Strength diagrams of fibrous composites with unidirectional structure

The dependence of the composite strength on the volume proportion of fiber and the transfer factor is analyzed. Four types of diagrams are constructed for the strength of composites as a function of the volume proportion of fibers and the transfer factor

Join Execution Using Fragmented Columnar Indices on GPU and MIC

The paper describes an approach to the parallel natural join execution on computing clusters with GPU and MIC Coprocessors. This approach is based on a decomposition of natural join relational operator using the column indices and domain-interval fragmentation. This decomposition admits parallel executing the resource-intensive relational operators without data transfers. All column index fragments are stored in main memory. To process the join of two relations, each pair of index fragments corresponding to particular domain interval is joined on a separate processor core. Described approach allows efficient parallel query processing for very large databases on modern computing cluster systems with many-core accelerators. A prototype of the DBMS coprocessor system was implemented using this technique. The results of computational experiments for GPU and Xeon Phi are presented. These results confirm the efficiency of proposed approach

Institutions and the Emergence of Markets - Transition in the Murmansk Forest Sector

The research for this report was part of the project "Institutional Change in Forestry Management in Murmansk Oblast" funded by NFR, the Research Council of Norway (Program for East- and Central Europe). NFR is the Norwegian National Member Organization of IIASA and the work has been performed in collaboration with IIASA's Sustainable Boreal Forest Resources (FOR) project

Six-loop ε\varepsilon expansion study of three-dimensional nn-vector model with cubic anisotropy

The six-loop expansions of the renormalization-group functions of $\varphi^4$ $n$-vector model with cubic anisotropy are calculated within the minimal subtraction (MS) scheme in $4 - \varepsilon$ dimensions. The $\varepsilon$ expansions for the cubic fixed point coordinates, critical exponents corresponding to the cubic universality class and marginal order parameter dimensionality $n_c$ separating different regimes of critical behavior are presented. Since the $\varepsilon$ expansions are divergent numerical estimates of the quantities of interest are obtained employing proper resummation techniques. The numbers found are compared with their counterparts obtained earlier within various field-theoretical approaches and by lattice calculations. In particular, our analysis of $n_c$ strengthens the existing arguments in favor of stability of the cubic fixed point in the physical case $n = 3$

Prospects for the implementation of the "digital government" project of the Russian Federation

The theoretical foundations for the development of the digital economy and e-government are considered in this article. The main objectives of Russia's transition to the digital economy are listed. The relevance of the development of the digital government concept is considered. The indicators of the current level of state development in the framework of digitalization and the formation of e-government are analyzed. The main problems, the solution of which makes the implementation of the digital government project of the Russian Federation possible, are singled out. Digital economy development is associated not only with the progress of the information technology and innovation industry, but also with the improvement of the labor market, where new jobs, professions and personnel are created. In this regard, there is a rapid process of the foundation of society, where one job becomes low-paid, and new professions allow one to receive a personal income at the level of top managers of small and medium-sized enterprises.peer-reviewe

COMPARATIVE STUDY OF PATIENTS AND HEALTHY CARRIERS OF ANTIBODIES AGAINST VARICELLAZOSTER VIRUS

The degree of seropositiveness against Varicella-zoster virus (VZV) were established by examination of 737 single serum samples from clinically healthy donors aged 1-60 years by using CF-test. Comparatively, 91 of them were studied by ELISA (Behring-Germany, Ensygnost-Varicella-Zoster). Additionally, 37 single serum samples of patients with clinical symptoms of herpes zoster were examined by CF-test. The results of the healthy population showed a mean seroprevalence of 48,98 % by CF-test and of 60,44 % by ELISA. The difference of seroprevalence of healthy and ill individuals (with typical clinical signs) in matched age intervals was considerable only with patients suffering from herpes zoster

ISOLATED INFLUENZA VIRUSES FROM CLINICALLY HEALTHY CHILDREN AT THE BEGINNING OF THE INFLUENZA WAVE IN 1983

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Intermediate Fusion Approach for Pneumonia Classification on Imbalanced Multimodal Data

In medical practice, the primary diagnosis of diseases should be carried out quickly and, if possible, automatically. The processing of multimodal data in medicine has become a ubiquitous technique in the classification, prediction and detection of diseases. Pneumonia is one of the most common lung diseases. In our study, we used chest X-ray images as the first modality and the results of laboratory studies on a patient as the second modality to detect pneumonia. The architecture of the multimodal deep learning model was based on intermediate fusion. The model was trained on balanced and imbalanced data when the presence of pneumonia was determined in 50% and 9% of the total number of cases, respectively. For a more objective evaluation of the results, we compared our model performance with several other open-source models on our data. The experiments demonstrate the high performance of the proposed model for pneumonia detection based on two modalities even in cases of imbalanced classes (up to 96.6%) compared to single-modality models’ results (up to 93.5%). We made several integral estimates of the performance of the proposed model to cover and investigate all aspects of multimodal data and architecture features. There were accuracy, ROC AUC, PR AUC, F1 score, and the Matthews correlation coefficient metrics. Using various metrics, we proved the possibility and meaningfulness of the usage of the proposed model, aiming to properly classify the disease. Experiments showed that the performance of the model trained on imbalanced data was even slightly higher than other models considered.In medical practice, the primary diagnosis of diseases should be carried out quickly and, if possible, automatically. The processing of multimodal data in medicine has become a ubiquitous technique in the classification, prediction and detection of diseases. Pneumonia is one of the most common lung diseases. In our study, we used chest X-ray images as the first modality and the results of laboratory studies on a patient as the second modality to detect pneumonia. The architecture of the multimodal deep learning model was based on intermediate fusion. The model was trained on balanced and imbalanced data when the presence of pneumonia was determined in 50% and 9% of the total number of cases, respectively. For a more objective evaluation of the results, we compared our model performance with several other open-source models on our data. The experiments demonstrate the high performance of the proposed model for pneumonia detection based on two modalities even in cases of imbalanced classes (up to 96.6%) compared to single-modality models’ results (up to 93.5%). We made several integral estimates of the performance of the proposed model to cover and investigate all aspects of multimodal data and architecture features. There were accuracy, ROC AUC, PR AUC, F1 score, and the Matthews correlation coefficient metrics. Using various metrics, we proved the possibility and meaningfulness of the usage of the proposed model, aiming to properly classify the disease. Experiments showed that the performance of the model trained on imbalanced data was even slightly higher than other models considered