Crystallinity Changes in Modified Cellulose Substrates Evidenced by Spectral and X-Ray Diffraction Data by Magdalena-Cristina Stanciu, Fulga Tanasă, Carmen-Alice Teacă

“…Due to their specific methodology, they can be used to analyze not only starting materials and their final products but also intermediates. Data obtained by these methods substantiated the structural changes in cellulose substrates, as well as the alterations that occurred in their supramolecular architectures. …”
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Defining environmentally heterogeneous sites when faced with conservation urgency and scarce in situ data by Tania Garrido-Garduño, Ella Vázquez-Domínguez, Patricia Dávila-Aranda, Rafael Lira-Saade, Maribel Arenas-Navarro, Oswaldo Téllez-Valdés

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Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices by Yangcan Bao, Xiaofeng Lin, Mingming Jia, Zhongyong Xiao, Cuiping Wang, Jiangfu Liao, Yinghui Zhang

“…Results showed that structural and physiological VIs significantly improved classification accuracy by 7–43% compared to the initial accuracy. …”
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Network controllability mediates the relationship between rigid structure and flexible dynamics by Shi Gu, Panagiotis Fotiadis, Linden Parkes, Cedric H. Xia, Ruben C. Gur, Raquel E. Gur, David R. Roalf, Theodore D. Satterthwaite, Dani S. Bassett

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Index-Based Neural Network Framework for Truss Structural Analysis via a Mechanics-Informed Augmented Lagrangian Approach by Hyeonju Ha, Sudeok Shon, Seungjae Lee

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Railway traffic characterisation data based on weigh-in-motion and machine learning: A case study in PortugalZenodo by Idilson A. Nhamage, Cláudio S. Horas, José A. Campos e Matos, João Poças Martins

Subjects: “…Railway traffic data…”
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Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle ships by Liliya Mileva

“…The methods used are SQL queries and Excel functions for data extraction and calculations for data to receive results about seasonality and structural change indexes in five years (2019-2023). …”
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Innovation-Based Research Using Structural Flexibility and Acceptance Model (SFAM) by   Solimun, Adji Achmad Rinaldo Fernandes

“…The research objectives are as follows: (1) Develop a solid structural model assuming normality and homoscedasticity. (2) Obtain the property estimator of the flexible and robust SFAM structural model. (3) Obtaining hypothesis testing of each relationship built from the flexible and strong SFAM structural model. …”
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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

“…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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Reliability parameters calculation of restorable objects with regard to inaccuracies in the initial data by L. I. Kulbak, T. S. Martinovich

“…The method of reliability parameters calculation of objects with regard to inaccuracies of source data is provided. In this case, only restorable objects are considered, the reliability of which is ensured by structural redundancy with limited multiplicity. …”
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The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random by Wahiba Bouabsa

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Civil structural health monitoring and machine learning: a comprehensive review by Asraar Anjum, Meftah Hrairi, Abdul Aabid, Norfazrina Yatim, Maisarah Ali

“…More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. …”
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Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review by Asraar Anjum, Meftah Hrairi, Abdul Aabid, Norfazrina Yatim, Maisarah Ali

“…More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. …”
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A structural biology compatible file format for atomic force microscopy by Yining Jiang, Zhaokun Wang, Simon Scheuring

“…Abstract Cryogenic electron microscopy (cryo-EM), X-ray crystallography, and nuclear magnetic resonance (NMR) contribute structural data that are interchangeable, cross-verifiable, and visualizable on common platforms, making them powerful tools for our understanding of protein structures. …”
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Advancing Structural Health Monitoring with Deep Belief Network-Based Classification by Álvaro Presno Vélez, Zulima Fernández Muñiz, Juan Luis Fernández Martínez

“…In recent years, deep learning techniques have emerged as powerful tools for analyzing the complex data generated by SHM systems. This study investigates the use of deep belief networks (DBNs) for classifying structural conditions before and after retrofitting, using both ambient and train-induced acceleration data. …”
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Structural Health Monitoring of Laminated Composites Using Lightweight Transfer Learning by Muhammad Muzammil Azad, Izaz Raouf, Muhammad Sohail, Heung Soo Kim

“…Therefore, deep learning-based methods that use sensor data to conduct autonomous health monitoring have drawn much interest in structural health monitoring (SHM). …”
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Remote sensing‐based mapping of structural building damage in the Ahr valley by Guilherme Samprogna Mohor, Tobias Sieg, Oliver Koch, Aaron Buhrmann, Holger Maiwald, Jochen Schwarz, Annegret H. Thieken

“…Abstract Flood damage data are needed for various applications. Structural damage of buildings can reflect not only the economic damage but also the life‐threatening condition of a building, which provide crucial information for disaster response and recovery. …”
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An Experimental Technique for Structural Diagnostic Based on Laser Vibrometry and Neural Networks by Paolo Castellini, Gian Marco Revel

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A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data by Mohamed El Amine Seghier Ben, Tam T. Truong, Christian Feiler, Daniel Höche

“…Atmospheric corrosion of maritime structures remains one of the most challenging issues facing offshore industry. …”
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Mapping the internal structures of fault zones of the sedimentary cover: a tectonophysical approach applied to interpret TDEM data (Kovykta gas condensate field) by K. Zh. Seminsky, I. V. Buddo, A. A. Bobrov, N. V. Misyurkeeva, Yu. P. Burzunova, A. S. Smirnov, I. A. Shelokhov

“…The article presents the results of studying the internal structures of platform fault zones with the use of a new tectonophysical approach to processing and interpretation of electrical exploration data obtained by the transient elec‐ tromagnetic method in the near field zone (TEM). …”
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