Large Decreases in Tailpipe Criteria Pollutant Emissions from the U.S. Light-Duty Vehicle Fleet Expected in 2020–2040 by Rachael H. Dolan (17918706)

“…Reductions in CO₂ emissions follow a similar pattern. Large decreases in criteria pollutant and CO₂ emissions from light duty vehicles lie ahead.…”

Large Decreases in Tailpipe Criteria Pollutant Emissions from the U.S. Light-Duty Vehicle Fleet Expected in 2020–2040 by Rachael H. Dolan (17918706)

“…Reductions in CO₂ emissions follow a similar pattern. Large decreases in criteria pollutant and CO₂ emissions from light duty vehicles lie ahead.…”

Largest twenty coefficients by absolute value as defined by logistic regression. by Roshan Karri (11060943)

DataSheet1_Remote sensing modeling of environmental influences on lake fish resources by machine learning: A practice in the largest freshwater lake of China.docx by Tan Chen (571832)

“…The results also showed that fish catches of aquatic species in Poyang Lake are more susceptible to water ecological variables than water quality refers to the model performance improvements by different input variable selections. In addition, a multi-dimension variable combination involving hydrometeorological conditions, water quality, and water ecological variables derived from remote sensing can maximally optimize the model performance of fish catch simulation (R increased by 21%, and RMSE decreased by 4,300 tons). …”

The numerical solution of model (1) for infected compartment vs. time with different scenarios of Phase 2 in the First Case: 1^<i>st</i> scenario, no lockdown is implemented; 2^<i>nd</i> scenario, no lockdown while <i>SD</i> increases; 3^<i>rd</i> scenario, lockdown increases; 4^<i>th</i> scenario, <i>SD</i> increases; 5^<i>th</i> scenario, lockdown decreases; 6^<i>th</i> scenario, <i>SD</i> decreases; 7^<i>th</i> scenario, lockdown and <i>SD</i> increases; 8^<i>th</i> scenario, lockdown decreases and <i>SD</i> increases; 9^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 1; 10^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 1 while <i>SD</i> increases; 11^<i>th</i> scenario, <i>ρ</i> = 0.75 in all Phase 2; 12^<i>th</i> scenario, <i>ρ</i> = 0.75 while <i>SD</i> increases. by Sara K. Al-Harbi (12418482)

“…<p>The numerical solution of model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0265779#pone.0265779.e002" target="_blank">(1)</a> for infected compartment vs. time with different scenarios of Phase 2 in the First Case: 1^<i>st</i> scenario, no lockdown is implemented; 2^<i>nd</i> scenario, no lockdown while <i>SD</i> increases; 3^<i>rd</i> scenario, lockdown increases; 4^<i>th</i> scenario, <i>SD</i> increases; 5^<i>th</i> scenario, lockdown decreases; 6^<i>th</i> scenario, <i>SD</i> decreases; 7^<i>th</i> scenario, lockdown and <i>SD</i> increases; 8^<i>th</i> scenario, lockdown decreases and <i>SD</i> increases; 9^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 1; 10^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 1 while <i>SD</i> increases; 11^<i>th</i> scenario, <i>ρ</i> = 0.75 in all Phase 2; 12^<i>th</i> scenario, <i>ρ</i> = 0.75 while <i>SD</i> increases.…”

The time series depicts cases in which the period length remains relatively constant despite increasing reaction rates in the circadian clock model (A: reference set case, B: incre... by Shingo Gibo (21762920)

“…<p>Additionally, synchronization regions for these cases are presented (C: reference set case, D: increased reaction rates case). …”

Absence of haptoglobin increase in the first 34 weeks of 2020 in the US cohort. by Thierry Poynard (38084)

<i>TP53</i> mutated cases in the ACC-TCGA dataset showed increased mRNA expression involved in the M phase. by Akira Ikeya (8707968)

Acute phase protein concentrations with increasing viral load. by Katie L. Edwards (6808013)

Subjects:

The COVID-19 pandemic affects seasonality with increasing cases of new-onset type 1 diabetes in children from the worldwide SWEET registry by Felix Reschke (13532003)

“…<p>  </p> <p><strong>Aims: </strong>To analyze whether the COVID-19 pandemic increased the number of cases or impacted seasonality of new-onset type 1 diabetes (T1D) in large pediatric diabetes centers globally. …”

The numerical solution of model (1) for infected compartment vs. time with different scenarios of Phase 3 in the First Case: 1^<i>st</i> scenario, no lockdown is implemented; 2^<i>nd</i> scenario, no lockdown while <i>SD</i> increases; 3^<i>rd</i> scenario, lockdown increases; 4^<i>th</i> scenario, <i>SD</i> increases; 5^<i>th</i> scenario, lockdown decreases; 6^<i>th</i> scenario, <i>SD</i> decreases; 7^<i>th</i> scenario, lockdown and <i>SD</i> increases; 8^<i>th</i> scenario, lockdown decreases and <i>SD</i> increases; 9^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 2; 10^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 2 while <i>SD</i> increases. by Sara K. Al-Harbi (12418482)

“…<p>The numerical solution of model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0265779#pone.0265779.e002" target="_blank">(1)</a> for infected compartment vs. time with different scenarios of Phase 3 in the First Case: 1^<i>st</i> scenario, no lockdown is implemented; 2^<i>nd</i> scenario, no lockdown while <i>SD</i> increases; 3^<i>rd</i> scenario, lockdown increases; 4^<i>th</i> scenario, <i>SD</i> increases; 5^<i>th</i> scenario, lockdown decreases; 6^<i>th</i> scenario, <i>SD</i> decreases; 7^<i>th</i> scenario, lockdown and <i>SD</i> increases; 8^<i>th</i> scenario, lockdown decreases and <i>SD</i> increases; 9^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 2; 10^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 2 while <i>SD</i> increases.…”

The numerical solution of model (1) for infected compartment vs. time with different scenarios of Phase 6 in the First Case: 1^<i>st</i> scenario, no lockdown is implemented; 2^<i>nd</i> scenario, no lockdown while <i>SD</i> increases; 3^<i>rd</i> scenario, lockdown increases; 4^<i>th</i> scenario, <i>SD</i> increases; 5^<i>th</i> scenario, lockdown decreases; 6^<i>th</i> scenario, <i>SD</i> decreases; 7^<i>th</i> scenario, lockdown and <i>SD</i> increases; 8^<i>th</i> scenario, lockdown decreases and <i>SD</i> increases; 9^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 5; 10^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 5 while <i>SD</i> increases. by Sara K. Al-Harbi (12418482)

“…<p>The numerical solution of model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0265779#pone.0265779.e002" target="_blank">(1)</a> for infected compartment vs. time with different scenarios of Phase 6 in the First Case: 1^<i>st</i> scenario, no lockdown is implemented; 2^<i>nd</i> scenario, no lockdown while <i>SD</i> increases; 3^<i>rd</i> scenario, lockdown increases; 4^<i>th</i> scenario, <i>SD</i> increases; 5^<i>th</i> scenario, lockdown decreases; 6^<i>th</i> scenario, <i>SD</i> decreases; 7^<i>th</i> scenario, lockdown and <i>SD</i> increases; 8^<i>th</i> scenario, lockdown decreases and <i>SD</i> increases; 9^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 5; 10^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 5 while <i>SD</i> increases.…”

Large direct shear test system. by Changbo Du (14014481)

Entropy analysis for the large data set including the GST dataset. by Aleksi Sutinen (10039145)

Study variables. by Amit Timilsina (15203538)

“…We employed a logit model on the geo-linked NDHS datasets to compute the marginal effect of forest cover on ARI, diarrhoea, and fever.</p><p>Results</p><p>From 2011 to 2016, the prevalence of fever increased from 18% to 20%, while the prevalence of diarrhoea decreased from 14% to 7%, and ARI prevalence decreased from 5% to 3%. …”

Descriptive statistics. by Amit Timilsina (15203538)

“…We employed a logit model on the geo-linked NDHS datasets to compute the marginal effect of forest cover on ARI, diarrhoea, and fever.</p><p>Results</p><p>From 2011 to 2016, the prevalence of fever increased from 18% to 20%, while the prevalence of diarrhoea decreased from 14% to 7%, and ARI prevalence decreased from 5% to 3%. …”

Analysing perfusion response. by Jakub Köry (17980273)

Estimated reduction in dengue incidence with increasing <i>w</i>Mel prevalence in <i>Aedes aegypti</i> populations in Niterói neighbourhoods–sensitivity analysis. by Sofia B. Pinto (11114099)

Network resistance. by Jakub Köry (17980273)

Chart summarizing the key components of the present study. by Jakub Köry (17980273)