands goes into the Central tension, and the value of the load from the mutual pressure is no longer increased (tends to constant). To simulate the described processes, the basic geometrical and mechanical parameters of the material affecting the process of forming are introduced, the necessary terminology and description of the characteristics are given. Due to the high geometric nonlinearity of the all processes described in the increments, as in the initial load values there is a significant deformation of the reinforcing layer. For the quantitative and qualitative description of mechanical deformation processes occurring in the reinforcing layer, analytical dependences are derived to determine the increment of the angle of straightening of reinforcement filaments and the load caused by the mutual pressure of the cross-lying filaments at each step of the load increment. For testing of obtained dependencies shows an example of their application for flexible woven composites brands VP4126, VP6131 and VP6545. The simulation results confirmed the assumptions about the processes of straightening the threads and slowing the increase in mutual pressure of the threads. The results and dependences presented in this paper are directly related to the further generalization of the previously proposed analytical models for biaxial tension, since stretching in two directions will significantly reduce the straightening of the threads and increase the amount of mutual pressure under similar loads.

This article studies a method of constructing a predictive neural network model of a time series based on determining the composition of input variables, constructing a training sample and training itself using the back propagation method. Traditional methods of constructing predictive models of the time series are: the autoregressive model, the moving average model or the autoregressive model — the moving average allows us to approximate the time series by a linear dependence of the current value of the output variable on a number of its previous values. Such a limitation as linearity of dependence leads to significant errors in forecasting.

Mining Technologies using neural network modeling make it possible to approximate the time series by a nonlinear dependence. Moreover, the process of constructing of a neural network model (determining the composition of input variables, the number of layers and the number of neurons in the layers, choosing the activation functions of neurons, determining the optimal values of the neuron link weights) allows us to obtain a predictive model in the form of an analytical nonlinear dependence.

The determination of the composition of input variables of neural network models is one of the key points in the construction of neural network models in various application areas that affect its adequacy. The composition of the input variables is traditionally selected from some physical considerations or by the selection method. In this work it is proposed to use the behavior of the autocorrelation and private autocorrelation functions for the task of determining the composition of the input variables of the predictive neural network model of the time series.

In this work is proposed a method for determining the composition of input variables of neural network models for stationary and non-stationary time series, based on the construction and analysis of autocorrelation functions. Based on the proposed method in the Python programming environment are developed an algorithm and a program, determining the composition of the input variables of the predictive neural network model — the perceptron, as well as building the model itself. The proposed method was experimentally tested using the example of constructing a predictive neural network model of a time series that reflects energy consumption in different regions of the United States, openly published by PJM Interconnection LLC (PJM) — a regional network organization in the United States. This time series is non-stationary and is characterized by the presence of both a trend and seasonality. Prediction of the next values of the time series based on previous values and the constructed neural network model showed high approximation accuracy, which proves the effectiveness of the proposed method.

The paper is devoted to convex-concave saddle point problems where the objective is a sum of a large number of functions. Such problems attract considerable attention of the mathematical community due to the variety of applications in machine learning, including adversarial learning, adversarial attacks and robust reinforcement learning, to name a few. The individual functions in the sum usually represent losses related to examples from a data set. Additionally, the formulation admits a possibly nonsmooth composite term. Such terms often reflect regularization in machine learning problems. We assume that the dimension of one of the variable groups is relatively small (about a hundred or less), and the other one is large. This case arises, for example, when one considers the dual formulation for a minimization problem with a moderate number of constraints. The proposed approach is based on using Vaidya’s cutting plane method to minimize with respect to the outer block of variables. This optimization algorithm is especially effective when the dimension of the problem is not very large. An inexact oracle for Vaidya’s method is calculated via an approximate solution of the inner maximization problem, which is solved by the accelerated variance reduced algorithm Katyusha. Thus, we leverage the structure of the problem to achieve fast convergence. Separate complexity bounds for gradients of different components with respect to different variables are obtained in the study. The proposed approach is imposing very mild assumptions about the objective. In particular, neither strong convexity nor smoothness is required with respect to the low-dimensional variable group. The number of steps of the proposed algorithm as well as the arithmetic complexity of each step explicitly depend on the dimensionality of the outer variable, hence the assumption that it is relatively small.

This article discusses the mechanical properties of carbon nanotube (CNT)-reinforced platinum under uniaxial tensile loading using the molecular dynamics method. A review of current computational and experimental studies on the use of carbon nanotube-reinforced composites from a structural point of view. However, quantitative and qualitative studies of CNTs to improve the properties of composites are still rare. Composite selection is a promising application for platinum alloys in many cases where they may be subjected to mechanical stress, including in biocompatibility sources. Pt-reinforced with CNTs may have additional possibilities for implantation of the implant and at the same time obtain the required mechanical characteristics.

The structure of the composite is composed of a Pt crystal with a face-centered cubic lattice with a constant of 3.92 Å and a carbon nanotube. The Pt matrix has the shape of a cube with dimensions of $43.1541 Å \times 43.1541 Å \times 43.1541 Å$. The hole size in the average platinum dimension is the radius of the carbon nanotube of the «zigzag» type (8,0), which is 2.6 Å. A carbon nanotube is placed in a hole with a radius of 4.2 Å. At such parameters, the maximum energy level was mutually observed. The model under consideration is contained in 320 atomic bombs and 5181 atomic platinum. The volume fraction of deaths in the Pt-C composite is 5.8%. At the first stage of the study, the strain rate was analyzed for stress-strain and energy change during uniaxial action on the Pt-C composite.

Analysis of the strain rate study showed that the consumption yield strength increases with high strain rate, and the elasticity has increased density with decreasing strain rate. This work also increased by 40% for Pt-C, the elasticity of the composite decreased by 42.3%. In general, fracture processes are considered in detail, including plastic deformation on an atomistic scale.

The paper contains numerical simulation of nonisothermal nonlinear flow in a porous medium. Twodimensional unsteady problem of heavy oil, water and steam flow is considered. Oil phase consists of two pseudocomponents: light and heavy fractions, which like the water component, can vaporize. Oil exhibits viscoplastic rheology, its filtration does not obey Darcy's classical linear law. Simulation considers not only the dependence of fluids density and viscosity on temperature, but also improvement of oil rheological properties with temperature increasing.

To solve this problem numerically we use streamline method with splitting by physical processes, which consists in separating the convective heat transfer directed along filtration from thermal conductivity and gravitation. The article proposes a new approach to streamline methods application, which allows correctly simulate nonlinear flow problems with temperature-dependent rheology. The core of this algorithm is to consider the integration process as a set of quasi-equilibrium states that are results of solving system on a global grid. Between these states system solved on a streamline grid. Usage of the streamline method allows not only to accelerate calculations, but also to obtain a physically reliable solution, since integration takes place on a grid that coincides with the fluid flow direction.

In addition to the streamline method, the paper presents an algorithm for nonsmooth coefficients accounting, which arise during simulation of viscoplastic oil flow. Applying this algorithm allows keeping sufficiently large time steps and does not change the physical structure of the solution.

Obtained results are compared with known analytical solutions, as well as with the results of commercial package simulation. The analysis of convergence tests on the number of streamlines, as well as on different streamlines grids, justifies the applicability of the proposed algorithm. In addition, the reduction of calculation time in comparison with traditional methods demonstrates practical significance of the approach.

The biomass growth yield is the ratio of the newly synthesized substance of growing cells to the amount of the consumed substrate, the source of matter and energy for cell growth. The yield is a characteristic of the efficiency of substrate conversion to cell biomass. The conversion is carried out by the cell metabolism, which is a complete aggregate of biochemical reactions occurring in the cells.

This work newly considers the problem of maximal cell growth yield prediction basing on balances of the whole living cell metabolism and its fragments called as partial metabolisms (PM). The following PM’s are used for the present consideration. During growth on any substrate we consider i) the standard constructive metabolism (SCM) which consists of identical pathways during growth of various organisms on any substrate. SCM starts from several standard compounds (nodal metabolites): glucose, acetyl-CoA 2-oxoglutarate, erythrose-4-phosphate, oxaloacetate, ribose-5- phosphate, 3-phosphoglycerate, phosphoenolpyruvate, and pyruvate, and ii) the full forward metabolism (FM) — the remaining part of the whole metabolism. The first one consumes high-energy bonds (HEB) formed by the second one. In this work we examine a generalized variant of the FM, when the possible presence of extracellular products, as well as the possibilities of both aerobic and anaerobic growth are taken into account. Instead of separate balances of each nodal metabolite formation as it was made in our previous work, this work deals at once with the whole aggregate of these metabolites. This makes the problem solution more compact and requiring a smaller number of biochemical quantities and substantially less computational time. An equation expressing the maximal biomass yield via specific amounts of HEB formed and consumed by the partial metabolisms has been derived. It includes the specific HEB consumption by SCM which is a universal biochemical parameter applicable to the wide range of organisms and growth substrates. To correctly determine this parameter, the full constructive metabolism and its forward part are considered for the growth of cells on glucose as the mostly studied substrate. We used here the found earlier properties of the elemental composition of lipid and lipid-free fractions of cell biomass. Numerical study of the effect of various interrelations between flows via different nodal metabolites has been made. It showed that the requirements of the SCM in high-energy bonds and NAD(P)H are practically constants. The found HEB-to-formed-biomass coefficient is an efficient tool for finding estimates of maximal biomass yield from substrates for which the primary metabolism is known. Calculation of ATP-to-substrate ratio necessary for the yield estimation has been made using the special computer program package, GenMetPath.

The article is devoted to the application of various methods of mathematical analysis, search for patterns and studying the composition of nucleotides in DNA sequences at the genomic level. New methods of mathematical biology that made it possible to detect and visualize the hidden ordering of genetic nucleotide sequences located in the chromosomes of cells of living organisms described. The research was based on the work on algebraic biology of the doctor of physical and mathematical sciences S. V. Petukhov, who first introduced and justified new algebras and hypercomplex numerical systems describing genetic phenomena. This paper describes a new phase in the development of matrix methods in genetics for studying the properties of nucleotide sequences (and their physicochemical parameters), built on the principles of finite geometry. The aim of the study is to demonstrate the capabilities of new algorithms and discuss the discovered properties of genetic DNA and RNA molecules. The study includes three stages: parameterization, scaling, and visualization. Parametrization is the determination of the parameters taken into account, which are based on the structural and physicochemical properties of nucleotides as elementary components of the genome. Scaling plays the role of “focusing” and allows you to explore genetic structures at various scales. Visualization includes the selection of the axes of the coordinate system and the method of visual display. The algorithms presented in this work are put forward as a new toolkit for the development of research software for the analysis of long nucleotide sequences with the ability to display genomes in parametric spaces of various dimensions. One of the significant results of the study is that new criteria were obtained for the classification of the genomes of various living organisms to identify interspecific relationships. The new concept allows visually and numerically assessing the variability of the physicochemical parameters of nucleotide sequences. This concept also allows one to substantiate the relationship between the parameters of DNA and RNA molecules with fractal geometric mosaics, reveals the ordering and symmetry of polynucleotides, as well as their noise immunity. The results obtained justified the introduction of new terms: “genometry” as a methodology of computational strategies and “genometrica” as specific parameters of a particular genome or nucleotide sequence. In connection with the results obtained, biosemiotics and hierarchical levels of organization of living matter are raised.