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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Integrating numerical simulation and experimental data for enhanced structural health monitoring of bridges by Om Narayan Singh, Kaushik Dey

“… The research described in this paper aims to enhance the structural health monitoring (SHM) of highway bridges by integrating numerical simulations with experimental data. …”
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New data on the geological structure and zonality of the Vorontsovka gold field in the Northern Urals by Vikentyev I. V., Vikentieva O. V., Blokov I. V., Vykhristenko R. I., Yushko N. A., Krinochkin L. A.

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Structural Safety Evaluation of Tunnel Based on the Dynamic Monitoring Data during Construction by Xiaokun Yan, Hu Li, Feng Liu, Yang Liu

“…It is still a challenge to accurately evaluate the structural safety of tunnel during the process of construction. …”
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The Impact of Structural Funds on Regional Growth: A Panel Data Spatial Analysis by Micaela Antunes, Miguel Viegas, Celeste Varum, Carlos Pinho

“…However, the new economic geography states that structural funds may also facilitate the geographic concentration of economic activities, thus perpetuating regional imbalances. …”
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THE CENOZOIC CRUSTAL STRESS STATE OF MONGOLIA ACCORDING TO GEOLOGICAL AND STRUCTURAL DATA (REVIEW) by V. A. Sankov, A. V. Parfeevets

“…The studies commenced in the southern Baikal rift zone in the 1970s and were extended further southwards to cover mobile regions neighbouring the Siberian platform. Geological, structural and morphostructural data were collected and analysed to define the crustal stress types and spatial characteristics. …”
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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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Cross-Filter Structured Pruning for Efficient Sparse CNN Acceleration by Ngoc-Son Pham, Sangwon Shin, Lei Xu, Weidong Shi, Taeweon Suh

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DEEP STRUCTURE OF THE ALTAI MOUNTAINS AND MODERN GRAVITY FIELD MODELS by V. Yu. Timofeev, D. G. Ardyukov, D. N. Goldobin, A. V. Timofeev, D. A. Nosov, I. S. Sizikov, E. N. Kalish, Yu. F. Stus

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RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network by Xiaoteng Yang, Zhenqiang Wu, Shumaila Javaid

“…In the presented dynamically evolving network model, new directed edges are generated depending on the existing nodes and the hierarchical structures among the network, and these edges decay over time. …”
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Guided Regularizers for Structured Reduction of Neural Networks by Ali Haisam Muhammad Rafid, Adrian Sandu

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SemantiPack: An Efficient Real-World Data Compressor Using Structural and Semantic Metadata by Yoshiteru Nagata, Daiki Kohama, Yoshiki Watanabe, Shin Katayama, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi

“…The exponential growth of Real-World Data (RWD), primarily collected from IoT sensors and spanning domains such as mobility, environment, and energy consumption, presents critical challenges due to its scale, heterogeneity, and structural variability. …”
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Modal Parameters Estimation of Building Structures from Vibration Test Data Using Observability Measurement by Jae-Seung Hwang, Hongjin Kim, Bong-Ho Cho

“…The numerical simulation is performed to evaluate the proposed procedure, and the results show that the modal participation factor and mode shapes are estimated from the structural responses accurately. The procedure is also applied to the experimental data obtained from the shaking table test of a three-story shear building model.…”
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Delineation of mineralization-related geologic structures and lithological units using airborne magnetic and radiometric data by Christian Ordóñez, Stephen Eguba Ekwok, Ahmad Alkhayaat, Pardeep Singh Bains, Rohit Sharma, Raman Kumar, Ankur Kulshreshta, Vikasdeep Singh Mann, Yasser Elmasry

Subjects: “…Geologic structures…”
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Characterizing Subsurface Structures From Hard and Soft Data With Multiple‐Condition Fusion Neural Network by Zhesi Cui, Qiyu Chen, Jian Luo, Xiaogang Ma, Gang Liu

“…In this study, we introduce a multiple‐condition fusion network (MCF‐Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple‐source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. …”
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The intraorganic architecture of musculoskeletal structures in maxillofacial area of children according to x-ray diagnostics data

“…The combined use of the methods of teleradiography, computer and magnetic resonance imaging to study the structure of bone structures of MFA on the same object provides information that significantly reinforce the concept of morpho-functional unity of all structural components of the facial part of the head.…”
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A Method for Compressing Event Log Data Based on Combinatorial Generation Using AND/OR Tree Structures by Yuriy Shablya

“…Based on this idea, a method for compressing event log data based on combinatorial generation using AND/OR tree structures is proposed. …”
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Gravity Data Fusion and Imaging of Geological Structures in the Red River Fault Zone and Adjacent Areas by Guiju Wu, Fei Yu, Hongbo Tan, Jiapei Wang, Weihua Liu

“…The geological structure in the Red River fault zone (RRF) and adjacent areas is complex. …”
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