Study on factors influencing sand migration in wellbores by Liu Shanshan

“…Based on the simulation results and dimensional analysis, a prediction model for the volume concentration of sand particles in the wellbore was established, which is convenient for engineering applications to quickly determine the flow state of the wellbore. …”
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Sports Big Data: Management, Analysis, Applications, and Challenges by Zhongbo Bai, Xiaomei Bai

“…Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. …”
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Bench Testing and Modeling Analysis of Optimum Shifting Point of HMCVT by Yu Qian, Zhun Cheng, Zhixiong Lu

“…To decrease shift impact on agricultural tractors with HMCVT (Hydromechanical Continuously Variable Transmission) and find the best working point, the following methods are proposed in this paper: firstly, conducting all-factor method and response surface method (RSM) to design and carry out bench test of shift impact; secondly, using the basic linear fitting, RSM, and stepwise regression analysis to establish comprehensive mathematical models and selecting models with high prediction accuracy; thirdly, putting forward the method of determining the weight of variance to match parameters of three quality evaluation indexes with inconsistent correlation; finally, establishing the total shift quality evaluation index and obtaining the optimal shift working point. …”
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Genetic polymorphism of hepatitis C virus and risk of hepatocellular carcinoma by S. N. Mammayev, A. M. Karimova

“…Early detection mutant Glu 70, Met 91 forms of HCV genotype 1b will provide prediction of HCC risk in CHC patients and to begin adequate etiological treatment for preventive maintenance of this dangerous complication intime.…”
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Investigations on tool wear behavior in turning AISI 304 stainless steel: An empirical and neural network modeling approach by Satish Chinchanikar, Mahendra Gadge

“…In this study, a higher prediction accuracy of 0.9975 was achieved with ANN model as compared to the empirical model. …”
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Research on Multistage Dynamic Trading Model Based on Gray Model and Auto-Regressive Integrated Moving Average Model by Zishan Xu, Chuanggeng Lin, Zhe Zhuang, Lidong Wang

“…Quantitative portfolio investment mainly depends on historical data analysis and market trend prediction to make appropriate decisions, which is an important mean to reduce risks and increase returns. …”
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MODELING AND SIMULATION OF COAL-ROCK RECOGNITION SYSTEM OF SHEARER BASED ON CYBER-PHYSICAL SYSTEM (MT) by ZHAO LiJuan, SI HuanHuan, SHI Lei, JIN Xin, ZHANG MeiChen

“…The results obtained through the simulation of the hybrid system more truly reflect the coal-rock identification process of the shearer, and the prediction accuracy is higher. The research provides a certain contribution to the modeling and simulation of the CPS hybrid system.…”
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Reservoir Flood Forecasting Based on Long-Short-Term Memory Neural Network by LUO Zhaolin, ZHANG Bo, MENG Qingkui, CHEN Wufen

“…Accurate flood forecasting is one of the main means to well perform flood control and drainage,and the long-short-term memory neural network (LSTM) has a strong ability to fit time series relationships,which thus is very suitable for simulating and forecasting the complex time series process of basin runoff generation and confluence.To explore the applicability of LSTM in the field of reservoir flood forecasting,this paper established an LSTM model according to different forecast periods in the Baipenzhu Basin and compared it with Xinanjiang model.The LSTM model uses the rainfall and water level data in the basin as input and adopts the water levels of the reservoir at different forecast periods as output.The calibration period is five years,and the verification period is one year.The results show that LSTM has high forecast accuracy when the forecast period is 1~6 h,and the forecast accuracy is the highest when the forecast period is 1h,reaching 0.991.As the forecast period increases,the accuracy of the LSTM model gradually decreases,but its forecast accuracy is higher than that of Xinanjiang model.In addition,reflecting the complexity of the neural network,the prediction period and the number of neurons in the hidden layer will affect not only the forecast accuracy but also the training speed of the model.It is proven that the LSTM model has high forecast accuracy and is of guiding significance to reservoir flood forecasting.…”
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Dynamical Models of Tuberculosis and Their Applications by Carlos Castillo-Chavez, Baojun Song

“…Theearliest mathematical models describing the TB dynamics appeared inthe 1960s and focused on the prediction and control strategies usingsimulation approaches. …”
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Discriminating the Geographical Origins of Chinese White Lotus Seeds by Near-Infrared Spectroscopy and Chemometrics by Lu Xu, Chen-Bo Cai, Yuan-Bin She, Li-Juan Chen

“…By the fusion of LS-SVM and OCPLS, the best prediction sensitivity and specificity were 0.900 and 0.973, respectively. …”
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Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example by Xie Renjun, Yuan Junliang, Wu Yi, Shu Mengcheng

“…First, input the image into the ResNet-50 feature extraction network to obtain the corresponding feature map, then use the RPN structure to generate the candidate frame, and project the candidate frame generated by the RPN to the feature map to obtain the corresponding feature matrix, and finally, through the ROI pooling layer, each of the feature matrix is scaled to a fixed-size feature map, and then the feature map is flattened through a series of fully connected layers to obtain the prediction result. ResNet-50 mainly solves the problem of network degradation and overfitting caused by deepening of the network layer when extracting the deep features of faults. …”
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Regional simulations of equatorial spread F driven with, and an analysis of, WAM-IPE electric fields by Aaron Kirchman, David Hysell, Tzu-Wei Fang

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Hybrid Time Series Method for Long-Time Temperature Series Analysis by Guangdong Huang, Jiahong Li

“…Moreover, the bigger decrease in RMSE of the DAX model than the EWA model represents that the proposed DAX model has significant superiority in combining models which proves that DAX has significant improvement in prediction as well.…”
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Rapid and Nondestructive Detection of Proline in Serum Using Near-Infrared Spectroscopy and Partial Least Squares by Kejing Zhu, Shengsheng Zhang, Keyu Yue, Yaming Zuo, Yulin Niu, Qing Wu, Wei Pan

“…The root mean square errors of prediction were 0.00111, 0.00150, 0.000770, and 0.000449, and the correlation coefficients (Rp) were 0.84, 0.67, 0.91, and 0.97, respectively. …”
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Changing Positive Academic Emotions of Art Students Utilizing Computer Information Technology Based on the Perspective of Teaching by Yihong Sun

“…It is concluded that the outcomes could provide a reference for the prediction of academic mood changes in art students.…”
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Plasticity and Awareness of Bodily Distortion by Mariella Pazzaglia, Marta Zantedeschi

“…Despite multiple sources, the immediate prediction, construction, and evaluation of one’s body are distorted. …”
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DTA measurements in the ternary Ag-Au-Ga system by Jendrzejczyk-Handzlik D., Handzlik P.

“…Finally, the experimental results were compared with the isopleths obtained from the prediction and calculation of the phase diagram which were done by using CALPHAD method. …”
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Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm Optimization by Naveen Kumar, Ashish Kumar, Deepak Sinwar, Monika Saini

“…The proposed methodology can be utilized to predict the availability of other process industries. …”
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Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data by Wenchen Shan, Xianqiang Wang, Yubo Jiao

“…Results indicate that the SVR exhibits a better reproduction and prediction capability than BPNN and MLR models for predicting the modal frequencies with respect to nonuniformly distributed temperatures. …”
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Rainfall-Altitude Relationship in Saudi Arabia by Khalid Al-Ahmadi, Sharaf Al-Ahmadi

“…The authors concluded that the use of a nonstationary local model such as GWR enabled them to provide a deeper explanation of relations between rainfall and the altitude of the terrain than a global model such as OLS in terms of spatial estimation and prediction.…”
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