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This article considers the task of multiclass classification of coniferous trees with varying degrees of damage by insect pests on images obtained using unmanned aerial vehicles (UAVs). We propose the use of convolutional neural networks (CNNs) for the classification of fir trees Abies sibirica and Siberian pine trees Pinus sibirica in unmanned aerial vehicles (UAV) imagery. In our approach, we develop three CNN models based on the classical U-Net architecture, designed for pixel-wise classification of images (semantic segmentation). The first model, Mo-U-Net, incorporates several changes to the classical U-Net model. The second and third models, MSC-U-Net and MSC-Res-U-Net, respectively, form ensembles of three Mo-U-Net models, each varying in depth and input image sizes. Additionally, the MSC-Res-U-Net model includes the integration of residual blocks. To validate our approach, we have created two datasets of UAV images depicting trees affected by pests, specifically Abies sibirica and Pinus sibirica, and trained the proposed three CNN models utilizing mIoULoss and Focal Loss as loss functions. Subsequent evaluation focused on the effectiveness of each trained model in classifying damaged trees. The results obtained indicate that when mIoULoss served as the loss function, the proposed models fell short of practical applicability in the forestry industry, failing to achieve classification accuracy above the threshold value of 0.5 for individual classes of both tree species according to the IoU metric. However, under Focal Loss, the MSC-Res-U-Net and Mo-U-Net models, in contrast to the third proposed model MSC-U-Net, exhibited high classification accuracy (surpassing the threshold value of 0.5) for all classes of Abies sibirica and Pinus sibirica trees. Thus, these results underscore the practical significance of the MSC-Res-U-Net and Mo-U-Net models for forestry professionals, enabling accurate classification and early detection of pest outbreaks in coniferous trees.

The traditional approach to studying inter-journal communication involves analyzing journal citation graphs. This paper proposes a method for analyzing journal networks using a new type of bibliometric graph — a journal intersection graph based on the binary operation of set intersection — employing techniques grounded in the friendship index and the disparity function. The approach is demonstrated using a relatively small example of a real journal network, with data sourced from the All-Russian portal Math-Net.Ru information system: 63 journals from 2008–2021 meeting specific criteria, containing almost 69 thousand articles authored by 54 thousand individuals. The mathematical model of this real-world network is represented as an intersection graph using the Jaccard coefficient, which exhibits specific properties: low dimensionality, high graph density, and an edge weight distribution that is not approximated by a power law function. The obtained results include the network structure of connections within the studied set of journals, accounting for their degree of interaction, and the identification of significant vertices using the friendship index. This captures the graph’s structural properties, offers an obvious substantive interpretation, and allows for ranking journals by this metric. Thus, the method implements a tool for distinguishing between vertices that are leaders in terms of the friendship index and “network integrators” (based on closeness/betweenness centrality). It also demonstrates a qualitative change in structural properties when reducing graph density while maintaining connectivity, achieved by applying the disparity function. The sequential application of the disparity function while lowering the significance threshold allows for the identification of the graph’s core, containing the most strongly connected vertices. This, in turn, enables the determination of a set of vertices (and corresponding journals) that are simultaneously part of the core and have the highest significance according to the friendship index. An analysis of the levels of this resulting journal set within the “Belyi Spisok” (“White List”) shows these journals have a high rating. The findings provide a deeper understanding of the relationship structure within scientific journal networks and define new approaches for their study.

The article describes a model of a human in the informational economy and demonstrates the multicriteria optimizational approach to the metric analysis of model-generated data. The traditional approach using the identification and study involves the model’s identification by time series and its further prediction. However, this is not possible when some variables are not explicitly observed and only some typical borders or population features are known, which is often the case in the social sciences, making some models pure theoretical. To avoid this problem, we propose a method of metric data analysis (MMDA) for identification and study of such models, based on the construction and analysis of the Kolmogorov – Shannon metric nets of the general population in a multidimensional space of social characteristics. Using this method, the coefficients of the model are identified and the features of its phase trajectories are studied. In this paper, we are describing human according to his role in information processing, considering his awareness and cognitive abilities. We construct two lifetime indices of human capital: creative individual (generalizing cognitive abilities) and productive (generalizing the amount of information mastered by a person) and formulate the problem of their multi-criteria (two-criteria) optimization taking into account life expectancy. This approach allows us to identify and economically justify the new requirements for the education system and the information environment of human existence. It is shown that the Pareto-frontier exists in the optimization problem, and its type depends on the mortality rates: at high life expectancy there is one dominant solution, while for lower life expectancy there are different types of Paretofrontier. In particular, the Pareto-principle applies to Russia: a significant increase in the creative human capital of an individual (summarizing his cognitive abilities) is possible due to a small decrease in the creative human capital (summarizing awareness). It is shown that the increase in life expectancy makes competence approach (focused on the development of cognitive abilities) being optimal, while for low life expectancy the knowledge approach is preferable.