All issues

In this work, we consider a special approximation of the general perturbation formula for the electromagnetic field by a set of electrically small inhomogeneities located in the domain of interest. The problem considered in this paper arises in many applications of technical electrodynamics, radar technologies and subsurface remote sensing. In the general case, it is formulated as follows: at some point in the perturbed domain, it is necessary to determine the amplitude of the electromagnetic field. The perturbation of electromagnetic waves is caused by a set of electrically small scatterers distributed in space. The source of electromagnetic waves is also located in perturbed domain. The problem is solved by introducing the far field approximation and through the formulation for the scatterer radar cross section value. This, in turn, allows one to significantly speed up the calculation process of the perturbed electromagnetic field by a set of a spherical inhomogeneities identical to each other with arbitrary electrophysical parameters. In this paper, we consider only the direct scattering problem; therefore, all parameters of the scatterers are known. In this context, it may be argued that the formulation corresponds to the well-posed problem and does not imply the solution of the integral equation in the generalized formula. One of the features of the proposed algorithm is the allocation of a characteristic plane at the domain boundary. All points of observation of the state of the system belong to this plane. Set of the scatterers is located inside the observation region, which is formed by this surface. The approximation is tested by comparing the results obtained with the solution of the general formula method for the perturbation of the electromagnetic field. This approach, among other things, allows one to remove a number of restrictions on the general perturbation formula for E-filed analysis.

The paper is devoted to the secondary use of spectrum in telecommunication networks. It is emphasized that one of the solutions to this problem is the use of cognitive radio technologies and dynamic spectrum access for the successful functioning of which a large amount of information is required, including the parameters of base stations and network subscribers. Storage and processing of information should be carried out using a radio environment map, which is a spatio-temporal database of all activity in the network and allows you to determine the frequencies available for use at a given time. The paper presents a two-level model for forming a map of the radio environment of a cellular communication system LTE, in which the local and global levels are highlighted, which is described by the following parameters: a set of frequencies, signal attenuation, signal propagation map, grid step, current time count. The key objects of the model are the base station and the subscriber unit. The main parameters of the base station include: name, identifier, cell coordinates, range number, radiation power, numbers of connected subscriber devices, dedicated resource blocks. For subscriber devices, the following parameters are used: name, identifier, location, current coordinates of the device cell, base station identifier, frequency range, numbers of resource blocks for communication with the station, radiation power, data transmission status, list of numbers of the nearest stations, schedules movement and communication sessions of devices. An algorithm for the implementation of the model is presented, taking into account the scenarios of movement and communication sessions of subscriber devices. A method for calculating a map of the radio environment at a point on a coordinate grid, taking into account losses during the propagation of radio signals from emitting devices, is presented. The software implementation of the model is performed using the MatLab package. The approaches are described that allow to increase the speed of its work. In the simulation, the choice of parameters was carried out taking into account the data of the existing communication systems and the economy of computing resources. The experimental results of the algorithm for the formation of a radio environment map are demonstrated, confirming the correctness of the developed model.

The paper is a study of the mathematical model and kinematics of a parallel spherical manipulator. This type of manipulator was proposed back in the 80s of the last century and has since found application in exoskeletons and rehabilitation robots due to its structure, which allows imitating natural joint movements of the human body.

The Parallel Spherical Manipulator is a robot with three legs and two platforms, a base platform and a mobile platform. Its legs consist of two support links that are arc-shaped. Mathematically, the manipulator can be described using two virtual pyramids that are placed on top of each other.

The paper considers two types of manipulator configurations: classical and asymmetric, and solves basic kinematic problems for each. The study shows that the asymmetric design of the manipulator has the maximum workspace, especially when the motors are mounted at the joints of the manipulator’s links inside legs.

To optimize the parameters of the parallel spherical manipulator, we introduced a metric of usable workspace volume. This metric represents the volume of the sector of the sphere in which the robot does not experience internal collisions or singular states. There are three types of singular states possible within a parallel spherical manipulator — serial, parallel, and mixed singularity. We used all three types of singularities to calculate the useful volume. In our research work, we solved the problem related to maximizing the usable volume of the workspace.

Through our research work, we found that the asymmetric configuration of the spherical manipulator maximizes the workspace when the motors are located at the articulation point of the robot leg support arms. At the same time, the parameter $\beta_1$ must be zero degrees to maximize the workspace. This allowed us to create a prototype robot in which we eliminated the use of lower links in legs in favor of a radiused rail along which the motors run. This allowed us to reduce the linear dimensions of the robot itself and gain on the stiffness of the structure.

The results obtained can be used to optimize the parameters of the parallel spherical manipulator in various industrial and scientific applications, as well as for further research of other types of parallel robots and manipulators.

The article shows the relevance of research work in the field of creating control systems for unmanned vehicles based on computer vision technologies. Computer vision tools are used to solve a large number of different tasks, including to determine the location of the car, detect obstacles, determine a suitable parking space. These tasks are resource intensive and have to be performed in real time. Therefore, it is important to develop effective models, methods and tools that ensure the achievement of the required time and accuracy for use in unmanned vehicle control systems. In this case, the choice of the image representation model is important. In this paper, we consider a model based on the wavelet transform, which makes it possible to form features characterizing the energy estimates of the image points and reflecting their significance from the point of view of the contribution to the overall image energy. To form a model of energy characteristics, a procedure is performed based on taking into account the dependencies between the wavelet coefficients of various levels and the application of heuristic adjustment factors for strengthening or weakening the influence of boundary and interior points. On the basis of the proposed model, it is possible to construct descriptions of images their characteristic features for isolating and analyzing, including for isolating contours, regions, and singular points. The effectiveness of the proposed approach to image analysis is due to the fact that the objects in question, such as road signs, road markings or car numbers that need to be detected and identified, are characterized by the relevant features. In addition, the use of wavelet transforms allows to perform the same basic operations to solve a set of tasks in onboard unmanned vehicle systems, including for tasks of primary processing, segmentation, description, recognition and compression of images. The such unified approach application will allow to reduce the time for performing all procedures and to reduce the requirements for computing resources of the on-board system of an unmanned vehicle.

The functioning of DNA regulatory regions rely primarily on their physicochemical and structural properties but not on nucleotide sequences, i.e. ‘genetic text’. The formers are responsible for coding of DNA-protein interactions that govern various regulatory events. One of the characteristics is SIDD (Stress-Induced Duplex Destabilization) that quantify DNA duplex region propensity to melt under the imposed superhelical stress. The duplex property has been shown to participate in activity of various regulatory regions. Here we employ the SIDD model to calculate melting probability profiles for T7 bacteriophage promoter sequences. The genome is characterized by small size (approximately 40 thousand nucleotides) and temporal organization of expression: at the first stage of infection early T7 DNA region is transcribed by the host cell RNA polymerase, later on in life cycle phage-specific RNA polymerase performs transcription of class II and class III genes regions. Differential recognition of a particular group of promoters by the enzyme cannot be solely explained by their nucleotide sequences, because of, among other reasons, it is fairly similar among most the promoters. At the same time SIDD profiles obtained vary significantly and are clearly separated into groups corresponding to functional promoter classes of T7 DNA. For example, early promoters are affected by the same maximally destabilized DNA duplex region located at the varying region of a particular promoter. class II promoters lack substantially destabilized regions close to transcription start sites. Class III promoters, in contrast, demonstrate characteristic melting probability maxima located in the near-downstream region in all cases. Therefore, the apparent differences among the promoter groups with exceptional textual similarity (class II and class III differ by only few singular substitutions) were established. This confirms the major impact of DNA primary structure on the duplex parameter as well as a need for a broad genetic context consideration. The differences in melting probability profiles obtained using SIDD model alongside with other DNA physicochemical properties appears to be involved in differential promoter recognition by RNA polymerases.

A simple model based on a kinetic-type equation is proposed to describe the spread of a virus in space through the migration of virus carriers from a certain center. The consideration is carried out on the example of three countries for which such a one-dimensional model is applicable: Russia, Italy and Chile. The geographical location of these countries and their elongation in the direction from the centers of infection (Moscow, Milan and Lombardia in general, as well as Santiago, respectively) makes it possible to use such an approximation. The aim is to determine the dynamic density of the infected in time and space. The model is two-parameter. The first parameter is the value of the average spreading rate associated with the transfer of infected moving by transport vehicles. The second parameter is the frequency of the decrease of the infected as they move through the country, which is associated with the passengers reaching their destination, as well as with quarantine measures. The parameters are determined from the actual known data for the first days of the spatial spread of the epidemic. An analytical solution is being built; simple numerical methods are also used to obtain a series of calculations. The geographical spread of the disease is a factor taken into account in the model, the second important factor is that contact infection in the field is not taken into account. Therefore, the comparison of the calculated values with the actual data in the initial period of infection coincides with the real data, then these data become higher than the model data. Those no less model calculations allow us to make some predictions. In addition to the speed of infection, a similar “speed of recovery” is possible. When such a speed is found for the majority of the country's population, a conclusion is made about the beginning of a global recovery, which coincides with real data.

The numerical methods used to simulate the evolution of hydrodynamic systems require the considerable use of computational resources thus limiting the number of possible simulations. The data-driven simulation technique is one promising approach to the development of heuristic models, which may speed up the study of such models. In this approach, machine learning methods are used to tune the weights of an artificial neural network that predicts the state of a physical system at a given point in time based on initial conditions. This article describes an original neural network architecture and a novel multi-stage training procedure which create a heuristic model of a two-phase flow in a heterogeneous porous medium. The neural network-based model predicts the states of the grid cells at an arbitrary timestep (within the known constraints), taking in only the initial conditions: the properties of the heterogeneous permeability of the medium and the location of sources and sinks. The proposed model requires orders of magnitude less processor time in comparison with the classical numerical method, which served as a criterion for evaluating the effectiveness of the trained model. The proposed architecture includes a number of subnets trained in various combinations on several datasets. The techniques of adversarial training and weight transfer are utilized.

Examining the spectral response of plants from data collected using remote sensing has a lot of potential for solving real-world problems in different fields of research. In this study, we have used the spectral property to identify the invasive plant Heracleum sosnowskyi Manden from satellite imagery. H. sosnowskyi is an invasive plant that causes many harms to humans, animals and the ecosystem at large. We have used data collected from the years 2018 to 2020 containing sample geolocation data from the Moscow Region where this plant exists and we have used Sentinel-2 imagery for the spectral analysis towards the aim of detecting it from the satellite imagery. We deployed a Random Forest (RF) machine learning model within the framework of Google Earth Engine (GEE). The algorithm learns from the collected data, which is made up of 12 bands of Sentinel-2, and also includes the digital elevation together with some spectral indices, which are used as features in the algorithm. The approach used is to learn the biophysical parameters of H. sosnowskyi from its reflectances by fitting the RF model directly from the data. Our results demonstrate how the combination of remote sensing and machine learning can assist in locating H. sosnowskyi, which aids in controlling its invasive expansion. Our approach provides a high detection accuracy of the plant, which is 96.93%.

The article presents the results of computer simulations aimed to assess the influence of tree spatial locations (planting schemes) on the productivity and the dynamics of soil fertility in forest plantations. The growth of aspen (Populus tremula L.) in plantations with short rotation (30 years) was simulated in the EFIMOD system of models with the soil and climatic data matching forested lands in the Mari El Republic. The outcome reveals that higher biomass rates, increase in soil organic matter stocks, and the minimal loss of soil nitrogen can be obtained when the distance between trees in the row equals 1–4 m and 4–6 м in aisles.

The paper presents agent-based model for city formation named Technoscape which is both local and nonlocal. Technoscape can, to a certain degree, be also assumed as a model for emergence of global economy. The current version of the model implements very simple way of agents’ behavior and interaction, still the model provides rather interesting spatio-temporal patterns.

Locality and non-locality mean here the spatial features of the way the agents interact with each other and with geographical space upon which the evolution takes place. Technoscape agent is some conventional artisan, family, or а producing and trading firm, while there is no difference between production and trade. Agents are located upon and move through bounded two-dimensional space divided into square cells. The model demonstrates processes of agents’ concentration in a small set of cells, which is interpreted as «city» formation. Agents are immortal, they don’t mutate and evolve, though this is interesting perspective for the evolution of the model itself.

Technoscape provides some distinctively new type of self-organization. Partially, this type of selforganization resembles the behavior of segregation model by Thomas Shelling, still that model has evolution rules substantially different from Technoscape. In Shelling model there exist avalanches still simple equilibria exist if no new agents are added to the game board, while in Technoscape no such equilibria exist. At best, we can observe quasi-equilibrium, slowly changing global states.

One non-trivial phenomenon Technoscape exhibits, which also contrasts to Shelling segregation model, is the ability of agents to concentrate in local cells (interpreted as cities) even explicitly and totally ignoring local interactions, using non-local interactions only.

At the same time, while the agents tend to concentrate in large one-cell cities, large scale of such cities does not guarantee them from decay: there always exists a process of «enticement» of agents and their flow to new cities.