Setting up for VFSS image. حسب Rie Asayama (693361)

الموضوعات:

Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH₃NH₃PbBr₃ حسب Alejandro Garrote-Márquez (16796896)

الموضوعات:

Interaction panel of Treat × Period effect (T × P) for body measurements as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein calves (n =... حسب Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for ether extract intake (g/d) as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein cal... حسب Shahryar Kargar (18880600)

Improved Accuracy and Reliability in Untargeted Analysis with LC-ESI-QTOF/MS¹ by Ensemble Averaging حسب Guillaume Laurent Erny (21000926)

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Improved Accuracy and Reliability in Untargeted Analysis with LC-ESI-QTOF/MS¹ by Ensemble Averaging حسب Guillaume Laurent Erny (21000926)

الموضوعات:

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... حسب Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for eating time (min/8-h) and sorting index (%) on sieve 2.36 mm and bottom pan as influenced by feeding pasteurized waste milk (... حسب Shahryar Kargar (18880600)

Mouse and human T-ALL samples show differential expression of genes involved in sensitivity to NAMPT inhibition. حسب Chelsea Vrana (21560473)

Major hyperparameters of RF-SVR. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. …"

Statins induced flatter neuronal somas. حسب Shuk C. Tsoi (21192243)

Top 10 significant functional annotations of up-regulated DEGs. حسب Meitner Cadena (22216261)

"…Functional annotations are ordered by decreasing significance, with color indicating significance according to the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…"

Top 10 significant functional annotations of down-regulated DEGs. حسب Meitner Cadena (22216261)

"…Functional annotations are ordered by decreasing significance, with color indicating significance level based on the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…"

Comparison of the cohesion ranges of different food categories under IDDSI levels. حسب Muxi Chen (9294270)

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Burn out assessment throughout demographic structure of the participant. حسب Nehal Mohamed Eisa (21148787)

الموضوعات:

Relationships between cohesiveness and IDDSI levels. حسب Muxi Chen (9294270)

الموضوعات: