Reliability and information content of tests with cardioleader in cyclic types of sports

Tests with cardioleader to control the physical, technical and tactical preparedness of athletes in cyclic types of sports are discussed. Ways of increasing the reliability and information content of the tests were studied

Piecewise smooth systems near a co-dimension 2 discontinuity manifold: can one say what should happen?

We consider a piecewise smooth system in the neighborhood of a co-dimension 2 discontinuity manifold $\Sigma$. Within the class of Filippov solutions, if $\Sigma$ is attractive, one should expect solution trajectories to slide on $\Sigma$. It is well known, however, that the classical Filippov convexification methodology is ambiguous on $\Sigma$. The situation is further complicated by the possibility that, regardless of how sliding on $\Sigma$ is taking place, during sliding motion a trajectory encounters so-called generic first order exit points, where $\Sigma$ ceases to be attractive. In this work, we attempt to understand what behavior one should expect of a solution trajectory near $\Sigma$ when $\Sigma$ is attractive, what to expect when $\Sigma$ ceases to be attractive (at least, at generic exit points), and finally we also contrast and compare the behavior of some regularizations proposed in the literature. Through analysis and experiments we will confirm some known facts, and provide some important insight: (i) when $\Sigma$ is attractive, a solution trajectory indeed does remain near $\Sigma$, viz. sliding on $\Sigma$ is an appropriate idealization (of course, in general, one cannot predict which sliding vector field should be selected); (ii) when $\Sigma$ loses attractivity (at first order exit conditions), a typical solution trajectory leaves a neighborhood of $\Sigma$; (iii) there is no obvious way to regularize the system so that the regularized trajectory will remain near $\Sigma$ as long as $\Sigma$ is attractive, and so that it will be leaving (a neighborhood of) $\Sigma$ when $\Sigma$ looses attractivity. We reach the above conclusions by considering exclusively the given piecewise smooth system, without superimposing any assumption on what kind of dynamics near $\Sigma$ (or sliding motion on $\Sigma$) should have been taking place.Comment: 19 figure

Sliding mode control of quantum systems

This paper proposes a new robust control method for quantum systems with uncertainties involving sliding mode control (SMC). Sliding mode control is a widely used approach in classical control theory and industrial applications. We show that SMC is also a useful method for robust control of quantum systems. In this paper, we define two specific classes of sliding modes (i.e., eigenstates and state subspaces) and propose two novel methods combining unitary control and periodic projective measurements for the design of quantum sliding mode control systems. Two examples including a two-level system and a three-level system are presented to demonstrate the proposed SMC method. One of main features of the proposed method is that the designed control laws can guarantee desired control performance in the presence of uncertainties in the system Hamiltonian. This sliding mode control approach provides a useful control theoretic tool for robust quantum information processing with uncertainties.Comment: 18 pages, 4 figure

CONSTRUCTION OF A DNA-MICROARRAY FOR DIFFERENTIATION BETWEEN THE MAIN AND NON-MAIN SUBSPECIES AND BIOVARS OF THE MAIN SUBSPECIES OF YERSINIA PESTIS

Objective of the study is to design the DNA-microarray for differentiation of Y. pestis strains of the main and non-main subspecies and biovars of the main subspecies. Materials and methods. Efficiency analysis for the devised means was conducted using 62 Y. pestis strains of various subspecies and biovars, isolated in the natural foci of Russia and neighboring countries. Results and conclusions. Selected have been the DNA-targets, probes and primers – calculated. Enhanced is the method of sub-specific and biovar differentiation of Y. pestis strains by means of DNA-microarray. DNA-chip with “Med24”, “glpD(-93)”, and “45” targets allows for prompt differentiation of the strains of the main and non-main subspecies and biovars of the main subspecies based on the presence and absence of fluorescent signal by the specific for the main subspecies and its biovars DNA-targets

Искусственный интеллект в медицине: современное состояние и основные направления развития интеллектуальной диагностики

The main difference between artificial intelligence (AI) systems and simple automated algorithms is the ability to learn, synthesize and conclude. The AI system is trained on a set of examples, including pictures, characteristics of patients with a certain disease, then it allows to generalize a lot of such examples and get some general functional dependence, which brings in line the patient data and a certain diagnosis. The system can be named intelligent if this synthetizing ability is realized. Although the AI systems are now becoming more understood and accepted by doctors, a deeper understanding of «how it works» is needed. The article provides a detailed review of the application of methods and models of artificial intelligence in the diagnostics of cancer based on the of multimodal instrumental data. The basic concepts of artificial intelligence and directions of its development are presented. From the point of view of data processing, the stages of development of AI systems are identical. The stages of intellectual processing of diagnostic data are considered in the paper. They include the acquisition and use of training databases of oncological diseases, pre-processing of images, segmentation to highlight the studied objects of diagnosis and classification of these objects to determine whether they are malignant or benign. One of the problems limiting the acceptance of AI systems development by the medical community is the imperfection of the explainability of the results obtained by intelligent systems. Authors pay attention to importance of the development of so-called explanatory intelligence, because its absence currently significantly inhibits the introduction and use of intelligent diagnostic systems in medicine. In addition, the purpose of the article is a way to develop the interaction between a radiologists and data scientists.Главное отличие систем искусственного интеллекта (ИИ) от простых автоматизированных алгоритмов заключается в способности к обучению, обобщению и выводу. Система ИИ обучается на множестве примеров, включая снимки, характеристики пациентов с определенным заболеванием, далее она позволяет обобщить множество таких примеров и получить некоторую общую функциональную зависимость, которая приводит в соответствие данные о пациенте и определенный диагноз. Интеллектуальной система становится при реализации этой обобщающей способности. Несмотря на то, что в настоящее время тематика ИИ становится более понимаемой и принимаемой врачами, необходимо более глубокое понимание «как это работает». В статье приводится детальный обзор применения методов и моделей искусственного интеллекта в диагностике онкологических заболеваний на основе данных мультимодальной лучевой диагностики. Даны основные понятия искусственного интеллекта и направления его использования. С точки зрения обработки данных этапы разработки систем ИИ идентичны. В статье рассмотрены этапы интеллектуальной обработки диагностических данных, которые включают создание и использование обучающих баз данных онкологических заболеваний, предварительную обработку снимков, сегментацию изображений для выделения исследуемых объектов диагностики и классификацию этих объектов для определения, являются ли они злокачественными или доброкачественными. Одной из проблем, ограничивающих принятие развития систем ИИ медицинским сообществом, является несовершенство объяснимости результатов, получаемых при помощи интеллектуальных систем. В статье затронуты важные вопросы разработки объяснительного интеллекта, отсутствие которого в настоящее время существенно тормозит внедрение и использование интеллектуальных систем диагностики в медицине. Кроме того, цель статьи — путь к развитию взаимодействия между врачом и специалистом по искусственному интеллекту

Development of an Integrated System for Molecular-Genetic Identification of <i>Yersinia pestis</i> Strains

The paper describes a developed comprehensive system for molecular-genetic identification of Yersinia pestis strains according to their appurtenance to certain subspecies, biovars, phylo-geographic populations, using realtime PCR (RT-PCR), allele-specific RT-PCR, and multiplex PCR with hybridization fluorescent registration of results on a solid substrate. Application of this system makes it possible to establish the appurtenance of Y. pestis strains to the following phylogenetic branches: 0.ANT1, 0.ANT2, 0.ANT3, 0.ANT5, 3.ANT, 4.ANT of antique biovar of the main subspecies; 2.MED0, 2.MED1, 2.MED2, 2.MED3, 2.MED4 of medieval biovar of the main subspecies; 1.IN1, 1.IN2, 1.IN3 of intermedium biovar of the main subspecies; 1.ORI1, 1.ORI2, 1.ORI3 of oriental biovar of the main subspecies; 0.PE3 (angolica subspecies), 0.PE7 (tibetica subspecies) and 0.PE10 (qinghaica subspecies). The first stage of the studies within the frames of the developed system is indication of plague agent using registered diagnostic drugs. The second stage is the determination of belonging to individual subspecies through RT-PCR or by the method of multiplex PCR system with hybridization-fluorescent registration of results on a solid substrate, which also allows for establishing to which biovars of the main subspecies and the main phylogenetic lines of the ancient biovar the strains belong. The third stage is the identification of strain appurtenance to phylogenetic branches by the AS-RT-PCR method. The designed complex system for molecular-genetic identification of Y. pestis strains can be applied at the regional and federal levels of the laboratory network of the Russian Federation for diagnostics of infectious diseases. Its use will considerably facilitate and increase the efficiency of intraspecific differentiation of Y. pestis strains within the framework of the epidemiological investigation of outbreaks or importation of strains of plague pathogen into the territory of the Russian Federation or during the certification of strains in collection activities

Procjena varijabli stanja sustava s gorivnim člankom i uzlaznim pretvaračem metodom brzog uzorkovanja signala

Estimation of state variables of a peak current mode (PCM) controlled DC-DC boost converter supplied by a PEM fuel cell is described in this paper. Since this system is highly nonlinear and non-minimum phase, its state variables are estimated by using fast output sampling method. Estimated state variables are the converter output voltage and its first derivative, and they are suitable for model reference adaptive control or sliding mode based control techniques. The estimator has been designed in a way that it gives a good estimate of the state variables in the continuous and in the discontinuous conduction mode of the converter, and in the presence of measurement and process noise caused by converter switching-mode operation. Experimental results of estimating the state variables on a 450 W boost converter supplied by the emulator of the PEM fuel cell BCS 64-32 show good results of the estimation, regardless of the conduction mode of the converter, i.e. the operating point determined by its output current.U ovom radu obrađena je procjena varijabli stanja sustava s istomjernim uzlaznim pretvaračem u vršnom strujnom načinu upravljanja napajanim PEM gorivnim člankom. Budući da je taj sustav izrazito nelinearan te neminimalno-fazan, za procjenu njegovih varijabli stanja upotrebljena je metoda brzog uzorkovanja izlaznog signala. Procjenjene varijable stanja su izlazni napon uzlaznog pretvarača te njegova prva derivacija, te su pogodne za adaptivno upravljanje s referentnim modelom i upravljanje temeljeno na kliznim režimima. Procjenitelj je projektiran na način da daje dobru procjenu varijabli stanja u kontinuiranom i diskontinuiranom režimu rada pretvarača, te u uvjetima mjernog i procesnog šuma uzrokovanog sklopnim načinom rada pretvarača. Eksperimentalni rezultati procjene varijabli stanja na uzlaznom pretvaraču snage 450 W napajanim emulatorom gorivnog članka BCS 64-32 pokazuju dobre rezultate procjene, neovisno o režimu rada pretvarača, odnosno radnoj točki određenoj njegovom izlaznom strujom