The correlogram. by Bingtao Su (4740009)

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The environmental footprint of diabetes and prediabetes groups. by Bingtao Su (4740009)

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The food consumption quantity. by Bingtao Su (4740009)

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Deviation from the balanced dietary patterns outlined in the 2022 CDG. by Bingtao Su (4740009)

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Dataset. by Bingtao Su (4740009)

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PPI network analysis. by Du Leng (20421711)

Example of Group C scene material. by Chengmin Zhou (14554562)

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Conditional autonomous driving scenario complexity hierarchy. by Chengmin Zhou (14554562)

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Mean (SD) of fixation duration, fixation counts, and SAGAT accuracy according to scene complexity and autonomous driving information display type. by Chengmin Zhou (14554562)

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Point in time of the first visible deviation from the trail in each scenario. by Chengmin Zhou (14554562)

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Displays types of autonomous driving information: a) P3D display on the W-HUD; b) SR display on the W-HUD; c) display on the AR-HUD. by Chengmin Zhou (14554562)

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Experiment setting. by Chengmin Zhou (14554562)

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Supplementary raw data file of eye-tracking experiment for subjects. by Chengmin Zhou (14554562)

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The mean and standard error of SAGAT accuracy for type A, type B, and type C autonomous driving information display types under various scenario complexities. by Chengmin Zhou (14554562)

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A path analysis model of the relationship between various scenario elements and traffic scenarios. by Chengmin Zhou (14554562)

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Experimental flow. by Chengmin Zhou (14554562)

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Schematic flow chart of driving take-over reaction experiment. by Chengmin Zhou (14554562)

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Three accident scenarios: a) forward vehicle collision; b) The sudden cut-in of lateral vehicles ahead; c) adjacent vehicle occupies the lane. by Chengmin Zhou (14554562)

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Conditional autonomous driving scenario complexity factor perceptual weights. by Chengmin Zhou (14554562)

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The mean and standard error of take-over reaction time for type A, type B, and type C autonomous driving information display types under various scenario complexities. by Chengmin Zhou (14554562)

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