Local consumer escalation process (adapted from Local Health Network (CALHN) procedure 2019) [16]. by Lisa Thiele (6468056)

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SEM image of 10-micron iron oxide particles. by Yang Li (7082)

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Particle size distribution of iron oxide. by Yang Li (7082)

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Relevant parameters of test piece. by Yang Li (7082)

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Respondent characteristics. by Lisa Thiele (6468056)

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Graph of wear volume variation for test groups with different particle sizes at two concentrations. by Yang Li (7082)

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(a). Initial microscopic morphology map of test specimen surface. (b). Initial three-dimensional morphology map of test specimen surface. by Yang Li (7082)

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Graphical representation of surface roughness values on the specimen surface after tests conducted at different time intervals (a) Ra, Rq (b) Rz (c) Sa, Sq (d) Sz, Sp, Sv. by Yang Li (7082)

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Predicted confidence scores and 95% confidence intervals for the range of observed knowledge scores for barrier scores of 12, 23 and 36. by Lisa Thiele (6468056)

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Parameter diagram for the raised portions on the surface of the test specimen (a) Total volume (b) Total surface area (c) Average height (d) Cross-sectional area. by Yang Li (7082)

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Graph of friction coefficient variation for test groups with different particle sizes at two concentrations. by Yang Li (7082)

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Graph of surface roughness values of specimens after testing with different particle sizes at a concentration of 0.4 mg/mL (a) Ra, Rq (b) Rz (c) Sa, Sq (d) Sz, Sp, Sv. by Yang Li (7082)

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Graph of surface roughness values of specimens after testing with different particle sizes at a concentration of 4 mg/mL (a) Ra, Rq (b) Rz (c) Sa, Sq (d) Sz, Sp, Sv. by Yang Li (7082)

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Particle size distribution chart of 10-micron iron oxide particles. by Yang Li (7082)

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Graph of temperature rise variation for test groups with different particle sizes at two concentrations. by Yang Li (7082)

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Parameter diagram for pit features on the specimen surface (a) Volume (b) Surface Area (c) Depth (d) Cross-sectional area. by Yang Li (7082)

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IL-10 levels in tumor lysates post-treatment. by Hedieh Zargaran (21410233)

Example of sample data. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

Structure of BPNN. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”

The workflow of EGA-BPNN. by Xiying Wang (4859998)

“…The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”