Remote sensing imagery schedule. by Yuxin Wei (11510974)

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Spatial dry and wet side fitting of the TS-NDVI characteristics for different time periods in the Santun River Irrigation Area: (a) 2006/05; (b) 2007/04; (c) 2008/10; (d) 2009/08;... by Yuxin Wei (11510974)

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Age-standardized four major female-specific cancers mortality (2018-2022) among non-Hispanic White population across 51 U.S. states. by Ze Zhang (47230)

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Joinpoint trends in age-standardized incidence rates for the major female-specific cancers by race and ethnicity in the United States, 2000–2021. by Ze Zhang (47230)

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Insulin-free survival in patients with and without FHD. by Clemens Plattner (21567706)

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Event-free survival analysis of microvascular outcome. by Clemens Plattner (21567706)

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Characteristics of propensity score matched groups. by Clemens Plattner (21567706)

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Event-free survival analysis of macrovascular outcome. by Clemens Plattner (21567706)

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Clinical characteristics. by Clemens Plattner (21567706)

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Characteristics of propensity score matched groups. by Clemens Plattner (21567706)

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Trends of incident opioid users, L-TOT users, and L-TOT discontinuers between 2009 and 2013. by Qian Cai (112039)

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The yearly numerator, denominator and rate of incident opioid users, L-TOT users and L-TOT discontinuers from 2009 to 2019. by Qian Cai (112039)

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Incidence rate ratios of negative binominal regression models for yearly rates of incident opioid users, L-TOT users and L-TOT discontinuers during 2009–2013 and 2015–2019. by Qian Cai (112039)

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Demographic, comorbidity, concurrent drug, and opioid characteristics, among L-TOT users and L-TOT discontinuers between 2009–2019. by Qian Cai (112039)

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Original uncropped Western blot images. by Yaping Yuan (2734228)

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Testing set error. 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. …”

Internal structure of an LSTM cell. 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. …”

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. …”

The kernel density plot for data of each feature. 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. …”