Study Data. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Study outcomes. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Number of visits per clinic type, 2013-2015. by Jacob Dreiher (14856863)

“…During PE, statistically significant longer waiting times were found for surgery (+1.0 day) and imaging (+1.1 days), while a 2.4 days decrease was noted in pediatrics, controlled for age, sex, ethnicity and the daily number of visits. …”

Patient characteristics. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Treatment with vitamin D3 reduced the viability of cancer cell lines: <i>1A & 1B.</i> by Vidya G. Bettada (22208808)

“…Mouse EAC cells showed a decrease in cell viability starting from 250 µM at 24h of treatment. …”

Vitamin D3, but not the Cisplatin, could moderately reduce STZ-induced hyperglycemia in mice (a) Schematic representation of experimental protocol followed in the study: After accl... by Vidya G. Bettada (22208808)

“…(c) Vitamin D3 and the positive control Cisplatin differently modulated FBG in hyperglycaemic mice: Intraperitoneal administration of vitamin D3 very minimally decreased FBG compared to vehicle control at the end of the study. …”

Opioid consumption data. by Wali U. Pirzada (22278071)

“…No significant difference was observed in the mean percentage of prescribed MMEs leftover between the two cohorts (Orthopaedic 47% vs. …”

Prescription data. by Wali U. Pirzada (22278071)

“…No significant difference was observed in the mean percentage of prescribed MMEs leftover between the two cohorts (Orthopaedic 47% vs. …”

Refill rate by surgical specialty. by Wali U. Pirzada (22278071)

“…No significant difference was observed in the mean percentage of prescribed MMEs leftover between the two cohorts (Orthopaedic 47% vs. …”

Noncontinuous data on opioid use. by Wali U. Pirzada (22278071)

“…No significant difference was observed in the mean percentage of prescribed MMEs leftover between the two cohorts (Orthopaedic 47% vs. …”

Comparative expression between platelets and leukocytes in levels of transcripts related to thrombosis, coagulation and platelet-leukocyte interactions. by Heather Learnard (22075952)

PRR expression in platelets and leukocytes and their changes with SARS-CoV-2 infection. by Heather Learnard (22075952)

Testing set error. by Xiangjuan Liu (618000)

“…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Internal structure of an LSTM cell. by Xiangjuan Liu (618000)

“…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

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

“…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Estimated results of the mediation effect. by Getachew Magnar Kitila (19935139)

“…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …”

The kernel density plot for data of each feature. by Xiangjuan Liu (618000)

“…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Panel unit root test result. by Getachew Magnar Kitila (19935139)

“…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …”

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

“…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Flowchart of the STL. by Xiangjuan Liu (618000)

“…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”