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The paper presents several projects of schools in Ekaterinburg with description and analysis of emerging problems in dense urban areas. This topic was presented in the framework of the international forum "100+ Technobuild" in the section "Schools of modern cities". Presently, Russia is implementing a large-scale program for the construction of new schools and the reconstruction of existing ones. In recent years, not just school buildings have been built, but a rethinking of the educational space is taking place, which is flexible. Such trends lead to the fact that a school building should become capable of changes and transformations, comfortable and multifunctional. В данной статье представлены проекты школ Екатеринбурга с описанием и анализом возникающих проблем в плотной городской застройке. Доклад в рамках секции «Школы современных городов» был представлен на международном строительном форуме "100+ Technobuild". В настоящее время в России реализуется масштабная программа по строительству новых школ и реконструкции существующих. В последние годы строятся уже не просто школьные здания, а происходит переосмысление образовательного пространства, которое отличается гибкостью и индивидуальным подходом к каждому ученику. Подобные тенденции подводят к тому, что школьное здание должно стать способным к изменениям и трансформациям, комфортным и многофункциональным.

The problem of chemotherapy of a malignant tumor growing according to the Gompertz law is considered. The mathematical model is a system of two ordinary differential equations. We study a problem of optimal control (optimal therapy) aiming at the minimization of the malignant cells in the body at a given terminal time T. The viability set of this problem, i.e., the set of initial states of the model (the volume of the tumor and the amount of the drug in the body) for which an optimal control guarantees that the dynamics of the system up to the time T is compatible with life in terms of the volume of the tumor, is constructed analytically. © 2020 Krasovskii Institute of Mathematics and Mechanics. All rights reserved. This work was supported by the Russian Foundation for Basic Research (project no. 20-01-00362).

The paper presents the new approach to the semi-supervised fuzzy clustering, which is based on the extended optimization function and the proposed procedure to the active constraints selection. The approach is tested on the artificial and real data sets. Clustering results, obtained by proposed approach, are more accurate relative to the ground truth due to utilization of the additional information about class labels and its active selection.

In outbred male rats, the presence of quantitative dominance of synaptosomal phospholipids of the left hemisphere is found. It is suggested that the revealed left-hemispheric asymmetry of phospholipid content is associated not with the predominance of the level of phospholipid biosynthesis in the left hemisphere but rather with an increased number of neurons and / or nerve endings in this hemisphere

The paper presents the new approach to the supervised gene selection by means of gene clustering for the microarray data, which belong to more than two phenotypes (classes). The main distinction from the previous approaches, that are based on the splitting the multi-class task into several binary ones, is the application of the HUM (hypervolume under the manifold) score, that guides the search for the most discriminative gene clusters that simultaneously differentiate all the classes. The results of comparative analysis with other methods shows the advantages of our approach both in classification rate of the new samples and in the lower number of gene clusters. The application of our approach to a randomly permuted data shows that the identified structure is more than just a noise artifact.

This paper presents two novel approaches to evolutionary design of the classifier ensemble. The first one presents the task of one-objective optimization of feature set partitioning together with feature weighting for the construction of the inividual classifiers. The second approach deals with multi-objective optimization of classifier ensemble design. The proposed approaches have been tested on two data sets from the machine learning repository and one real data set on transient ischemic attack. The experiments show the advantages of the feature weighting in terms of classification accuracy when dealing with multivariate data sets and the possibility in one run of multi-objective genetic algorithm to get the non-dominated ensembles of different sizes and thereby skip the tedious process of iterative search for the best ensemble of fixed size. У статті запропоновано два нові підходи до еволюційної побудови ансамблю класифікаторів. Перший підхід є завданням одинкритерійної оптимізації розбиття безлічі ознак на окремі підмножини, які використовуються для побудови класифікаторів ансамблю. Другий підхід здійснює багатокритеріальну оптимізацію структури ансамблю класифікаторів. В статье предложены два новых подхода к эволюционному построению ансамбля классификаторов. Первый подход представляет собой задачу однокритериальной оптимизации разбиения множества признаков на отдельные подмножества, которые используются для построения классификаторов ансамбля. Второй подход осуществляет многокритериальную оптимизацию структуры ансамбля классификаторов.

the original method of fuzzy clustering using genetic algorithm is proposed. The chromosomes of the population of genetic algorithm have variable length and encode the possible partition of the data. The method enables in one GA run to find the near optimal data partition into clusters, to determine the cluster number and simultaneously to speed up clustering process comparing to other known methods. Classification rules, constructed on the basis of clustering results, obtained by proposed method, provide more accurate classification in comparison with results of classical FCM-method.