Residual stress of various samples: (a) surface residual stress and (b) residual stress in the depth direction. by Huashen Guan (20454677)

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Surface displacements after shot peening in the finite element model for different projectile diameters: (a) 0.4 mm, (b) 0.6 mm, (c) 0.8 mm, (d) 1.0 mm, and (e) surface roughness P... by Huashen Guan (20454677)

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Material parameters of the sample and projectile [21]. by Huashen Guan (20454677)

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Residual stress results of the finite element model: (a) comparison between surface residual stress and test values; (b) distribution along the depth direction. by Huashen Guan (20454677)

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Surface roughness of various samples. by Huashen Guan (20454677)

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Microstructure of various samples: (a) UT, (b) SP1, (c) SP2, and (d) SP3. by Huashen Guan (20454677)

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Friction coefficients of various samples: (a) UT, (b) SP1, (c) SP2, and (d) SP3. by Huashen Guan (20454677)

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Prediction formulas for fitting different indicators. by Yi Wang (32470)

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The variables that significantly predicted litter clump weight in shelter cats. by Allison Andrukonis (11846404)

SEM images of improved soil under different freeze-thaw cycles. by Hongqi Wang (2208238)

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The relationship between the thermal conductivity of the improved soil and the freeze-thaw cycle. by Hongqi Wang (2208238)

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Cat Stress Score (CSS) was significantly predicted by intake type and days in the study. by Allison Andrukonis (11846404)

SARIMA predicts season components. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Analysis of raw data prediction results. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Analysis of STL-PCA prediction results. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Network pharmacology analysis predicted the targets of NS on atherosclerosis. by Yihua Wang (491766)

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Prediction effect of each model after STL. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

BWO-BiLSTM model prediction results. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”

Multivariate regression analysis to explain the decrease in visibility and palpability. by Miharu Matsumoto (21304390)

The most common machine learning methods to predict ν_s. by Fahd Saeed Alakbari (10701871)

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