Graph neural network approach with spatial structure to anomaly detection of network data by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…Abstract Network anomaly detection using graph-structured data is a critical task in data mining and cybersecurity, involving the identification of unusual patterns within a network by analyzing its structure as a graph. …”
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Accounting for population structure and data quality in demographic inference with linkage disequilibrium methods by Enrique Santiago, Carlos Köpke, Armando Caballero

“…Abstract Linkage disequilibrium methods for demographic inference usually rely on panmictic population models. However, the structure of natural populations is generally complex and the quality of the genotyping data is often suboptimal. …”
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POSSIBILITIES OF THE STRUCTURE CONTROL OF THE CAST-IRON AND STEEL ARTICLES BY DATA OF ULTRASONIC MEASURING by V. R. Bajev, G. E. Konovalov, Ja. V. Artemenko, M. V. Asadchaja, K. A. Filippov, P. S. Gurchenko, M. A. Tishchenko

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Structural Analysis of Huaguangtan Arch Dam Based on Data Assimilation Parametric Inversion by LIN Xianzhi, LUO Zenghong, XU Zheng, MIN Jiesheng, CAO Chuanbo, ZHAO Quan

“…Aiming at the difficulty of the accurate safety assessment of the overall structural state of Huaguangtan Arch Dam,a finite element model of the arch dam is established,and the data assimilation algorithm is used to invert the material parameters based on the results of the sensitivity analysis of the mechanical parameters of the arch dam concrete and bedrock.Finally,the material parameters of finite element calculation are calibrated by parameter inversion results.The results show that the residual between the finite element displacement simulation result and the monitoring data conforms to the Gaussian distribution,and its standard deviation after data assimilation parametric inversion is reduced by 81.2%,which improves the accuracy of finite element calculation.This method enables automated operation and provides a reasonable and reliable new idea for the structural safety assessment of arch dams.…”
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Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality by Ali Azadi, Francisco José García-Peñalvo

“…Electronic medical records (EMRs) are fundamental to clinical decision support systems (CDSS). Conventional EMR structures still fail to capture the cyclical nature of treatment plans, leading to fragmented data representation and reduced decision accuracy. …”
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Review of the technology used for structural characterization of the GMO genome using NGS data by Kahee Moon, Prakash Basnet, Taeyoung Um, Ik-Young Choi

“…According to CODEX standards, this characterization involves evaluating the presence of introduced genes, insertion sites, copy number, and nucleotide sequence structure. Advances in technology have led to the increased use of next-generation sequencing (NGS) over traditional methods such as Southern blotting. …”
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3DB-ROC Data Structure Organization for Three-Dimensional Building Models by Jiangtao Lei, Bin Fan, Qing Liu, Shuhong Mei, Yongsheng Huang, Xiaoli Yang

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Yale Brain Atlas to interactively explore multimodal structural and functional neuroimaging data by Evan Collins, Evan Collins, Evan Collins, Omar Chishti, Omar Chishti, Hari McGrath, Hari McGrath, Sami Obaid, Sami Obaid, Sami Obaid, Alex King, Alex King, Edwin Qiu, Ellie Gabriel, Xilin Shen, Jagriti Arora, Xenophon Papademetris, Xenophon Papademetris, Xenophon Papademetris, Xenophon Papademetris, R. Todd Constable, R. Todd Constable, R. Todd Constable, R. Todd Constable, Dennis D. Spencer, Hitten P. Zaveri

“…For structural data, it incorporates white matter connectomes of 1,065 subjects and cortical thickness profiles of 200 subjects both from the Human Connectome Project. …”
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The Role of Organisational Culture and Structure in Data-driven Green Policy and Decision-making by Laetitia Van der Merwe, Calayde Davey

Subjects: “…Organisational structure…”
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Structure of Patients with Hepatocellular Cancer (Data Analysis from Two Specialised Centres) by M. V. Mayevskaya, M. S. Novruzbekov, I. M. Borovkov, D. G. Trofimova, M. S. Zharkova, V. D. Lunkov, K. N. Lutsyk, O. D. Olisov

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Data collection and transmission strategies for structural safety monitoring of large-scale infrastructure by Feng Zhou

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THE NEOTECTONIC STRUCTURE OF INTERMOUNTAIN BASINS OF MOUNTAINOUS ALTAI ACCORDING TO ELECTROMAGNETIC AND GEOLOGICAL DATA by N. N. Nevedrova, A. M. Sanchaa, E. V. Deev, S. M. Babushkin

“…The article presents new data on the geoelectrical structure of Chuya, Kuray and Uymon intermountain basins, which are the three largest ones in Mountainous Altai, Russia. …”
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Monte Carlo Evaluation of the Methods Estimating Structural Change Point in Panel Data by Selim Dağlıoğlu, Mehmet Akif Bakır

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Information hiding algorithm based on mapping and structure data of 3D model by Shuai REN, Zhen WANG, Dongxu SU, Tao ZHANG, Dejun MU

“…The existing 3D information hiding schemes are not robust enough against the joint attacks,as a result the secret information will be vulnerable and cannot be extracted correctly.In order to solve the above problem,an information hiding algorithm based on mapping and structure data of 3D models was proposed.First,several texture maps of the original 3D models in .stl format were picked from the standard model library,so the backup secret data after twice two-dimension discrete Daubechies transform can be embedded using dbl function just as the watermark.Secondly,the original 3D model in .stl format was operated by frame sampling in wavelet domain to obtain the coefficient in transform domain,thus the secret data was embedded into the corresponding transform coefficient.Finally,the .obj documents with the secret information were generated by multiplying the 2D texture map data and the 3D .stl data matrix based on orthogonal projection.Texture maps and coordinate space of 3D model were both used to embed the secret information repeatedly in order to enhance the robustness.The experiment analysis indicated that the imperceptibility,robustness and resistance against analysis are improved and information transmission safety in complex environment can be achieved based on the redundancy space of multi-type carriers.…”
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MRS-BIDS, an extension to the Brain Imaging Data Structure for magnetic resonance spectroscopy by Amy E. Bouchard, Dickson Wong, Wolfgang Bogner, Rémi Gau, Yaroslav O. Halchenko, Damon G. Lamb, Christopher J. Markiewicz, Paul G. Mullins, Guiomar Niso, Georg Oeltzschner, BIDS Maintainers, William T. Clarke, Martin Wilson, Mark Mikkelsen

“…The Brain Imaging Data Structure (BIDS) is an increasingly adopted standard for organizing scientific data and metadata. …”
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Combining Several Substitution Cipher Algorithms using Circular Queue Data Structure by Noor Ibraheem, Mokhtar Hasan

“…Lots of algorithms and techniques are available for data security.  This paper presents a cryptosystem that combines several Substitution Cipher Algorithms along with the Circular queue data structure. …”
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Fast and accurate short-read alignment with hybrid hash-tree data structure by Junichiro Makino, Toshikazu Ebisuzaki, Ryutaro Himeno, Yoshihide Hayashizaki

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A CDE-based data structure for radiotherapeutic decision-making in breast cancer by Fabio Dennstädt, Maximilian Schmalfuss, Johannes Zink, Janna Hastings, Roberto Gaio, Max Schmerder, Nikola Cihoric, Paul Martin Putora

“…Abstract Background The growing complexity of oncology and radiation therapy demands structured and precise data management strategies. The National Institutes of Health (NIH) have introduced Common Data Elements (CDEs) as a uniform approach to facilitate consistent data collection. …”
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Determination of Spatial-Temporal Correlation Structure of Troposphere Ozone Data in Tehran City by S.S. Mousavi, M. Mohammadzadeh

“…For eliminating the trend of data, a dynamic linear model is used, then some features of correlation structure of de-trended data, such as stationarity, symmetry and separability are considered. …”
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Research on Smart Scaling Mechanism and Structure for Big Data Network Server Groups by Hongyao Ju

“…The principle and construction of the structure of smart telescopic on big data network server groups were elaborated.The key technologies involved in the smart scaling of big data network server groups were discussed.After investigating key technologies,including the monitoring of the workload of the server group,the smart control of the number of servers,and the scheduling of access loads,the principle and realization method for the smart scaling of big data network server groups were proposed and technical supports were provided for the smart scaling and effective energy conservation of big data network server groups.According to the proposed construction method and key technologies,a smart scaling model for big data network server groups was provided as well.…”
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