Showing 1,681 - 1,700 results of 53,088 for search '((structure OR ((structures OR structural) OR (structures OR structural))) OR structured) data', query time: 0.63s Refine Results
  1. 1681

    Debiasing Structure Function Estimates from Sparse Time Series of the Solar Wind: A Data-driven Approach by Daniel Wrench, Tulasi N. Parashar

    Published 2025-01-01
    “…Structure functions (SFs), which quantify the moments of increments of a stochastic process, are essential complementary statistics to power spectra for analyzing the self-similar behavior of a time series. …”
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    Article
  2. 1682

    Grain Protein Function Prediction Based on CNN and Residual Attention Mechanism with AlphaFold2 Structure Data by Jing Liu, Xinping Zhang, Kai Huang, Yuqi Wei, Xiao Guan

    Published 2025-02-01
    “…The experimental results indicate that secondary structure and spatial structure information contribute to improving model performance. …”
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    Article
  3. 1683

    DETERMINANTS OF STRUCTURAL DIMENSION OF DAILY BEHAVIOUR IN A TRADITIONAL AFRICAN CITY: A CASE STUDY OF ILORIN, NIGERIA by Moses Olutoyin ADEDOKUN

    Published 2013-10-01
    “…The study also shows clearly that the temporal structure of activity in Ilorin is different from what obtains in Western cities where there is flexibility in the usage of time. …”
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    Article
  4. 1684

    SV-MeCa: an XGBoost-based meta-caller approach for structural variant calling from short-read data by Rudel Christian Nkouamedjo Fankep, Arda Söylev, Anna-Lena Kobiela, Jochen Blom, Corinna Ernst, Susanne Motameny

    Published 2025-08-01
    “…Abstract Background Calling structural variants (SVs), i.e., genomic alterations of $$\ge $$ 50bp, from whole genome short-read data remains challenging, as existing callers are known to lack accuracy and robustness. …”
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    Article
  5. 1685

    Structural Characteristics of the Turning End of the Kaiping Syncline and Its Influence on Coal Mine Gas by Zhenning Chen, Yanming Zhu, Hanyu Zhang, Jin Li

    Published 2024-12-01
    “…This study, drawing on previous theories, research, and practical coal mine production data, analyzes the structural characteristics of the Kaiping syncline, with particular emphasis on the structural differentiation at its northeastern uplifted end. …”
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    Article
  6. 1686

    A structured-illumination reflectance imaging dataset for woody breast assessment of broiler meatZenodo by Yuzhen Lu, Hamed Sardari

    Published 2025-06-01
    “…An image dataset was created to assess WB conditions in broiler breast fillets using structured-illumination reflectance imaging (SIRI) as a non-destructive, objective means for WB assessment. …”
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    Article
  7. 1687
  8. 1688

    A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen by Naiwei Lu, Yiru Liu, Jian Cui, Xiangyuan Xiao, Yuan Luo, Mohammad Noori

    Published 2024-12-01
    “…Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. …”
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    Article
  9. 1689

    Damage Detection in Beam Structures Based on Frequency-Domain Analysis Methods for Nonlinear Systems by Wenbo Zhang, Xiaoyue Guo, Liangliang Cheng, Bo Zhang

    Published 2025-05-01
    “…To improve the accuracy of the Nonlinear Auto-Regressive with eXogenous inputs (NARX) model and reduce the impact of noise interference, we proposed a Multi-input Multi-output Forward Regression Orthogonal Least Squares (MFROLS) algorithm for processing multi-input multi-output data to identify the NARX model of the same structural system. …”
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    Article
  10. 1690

    STRUCTURAL-COMPOSITIONAL MODEL OF THE NYURBINSKAYA KIMBERLITE PIPE FORMATION (SREDNE-MARKHA AREA OF THE YAKUTIAN DIAMONDIFEROUS PROVINCE) by A. S. Gladkov, D. A. Koshkarev, A. V. Cheremnykh, F. João, M. A. Karpenko, M. V. Marchuk, I. A. Potekhina

    Published 2016-09-01
    “…Analysis of different faults and tectonic fracturing allowed to reconstruct the tectonic stress fields acting at the stage of the kimberlite body formation and to determine their occurrence sequence in time. The data obtained about regularities of the Nyurbinskaya pipe compositional structure and results of geologo-structural studies are combined in a single structural-compositional model of the deposit formation. …”
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    Article
  11. 1691
  12. 1692

    Utility of UAS-LIDAR for estimating forest structural attributes of the Miombo woodlands in Zambia. by Hastings Shamaoma, Paxie W Chirwa, Jules C Zekeng, Abel Ramoelo, Andrew T Hudak, F Handavu, Stephen Syampungani

    Published 2025-01-01
    “…The estimation of forest structural data in area-based forest inventories relies on the relationship between field-based estimates of forest structural attributes (FSA) and lidar-derived metrics at plot level, which can be modeled using either parametric or non-parametric regression techniques. …”
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    Article
  13. 1693

    Structural Feature-Preserving Point Cloud Denoising Method for Aero-Engine Profile by Jieqiong Yan, Laishui Zhou, Jun Wang, Xiaoping Wang, Xia Liu

    Published 2022-01-01
    “…In order to ensure that noise is removed without blurring or distorting structural features, a structural feature-preserving point cloud denoising method is proposed. …”
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    Article
  14. 1694

    Structural Component Identification and Damage Localization of Civil Infrastructure Using Semantic Segmentation by Piotr Tauzowski, Mariusz Ostrowski, Dominik Bogucki, Piotr Jarosik, Bartłomiej Błachowski

    Published 2025-07-01
    “…The data includes semantic segmentation masks for both categorizing structural elements (slabs, beams, and columns) and assessing structural damage (concrete spalling or exposed rebars). …”
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    Article
  15. 1695

    Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy by Aileen Luo, Tao Zhou, Martin V. Holt, Andrej Singer, Mathew J. Cherukara

    Published 2025-07-01
    “…Abstract Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor—corresponding to dilation and two potential rigid body rotations of the unit cell. …”
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  16. 1696

    Interpretive Structural Modeling of Factors Affecting Organizational Ambidexterity in the Ministry of Sports and Youth by Ainolah Ahmadnejad Joghie, Mozafar Yektayar, Mozhgan Khodamoradpoor

    Published 2022-07-01
    “…The statistical population of this study consisted of experts who were selected purposefully based on theoretical saturation of 15 of them. Semi-structured interviews were used to collect data in the qualitative study and a questionnaire was used to collect data in the quantitative study. …”
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    Article
  17. 1697

    Intelligent Identification of Structural Damage Based on the Curvature Mode and Wavelet Analysis Theory by Longsheng Bao, Yue Cao, Xiaowei Zhang

    Published 2021-01-01
    “…Data fitting is then performed to estimate the degree of structural damage. …”
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  18. 1698

    Predictive modeling of visible-light azo-photoswitches’ properties using structural features by Said Byadi, P. K. Hashim, Pavel Sidorov

    Published 2025-04-01
    “…Abstract In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous benchmark to select the best feature type and modeling approach. …”
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  19. 1699

    Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration by Pushpendra Raghav, Mukesh Kumar, Yanlan Liu

    Published 2024-08-01
    “…In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. …”
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    Article
  20. 1700

    Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

    Published 2024-10-01
    “…This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). …”
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