Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks by Lei Cheng, Zhi Yu, Xinxi Liu

“…Subsequently, a three-dimensional finite difference scheme for the theoretical model is derived to implement a numerical algorithm. Finally, using argillaceous siltstone as an example, the validity of the theoretical model and its algorithmic implementation is verified through experimental testing, result analysis, model construction, secondary development, and numerical simulation. …”
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Study on multi-parameter optimization of seismic isolation bearings for continuous girder bridges considering interactions among key parameters by Zhaolan Wei, Bowen Yang, Qixuan You, Konstantinos Daniel Tsavdaridis, Shaomin Jia

“…First, using a conventional particle swarm optimization (PSO) algorithm, the individual and combined effects of each parameter on key response indicators are identified. …”
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Physics-Informed Neural Network for Load Margin Assessment of Power Systems with Optimal Phasor Measurement Unit Placement by Murilo Eduardo Casteroba Bento

“…The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction.…”
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Used economy market insight: Sailboat industry pricing mechanism and regional effects. by Zhanni Huang, Hansheng Hu, Di Wu

“…Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data. …”
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Chaotic Behaviors and Coexisting Attractors in a New Nonlinear Dissipative Parametric Chemical Oscillator by Y. J. F. Kpomahou, A. Adomou, J. A. Adéchinan, A. E. Yamadjako, I. V. Madogni

“…The performed numerical simulations confirm the obtained analytical predictions. Second, the prediction of coexisting attractors is investigated by solving numerically the new nonlinear parametric ordinary differential equation via the fourth-order Runge–Kutta algorithm. …”
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Rapid and Accurate Acquisition of Equivalent Economic Scale in Flood Protection Area Based on Multi-source Big Data by ZHENG Yong, XU Zhangfan, ZHOU Xiaoxin, CAI Jihong, JI Hongxiang, QIN Yan

“…The results show that the accuracy of the population prediction model is 82%, and the accuracy of the GDP prediction model is 76%. …”
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A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring by Can WANG, Jianxin PENG, Xiaowen ZHANG

“…A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. …”
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D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
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A New State Estimation Method with Radar Measurement Missing by Hongjian Wang, Cun Li, Ying Wang, Qing Li, Xicheng Ban

“…The state transition model based on the historical observation data fit prediction is designed because the existing state estimation method can only use the system model prediction while the observation is missing. …”
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Artificial intelligence in drug development: reshaping the therapeutic landscape by Sarfaraz K. Niazi, Zamara Mariam

“…Over the past 30 years, machine learning, deep learning, and neural networks have revolutionized drug design, target identification, and clinical trial predictions. AI has boosted pharmaceutical R&D (research and development) by identifying new therapeutic targets, improving chemical designs, and predicting complicated protein structures. …”
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Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era by Gabriel James, Anietie Ekong, Aloysius Akpanobong, Enefiok Etuk, Saviour Inyang, Samuel Oyong, Ifeoma Ohaeri, Chikodili Orazulume, Peace Okafor

“…This study models a system that employs a Random Forest algorithm for prediction and classification, using age, gender, hours spent, and technological experience as parameters to categorize stress into high, moderate, and low levels. …”
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The Multiobjective Based Large-Scale Electric Vehicle Charging Behaviours Analysis by Yimin Zhou, Zhifei Li, Xinyu Wu

“…The residential traveling historical data of EVs are analyzed and fitted to predict their probability distribution, so that the models of the traveling patterns can be established. …”
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An Early Warning Method of Unbalanced Power Battery Capacity Attenuation Based on ARIMA Model by KANG Zuchao, XIONG Gang, WANG Wenming, ZHONG Xiongwu, ZHOU Yanhui

“…It is of important significance to study a timely warning algorithm of the unbalance of power battery capacity attenuation. …”
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Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks by Xiangwu Deng, Zhiping Peng, Delong Cui

“…Motivated by this, this article has studied soft-sensing technology for measuring diene yields. A diene yield prediction method based on a deep belief network algorithm network is proposed, and the regularity of historical diene yield data is fully explored by the method. …”
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A mode of action protein based approach that characterizes the relationships among most major diseases by Hongyi Zhou, Brice Edelman, Jeffrey Skolnick

“…Abstract Disease classification is important for understanding disease commonalities on both the phenotypical and molecular levels. Based on predicted disease mode of action (MOA) proteins, our algorithm PICMOA (Pan-disease Classification in Mode of Action Protein Space) classifies 3526 diseases across 20 clinically classified classifications (ICD10-CM major classifications). …”
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Matching of Observation Footprints in the FY-3G MWRI-RM Using BGI by Ke Chen, Bowen Cai, Wei Han, Zihao Suo

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A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images by B.E. Malyugin, S.N. Sakhnov, L.E. Axenova, K.D. Axenov, E.V. Kozina, V.V. Vronskaya, V.V. Myasnikova

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Federated Transfer Learning for IIoT Devices With Low Computing Power Based on Blockchain and Edge Computing by Peiying Zhang, Hao Sun, Jingyi Situ, Chunxiao Jiang, Dongliang Xie

“…The experimental results show that our algorithm can achieve high security and high training accuracy.…”
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Method for detecting cracks in retaining walls based on improved YOLOv5s by Yanhai Wang, Chenxin Guo, Guoyong Duan, Yuhao Zhang, Chao Yang, Huafeng Deng

“…The experimental results showed that the improved algorithm reduced the number of YOLOv5s parameters by 12% and increased the average precision by 1.5% compared to YOLOv5s using only the rotating prediction frames. …”
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Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment by Zhenzhen Cheng, Yifan Cheng, Bailing Miao, Tingting Fang, Shoufu Gong

“…This study proposes a multi-objective learning algorithm that leverages RGB-D multimodal information to estimate these quality metrics. …”
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