The relationship between thermal conductivity and water content. by Hongqi Wang (2208238)

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Model prediction error analysis. by Hongqi Wang (2208238)

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The difference of BMD between 2019 and 2022 classify by grop of urinary camium levels measured in 2019. by Aroon La-up (14095691)

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Prevalence of osteoporosis classified by sex, and urinary cadmium level compare between 2019 and 2022. by Aroon La-up (14095691)

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Data. by Aroon La-up (14095691)

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Stratified analysis of the association between urinary cadmium and bone mineral density, by sex. by Aroon La-up (14095691)

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Association between baseline urinary cadmium and the three-year change in bone mineral density (ΔBMD). by Aroon La-up (14095691)

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Characteristics of the study participants for continuous variables. by Aroon La-up (14095691)

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Multiple bacterial subpopulations are present within splenic microcolonies. by Justin Greene (306625)

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Droplet-based microfluidics can be used to model clustered bacterial growth. by Justin Greene (306625)

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T3SS induction reduces gentamicin susceptibility, but S10 expression predicts survival. by Justin Greene (306625)

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S10 and T3SS reporter signals do not correlate in the absence of MgOx treatment. by Justin Greene (306625)

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Smoking Behaviors of the participants categorized by the respective year. by Jae Yeon Jeong (11979030)

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Drinking Behaviors of the male participants categorized by the respective year. by Jae Yeon Jeong (11979030)

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Drinking Behaviors of the participants categorized by the respective year. by Jae Yeon Jeong (11979030)

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Sleep and Mental health of the participants categorized by the respective year. by Jae Yeon Jeong (11979030)

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General characteristics of the participants categorized by the respective year. by Jae Yeon Jeong (11979030)

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Difference in drinking, smoking, and suicidal ideation by year. by Jae Yeon Jeong (11979030)

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Drinking Behaviors of the female participants categorized by the respective year. by Jae Yeon Jeong (11979030)

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A Locally Linear Dynamic Strategy for Manifold Learning. by Weifan Wang (4669081)

“…For 10-30% noise, where the Hebbian network employs a local linear transform, learning selectively increases signal direction alignment (blue) while simultaneously decreasing noise direction alignment (orange). …”