Navigation at baseline predicts worsening subjective concerns. by Gillian Coughlan (5111441)

“…<p>Red line represents a significant association between baseline VST central navigation performance and change on CCI-episodic memory concern in ε3ε4 carriers. …”

The previous history concerning hospitalization due to flu infection, receiving a flu vaccination, and frequency of vaccination (total number = 611, ever vaccinated = 267). by Norah Alhatim (12342405)

Risk ratios (RRs) at different WBGT threshold candidates for (a) different lag days for the entire population and (b) different sex and age groups on day 0, with the reference-adju... by Shih-Chun Candice Lung (12965438)

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Two-fold Blinder-Oaxaca decomposition of urban-rural disparity in 4⁺ANC and 1⁺ANC into the significant predictors. by Biplab Biswas (1984003)

Significance of FakeNET with k-fold validation. by Turki Aljrees (16715369)

Comparison of variations on particle count induced by control leaks and significant events during the whole procedures: The significant events generate lower levels of particles th... by Valentin Favier (4691665)

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Predicted probabilities of thinking SG will make a difference, being concerned about SG, wanting to invest in SG, reducing emissions, or both, by political ideology and familiarity... by Beatrice Magistro (14645040)

Change in per capita visits to breweries in each state from March to June in 2020. by Yingjie Hu (1579000)

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Predicted and observed wine sales for individual states. by Yingjie Hu (1579000)

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Feature importance of the numeric input variables in the optimized RF models for the estimation of spirits, wine, and beer sales. by Yingjie Hu (1579000)

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The architecture of the DNN model for estimating alcohol sales. by Yingjie Hu (1579000)

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Fig 7 - by Yingjie Hu (1579000)

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RMSE values of including and not including grocery store visits for alcohol sales prediction based on the RF model. by Yingjie Hu (1579000)

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Four categories of input features for the machine learning models. by Yingjie Hu (1579000)

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VIF values of the variables in three MLR models. by Yingjie Hu (1579000)

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Geographic differences for how the per capita visits to liquor stores changed in response to COVID-19. by Yingjie Hu (1579000)

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Change in per capita visits to drinking places in each state from March to June in 2020. by Yingjie Hu (1579000)

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Fig 15 - by Yingjie Hu (1579000)

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Fig 5 - by Yingjie Hu (1579000)

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Change in per capita visits to wineries in each state from March to June in 2020. by Yingjie Hu (1579000)

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