Estimating vegetation structure and aboveground carbon storage in Western Australia using GEDI LiDAR, Landsat and Sentinel data by Natasha Lutz, Pedro Rodriguez-Veiga, Imma Oliveras Menor

“…Worsening climate change impacts are amplifying the need for accurate estimates of vegetation structure and aboveground biomass density (AGBD) to assess changes in biodiversity and carbon storage. …”
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DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors by Yuanyuan Zhao, Jinzhu Jia

“…However, existing DAG structure learning algorithms still have limitations in handling mixed-type data (including continuous and discrete variables), which restricts their practical utility. …”
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Hierarchical genetic structure in an evolving species complex: Insights from genome wide ddRAD data in Sebastes mentella. by Atal Saha, Matthew Kent, Lorenz Hauser, Daniel P Drinan, Einar E Nielsen, Jon-Ivar Westgaard, Sigbjørn Lien, Torild Johansen

“…Sebastes mentella displays a particular life-history as livebearers, for which existence of multiple ecotypes has been suspected to complicate the genetic population structure of the species. Double digest restriction-site associated DNA was used to investigate genetic population structure in S. mentella and to scan for evidence of selection. …”
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Lack of genetic structure of Cypriot Alectoris chukar (Aves, Galliformes) populations as inferred from mtDNA sequencing data by M. Guerrini, P. Panayides, P. Hadjigerou, L. Taglioli, F. Dini, F. Barbanera

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Geological structure of central Vietnam by interpretation processing of gravitational survey data using the “COSCAD 3D” computer technology by T. H. Phan, A. V. Petrov, M. Ph. Do, M. G. Lai, T. L. Nguyen

“…Interpretation processing of gravity data was carried out using the “COSCAD 3D” computer technology of statistical and spectral correlation data analysis.Results. …”
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Changes in the extent and structure of Norway spruce stands in the Ukrainian Carpathians based on forest survey data from 1988 to 2018 by Lavnyy Vasyl, Khomiuk Petro, Gusti Mykola, Havryliuk Serhii, Korol Mykola, Matusevych Oleksandr, Savchyn Volodymyr, Vonderach Christian, Kahle Hans-Peter

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Evaluation of structured data from electronic health records to identify clinical classification criteria attributes for systemic lupus erythematosus by Rosalind Ramsey-Goldman, Theresa L Walunas, Abel N Kho, Anika S Ghosh, Jennifer A Pacheco, Vesna Mitrovic, Andy Wu, Kathryn L Jackson, Ryan Schusler, Anh Chung, Daniel Erickson, Karen Mancera-Cuevas, Yuan Luo

“…We implemented rule-based algorithms using structured data within the EHR system for each attribute of the three classification criteria. …”
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Indicator-Type Grey Structure Incidence Analysis Method for Panel Data and Its Application in Identifying Technological Innovation Factors by Shuqin Gu, Yong Liu, Lu Yue

“…There exist many panel data decision problems in real life, and they take on obvious structural similarities and lag effects among decision objects or indicators, which are difficult to solve effectively based on traditional panel data analysis methods. …”
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Intelligent Computerized Video Analysis for Automated Data Extraction in Wave Structure Interaction; A Wave Basin Case Study by Samuel Hugh Wolrige, Damon Howe, Hamed Majidiyan

“…Despite advancements in direct sensing technologies, accurately capturing complex wave–structure interactions remain a significant challenge in ship and ocean engineering. …”
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A large-scale dataset of AI-related tweets: Structure and descriptive statisticsGitHubDataverse by Nathalie de Marcellis-Warin, Daniel Kouloukoui, Thierry Warin

“…The final dataset includes structured metadata such as media elements (images, videos, and URLs), user engagement metrics (likes, retweets, replies), hashtags, language codes, and temporal indicators (hour and weekday of posting). …”
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Graph Neural Networks for Analyzing Trauma-Related Brain Structure in Children and Adolescents: A Pilot Study by Harim Jeong, Minjoo Kang, Shanon McLeay, R. J. R. Blair, Unsun Chung, Soonjo Hwang

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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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Real-time data in cancer registries: Validation of an automated data extraction system by Sylvie A.M. Langhout, Sjoerd J.F. Hermans, Anna J.T. Smit, Elizabeth Berkx, Sophie A. Kurk, Keetje J. Schade, Eduardus F.M. Posthuma, Otto Visser, Jan J. Cornelissen, Peter C. Huijgens, Jurjen Versluis, Maarten van der Wilt, Avinash G. Dinmohamed

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Encryption of sensor data using the Lorentz attractor in embedded systems by Akbota Kulzhanova, Sholpan Jomartova, Talgat Mazakov

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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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Change probability-aware graph for multimodal change detection by Te Han, Yuqi Tang, Yuzeng Chen, Yuqiang Guo, Bin Zou, Huihui Feng

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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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