Baseline characteristics of overall population. by Krystal Hunter (6820052)

Subjects:

Diagram of study population. by Krystal Hunter (6820052)

Subjects:

Baseline characteristics. by A. H. Ayesha Lavell (16734002)

“…No differences were found regarding patient care roles, prior SARS-CoV-2 infection, years of work experience, or hospital workdays per week. Mental health significantly declined during the COVID-19 pandemic, after which mental health symptoms returned to baseline as the burden of COVID-19 patients decreased and public measures were lifted. …”

De-identified participant survey data. by A. H. Ayesha Lavell (16734002)

“…No differences were found regarding patient care roles, prior SARS-CoV-2 infection, years of work experience, or hospital workdays per week. Mental health significantly declined during the COVID-19 pandemic, after which mental health symptoms returned to baseline as the burden of COVID-19 patients decreased and public measures were lifted. …”

Example of sample data. by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

Structure of BPNN. by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

The workflow of EGA-BPNN. by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

S1 Data - by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

Algorithm flow of the GA-BPNN model. by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

Schematic presentation <i>M</i>. <i>bovis</i> PG45 5′ nucleotidase and phylogenetic tree of its homologs. by Aga E. Gelgie (17661720)

Subjects:

Change in Watt’s Connectedness Scale, Subscales by Treatment Group. by Benjamin R. Lewis (22279166)

Subjects:

Composition of different formulations with their responses in I optimal design for optimization of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)

Subjects:

The composition and validation of the optimized formula with its predicted responses according to I Optimal Design. by Randa Mohammed Zaki (21637175)

Subjects:

Primary and secondary efficacy end points (intention-to-treat population). by Benjamin R. Lewis (22279166)

Subjects:

Demographic and clinical characteristics of participants at baseline. by Benjamin R. Lewis (22279166)

Subjects:

<i>M</i>. <i>bovis</i> 0690::Tn mutant failed to elicit the recruitment of neutrophils into milk spaces. by Aga E. Gelgie (17661720)

Subjects:

Data supporting the findings of this study. by Aga E. Gelgie (17661720)

Subjects:

Change in QIDS-SR-16 and MBI-HSS-MP by Treatment Group. by Benjamin R. Lewis (22279166)

Subjects:

I Optimal design for optimization of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)

Subjects:

ANOVA for I optimal design of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)

Subjects: