Results of the linear models for the amplitudes (in <i>μ</i>V) of the components P1, N1, P2, and N400. by Elena Bolt (20396190)

Results of the generalized linear mixed model (GLMM) for correct responses in the speech recognition task. by Elena Bolt (20396190)

Speech material modulation spectrum and behavioral speech recognition task results. by Elena Bolt (20396190)

Responses to speech onset and target nouns in relation to cognitive decline and context. by Elena Bolt (20396190)

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. …”

Schematic diagram of the basic principles of 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. …”

Attitude towards NTDs in the study Area. by Uchechukwu M. Chukwuocha (6685790)

“…<div><p>Background</p><p>Neglected Tropical Diseases (NTDs) continue to significantly impact marginalized communities, contributing to high morbidity, stigma, and social exclusion. …”

Dataset of results. by Uchechukwu M. Chukwuocha (6685790)

“…<div><p>Background</p><p>Neglected Tropical Diseases (NTDs) continue to significantly impact marginalized communities, contributing to high morbidity, stigma, and social exclusion. …”

Respondents’ perception about the public artwork. by Uchechukwu M. Chukwuocha (6685790)

“…<div><p>Background</p><p>Neglected Tropical Diseases (NTDs) continue to significantly impact marginalized communities, contributing to high morbidity, stigma, and social exclusion. …”

Characterization of Developmentally Dynamic Promoters (DDPs). by Jiang Tan (260415)

Sex- and age-related changes in individual variance and behavioural repeatability. by Andréa L. Johnson (22505376)

Qualitative changes in the implant subgroup. by Adela Klezlova (22608008)

“…In the study group, the IOP was statistically significantly lower by 29% at the end of the follow-up compared to the preoperative measurements (<i><i>p</i></i> = 0.009). …”

Results of the colorimetric MTT test. by Adela Klezlova (22608008)

“…In the study group, the IOP was statistically significantly lower by 29% at the end of the follow-up compared to the preoperative measurements (<i><i>p</i></i> = 0.009). …”

IOP fluctuation. by Adela Klezlova (22608008)

“…In the study group, the IOP was statistically significantly lower by 29% at the end of the follow-up compared to the preoperative measurements (<i><i>p</i></i> = 0.009). …”

IOP fluctuation. by Adela Klezlova (22608008)

“…In the study group, the IOP was statistically significantly lower by 29% at the end of the follow-up compared to the preoperative measurements (<i><i>p</i></i> = 0.009). …”

Combined 3D-MRI showing the patellar cartilage superimposed on the femoral trochlear cartilage before and after OWHTO in all cases. by Yusuke Fuchioka (22806178)

Data. by Michael C. Payne (2664379)

“…Similarly, semiannual MDA lowered Mf prevalence from 23.3% (CI, 21.4-25.4%) to 0.3% (CI, 0.1-0.7%). …”