Application of deep learning for diagnosis of shoulder diseases in older adults: a narrative review by Sung Min Rhee

“…Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. …”
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Research status of soft anthropomorphic dexterous hands by LIU Yibo, XIAO Huaping, LIU Chuanwang, SUN Zhenhao, HAO Tianze, LIU Shuhai

“…Finally, taking the maximum thrust of the linear actuator during the lifting and tilting process as the optimization objective, through sensitivity analysis, the hinge joints that have a greater impact on the optimization objective were selected as design variables, and performance constraints and interference constraint functions were set. …”
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Multi-Layer Combinatorial Fusion Using Cognitive Diversity by Landon Hurley, Bruce S. Kristal, Suman Sirimulla, Christina Schweikert, D. Frank Hsu

“…Multiple scoring systems (including rank and score functions; MSS) have been widely used in multiple regression, intelligent biometric systems, multiple artificial neural nets, combining pattern classifiers, ensemble methods, machine learning and artificial intelligence (AI), data and information fusion, preference ranking, and deep learning. …”
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An intelligent prediction method of gas concentration in coal mines based onimproved TCN-TimeGAN by Qingsong HU, Shuo ZHENG, Shiyin LI, Yanjing SUN

“…In the design of loss function, Wasserstein distance is used to measure the distribution of gas data, and the gradient penalty term of adaptive weight is added to the identification network loss function, so as to solve the problems of data irregularity and gradient disappearance, and improve training stability and prediction accuracy. …”
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Development of an Electro-Hydraulic System for Reverse Control of an Attachment by Anton A. Ananchikov, Nikolay N. Ishin, Dmitry V. Semashko

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Recognition of Manipulative Text: Structure and Dominant Language Parameters by Gulmira N. Kenzhebalina, Gulvira K. Shaikova, Maygul T. Shakenova, Inessa G. Akoyeva

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Gravitational-wave Parameter Estimation in Non-Gaussian Noise Using Score-based Likelihood Characterization by Ronan Legin, Maximiliano Isi, Kaze W. K. Wong, Yashar Hezaveh, Laurence Perreault-Levasseur

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Variable Admittance Control of High Compatibility Exoskeleton Based on Human–Robotic Interaction Force by Jian Cao, Jianhua Zhang, Chang Wang, Kexiang Li, Jianjun Zhang, Guihua Wang, Hongliang Ren

“…Experimental results show that the designed physiologically functional bionic exoskeleton and adaptive admittance controller can significantly improve the accuracy of human–robotic joint motion tracking, effectively reducing human–machine interaction forces and improving the comfort and safety of the wearer. …”
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Efficient Solution of Fokker–Planck Equations in Two Dimensions by Donald Michael McFarland, Fei Ye, Chao Zong, Rui Zhu, Tao Han, Hangyu Fu, Lawrence A. Bergman, Huancai Lu

“…Finite element analysis (FEA) of the Fokker–Planck equation governing the nonstationary joint probability density function of the responses of a dynamical system produces a large set of ordinary differential equations, and computations become impractical for systems with as few as four states. …”
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Effects of normal stress on shear properties and acoustic emission characteristics of bonded rock-concrete interfaces by Yan Chen, Jiangfan Yang, Jintao Wang, Shuai Heng, Zhiqiang Hou

“…Additionally, the proportion of shear damage signals in specimens, as determined by the joint Gaussian Mixture Model (GMM) and Support Vector Machine (SVM), was found to exceed 75% and to rise with higher normal stress levels. …”
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Indicators of insulin resistance as predictors of 28-day mortality in patients with VA-ECMO: a retrospective study by You Zhou, Zhi Cheng, Pingping Gu, Yu Zhang, Wanying Xu, Xin Wang

“…Firstly, risk factors were screened through univariate and multivariate Cox regression; Further combine Least Absolute Shrinkage and Selection Operator (LASSO) regression (L1 regularization), random forest and gradient boosting machine (GBM) for multi-method feature screening, and use ridge regression (L2 regularization) to control collinearity to construct a joint prediction model; Finally, the model efficacy was verified through C-index, time-dependent receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), net reclassification improvement index (NRI), and comprehensive discriminant improvement index (IDI).ResultsTyG, METS-IR, TG/HDL-C, and TyG-BMI independently predicted an increased risk of death (all p < 0.01). …”
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Artificial intelligence in de novo protein design by Jiawei Yao, Xiaogang Wang

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Minimalist Design for Multi-Dimensional Pressure-Sensing and Feedback Glove with Variable Perception Communication by Hao Ling, Jie Li, Chuanxin Guo, Yuntian Wang, Tao Chen, Minglu Zhu

“…Strain sensors located at the finger joints can simultaneously project the bending motion of the individual joint into the virtual space or robotic hand. …”
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SILF Dataset: Fault Dataset for Solar Insecticidal Lamp Internet of Things Node by Xing Yang, Liyong Zhang, Lei Shu, Xiaoyuan Jing, Zhijun Zhang

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Improved Black-Winged Kite Algorithm with Multi-Strategy Optimization for Identifying <i>Dendrobium huoshanense</i> by Chaochuan Jia, Ting Yang, Maosheng Fu, Yu Liu, Xiancun Zhou, Zhendong Huang, Fang Wang, Wenxia Li

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A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning by Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

“…DRL agents acquire knowledge and make decisions through unsupervised interactions with their environment without requiring explicit knowledge of the joint dynamics of portfolio assets. Among these DRL algorithms, the combination of actor-critic algorithms and deep function approximators is the most widely used DRL algorithm. …”
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Acoustic Emission-Based Small Leak Detection of Propulsion System Pipeline of Sounding Rocket by Lin Gao, Lili Dong, Jianguo Cao, Shaofeng Wang, Wenjing Liu

“…It is verified that the main frequency of the AE small leak signal due to the failure of the pipe joint is focused in the range of 33–45 kHz, and the algorithms based on SVM with kernel functions all can reach a better estimation accuracy of 98% using the feature “envelope area” or the feature set {standard deviation (STD), root mean square (RMS), energy, average frequency}.…”
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Ischemic Postconditioning Regulates New Cell Death Mechanisms in Stroke: Disulfidptosis by Shanpeng Liu, Qike Wu, Can Xu, Liping Wang, Jialing Wang, Cuiying Liu, Heng Zhao

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Integrative multi-omics analysis reveals the interaction mechanisms between gut microbiota metabolites and ferroptosis in rheumatoid arthritis by Lifang Liang, Lifang Liang, Lifang Liang, Huaguo Liang, Min He, Min He, Min He, Huiling Zhang, Huiling Zhang, Huiling Zhang, Peifeng Ke, Peifeng Ke, Peifeng Ke

“…BackgroundRheumatoid arthritis (RA) is an autoimmune disease characterized by chronic synovitis and joint destruction. To systematically investigate the regulatory relationship between key ferroptosis genes and gut metabolites in RA, this study employed an integrative multi-omics approach combined with machine learning algorithms and single-cell transcriptomic data, identifying and validating GPX3 and MYC as potential critical ferroptosis regulators in RA.Methods and resultsFirst, 16 candidate genes were obtained by intersecting WGCNA, differential expression analysis results, and targets related to ferroptosis and gut microbiota. …”
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Type 1 diabetes prevention clinical trial simulator: Case reports of model‐informed drug development tool by Juan Francisco Morales, Marian Klose, Yannick Hoffert, Jagdeep T. Podichetty, Jackson Burton, Stephan Schmidt, Klaus Romero, Inish O'Doherty, Frank Martin, Martha Campbell‐Thompson, Michael J. Haller, Mark A. Atkinson, Sarah Kim

“…We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. …”
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