Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling by Taseef Hasan Farook, James Dudley

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DESIGN AND STUDY OF DRIVE SWIVEL JOINTS FOR HYDRAULIC MANIPULATION SYSTEMS OF MOBILE TRANSPORT-TECHNOLOGICAL MACHINES by Lagerev A.V., Lagerev I.A.

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A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning by Congyang Li, Chenggang Wang, Jiru Zhang, Wenjun Zheng, Jing Shi, Li Li, Xuezhi Shi

“…ObjectiveCurrently, there is no individualized prediction model for joint function recovery after ankle fracture surgery. …”
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Instantaneous Metabolic Energetics: Data-Driven Modeling Using Function-Based Surrogates and Gradient Boosting by Christopher Buglino, William Z. Peng, Stacy Ashlyn, Hyunjong Song, Howard J. Hillstrom, Joo H. Kim

“…We use surrogate learners to encode fundamental information about the time-varying properties of MEE. A gradient-boosted machine-learning model was then trained on the surrogate functions’ outputs. …”
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Optimization of machining process route for internal joint parts using artificial fish swarm algorithm by Jun HAN, Junwei ZHU, Yang ZHANG, Zhenyao ZHAO, Zexi AN

“…A reasonable constraint matrix model is constructed. The objective function is to determine the minimum number of machine tool, cutting tool, and clamping type changes. …”
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RELIABILITY ANALYSIS OF REACTION FORCE DEVELOPED IN THE LUBRICATED REVOLUTE JOINT FOR A SLIDER-CRANK SYSTEM INCLUDING JOINT WITH CLEARANCE AND LUBRICATION by ZHAO Kuan, XUE He, CHEN JianJun, QIAO XinZhou

“…In order to overcome this limitation,the parameters are pretreated through Genetic Algorithm to get the optimum parameters values. Then approximate function relation between random parameters and performance function is derived and the reliability of the reaction force developed in the lubricated revolute joint of the crank-slider mechanism is discussed by Monte-Carlo method. …”
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Joint image reconstruction and segmentation of real-time cardiovascular magnetic resonance imaging in free-breathing using a model based on disentangled representation learning by Tobias Wech, Oliver Schad, Simon Sauer, Jonas Kleineisel, Nils Petri, Peter Nordbeck, Thorsten A. Bley, Bettina Baeßler, Bernhard Petritsch, Julius F. Heidenreich

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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

“…Six machine learning algorithms were selected and trained by our dataset. …”
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SELECTION OF SCHEMES AND CALCULATION OF FUNCTIONAL CHARACTERISTICS OF HYDROSTATIC TRANSMISSIONS FOR MOBILE MACHINES by O. V. Brilevski, A. P. Stezko, V. S. Shevchenko

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Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…This study introduces a multi-scale machine learning framework leveraging whole-body magnetic resonance imaging (MRI) and clinical data to predict regional, muscle, joint, and functional progression in FSHD. …”
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ProbC: joint modeling of epigenome and transcriptome effects in 3D genome by Emre Sefer

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Review of Research on Artificial Intelligence-driven Joint Radar Communication by Xianmei WANG, Xiangbo LIU, Yuzheng REN, Yang LU, Haijun ZHANG

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A METHOD FOR INVESTIGATING MACHINE LEARNING ATTACKS ON ARBITER-TYPE PHYSICALLY UNCLONABLE FUNCTIONS by Yuri A. Korotaev

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A Proposal for a New Python Library Implementing Stepwise Procedure by Luiz Paulo Fávero, Helder Prado Santos, Patrícia Belfiore, Alexandre Duarte, Igor Pinheiro de Araújo Costa, Adilson Vilarinho Terra, Miguel Ângelo Lellis Moreira, Wilson Tarantin Junior, Marcos dos Santos

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Application of Machine Learning for Identifying Factors Associated with Renal Function Impairment in Gouty Arthritis Patients by Osman Cüre, Fatih Bal

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Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact by Enrique Mondragon-Estrada, Jane W. Newburger, Steven R. DePalma, Martina Brueckner, John Cleveland, Wendy K. Chung, Bruce D. Gelb, Elizabeth Goldmuntz, Donald J. Hagler, Jr., Hao Huang, Patrick McQuillen, Thomas A. Miller, Ashok Panigrahy, George A. Porter, Jr., Amy E. Roberts, Caitlin K. Rollins, Mark W. Russell, Martin Tristani-Firouzi, P. Ellen Grant, Kiho Im, Sarah U. Morton

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Innovative diagnostic framework for shoulder instability: a narrative review on machine learning-enhanced scapular dyskinesis assessment in sports injuries by Yahui Fu, Shuai Ma, Benxu Ma, Meng Han, Dongxue Zhao, Ziteng Li

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Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning by Jared Pilet, Scott A. Beardsley, Chad Carlson, Christopher T. Anderson, Candida Ustine, Sean Lew, Wade Mueller, Manoj Raghavan

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Knee Joint Torque Estimation in Femoral Prosthesis Users Using MMG and Low-Frequency Acceleration by Nobuhiro Hondo, Toshiaki Tsuji

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Predicting 30-Day Venous Thromboembolism Following Total Joint Arthroplasty: Adjusting for Trends in Annual Length of Stay by Johnathan R. Lex, MBChB, MASc, Robert Koucheki, MD, MEng, Aazad Abbas, MD, Jesse I. Wolfstadt, MD, MSc, FRCSC, FAAOS, Alexander S. McLawhorn, MD, MBA, Bheeshma Ravi, MD, PhD, FRCSC

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