<?xml version="1.0" encoding="utf-8"?>
<journal>
  <titleid>https://www.elibrary.ru/title_about_new.asp?i</titleid>
  <issn>1605-8119</issn>
  <journalInfo lang="ENG">
    <title>Materials physics and mechanics</title>
  </journalInfo>
  <issue>
    <volume>9</volume>
    <number>3</number>
    <altNumber> </altNumber>
    <dateUni>2010</dateUni>
    <pages>1-79</pages>
    <articles>
      <article>
        <artType>RAR</artType>
        <langPubl>RUS</langPubl>
        <pages>167-174</pages>
        <authors>
          <author num="001">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Bezrukova</surname>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
          <author num="002">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Vlasova</surname>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
        </authors>
        <artTitles>
          <artTitle lang="ENG">Aggregation of Protein Nanoparticles Testing by Optical Spectroscopy</artTitle>
        </artTitles>
        <abstracts>
          <abstract lang="ENG">In this contribution we report on the inverse optical problem solution for characterizing protein nanoparticles aggregation by optical spectroscopy: absorption spectra, integral static (time average) light scattering spectra and intensity of differential static light scattering. The measurements are compatible, non-destructive and can provide information about the processes in "ill-defined" three-dimensional disperse systems (3D DS) with nanoparticles.</abstract>
        </abstracts>
        <codes/>
        <keywords>
          <kwdGroup lang="ENG">
            <keyword>Aggregation</keyword>
            <keyword>Protein</keyword>
            <keyword>Nanoparticles</keyword>
            <keyword>Optical Spectroscopy</keyword>
          </kwdGroup>
        </keywords>
        <files>
          <furl>https://mpm.spbstu.ru/article/2010.14.1/</furl>
          <file>MPM_9_3_P01.pdf</file>
        </files>
      </article>
      <article>
        <artType>RAR</artType>
        <langPubl>RUS</langPubl>
        <pages>175-184</pages>
        <authors>
          <author num="001">
            <individInfo lang="ENG">
              <orgName>United Institute of Informatics Problems of National Academy of Sciences of Belarus</orgName>
              <surname>Prus</surname>
              <address>Minsk, Belarus </address>
            </individInfo>
          </author>
          <author num="002">
            <individInfo lang="ENG">
              <orgName>United Institute of Informatics Problems of National Academy of Sciences of Belarus</orgName>
              <surname>Dmitruk</surname>
              <address>Minsk, Belarus </address>
            </individInfo>
          </author>
          <author num="003">
            <individInfo lang="ENG">
              <surname>Kharuzhik</surname>
            </individInfo>
          </author>
          <author num="004">
            <individInfo lang="ENG">
              <orgName>United Institute of Informatics Problems of National Academy of Sciences of Belarus</orgName>
              <surname>Snezhko</surname>
              <address>Minsk, Belarus </address>
            </individInfo>
          </author>
          <author num="005">
            <individInfo lang="ENG">
              <orgName>United Institute of Informatics Problems of National Academy of Sciences of Belarus</orgName>
              <surname>Kovalev</surname>
              <address>Minsk, Belarus </address>
            </individInfo>
          </author>
        </authors>
        <artTitles>
          <artTitle lang="ENG">Distributed Computing in the National-Wide Lung Screening and Diagnosis System: First Steps</artTitle>
        </artTitles>
        <abstracts>
          <abstract lang="ENG">The paper is devoted to consideration of methodological, large-scale data management, medical imaging and software developing issues concerned with the distributed computing in a national-wide telemedicine system. The system substantially exploits the image data provided with the complementary pulmonary X-ray and chest computed tomography (CT) image modalities; its aim is to provide the computerized support of lung disease diagnosis. The computational environment is heterogeneous and is based on PCs and dedicated servers located in medical institutions, as well as on the supercomputer installed in the National Supercomputer Center of Belarus. For utilizing the grid infrastructure, the distributed computing architecture employs the Unicore as a middleware, as well as the Message Passing Interface. At present the project is on its early stage. However, the preliminary research and experimentation performed in framework of the project already involves X-ray lung image data of more than 100 thousands of patients and about a hundred of 3D CT scans of the lungs accumulated in the database.</abstract>
        </abstracts>
        <codes/>
        <keywords>
          <kwdGroup lang="ENG">
            <keyword>Computing</keyword>
            <keyword>Lung Screening</keyword>
            <keyword>Diagnosis System</keyword>
          </kwdGroup>
        </keywords>
        <files>
          <furl>https://mpm.spbstu.ru/article/2010.14.2/</furl>
          <file>MPM_9_3_P02.pdf</file>
        </files>
      </article>
      <article>
        <artType>RAR</artType>
        <langPubl>RUS</langPubl>
        <pages>185-193</pages>
        <authors>
          <author num="001">
            <individInfo lang="ENG">
              <orgName>Children Hospital of Vilnius University </orgName>
              <surname>Samaitiene</surname>
              <address>Vilnius, Lithuania</address>
            </individInfo>
          </author>
          <author num="002">
            <individInfo lang="ENG">
              <orgName>Vilnius University</orgName>
              <surname>Kazakeviciute</surname>
              <address>Vilnius, Lithuania</address>
            </individInfo>
          </author>
          <author num="003">
            <individInfo lang="ENG">
              <orgName>Vilnius University</orgName>
              <surname>Juozapavicius</surname>
              <address>Vilnius, Lithuania</address>
            </individInfo>
          </author>
        </authors>
        <artTitles>
          <artTitle lang="ENG">Morphological Filtering of EEG</artTitle>
        </artTitles>
        <abstracts>
          <abstract lang="ENG">In the diagnosis of epilepsy long-term monitoring of electroencephalograms (EEG) data may be required to document and study interictal activities which appear as spikes in EEG signals. However visual inspection of EEG done by an expert neurologist is much time consuming. Here an automatic EEG spike detection method that uses morphological filter is described. The goal is to construct a database with data such as parameters of the detected spikes, the amount of different waves in EEG signal. The following analysis of the data is planned to be performed using methods of data mining to find the correlation of spiky signal areas with brain areas, the influence of amount of different waves and number of spikes in the signal on the nature of epilepsy. The grid environment is used for calculations.</abstract>
        </abstracts>
        <codes/>
        <keywords>
          <kwdGroup lang="ENG">
            <keyword>Morphological Filtering</keyword>
            <keyword>Epilepsy</keyword>
            <keyword>Electroencephalograms</keyword>
            <keyword>EEG</keyword>
          </kwdGroup>
        </keywords>
        <files>
          <furl>https://mpm.spbstu.ru/article/2010.14.3/</furl>
          <file>MPM_9_3_P03.pdf</file>
        </files>
      </article>
      <article>
        <artType>RAR</artType>
        <langPubl>RUS</langPubl>
        <pages>194-209</pages>
        <authors>
          <author num="001">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Vorobyeva</surname>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
          <author num="002">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Melker</surname>
              <initials>A.I.</initials>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
          <author num="003">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Starovoitov</surname>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
        </authors>
        <artTitles>
          <artTitle lang="ENG">Heat, Temperature, Entropy</artTitle>
        </artTitles>
        <abstracts>
          <abstract lang="ENG">The history of such notions as heat, temperature, and entropy is considered. The notions were created and evolved both with technique’s requirements and during the advancement of heat science. Some examples from different fields of mathematics which had a great impact on the notion evolution are given. The deduction of Maxwell and Boltzmann distributions is fully considered. The tragedy of pioneers, who worked in this filed, is also discussed.</abstract>
        </abstracts>
        <codes/>
        <keywords>
          <kwdGroup lang="ENG">
            <keyword>Heat</keyword>
            <keyword>Temperature</keyword>
            <keyword>Entropy</keyword>
          </kwdGroup>
        </keywords>
        <files>
          <furl>https://mpm.spbstu.ru/article/2010.14.4/</furl>
          <file>MPM_9_3_P04.pdf</file>
        </files>
      </article>
      <article>
        <artType>RAR</artType>
        <langPubl>RUS</langPubl>
        <pages>210-227</pages>
        <authors>
          <author num="001">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Vorobyeva</surname>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
          <author num="002">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Melker</surname>
              <initials>A.I.</initials>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
          <author num="003">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Starovoitov</surname>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
        </authors>
        <artTitles>
          <artTitle lang="ENG">Information, Entropy, Temperature</artTitle>
        </artTitles>
        <abstracts>
          <abstract lang="ENG">The evolution of such notions as heat, temperature, and entropy in the twentieth century is considered along with new relative notions. The new notions were created in connection with the demands of informatics as well as a result of the appearance of new scientific lines (computer simulation of physical properties, theory of nonlinear oscillations, radiation solid state physics). Some examples from different fields of physics and mathematics which had a great impact on the notion evolution are given. The relation between scientific uncertainty and information, molecular dynamics method, and temperature of a solid are considered in detail. An example of self-organization of chaos is briefly discussed.</abstract>
        </abstracts>
        <codes/>
        <keywords>
          <kwdGroup lang="ENG">
            <keyword>Information</keyword>
            <keyword>Entropy</keyword>
            <keyword>Temperature</keyword>
          </kwdGroup>
        </keywords>
        <files>
          <furl>https://mpm.spbstu.ru/article/2010.14.5/</furl>
          <file>MPM_9_3_P05.pdf</file>
        </files>
      </article>
      <article>
        <artType>RAR</artType>
        <langPubl>RUS</langPubl>
        <pages>228-235</pages>
        <authors>
          <author num="001">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Galich</surname>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
        </authors>
        <artTitles>
          <artTitle lang="ENG">Wavelets of the Immunofluorescence Distributions; Shannon Entropies, their Central Moments and Fractal Dimensions for Medical Diagnostics</artTitle>
        </artTitles>
        <abstracts>
          <abstract lang="ENG">Communication contains the immunology data treatment. New nonlinear methods of immunofluorescence statistical analysis of peripheral blood neutrophils have been developed. We used technology of respiratory burst reaction of DNA fluorescence in the neutrophils cells nuclei due to oxidative activity. The histograms of photon count statistics for the radiant neutrophils populations in flow cytometry experiments are considered. Distributions of the fluorescence flashes frequency as functions of the fluorescence intensity are analyzed. Square of deviations on the mean level of fluorescence flashes number considered as the probability measure of immunofluorescence fluctuations. Shannon information entropy, the square of fluctuations, their wavelet and wavelet spectra are investigated for medical diagnostics. Immunofluorescence fractal structure is analyzed for Hurst exponent and Shannon Weaver indices of biodiversity in the spaces of fluctuations, their wavelet and wavelet spectra. Biodiversity of fluorescence neutrophil populations has increasing tendency for oncology diseases, with anomalous increase of correspondence fractal dimension. We observe the universal exponential distribution of the central moments for information entropies. Parameters of exponential decrease are various for different health statuses. Therefore we have new tools to conduct diagnostics and monitoring of health statuses with the exponentially high sensitivity, precision and accuracy. Health or illness criteria are connected with statistics features of immunofluorescence histograms. Neutrophils populations fluorescence presents the sensitive clear indicator of health status.</abstract>
        </abstracts>
        <codes/>
        <keywords>
          <kwdGroup lang="ENG">
            <keyword>Immunofluorescence</keyword>
            <keyword>Shannon Entropies</keyword>
            <keyword>Medical Diagnostics</keyword>
          </kwdGroup>
        </keywords>
        <files>
          <furl>https://mpm.spbstu.ru/article/2010.14.6/</furl>
          <file>MPM_9_3_P06.pdf</file>
        </files>
      </article>
      <article>
        <artType>RAR</artType>
        <langPubl>RUS</langPubl>
        <pages>236-245</pages>
        <authors>
          <author num="001">
            <individInfo lang="ENG">
              <orgName>Peter the Great St. Petersburg Polytechnic University</orgName>
              <surname>Galich</surname>
              <address>St.Petersburg, Russia</address>
            </individInfo>
          </author>
        </authors>
        <artTitles>
          <artTitle lang="ENG">Stability, Tilting, Coup, Delay and Switching for Distributions of Neutrophils Fluorescence in Medical Diagnostics</artTitle>
        </artTitles>
        <abstracts>
          <abstract lang="ENG">Communication contains the immunology data treatment. New nonlinear methods of immunofluorescence statistical analysis of peripheral blood neutrophils have been developed. We used technology of respiratory burst reaction of DNA fluorescence in the neutrophils cells nuclei due to oxidative activity. The histograms of photon count statistics of the radiant neutrophils populations' in flow cytometry experiments are considered. Distributions of the fluorescence flashes frequency as functions of the fluorescence intensity are analyzed. Statistic peculiarities of histograms set allow divide all histograms into three classes. The classification is based on three different types of smoothing and long-range scale averaged the immunofluorescence distributions with maximum, minimum and monotonic change. First histograms group belongs to healthy donors. Two other groups belong to donors with inflammatory and autoimmune diseases. Some of the illnesses are not diagnosed by standards biochemical methods. Dynamics of a change in the immunofluorescence distributions of healthy and sick people in the process of medical treatment are examined; we observed the right and the contrary transitions or coup of the averaged distributions in histograms. Peculiarities of immunofluorescence for women in pregnant period are classified. Health or illness criteria are connected with statistics features of immunofluorescence histograms. Neutrophils populations' fluorescence presents the sensitive clear indicator of health status.</abstract>
        </abstracts>
        <codes/>
        <keywords>
          <kwdGroup lang="ENG">
            <keyword>Neutrophils Fluorescence</keyword>
            <keyword>Medical Diagnostics</keyword>
          </kwdGroup>
        </keywords>
        <files>
          <furl>https://mpm.spbstu.ru/article/2010.14.7/</furl>
          <file>MPM_9_3_P07.pdf</file>
        </files>
      </article>
      <article>
        <artType>RAR</artType>
        <langPubl>RUS</langPubl>
        <pages>246-250</pages>
        <authors>
          <author num="001">
            <individInfo lang="ENG">
              <orgName>Vilnius University</orgName>
              <surname>Bevainyte</surname>
              <address>Vilnius, Lithuania</address>
            </individInfo>
          </author>
          <author num="002">
            <individInfo lang="ENG">
              <orgName>Vilnius University</orgName>
              <surname>Būtenas</surname>
              <address>Vilnius, Lithuania</address>
            </individInfo>
          </author>
        </authors>
        <artTitles>
          <artTitle lang="ENG">Document Classification Using Weighted Ontology</artTitle>
        </artTitles>
        <abstracts>
          <abstract lang="ENG">This paper presents document comparison and classification model for Lithuanian language texts based on weighed ontology. The tests have been performed to measure several aspects: i) quality of comparison of documents; ii) optimal size of ontology; iii) type of part of speech words used to create ontology. Final results indicate 96% of correct classification cases and suggest that all the main part of speech terms should be used from the text. The proposed model can be used to classify texts more efficiently than keyword based systems.</abstract>
        </abstracts>
        <codes/>
        <keywords>
          <kwdGroup lang="ENG">
            <keyword>Ontology</keyword>
            <keyword>Lithuanian language texts</keyword>
          </kwdGroup>
        </keywords>
        <files>
          <furl>https://mpm.spbstu.ru/article/2010.14.8/</furl>
          <file>MPM_9_3_P08.pdf</file>
        </files>
      </article>
    </articles>
  </issue>
</journal>
