The problems of risk management are considered from different points of view. Most often, they are investigated as part of organizational management. The results obtained within this approach are widely used, but their validity is debated. On the other hand, risk management can be considered as a system of mathematical models. In this form, it is discussed in the mechanism design section of control theory. The results obtained with this approach have rigorous substantiation, but also require a large number of conditions to be met, which is often hard to achieve in practice. Therefore, the convergence of these approaches is an important practical problem. The proposed paper attempts to put a formal solution to building a risk-minimizing topology of a computer network into the risk management framework. When choosing the optimal set of countermeasures to protect a computer network, the possibility of modifying the network topology is usually not considered. At the same time, topology rebuilding is a natural process for some classes of networks (in particular, wireless mesh networks). This study considers the problem of designing the topology of computer networks that minimizes risk. The proposed solution is based on the results obtained previously in research on the influence of the structure of complex systems on their integral risk. The article considers a particular case of the exact solution of the problem for a peer-to-peer distributed network of the ’bus’ topology. In addition, it proposes an algorithm to design a risk-minimizing topology in attack scenarios of a simple chain structure.

Real-life applications in project planning often involve grappling with inaccurate data or unexpected events, which can impact the project duration and cost. The delay in the project execution can be overcome by investing in additional resources to avoid compromising the project duration. The goal of the resource leveling problems (RLP) is to determine the optimal amount of resources to invest in, aiming to minimize the associated complementary costs and adhere to the fixed deadline. To tackle data uncertainty in the RLP, the literature has predominantly focused on developing robust and stochastic approaches. In contrast, sensitivity analysis and reactive approaches have received comparatively less attention, especially concerning the generalized RLP with flexible job durations. In this problem, the duration of each job depends on the amount of resources available for its execution. Therefore, utilizing more resources may help reduce the project duration but at an additional cost. This paper introduces a novel approach that addresses the generalized RLP with uncertain job and resource parameters, incorporating reactive and sensitivity-based methodologies. The proposed approach extends the concept of evaluation metrics from machine scheduling to the domain of the RLP with flexible job durations. It is based on a metric-based function that estimates the impact of changes in input data on the solution quality, considering both optimality and feasibility for the newproblem instance. The approach is tested through numerical experiments conducted on benchmark instance sets to investigate the impact of variations in different problem parameters. The obtained results demonstrated a meaningful accuracy in estimating the impact on the value of the objective function. Additionally, they underscored the importance of utilizing optimality/feasibility preservation conditions, as for a significant portion of the tested instances, the use of these conditions gave a satisfactory outcome.

The article considers group management and control of collector motors with different types of drivers. The paper deals with a scheme where the microcontroller manages a cyberphysical system, which is a combination of heterogeneous motors and drivers with different control principles. Motors perform a single task in each group, and the composition of the group can be changed by reassigning each motor (or multiple motors) to other groups. We set the problem of controlling groups of motors under given conditions and propose the corresponding software solution. The presented software library allows to simplify the implementation of algorithms to control robotic devices by using a high-level programming interface to control the groups of collector motors, and create an abstraction layer to specify operating modes for single motors and their groups without taking into account specific electric control schemes and features of each model of a motor or its driver. This greatly simplifies the work with the motors for the end user, and allows the use of heterogeneous motors and drivers in a single robotic project, as well as the replacement of motor drivers with their compatible analogues. The proposed software library can be used both in Arduino projects, and other frameworks and environments for programming robotics.

The article discusses NP-hard optimization problems of irregular cutting and problems of packing objects of arbitrary geometry. When placing objects, their representation in the form of orthogonal polyhedra is used, obtained after applying voxelization algorithms to given models of objects of complex shape. A greedy heuristic for the placement of two- and three-dimensional orthogonal polyhedra is proposed, which ensures the selecting of the best orientation variant for each object to be placed when searching for the densest placement scheme. The analysis of the effectiveness of the application of this greedy placement heuristic on the problems of flat irregular cutting and packing of three-dimensional objects of non-regular shape is carried out. The application of the proposed greedy heuristic provides fast obtaining if high quality solutions to the problems of packing a large number of objects represented in the form of orthogonal polyhedra. It is shown that the best solutions are obtained as a result of the joint application of the genetic algorithm and the proposed heuristic.

Based on topography and gravitational field data, model variations in the crust thickness of Mars and Venus were calculated using the Love numbers method. The method takes into account the adjustment of the planetary interior to loads on the surface and in the interior. Numerical modeling was carried out using the expansion in spherical harmonics of the topography and gravitational field data up to the 90th degree and order for Mars and up to the 70th degree and order for Venus. The topography of the crust–mantle boundary suggests partial Airy isostatic compensation. The model of the Martian crust is consistent with the interval of crustal thickness values under the site of the InSight station in the southwestern part of Elysium Planitia obtained from the results of a seismic experiment. The comparison with the available global models of the crust of Mars and Venus was carried out.