An augmented GSNMF model for complete deconvolution of bulk RNA-seq data by Shaoyu Li, Su Xu, Xue Wang, Nilüfer Ertekin-Taner, Duan Chen

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Shell Model Reconstruction of Thin-Walled Structures from Point Clouds for Finite Element Modelling of Existing Steel Bridges by Tomoya Nakamizo, Mayuko Nishio

“…There are some studies that show finite element (FE) models from point cloud data. While most of those approaches focus on modelling by solid elements, modelling of some civil structures, such as bridges, requires various uses of beam and shell elements. …”
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Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model by Xianmei Zhou, Shanliang Zhu, Wentao Jia, Hengkai Yao

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Monitoring data of the openLAB research bridge – Part 1: Reference conditionOPARA by Andreas Jansen, Max Herbers, Bertram Richter, Maria Walker, Frank Jesse, Steffen Marx

“…Structural Health Monitoring (SHM) is emerging as an essential tool for ensuring the safety and longevity of an aging bridge infrastructure. …”
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Seismic comprehensive characterization technology for internal structures of deep single channels with few wells by WANG Jianwei, LEI Lei, SUN Li, LIU Mengying, WU Wenwen, LIU Shu

“…The target layer has a large burial depth， few drilled wells， and poor-quality seismic data. It is difficult to identify the channel sand bodies based on seismic data， and the development accuracy requirements are not supported. …”
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Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform by Florent Le Borgne, Camille Garnier, Camille Morisseau, Yanis Navarrete, Yanina Echeverria, Juan Mir, Jaume Calafell, Tanguy Perennec, Olivier Kerdraon, Jean-Sébastien Frenel, Judith Raimbourg, Mario Campone, Maria Fe Paz, François Bocquet

“…Results C-LAB ® achieved over 80% agreement with human extractions (precision, recall, and F1-score) in structuring biomarker data from complex, unstructured pathology reports, despite dataset variability and optical character recognition errors. …”
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State of India's Birds 2023: A framework to leverage semi‐structured citizen science for bird conservation by Ashwin Viswanathan, Karthik Thrikkadeeri, Pradeep Koulgi, Praveen J., Arpit Deomurari, Ashish Jha, Ashwin Warudkar, Kulbhushansingh Suryawanshi, M. D. Madhusudan, Monica Kaushik, Naman Goyal, Rajah Jayapal, Suhel Quader, Sutirtha Dutta, Tarun Menon, Vivek Ramachandran

“…Semi‐structured data are generated and uploaded by birdwatchers to citizen science platforms such as eBird. …”
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Multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands by Y. Shi, J. Wang, W. D. Kissling

“…<p>Recent years have seen a rapid surge in the use of light detection and ranging (lidar) technology for characterizing the structure of ecosystems. Even though repeated airborne laser scanning (ALS) surveys are becoming increasingly available across several European countries, so far, only a few studies have derived data products of ecosystem structure at a national scale, possibly due to a lack of free and open-source tools and the computational challenges involved in handling the large volumes of data. …”
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Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model. by Shozen Dan, Yu Chen, Yining Chen, Melodie Monod, Veronika K Jaeger, Samir Bhatt, André Karch, Oliver Ratmann, Machine Learning & Global Health network

“…The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported.…”
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Seasonal genetic variation and genetic structure of Spodoptera exigua in Liaoning Province, Northeast China: insights from 11 years of microsatellite data by Ming-Li Yu, Xian-Zhi Xiu, Jin-Yang Wang, Xin-Yi Cao, Fa-Liang Qin, Xing-Ya Wang, Li-Hong Zhou

“…Results Microsatellite data revealed moderate levels of genetic variation among 50 seasonal populations of BAW sampled from 2012–2022, along with significant genetic differentiation among these populations. …”
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The glucose-lowering therapy structure in special groups of type 2 diabetes mellitus patients based on data from the Moscow region register by Inna V. Misnikova, Yulia A. Kovaleva, Mikhail А. Isakov, Alexander V. Dreval

“…BACKGROUND: Data of real clinical practice in diabetes mellitus (DM) register allow to evaluate features and trends in structure of glucose-lowering therapy (GLT). …”
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Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia by Julien Lerat, Francis Chiew, David Robertson, Vazken Andréassian, Hongxing Zheng

Subjects: “…model structure…”
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Structural Feedback Analysis Based on Monitoring Data from Units with Different Spiral Case Embedding Methods at Three Gorges Hydropower Station by CHEN Qin, SU Hai-dong, DUAN Guo-xue, CUI Jian-hua, ZHOU Shi-hua

“…As the supporting system of hydroelectric generators, both the steel spiral cases and surrounding reinforced concrete structures have significant differences in construction processes and structural bearing characteristics due to different embedding methods. …”
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What determines export structure in the EU countries? The use of gravity model in international trade based on the panel data for the years 1995-2015 by Marek Maciejewski, Krzysztof Wach

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A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. by William T Harvey, Vinny Davies, Rodney S Daniels, Lynne Whittaker, Victoria Gregory, Alan J Hay, Dirk Husmeier, John W McCauley, Richard Reeve

“…We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. …”
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A dynamic key management and secure data transfer based on m-tree structure with multi-level security framework for Internet of vehicles by Hua Yi Lin, Meng-Yen Hsieh

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PATIENTS WITH ATRIAL FIBRILLATION IN MULTIDISCIPLINARY HOSPITAL: STRUCTURE OF HOSPITALIZATION, CONCOMITANT CARDIOVASCULAR DISEASES AND DRUG TREATMENT (DATA OF RECVASA AF-TULA REGISTRY) by M. N. Valiakhmetov, T. A. Gomova, M. M. Loukianov, S. Yu. Martsevich, K. N. Nadejkina, M. N. Artemova, D. N. Jilin, E. E. Fedotova, A. V. Zagrebelnyy, E. V. Kudryashov, S. A. Boytsov

“…All patients with a diagnosis of AF in the patient's chart (n=1225) were included into the RECVAZA AF-Tula registry; that is 4.2% of 29018 patients hospitalized to the Tula Regional Clinical Hospital in 2013. The structure of the associated cardiovascular diseases, as well as drug therapy, was evaluated on the basis of data in the medical documentation.Results. …”
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Detachment‐Fault Structure Beneath the TAG Hydrothermal Field, Mid‐Atlantic Ridge, Revealed From Dense Wide‐Angle Seismic Data by Szu‐Ying Lai, G. Bayrakci, B. J. Murton, T. A. Minshull

“…We used dense wide‐angle seismic data to define TAG's detachment structure at a finer scale than has previously been possible. …”
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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

“…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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A comparative study of regression methods to predict forest structure and canopy fuel variables from LiDAR full-waveform data by P. Crespo-Peremarch, L.A. Ruiz, A. Balaguer-Beser

“…Independent variables were calculated using metrics extracted from full-waveform LiDAR data, while the reference data used to generate the dependent variables for the prediction models were obtained from fieldwork in 78 plots of 16 m radius. …”
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