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. …”

Accuracy test results. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Experiment environment and parameter. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Test results for NME and FR. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

DARTS algorithm process. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Comparison result of memory usage. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

LKA model structure. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Test results on different datasets. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Comparison result of memory usage. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Residual configuration. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Test results for P, R, F1, and OA. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Schematic diagram of DARTS-VAN model structure. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

DARTS-VAN model unit search process. by Yuxuan Ji (13991895)

“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”

Prediction performance of each model. by Ahmed S. Alkhalifah (20721713)

“…Nonsurvivors had a significantly higher time-weighted average MP (TWA-MP) than survivors. …”

Patient inclusion flow chart. by Ahmed S. Alkhalifah (20721713)

“…Nonsurvivors had a significantly higher time-weighted average MP (TWA-MP) than survivors. …”

Proportion of large and small EEFs over time in the different culture conditions and for each biological replicate. by Erica M. Pasini (3146643)

Making Cells as a “Nirvana Phoenix”: Precise Coupling of Precursors Prior to ROS Bursts for Intracellular Synthesis of Quantum Dots by Juan Kong (2230867)

“…Such a comprehensive control strategy can inhibit the production of cytotoxic Se species and ROS bursts, significantly increasing the cell viability from 4 to 80% and enhancing the fluorescence of intracellularly synthesized Ag₂Se QDs by over 8.7 times. …”

Making Cells as a “Nirvana Phoenix”: Precise Coupling of Precursors Prior to ROS Bursts for Intracellular Synthesis of Quantum Dots by Juan Kong (2230867)

“…Such a comprehensive control strategy can inhibit the production of cytotoxic Se species and ROS bursts, significantly increasing the cell viability from 4 to 80% and enhancing the fluorescence of intracellularly synthesized Ag₂Se QDs by over 8.7 times. …”

Making Cells as a “Nirvana Phoenix”: Precise Coupling of Precursors Prior to ROS Bursts for Intracellular Synthesis of Quantum Dots by Juan Kong (2230867)

“…Such a comprehensive control strategy can inhibit the production of cytotoxic Se species and ROS bursts, significantly increasing the cell viability from 4 to 80% and enhancing the fluorescence of intracellularly synthesized Ag₂Se QDs by over 8.7 times. …”