Table 2_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

A student sleeping while studying. by Mohammed B. A. Sarhan (19838131)

“…The third theme included beliefs and behaviours that either increased or decreased COVID-19 risk. The final theme addressed schools’ responses to COVID-19, including factors such as maintaining connections with schools, preventive measures and the transition to remote learning.…”

A student playing video games. by Mohammed B. A. Sarhan (19838131)

“…The third theme included beliefs and behaviours that either increased or decreased COVID-19 risk. The final theme addressed schools’ responses to COVID-19, including factors such as maintaining connections with schools, preventive measures and the transition to remote learning.…”

Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH₃NH₃PbBr₃ by Alejandro Garrote-Márquez (16796896)

“…This study explores the dynamics of hydrogen bonds in CH₃NH₃PbBr₃ across a temperature range from 70 to 350 K, using molecular dynamics simulations with machine-learning force fields. …”

Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH₃NH₃PbBr₃ by Alejandro Garrote-Márquez (16796896)

“…This study explores the dynamics of hydrogen bonds in CH₃NH₃PbBr₃ across a temperature range from 70 to 350 K, using molecular dynamics simulations with machine-learning force fields. …”

Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH₃NH₃PbBr₃ by Alejandro Garrote-Márquez (16796896)

“…This study explores the dynamics of hydrogen bonds in CH₃NH₃PbBr₃ across a temperature range from 70 to 350 K, using molecular dynamics simulations with machine-learning force fields. …”

Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH₃NH₃PbBr₃ by Alejandro Garrote-Márquez (16796896)

“…This study explores the dynamics of hydrogen bonds in CH₃NH₃PbBr₃ across a temperature range from 70 to 350 K, using molecular dynamics simulations with machine-learning force fields. …”

Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH₃NH₃PbBr₃ by Alejandro Garrote-Márquez (16796896)

“…This study explores the dynamics of hydrogen bonds in CH₃NH₃PbBr₃ across a temperature range from 70 to 350 K, using molecular dynamics simulations with machine-learning force fields. …”

Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH₃NH₃PbBr₃ by Alejandro Garrote-Márquez (16796896)

“…This study explores the dynamics of hydrogen bonds in CH₃NH₃PbBr₃ across a temperature range from 70 to 350 K, using molecular dynamics simulations with machine-learning force fields. …”

Demographic and self-injury history characteristics for the sample. by Brooke H. Nancekivell (19949839)

Bivariate correlations and descriptive statistics for study variables. by Brooke H. Nancekivell (19949839)

Simple block slopes on proportion of risky decisions based on task. by Brooke H. Nancekivell (19949839)

Data Sheet 1_Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation.docx by Xianzhi Deng (20548430)

“…In this study, we proposed a deep learning model with enhanced interpretability based on attention and vegetation indices calculation for global spectral feature mining to accurately estimate photosynthetic capacity. …”

Image 1_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain–computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.…”

Image 8_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain–computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.…”

Image 6_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain–computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.…”

Image 2_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain–computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.…”

Image 7_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain–computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.…”

Image 5_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain–computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.…”