The Use of Column-Based DBMSS to Form Multidimensional Data Structures by Dmitry Kunitsky, Maxim Fomin

“…The key point in the structure of the information system is the way of transition from the “Data vault” model to a multidimensional model of data representation based on associative rules of dependence between information objects.…”
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Large Language Model-Driven Structured Output: A Comprehensive Benchmark and Spatial Data Generation Framework by Diya Li, Yue Zhao, Zhifang Wang, Calvin Jung, Zhe Zhang

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Constructing Rasterized Covariates from LiDAR Point Cloud Data via Structured Query Language by Rory Pittman, Baoxin Hu

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Enhancing structured data generation with GPT-4o evaluating prompt efficiency across prompt styles by Ashraf Elnashar, Jules White, Douglas C. Schmidt

Subjects: “…structured data generation…”
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UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data by Lijun Fan, Shichao Yi, Wenrui Guan, Pingxin Wang

“…Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have utilized GCNs for data with Euclidean structures. such as image data and language data, and achieved good results, expanding the application scope of GCN. …”
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Analysis of strengths, weaknesses, opportunities, and threats: Analytical hierarchy process based dental waste management in Odisha by Somalee Mahapatra, Manoranjan Dash, Subhashis Mohanty

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Directions of emerging structural changes in the use of biometric data in Russia by N. S. Seliverstova

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Discriminant analysis of Gaussian spatial data with exponential covariance structure by Kęstutis Dučinskas

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Covariate-adjusted functional data analysis for structural health monitoring by Philipp Wittenberg, Lizzie Neumann, Alexander Mendler, Jan Gertheiss

“…Structural health monitoring (SHM) is increasingly applied in civil engineering. …”
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Collaborative Governance Structures for Interoperability in the EU’s new data acts by Jens-Peter Schneider, Johannes Erny, Franka Enderlein, Herwig C.H. Hofmann, Felix Pflücke

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Connection between nuclear structure, dissipation, and time in fission data by Caamaño M., Ramos D., Fernández D., Mantovani G., Farget F., Rodríguez-Tajes C., Lemasson A., Rejmund M., Schmitt C., Ackermann D., Álvarez-Pol H., Audouin L., Benlliure J., Biswas S., Casarejos E., Clement E., Cortina D., Delaune O., Derkx X., Dijon A., Doré D., Durand D., Frankland J. D., Fernández-Domínguez B., de France G., Fregeau M. O., Galaviz D., Galiana-Baldó E., Heinz A., Henriques A., Jacquot B., Jurado B., Kim Y. H., Morfouace P., Paradela C., Piot J., Ralet D., Roger T., Salsac M. D., Teubig P., Tsekhanovich I.

“…This seemingly mundane property can be used to obtain information about the energy dissipated during the process and the role of structure in its dynamics. In this paper, the study of the even-odd effect for elasticand transfer-induced fission data is discussed. …”
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Deep Structure of the Middle Urals According to Magnetic and Gravitational Data by Byzov Denis, Martyshko Petr, Fedorova Natalia, Rublev Aleksey

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DATA PREPARATION METHODS FOR RECOVERING THREE-DIMENSIONAL STRUCTURE OF THE SCENE by Sergey Sergeyevich Taranenko, Oleg Fedorovich Kovalev

“…The problem of data preparation to recover the three-dimensional structure of the scene including points, segments, straight lines, etc., using video obtained from a moving camera around a static scene is considered. …”
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Crustal structure of the Ulaanbaatar region, Mongolia according to gravimetric data by B. Buyantogtokh, E. Kh. Turutanov, V. S. Kanaikin

“…Based on the medium-scale areal gravimetric survey data, a regional map of Bouguer gravity anomalies was constructed for theUlaanbaatar region, Central Mongolia.Interpreted gravimetric data from the solutions of the inverse problem of gravimetry based on the body-shape selection were used to developthe 3D models showing the largest crustal density inhomogeneities of the study area – the Tolskaya (Ulaanbaatar) basin, Nalaiha depression and Bogdoulin granite massif. …”
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Data Transfer Schemes in Rotorcraft Fluid-Structure Interaction Predictions by Young H. You, Deokhwan Na, Sung N. Jung

“…For a CFD (computation fluid dynamics)/CSD (computational structural dynamics) coupling, appropriate data exchange strategy is required for the successful operation of the coupling computation, due to fundamental differences between CFD and CSD analyses. …”
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Crop Yield Prediction: Data Structure and Ai-Powered Methods by V. K. Kalichkin, K. Yu. Maksimovich, O. A. Aleshchenko, V. V. Aleshchenko

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Segmentation study of nanoparticle topological structures based on synthetic data. by Fengfeng Liang, Yu Zhang, Chuntian Zhou, Heng Zhang, Guangjie Liu, Jinlong Zhu

“…In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. …”
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Structural controls of gold mineralisation in Birrimian structures, Western Mali: insights from magnetic data analysis by Imen Hamdi Nasr, Adama Youssouf Koné, Wajdi Belkhiria, Adnen Amiri, Aboubacar Denon, Souleymane Sangaré, Mohamed Hedi Inoubli, Saïdou Ly

Subjects: “…Aeromagnetic data…”
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STRUCTURAL FEATURES OF THE DEEP STRUCTURE OF THE SOUTHEASTERN YANA-KOLYMA FOLD SYSTEM FROM COMPLEX GEOPHYSICAL DATA by N. K. Gayday, N. A. Goryachev, I. M. Khasanov, E. Yu. Goshko

“…Ore deposits of the Magadan region are now in the focus of comprehensive studies as information on their deep structure is needed for both subsoil prospecting and regional development planning. …”
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Using Information Extraction to Normalize the Training Data for Automatic Radiology Report Generation by Yuxiang Liao, Haishan Xiang, Hantao Liu, Irena Spasic

“…We developed a joint entity and relation model and proposed a novel auxiliary component to enhance the model performance by interpreting token-level information. Using the annotated data, we trained the model for automatically converting information from a narrative radiology report into the structured representation, which achieved a micro-F1 of 96.6% and 96.1% on named entity recognition, 94.0% and 89.8% on entity attribute recognition, and 89.5% and 86.6% on relation extraction, on the MIMIC-CXR and CheXpert test sets, respectively. …”
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