Bidimensional High-Low Frequency (HF, LF) plane representation of the interaction among Phase and High-Low Clarity Groups_: In the Low Clarity (LC) group there was a larg... by David Vagni (8060432)

Differential gene sets associated with the transcriptional host response to SARS-CoV-2 infection across serially collected samples. by Vasanthi Avadhanula (259546)

Association between GSRS and CD8⁺ T cell infiltration levels in the TCGA-KIRC cohort, and the infiltration proportion of CD8⁺ T cells was evaluated using mult... by Yu Zhang (12946)

The constitutively active form of insulin receptor and knockdown of FoxO block high temperature-mediated body fat loss. by Tucker Hopkins (20790529)

High ambient temperatures reduce body fat and alter the expression of genes involved in lipid metabolism. by Tucker Hopkins (20790529)

Characteristics of HACE respondents by year. by David Henderson (4612354)

“…</p><p>Conclusion</p><p>Satisfaction overall, and with access to GP consultations, steadily declined between 2011/12 and 2021/22, with a more pronounced decrease following the COVID-19 pandemic, particularly among the most deprived and complex patients. …”

Bidimensional Low-High Frequency Normalized Difference and High-Low Frequency Power (LHFND, LHFP) plane representation of the interaction among Phase and High-Low Clarity Groups<su... by David Vagni (8060432)

Additional questions on vaccination. by Sara Saleh (6733619)

“…Chi-square test and linear regression were used to assess the association between KAP and sociodemographic factors with significance set at p < 0.05.</p><p>Results</p><p>Most mothers demonstrated good knowledge (86.3%) and positive attitudes (94.8%) toward vaccination. …”

The raw data file. by Sara Saleh (6733619)

“…Chi-square test and linear regression were used to assess the association between KAP and sociodemographic factors with significance set at p < 0.05.</p><p>Results</p><p>Most mothers demonstrated good knowledge (86.3%) and positive attitudes (94.8%) toward vaccination. …”

Impact of red blood cell-platelet ratio trajectories in the first four days before ICU admission on prognosis and clinical outcomes in the validation set. by Jie Zhao (49409)

Impact of red blood cell-platelet ratio trajectories on prognosis and clinical outcomes in the first four days before ICU admission in the training set. by Jie Zhao (49409)

A Comparison of Pediatric Prehospital Opioid Encounters and Social Vulnerability by Stephen Sandelich (19991783)

“…The analysis demonstrated that as socioeconomic status (SES) improves, the likelihood of opioid-related activations increases significantly supported by a significant negative linear trend (Estimate = −0.2971, SE = 0.1172, z = −2.54, <i>p</i> = 0.0112. …”

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. …”

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. …”

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. …”

SARIMA predicts season components. 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. …”

BWO-BiLSTM model 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. …”

Bi-LSTM architecture diagram. 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. …”