Examples of positive, negative, and neutral tweets. Each tweet is assigned to only one of the categories based on its sentiment score calculated by VADER. by Yuqi Zhang (286958)

Subjects:

Monthly volume distribution and sentiment score of five topic groups. by Yuqi Zhang (286958)

Subjects:

Top 10 words and their associated weights for the 10 topics prior to manual grouping. by Yuqi Zhang (286958)

Subjects:

The sentiment score and number of tweets per month from 09/01/2018 to 01/31/2020. by Yuqi Zhang (286958)

Subjects:

The theme, top terms, and description of five topic groups. by Yuqi Zhang (286958)

Subjects:

Volume distribution and sentiment of positive, negative, and neutral tweets. by Yuqi Zhang (286958)

Subjects:

Parallel trend test. by Wei Wang (17594)

“…After the policy was implemented (with a positive relative time), the regression coefficient increased significantly and reached a peak at a relative time of 3, and then decreased slightly. …”

The trend of ASDR for hypertension-related AA in global age groups from 2022 to 2050 predicted using the Bayesian age-period-cohort (BAPC) model. by Guanghui Yu (423945)

Subjects:

Stability differences between condition 1 and other conditions across participants on the non-hemiparetic side (NHP) side. by Hyung Suk Yang (20442438)

Subjects:

Stability differences between condition 1 and other conditions across participants on the hemiparetic (HP) side. by Hyung Suk Yang (20442438)

Subjects:

Autophagy significantly increases RGCs loss. by Hongmei Qian (538647)

Subjects:

Autophagy significantly increases RGCs loss. by Hongmei Qian (538647)

Subjects:

Comparing admission rate of each ward before and during COVID-19 outbreak. by Mahsa Motiei (18275525)

Subjects:

Comparing diagnosis based on ICD-10 criteria in patients before and after COVID-19 outbreak. by Mahsa Motiei (18275525)

Subjects:

Comparing demographic and clinical characteristics of patients before and COVID-19 outbreak. by Mahsa Motiei (18275525)

Subjects:

Analysis of the Nugent score before (T=0) and after (T=1) the use of the vaginal gel. Number of participants and respective percentages of Nugent scores before and after the intervention. A statistically significant decrease in Nugent scores was observed after the intervention (p value =0.0047). by Adriana Bittencourt Campaner (21175462)

“…Number of participants and respective percentages of Nugent scores before and after the intervention. A statistically significant decrease in Nugent scores was observed after the intervention (p value =0.0047).…”

Water footprint at regional levels in China. by Jianqin Ma (20328414)

Subjects:

Characteristics of water footprint by sector at the regional level in China. by Jianqin Ma (20328414)

Subjects:

The distribution of water footprint levels in China. a, 2005. b, 2010. c, 2015. d, 2022. by Jianqin Ma (20328414)

Subjects:

Synergy and trade-offs of sectoral water use at the provincial level. by Jianqin Ma (20328414)

Subjects: