Interaction panel of Treat × Period effect (T × P) for starter intake (g/d), body weight (kg), average daily gain (g/d), and feed efficiency (g/g) as influenced by feeding pasteuri... by Shahryar Kargar (18880600)

CTL response following combination therapy with NDV. by Hedieh Zargaran (21410233)

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

电针预处理通过心肌缺血损伤 棕色脂肪组织和 BMP3b/Smad1/5 通路小鼠 by Danying Qian (21730250)

Subjects: “…electroacupuncture significantly decreased…”

Box plots showing the taxa that were significantly different between thin and normal cows. by Bhuripit Saraphol (20855573)

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Green Hydrogen Economy: Scenarios versus Technologies by Hua Fan (495346)

“…Our findings reveal that operational scenarios can reduce LCOH more significantly than technological improvements alone. …”

Quantitative real-time PCR analysis of high-risk CRLs and co-expressed CRGs in GBM human cell lines and the GBM mouse model. by Yajia Chen (20685780)

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Expression of high-risk CRLs and co-expression CRGs in the GBM cuproptosis model. by Yajia Chen (20685780)

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Analysis of independent prognostic factors. by Yajia Chen (20685780)

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Evaluation of risk model. by Yajia Chen (20685780)

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Relationship of risk score with immune status. by Yajia Chen (20685780)

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Data processing and predictive model construction. by Yajia Chen (20685780)

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Drug concentrations for constructing a normal astrocyte cuproptosis model and expression of high-risk CRLs and co-expression CRGs in the human astrocyte cuproptosis model. by Yajia Chen (20685780)

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ELISA analysis revealed a significant decrease in SP (A & B) and CGRP (C & D) protein levels in the corneas and tear fluid of mice treated with 0.5% bupivacaine (BUP) every alternate day for 15 days (administered via subconjunctival injection (SCI) alone or in combination with topical (TOP) application) compared to PBS-treated controls. by Ananya Datta (19932144)

Microbiota compositions in percent relative abundance between healthy control and stroke patient individuals (the average microbiota between groups were displayed on the rightmost... by Duangnapa Roongpiboonsopit (12829198)

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Taxonomic composition of bacterial and fungal communities in <i>C. capitata</i> gut microbiota across control (C) and treated (T) samples. by Maria Cecilia Rasuk (4594303)

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