Unmanned aerial vehicles have shown promise in agriculture and nature management by providing rapid delivery of essential cargo to remote areas that are inaccessible by land transport routes. Given these considerations, an important research area involves modeling the task of creating scenarios for transporting diverse cargoes while accounting for constraints such as route network capacity, shortages of certain types of cargo, unmanned vehicle carrying capacity, and other relevant factors. In a previous study, the authors proposed a model to optimize the transportation plan for heterogeneous car-goes based on cost minimization for a route network of arbitrary structure. The model includes mechanisms to prioritize delivery requests. This work builds upon the previous study and addresses situations where constraints prevent the transportation of the entire volume of cargo intended for delivery. A model is proposed for maximizing the total volume of cargo passing through the route network in such cases. The proposed model is based on a multiproduct maximum flow problem and includes mechanisms to consider the prioritization of cargo delivery requests for different prioritization schemes under lexicographic preferences. An algorithm for finding the optimal plan is described. The experimental study results on modeling the process of cargo delivery to remote areas are presented, including visualization of the simulation results based on a digital terrain model.

Fish resources play a crucial role in Russia’s economy, especially given its extensive coastline, vast water areas, and rich marine and freshwater resources. Addressing the decline and restoration of fish populations, a consequence of farming practices, illegal fishing, and environmental catastrophes, stands as a critical issue in today’s world. In Russia in recent years, there has been active construction and devel-opment of fish farms, fisheries and biological laboratories, partly due to economic difficulties. One of the most valuable fish species is sturgeon, which requires special conditions of confinement compared to, for example, catfish. Production workers monitor the living conditions of the fish, including water parameters, and observe the growth and activity of these fish. This paper analyzes the different existing methods of weighing fish in production facilities. Nowadays, there is a trend toward digiti-zation of production and implementation of cyber-physical systems to improve the efficiency of production. Visual inspection, complemented by neural network anal-ysis, is emerging as a valuable approach for addressing the challenge of fish weight estimation. The study suggests a viable technique for automatically measuring stur-geon sizes at various developmental phases using YoLo9 and highlights potential areas for future research.

In the paper, we consider both finite-dimensional and infinite-dimensional optimization problems with inclusion-type and equality-type constraints. We obtain sufficient conditions for the stability in the weak topology of a solution to this problem with respect to small perturbations of the problem parameters. In the finite-dimensional case, conditions for the stability in the strong topology of the solution are obtained for the problem with equality-type constraints. These conditions are based on a certain implicit function theorem.

Distributions of in-degree and out-degree in directed graphs evolving by the linear preferential attachment (PA) without edge and node deletion were derived in Bollob\'{a}s, Riordan (2002). The same distributions, but in undirected graphs evolving by the PA with edge and node deletion, were obtained in Ghoshal et al. (2013). Our paper is devoted to a modeling %an empirical study of in- and out-degree distributions in directed graphs evolving by the PA and with edge and node deletion. The PA model is taken the same way as in Ghoshal et al. (2013). We show by a simulation study that the in- and out-degree distributions can be modeled by a power-law distribution or a power-law distribution with an exponential correction depending on the PA parameters.

This research considers a problem of distributed missions assignment in the heterogeneous group of aerial rescue robots, which function without obstacles on plain landscape. A new technique of distributed missions assignment problem solving is proposed, based on the metaheuristic optimization algorithms independent runs, which are launched on robots in a distributed manner. The novelty of the technique is that metaheuristics instances with varying computational complexities are formed and distributed through the heterogeneous robotic group with the usage of efficient algorithms for the block size forming and assignment. Computational complexities of metaheuristics instances are formed with two contradictory criteria: the first one is the minimization of the makespan of the missions assignment problem solving via independent runs, the second one is the metaheuristic instance maximization because of the solution quality degrading in case of small number of algorithm iterations. This improves the makespan of distributed missions assignment problem solving significantly without considerable degrading of the overall missions assignment result. Selected simulation results demonstrate the improvement of overall mission assignment time up to 2 times with the mission assignment solution degrading less than 26% in the worst case and without degrading in the best case.