Depth-specific soil moisture estimation in vegetated corn fields using a canopy-informed model: A fusion of RGB-thermal drone data and machine learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

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Syntactic Analysis of Movement Transformation in Bahasa Indonesia by Ruly Adha

“…This article aims to collect ideas, theories, and reported empirical data within the context of scholarship in the library. …”
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Fast, Robust, and Learned Distribution-Based Sorting by Paolo Ferragina, Mattia Odorisio

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Timeseria: An object-oriented time series processing library by Stefano Alberto Russo, Giuliano Taffoni, Luca Bortolussi

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Low-frequency ultrasonic pulse-echo datasets for geometry determination of a reinforced concrete step specimen with embedded polystyrene foam cuboids in non-destructive testingData... by Maria Grohmann, Stefan Maack, Matthias Behrens, Ernst Niederleithinger

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СТРУКТУРНЫЕ ДИСПРОПОРЦИИ КАК УГРОЗА ЭКОНОМИЧЕСКОЙ БЕЗОПАСНОСТИ РОССИИ by N. V. Osokina, E. V. Slesarenko

“…Results: basing on the analysis of scientific economic literature and statistical data, the historical background is revealed of the emergence of structural disproportions in the Russian economy, the origins of which had been laid long before the formation of the USSR and tended to strengthen during the Soviet period, and later during the period of transformation processes that swept the country at the turn of XX-XXI centuries; the influence of structural disproportions on the security of the Russian economy was described, which can be interpreted as a threat, characterized by a complete de-industrialization of the economy, reduction of innovation development, degradation of economic base and scientific potential, increasing dependence on vital consumer and industrial goods; there is a need to change the vector of socio-economic development of Russia and form a new model of structural modernization. …”
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Enhancing the assessment of in situ beam–column strength through probing and machine learning by Jin Terng Ma, Luke Lapira, M. Ahmer Wadee

“…Therefore, when assessing the structural health of an in situ beam–column, both of these load effects must be considered. …”
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Multiple Structural Breaks in Profit Share Rates of Participation Banks in Turkey: Are They Caused by the Recent Global Crises? by Mehmet Levent Erdas

“…I used the monthly data covering the period 1998-2018 for the estimated structural break dates. …”
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The 3‐D geological model around Chang'E‐3 landing site based on lunar penetrating radar Channel 1 data by Yuefeng Yuan, Peimin Zhu, Na Zhao, Long Xiao, Edward Garnero, Zhiyong Xiao, Jiannan Zhao, Le Qiao

“…Abstract High‐frequency lunar penetrating radar (LPR) data from an instrument on the lunar rover Yutu, from the Chang'E‐3 (CE‐3) robotic lander, were used to build a three‐dimensional (3‐D) geological model of the lunar subsurface structure. …”
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Optimising Sensor Placement in Heritage Buildings: A Comparison of Model-Based and Data-Driven Approaches by Estefanía Chaves, Alberto Barontini, Nuno Mendes, Víctor Compán

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Does teaching quality matter for Nordic primary school students’ mathematics confidence and mathematics achievement? A multilevel structural equation analysis of Nordic TIMSS 2019 grade 4 data by Lena Asp, Alli Klapp, Monica Rosén

“…To capture the hierarchical structure of the data, multilevel structural equation modelling (MSEM) is used as it keeps individual and classroom variation separate when investigating relations between teaching quality, mathematics confidence and mathematics achievement. …”
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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

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

“…The noisy point cloud is divided into casing feature data, pipeline feature data and complex shape feature data. …”
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Structural Component Identification and Damage Localization of Civil Infrastructure Using Semantic Segmentation by Piotr Tauzowski, Mariusz Ostrowski, Dominik Bogucki, Piotr Jarosik, Bartłomiej Błachowski

“…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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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

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

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

“…Data fitting is then performed to estimate the degree of structural damage. …”
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Predictive modeling of visible-light azo-photoswitches’ properties using structural features by Said Byadi, P. K. Hashim, Pavel Sidorov

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

“…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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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

“…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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