Inactivation of MnuA and 5′-NT reduced bacterial fitness and virulence of <i>M</i>. <i>bovis</i> PG45 in mammary glands. by Aga E. Gelgie (17661720)

Survival curve of ART Treatment outcomes. by Ekerette Emmanuel Udoh (7326194)

“…LTFU was shown to be low, decreasing significantly from 20.37 per 100PY in 2020 to 0.69 per 100PY in 2022. …”

Baseline characteristics of the participants. by Junichi Kushioka (12236447)

“…This significantly improves patient outcomes and marks a crucial advancement in digital health, addressing key challenges in management and care of LS.…”

Internal validation by cross-validation. by Junichi Kushioka (12236447)

“…This significantly improves patient outcomes and marks a crucial advancement in digital health, addressing key challenges in management and care of LS.…”

GrowSafe™ system and steer eating. by M. Jordana Rivero (14610947)

“…<div><p>Heat stress is a significant challenge in tropical beef production systems, affecting feed intake, water intake, and overall animal welfare. …”

Intergado™ System and steer drinking water. by M. Jordana Rivero (14610947)

“…<div><p>Heat stress is a significant challenge in tropical beef production systems, affecting feed intake, water intake, and overall animal welfare. …”

Structure used to provide shade. by M. Jordana Rivero (14610947)

“…<div><p>Heat stress is a significant challenge in tropical beef production systems, affecting feed intake, water intake, and overall animal welfare. …”

Green Hydrogen Economy: Scenarios versus Technologies by Hua Fan (495346)

“…Our findings reveal that operational scenarios can reduce LCOH more significantly than technological improvements alone. …”

Higher sampling flow rates result in higher viral recovery. by Eloise Parry-Nweye (7548998)

Jitter plot illustrating the distribution of three racial groups across varying rural percentages. by Michael Bradfield (3540005)

TAD boundary stability for different noise amplitudes for all chromosomes. by Lucas Hedström (21688891)

Ranking position of feature importance for each cluster in the post-vaccination period. by Jéssica Villar (21453137)

Ranking position of feature importance for each cluster in the pre-vaccination period. by Jéssica Villar (21453137)

SHAP Values per cluster in the pre-vaccination period show each feature’s importance on COVID-19 mortality. by Jéssica Villar (21453137)

SHAP Values per cluster in the post-vaccination period show each feature’s importance on COVID-19 mortality. by Jéssica Villar (21453137)

Participant characteristics by village. by Tyler M. Barrett (20846174)

“…These results suggest that climate change is a significant challenge for farmers in northeast Madagascar, yet adaptation is limited by existing socioeconomic inequalities involving access to market activities and gender.…”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”