Study area. by Yuxin Wei (11510974)

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Temporal trends of the TVDI in different seasons in the Santun River Irrigation Area. by Yuxin Wei (11510974)

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Trends of the temperature, precipitation, and TVDI in the Santun River Irrigation Area, Xinjiang, during 2005–2023. by Yuxin Wei (11510974)

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Trends in annual age-standardized rates for six major female-specific cancers incidence (2000-2021) by race and ethnicity in the USA. by Ze Zhang (47230)

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

“…</p><p>Results</p><p>WGCNA identified the turquoise module as significantly negatively correlated with ARDS. Five key overlapping genes (<i>GRIA1</i>, <i>OGN</i>, <i>COL14A1</i>, <i>FBLN1</i>, and <i>COL6A3</i>) were significantly downregulated in ARDS samples. …”

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. …”