Frequency of postural sway in the four sensory conditions before and after napping. by Hui-Ya Chen (13919141)

Velocity of postural sway in the four sensory conditions before and after napping. by Hui-Ya Chen (13919141)

Average EEG activity (power in %) relative to the condition of “eyes open, firm surface, before nap” in the alpha band (8-12 Hz). by Hui-Ya Chen (13919141)

Average EEG activity (power in %) relative to the condition of “eyes open, firm surface, before nap” in the theta band (4-7 Hz). by Hui-Ya Chen (13919141)

Average EEG activity (power in %) relative to the condition of “eyes open, firm surface, before nap” in the gamma band (31-50 Hz). by Hui-Ya Chen (13919141)

Average EEG activity (power in %) relative to the condition of “eyes open, firm surface, before nap” in the beta band (13-21 Hz). by Hui-Ya Chen (13919141)

Average EEG activity (power in %) relative to the condition of “eyes open, firm surface, before nap” in the fast beta band (22-30 Hz). by Hui-Ya Chen (13919141)

Reproducibility of response time and brain measures. by Jan Willem Koten (17743224)

DEGs and enrichment analysis in HepG2 cells expressing HBx. by Jianhua Zhong (11351726)

Demographic information. by Silas Manning (22693338)

“…<div><p>Disorders of decreased sound tolerance such as misophonia and hyperacusis cause significant distress through strong negative emotional and physiological reactions to everyday sounds. …”

Absolute β convergence results. by Ke Liu (121889)

“…The probability of the population agglomeration level remaining stable was at least 53.85%, indicating that there was a “Matthew effect” in which the rich become richer and the poor become poorer. Through the convergence models of α and β, the analysis suggested that there was no significant α convergence between the population agglomeration level of the Xi’an metropolitan agglomeration and that of other metropolitan agglomerations. …”

Conditional β convergence results. by Ke Liu (121889)

“…The probability of the population agglomeration level remaining stable was at least 53.85%, indicating that there was a “Matthew effect” in which the rich become richer and the poor become poorer. Through the convergence models of α and β, the analysis suggested that there was no significant α convergence between the population agglomeration level of the Xi’an metropolitan agglomeration and that of other metropolitan agglomerations. …”

Markov transition probability matrix (k = 4). by Ke Liu (121889)

“…The probability of the population agglomeration level remaining stable was at least 53.85%, indicating that there was a “Matthew effect” in which the rich become richer and the poor become poorer. Through the convergence models of α and β, the analysis suggested that there was no significant α convergence between the population agglomeration level of the Xi’an metropolitan agglomeration and that of other metropolitan agglomerations. …”

Markov transition probability matrix (k = 4). by Ke Liu (121889)

“…The probability of the population agglomeration level remaining stable was at least 53.85%, indicating that there was a “Matthew effect” in which the rich become richer and the poor become poorer. Through the convergence models of α and β, the analysis suggested that there was no significant α convergence between the population agglomeration level of the Xi’an metropolitan agglomeration and that of other metropolitan agglomerations. …”

Regression results. by Ke Liu (121889)

“…The probability of the population agglomeration level remaining stable was at least 53.85%, indicating that there was a “Matthew effect” in which the rich become richer and the poor become poorer. Through the convergence models of α and β, the analysis suggested that there was no significant α convergence between the population agglomeration level of the Xi’an metropolitan agglomeration and that of other metropolitan agglomerations. …”

Marginal effect of additional hot day by region and respondent characteristics. by Stephan Dietrich (6979985)

General characteristics of study subjects. by Soo-Hee Hwang (17767519)

“…COVID-19 did not affect inpatient mortality (p = 0.9608), but in-hospital mortality decreased from 12% to 7% in the medical aid group.</p><p>Conclusion</p><p>Overall, we found that COVID-19 had an impact on admission rates of patients with AMI but did not have a significant impact on in-hospital mortality. …”

Boxplots of food security metrics by income equivalence indices. by Daniel Fobi (13607663)

Boxplots of food-security metrics by household asset categories. by Daniel Fobi (13607663)

Raw data. by Chenchen Luo (20649067)

“…However, as the oil shale residue content further increases, the hydration product gradually decreases, and the microstructure becomes poorer. Therefore, the optimal oil shale residue content is 20%. …”