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  1. 2941
  2. 2942

    Practical concepts for the use of probabilistic methods in the structural analysis and reassessment of existing bridges - presentation of latest research and implementation by Thomas Braml, Christian Kainz

    Published 2022-08-01
    “…Structure-specific partial safety factors include measured data and can be used for the well-known and in the codes established semi-probabilistic design concept. …”
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    Article
  3. 2943

    New data on biology of siberian cisco <i>Coregonus sardinella</i> (Coregonidae) from the Anadyr River by Alexander V. Shestakov

    Published 2014-12-01
    “…Besides, the data collected previously in 1987 and 1992 were processed with modern research techniques. …”
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    Article
  4. 2944
  5. 2945

    Algorithm of athletes’ fitness structure individual features’ determination with the help of multidimensional analysis (on example of basketball) by Zh.L. Kozina, M. Cieslicka, K. Prusik, R. Muszkieta, I.N. Sobko, O.A. Ryepko, T.A. Bazilyuk, S.B. Polishchuk, A.V. Osiptsov, S.A. Korol

    Published 2017-10-01
    “…In every micro-cycle 30% is assigned for athletes’ individual training: athletes received individual tasks; groups on the base of cluster analysis data were formed, if necessary. Conclusions: when working out individual training programs, development of leading factors in individual factorial structure of athletes’ fitness shall be accented. …”
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    Article
  6. 2946

    Forestry Plans as the Source of Environmental Data for the Analysis of Bird Community Composition by Jakub Šimurda, Petr Šmilauer, Roman Fuchs

    Published 2025-05-01
    “…Forest management plans offer valuable data on forest species composition and structure, useful for large-scale bird conservation. …”
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    Article
  7. 2947
  8. 2948

    Monitoring data of the openLAB research bridge – Part 1: Reference conditionOPARA by Andreas Jansen, Max Herbers, Bertram Richter, Maria Walker, Frank Jesse, Steffen Marx

    Published 2025-06-01
    “…Structural Health Monitoring (SHM) is emerging as an essential tool for ensuring the safety and longevity of an aging bridge infrastructure. …”
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    Article
  9. 2949
  10. 2950
  11. 2951

    Relações empíricas entre a estrutura da vegetação e dados do sensor TM/LANDSAT Empirical relationship between vegetation structure and TM/LANDSAT data by Luciano J. de O. Accioly, Admilson Pachêco, Thomaz C. e C. da Costa, Osvaldo F. Lopes, Maria A. J. de Oliveira

    Published 2002-12-01
    “…<br>The use of spectral data to estimate the structural parameters of vegetation has been considered as one of the most important applications of the remote sensing of ecosystems. …”
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  12. 2952

    LOW AND MEAN RADIATION DOSES IMPACT ON THE CEREBRAL TRACTS STRUCTURE OF THE CHERNOBYL ACCIDENT LIQUIDATORS IN THE REMOTE PERIOD (BASED ON ROUTINE AND DIFFUSION-TENSOR MAGNETIC RESONANCE IMAGING DATA) by I. M. Levashkina, S. S. Aleksanin, S. V. Serebryakova, T. G. Gribanova

    Published 2018-01-01
    “…To evaluate correlation between brain structural damages and radiation exposure level for the Chernobyl nuclear power plant accident liquidators, routine and diffusion tensor magnetic resonance imaging methods are efficient to visualize and evaluate those damages; it is also important to compare magnetic resonance imaging data of liquidators with results, received for people of the same age and the same stage of cerebral vascular disease (the discirculatory encephalopathy of I and II stage), but who did not participate in the Chernobyl accident liquidation and did not suffer from other liquidation factors and radiation catastrophe aftermaths. …”
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    Article
  13. 2953

    CrystalShift: A Versatile Command-Line Tool for Crystallographic Structural Data Analysis, Modification, and Format Conversion Prior to Solid-State DFT Calculations of Organic Crystals by Ilona A. Isupova, Denis A. Rychkov

    Published 2025-06-01
    “…<i>CrystalShift</i> is an open-source computational tool tailored for the analysis, transformation, and conversion of crystallographic data, with a particular emphasis on organic crystal structures. …”
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    Article
  14. 2954

    Geological structure and occurrence conditions of the Bazhenov formation within THE South Tambey gas condensate field by A. A. Rasskazov, G. N. Potemkin

    Published 2023-06-01
    “…Determination of the structural features and occurrence conditions of the Bazhenov formation.Materials and methods. …”
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  15. 2955
  16. 2956

    3D Visualization Method for Surface Defects of Underwater Structures Based on Acoustic and Optical Combination Measurement by Xiang WANG, Jinchao WANG, Houcheng LIU, Wanpeng SONG, Shuqi LI

    Published 2025-03-01
    “…Finally, a three-dimensional visualization system for surface defects of underwater structures is developed by constructing a texture feature image enhancement algorithm, synchronously utilizing sound and light combination data. …”
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  17. 2957
  18. 2958

    Seismic Facies Classification of Salt Structures and Sediments in the Northern Gulf of Mexico Using Self-Organizing Maps by Silas Adeoluwa Samuel, Camelia C. Knapp, James H. Knapp

    Published 2025-05-01
    “…SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate potential storage reservoirs, gas hydrates, salt structures, associated radial faults, and areas with poor data quality due to the presence of the salt structures more than SOM Models 3 and 4. …”
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  19. 2959

    Impact of station number and duration time per station on the reliability of Objective Structured Clinical Examination (OSCE) scores: a systematic review and meta-analysis by Qiyun Peng, Jiani Luo, Chunyan Wang, Lei Chen, Shenglan Tan

    Published 2025-01-01
    “…Abstract Background The Objective Structured Clinical Examination (OSCE) is a systematic and structured assessment tool that evaluates candidates’ professional medical skills in a simulated environment through a series of time-limited stations. …”
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  20. 2960

    GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds by D. Oakley, D. Oakley, C. Loiselet, T. Coowar, T. Coowar, V. Labbe, J.-P. Callot

    Published 2025-02-01
    “…<p>The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. …”
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