All issues

A new grid and cloud services simulation for NICA accelerator complex data storage and processing system are described. This system is focused on improving the efficiency of the grid-cloud systems development by using work quality indicators of some real system to design and predict its evolution. For these purpose the simulation program are combined with real monitoring system of the grid-cloud service through a special database. An example of the program usage to simulate a sufficiently general cloud structure, which can be used for more common purposes, is given.

We present the iterative algorithm that solves numerically both Urysohn type Fredholm and Volterra nonlinear one-dimensional nonsingular integral equations of the second kind to a specified, modest user-defined accuracy. The algorithm is based on descending recursive sequence of quadratures. Convergence of numerical scheme is guaranteed by fixed-point theorems. Picard’s method of integrating successive approximations is of great importance for the existence theory of integral equations but surprisingly very little appears on numerical algorithms for its direct implementation in the literature. We show that successive approximations method can be readily employed in numerical solution of integral equations. By that the quadrature algorithm is thoroughly designed. It is based on the explicit form of fifth-order embedded Runge–Kutta rule with adaptive step-size self-control. Since local error estimates may be cheaply obtained, continuous monitoring of the quadrature makes it possible to create very accurate automatic numerical schemes and to reduce considerably the main drawback of Picard iterations namely the extremely large amount of computations with increasing recursion depth. Our algorithm is organized so that as compared to most approaches the nonlinearity of integral equations does not induce any additional computational difficulties, it is very simple to apply and to make a program realization. Our algorithm exhibits some features of universality. First, it should be stressed that the method is as easy to apply to nonlinear as to linear equations of both Fredholm and Volterra kind. Second, the algorithm is equipped by stopping rules by which the calculations may to considerable extent be controlled automatically. A compact C++-code of described algorithm is presented. Our program realization is self-consistent: it demands no preliminary calculations, no external libraries and no additional memory is needed. Numerical examples are provided to show applicability, efficiency, robustness and accuracy of our approach.

The article covers several mathematical problems relating to elastic stability of thin shells in view of inconsistencies that have been recently identified between the experimental data and the predictions based on the shallow- shell theory. It is highlighted that the contradictions were caused by new algorithms that enabled updating the values of the so called “low critical stresses” calculated in the 20th century and adopted as a buckling criterion for thin shallow shells by technical standards. The new calculations often find the low critical stress close to zero. Therefore, the low critical stress cannot be used as a safety factor for the buckling analysis of the thinwalled structure, and the equations of the shallow-shell theory need to be replaced with other differential equations. The new theory also requires a buckling criterion ensuring the match between calculations and experimental data.

The article demonstrates that the contradiction with the new experiments can be resolved within the dynamic nonlinear three-dimensional theory of elasticity. The stress when bifurcation of dynamic modes occurs shall be taken as a buckling criterion. The nonlinear form of original equations causes solitary (solitonic) waves that match non-smooth displacements (patterns, dents) of the shells. It is essential that the solitons make an impact at all stages of loading and significantly increase closer to bifurcation. The solitonic solutions are illustrated based on the thin cylindrical momentless shell when its three-dimensional volume is simulated with twodimensional surface of the set thickness. It is noted that the pattern-generating waves can be detected (and their amplitudes can by identified) with acoustic or electromagnetic devices.

Thus, it is technically possible to reduce the risk of failure of the thin shells by monitoring the shape of the surface with acoustic devices. The article concludes with a setting of the mathematical problems requiring the solution for the reliable numerical assessment of the buckling criterion for thin elastic shells.

The Industrial Internet of Things (IIoT) networks plays a significant role in enhancing industrial automation systems by connecting industrial devices for real time data monitoring and predictive maintenance. However, this connectivity introduces new vulnerabilities which demand the development of advanced intrusion detection systems. The nuclear facilities are considered one of the closest examples of critical infrastructures that suffer from high vulnerability through the connectivity of IIoT networks. This paper develops a robust intrusion detection approach using machine and ensemble learning algorithms specifically determined for IIoT networks. This approach can achieve optimal performance with low time complexity suitable for real-time IIoT networks. For each algorithm, Grid Search is determined to fine-tune the hyperparameters for optimizing the performance while ensuring time computational efficiency. The proposed approach is investigated on recent IIoT intrusion detection datasets, WUSTL-IIOT-2021 and Edge-IIoT-2022 to cover a wider range of attacks with high precision and minimum false alarms. The study provides the effectiveness of ten machine and ensemble learning models on selected features of the datasets. Synthetic Minority Over-sampling Technique (SMOTE)-based multi-class balancing is used to manipulate dataset imbalances. The ensemble voting classifier is used to combine the best models with the best hyperparameters for raising their advantages to improve the performance with the least time complexity. The machine and ensemble learning algorithms are evaluated based on accuracy, precision, recall, F1 Score, and time complexity. This evaluation can discriminate the most suitable candidates for further optimization. The proposed approach is called the XCL approach that is based on Extreme Gradient Boosting (XGBoost), CatBoost (Categorical Boosting), and Light Gradient- Boosting Machine (LightGBM). It achieves high accuracy, lower false positive rate, and efficient time complexity. The results refer to the importance of ensemble strategies, algorithm selection, and hyperparameter optimization in enhancing the performance to detect the different intrusions across the IIoT datasets over the other models. The developed approach produced a higher accuracy of 99.99% on the WUSTL-IIOT-2021 dataset and 100% on the Edge-IIoTset dataset. Our experimental evaluations have been extended to the CIC-IDS-2017 dataset. These additional evaluations not only highlight the applicability of the XCL approach on a wide spectrum of intrusion detection scenarios but also confirm its scalability and effectiveness in real-world complex network environments.

New key parameters, namely b₀ = tgθ₀, θ₀ — angle of throwing, R_a — top curvature radius and β₀ — dimensionless speed square on the top of low angular trajectory were suggested in classic problem of integrating nonlinear equations of point mass projectile motion with quadratic air drag. Very precise formulae were obtained in a new way for coordinates x(b), y(b) and fly time t(b), b = tgθ where θ is inclination angle. This method is based on Legendre transformation and its precision is automatically improved in wide range of the θ₀ values and drag force parameters α. The precision was monitored by Maple computing product.

In the paper the statistical relationships between the size and production characteristics of phytoplankton and zooplankton of the Vistula and Curonian lagoons, the Baltic Sea, were investigated. Research phytoplankton and zooplankton within the Russian part of the area of the Vistula and the Curonian lagoon was carried out on the monthly basis (from April to November) within the framework of long-term monitoring program on evaluating of ecological status of the lagoons. The size structure of plankton is the basis for understanding of the development of production processes, mechanisms of formation of the plankton species diversity and functioning of the lagoon ecosystems. As results of the work it was found that the maximum rate of photosynthesis and the integral value of the primary production with a change in cell volume of phytoplankton are changed according to a power law. The result shows that the smaller the size of algal cells in phytoplankton communities the more actively occur metabolism and the more effective they assimilate the solar energy. It is shown that the formation of plankton species diversity in ecosystems of lagoons is closely linked with the size structure of plankton communities and with features of development of the production processes. It is proposed the structure of a spatially homogenous mathematical model of the plankton food chain for the lagoon ecosystems taking into account the size spectrum and the characteristics of phytoplankton and zooplankton. The model parameters are the sizedependent indicators allometrically linked with average volumes of cells and organisms in different ranges of their sizes. In the model the algorithm for changes over time the coefficients of food preferences in the diet of zooplankton was proposed. Developed the size-dependent mathematical model of aquatic ecosystems allows to consider the impact of turbulent exchange on the size structure and temporal dynamics of the plankton food chain of the Vistula and Curonian lagoons. The model can be used to study the different regimes of dynamic behavior of plankton systems depending on the changes in the values of its parameters and external influences, as well as to quantify the redistribution of matter flows in ecosystems of the lagoons.

This article proposes the sumo-atclib framework, which provides a convenient uniform interface for testing adaptive control algorithms with different limitations, for example, restrictions on phase durations, phase sequences, restrictions on the minimum time between control actions, which uses the open source microscopic transport modeling environment SUMO. The framework shares the functionality of controllers (class TrafficController) and a monitoring and detection system (class StateObserver), which repeats the architecture of real traffic light objects and adaptive control systems and simplifies the testing of new algorithms, since combinations of different controllers and vehicle detection systems can be freely varied. Also, unlike most existing solutions, the road class Road has been added, which combines a set of lanes, this allows, for example, to determine the adjacency of regulated intersections, in cases when the number of lanes changes on the way from one intersection to another, and therefore the road graph is divided into several edges. At the same time, the algorithms themselves use the same interface and are abstracted from the specific parameters of the detectors, network topologies, that is, it is assumed that this solution will allow the transport engineer to test ready-made algorithms for a new scenario, without the need to adapt them to new conditions, which speeds up the development process of the control system, and reduces design overhead. At the moment, the package contains examples of MaxPressure algorithms and the Q-learning reinforcement learning method, the database of examples is also being updated. The framework also includes a set of SUMO scripts for testing algorithms, which includes both synthetic maps and well-verified SUMO scripts such as Cologne and Ingolstadt. In addition, the framework provides a set of automatically calculated metrics, such as total travel time, delay time, average speed; the framework also provides a ready-made example for visualization of metrics.

The efficiency of mobile robotic systems (MRS) that monitor the traffic situation, urban infrastructure, consequences of emergency situations, etc., directly depends on the quality of vision systems, which are the most important part of MRS. In turn, the accuracy of image processing in vision systems depends to a great extent on the accuracy of spatial orientation of the video camera placed on the MRS. However, when video cameras are placed on the MRS, the level of errors of their spatial orientation increases sharply, caused by wind and seismic vibrations, movement of the MRS over rough terrain, etc. In this connection, the paper considers a general solution to the problem of stochastic estimation of spatial orientation parameters of video cameras in conditions of both random mast vibrations and arbitrary character of MRS movement. Since the methods of solving this problem on the basis of satellite measurements at high intensity of natural and artificial radio interference (the methods of formation of which are constantly being improved) are not able to provide the required accuracy of the solution, the proposed approach is based on the use of autonomous means of measurement — inertial and non-inertial. But when using them, the problem of building and stochastic estimation of the general model of video camera motion arises, the complexity of which is determined by arbitrary motion of the video camera, random mast oscillations, measurement disturbances, etc. The problem of stochastic estimation of the general model of video camera motion arises. Due to the unsolved nature of this problem, the paper considers the synthesis of both the video camera motion model in the most general case and the stochastic estimation of its state parameters. The developed algorithm for joint estimation of the spatial orientation parameters of the video camera placed on the mast of the MRS is invariant to the nature of motion of the mast, the video camera, and the MRS itself, providing stability and the required accuracy of estimation under the most general assumptions about the nature of interference of the sensitive elements of the autonomous measuring complex used. The results of the numerical experiment allow us to conclude that the proposed approach can be practically applied to solve the problem of the current spatial orientation of MRS and video cameras placed on them using inexpensive autonomous measuring devices.

The key problems of remote forest pathology monitoring for coniferous forests affected by insect pests have been analyzed. It has been demonstrated that addressing these tasks requires the use of multiclass classification results for coniferous trees in high- and ultra-high-resolution images, which are promptly obtained through monitoring via satellites or unmanned aerial vehicles (UAVs). An analytical review of modern models and methods for multiclass classification of coniferous forest images was conducted, leading to the development of three fully convolutional neural network models: Mo-U-Net, At-Mo-U-Net, and Res-Mo-U-Net, all based on the classical U-Net architecture. Additionally, the Segformer transformer model was modified to suit the task. For RGB images of fir trees Abies sibirica affected by the four-eyed bark beetle Polygraphus proximus, captured using a UAV-mounted camera, two datasets were created: the first dataset contains image fragments and their corresponding reference segmentation masks sized 256 × 256 × 3 pixels, while the second dataset contains fragments sized 480 × 480 × 3 pixels. Comprehensive studies were conducted on each trained neural network model to evaluate both classification accuracy for assessing the degree of damage (health status) of Abies sibirica trees and computation speed using test datasets from each set. The results revealed that for fragments sized 256 × 256 × 3 pixels, the At-Mo-U-Net model with an attention mechanism is preferred alongside the Modified Segformer model. For fragments sized 480 × 480 × 3 pixels, the Res-Mo-U-Net hybrid model with residual blocks demonstrated superior performance. Based on classification accuracy and computation speed results for each developed model, it was concluded that, for production-scale multiclass classification of affected fir trees, the Res-Mo-U-Net model is the most suitable choice. This model strikes a balance between high classification accuracy and fast computation speed, meeting conflicting requirements effectively.