The Property of the <i>Gaia</i> Celestial Reference Frame 3 (<i>Gaia</i>-CRF3) by Guangyi Liu, Sufen Guo

“…Using the Fibonacci grid, approximately 430,000 uniformly distributed sources were selected from the 5-parameter solution of <i>Gaia</i> DR3. After VSH processing, the rotation vector and glide vector were determined as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="bold">R</mi><mo>=</mo><mo>(</mo><mn>10.7</mn><mo>±</mo><mn>3.1</mn><mo>,</mo><mn>2.2</mn><mo>±</mo><mn>2.7</mn><mo>,</mo><mo>−</mo><mn>2.5</mn><mo>±</mo><mn>4.0</mn><mo>)</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>as · year⁻¹ and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="bold">G</mi><mo>=</mo><mo>(</mo><mn>0.3</mn><mo>±</mo><mn>3.1</mn><mo>,</mo><mo>−</mo><mn>1.2</mn><mo>±</mo><mn>2.7</mn><mo>,</mo><mo>−</mo><mn>2.5</mn><mo>±</mo><mn>4.0</mn><mo>)</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>as · year⁻¹, respectively. …”
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Identification of DNA N6-methyladenine modifications in the rice genome with a fine-tuned large language model by Yichi Zhang, Hao Chen, Shicheng Xiang, Zhibin Lv

“…Through transfer learning, we fine-tune the model on the rice 6mA recognition task, converting raw DNA sequences into high-dimensional feature vectors. These features are then processed by an XGBoost algorithm to generate predictions. …”
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In the Face of the Strategic Choice: New Imperatives of World Policy by Mark A. Neymark

“…We study its problem nodes and key issues. The world political process is considered as an expression of a flexible mobility, variability, dynamic interaction of a multitude of objective and subjective factors that determine the content, nature and vectors of the evolution of the world community. …”
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Origin, trophic transfer and recycling of particulate organic matter in two upwelling bays of Humboldt Current System: Insights from compound-specific isotopic compositions of amin... by Benjamín Srain, Edgart Flores, Jorge Valdés, Andrés Camaño

“…Although significant knowledge exists regarding their hydrodynamic and ecological processes, the spatial dynamics of trophic transfer and heterotrophic resynthesis of organic matter remain insufficiently understood. …”
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Using mechanistic models to highlight research priorities for tick-borne zoonotic diseases: Improving our understanding of the ecology and maintenance of Kyasanur Forest Disease in... by Richard M J Hassall, Sarah J Burthe, Stefanie M Schäfer, Nienke Hartemink, Bethan V Purse

“…As such, effective control measures and interventions are often hindered by a limited ecological evidence base, which results in a limited understanding of epidemiologically relevant hosts or vectors and the processes that contribute to the maintenance of pathogens and spillover to humans. …”
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Research on civil aircraft cockpit display interface availability considering multidimensional indicators clustering and reduction by CHEN Dengkai, XIAO Yao, XIAO Jianghao, ZHOU Yao, YANG Cong

“…Finally, the support vector machine(SVM) classification model was employed to verify performance and reliability of both algorithms. …”
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Content-Enhanced Network Embedding for Academic Collaborator Recommendation by Jie Chen, Xin Wang, Shu Zhao, Yanping Zhang

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Spatial Characteristics and Driving Mechanisms of Carbon Neutrality Progress in Tourism Attractions in the Qinghai–Tibet Plateau Based on Remote Sensing Methods by Bing Xia

“…The carbon neutrality process in natural tourism attractions is marginally superior to that in cultural tourism attractions. …”
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Estimating the travel distance of channelized rock avalanches using genetic programming method by Zhang Yong, Wang Tao, Deng Mingfeng, Chen Ningsheng

“…Considering the advantages of machine learning in data processing, 49 channelized rock avalanche events and three machine learning methods (genetic programming (GP), support vector machine (SVM), and multilayer perceptron (MLP)) were selected for model optimization and establishment. …”
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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine by Jichang Peng, Ya Gao, Lei Cai, Ming Zhang, Chenghao Sun, Haitao Liu

“…In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%.…”
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A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts by Yunlin Guan, Yun Wang, Xuedong Yan, Haonan Guo, Yu Zhou

“…Parking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. …”
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Assessment of Current and Voltage Signature Analysis for the Diagnosis of Open-Phase Faults in Asymmetrical Six-Phase AC Permanent Magnet Synchronous Motor Drives by Yasser Gritli, Claudio Rossi, Angelo Tani, Domenico Casadei

“…It is shown that the presence of specific harmonics can significantly affect the diagnosis process. Here, two diagnostic space vectors elaborated in the fifth α-β plane, based on the current and voltage signals, are proposed to cope with this limitation. …”
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Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol by Qunling Luo, Zhiqiang Guo, Danping Lin, Boxue Chang, Yinlan Ruan

“…The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. …”
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Scanning Micromirror Calibration Method Based on PSO-LSSVM Algorithm Prediction by Yan Liu, Xiang Cheng, Tingting Zhang, Yu Xu, Weijia Cai, Fengtian Han

“…However, in practical applications, several factors inherent to the fabrication process and the surrounding usage environment exert a considerable influence on the accuracy of measurements obtained with the micromirror. …”
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The Algorithm to Automatically Extract Body Sizes and Shapes by Mong Hien Thi Nguyen, Tuong Quan Vo, Mai Huong Bui, Van Anh Pham

“…Furthermore, a simple approach based on vertices and surface normal vector data, together with optimal searching, is adapted to estimate the primary dimensions. …”
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Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy by Ting An, Yongwen Jiang, Hanting Zou, Xuan Xuan, Jian Zhang, Haibo Yuan

“…The intelligent perception of moisture content (MC) for tea leaves during the black tea withering process is an unsolved task because of the acquisition of limited sample characteristic information. …”
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Ship Target SAR Dataset Generation Based on Ray Tracing Including Multiple Scattering by Junjie Li, Ke Wu, Liuyan Wei, Chuanxiang Cheng, Gaohao Zhu

“…Finally, the raw echo signal of the ship target is processed by RD imaging algorithm, then the corresponding SAR simulated image is obtained. …”
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Structural synthesis of discrete adaptive tracking systems based on the combined maximum principle by Andrey A Kostoglotov, Sergey V. Lazarenko, Anton A Kuznetsov, Igor V. Deryabkin, Vitaly A Losev

“…Discrepancy of the mathematical model structure to the observed process is one of the causes for loss in the control system operation. …”
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Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints by Min Zeng, J. R. R. Mayer, Miao Feng, Elie Bitar-Nehme, Xuan Truong Duong

“…Initial processing of data using the SAMBA (scale and master ball artefact) method eliminates setup errors and provides estimates of inter- and intra-axis errors as well as volumetric errors vectors. …”
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Machine Learning Ship Classifiers for Signals from Passive Sonars by Allyson A. da Silva, Lisandro Lovisolo, Tadeu N. Ferreira

“…The classifiers included standard Support Vector Machines, K-Nearest Neighbors, Random Forests, Neural Networks and two less conventional approaches in this context: Linear Discriminant Analysis (LDA) and the XGBoost ensemble method. …”
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