The results of curve fitting analysis. by Hai-bo Ai (20790405)

Data Sheet 2_Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated f... by Rachid Tahzima (22106816)

“…<p>The functions of RNA-dependent RNA polymerases (RdRps) in RNA viruses are demonstrably modulated by native substrates of dynamic and interconvertible conformational ensembles. …”

Data Sheet 1_Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated f... by Rachid Tahzima (22106816)

“…<p>The functions of RNA-dependent RNA polymerases (RdRps) in RNA viruses are demonstrably modulated by native substrates of dynamic and interconvertible conformational ensembles. …”

Data Sheet 3_Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated f... by Rachid Tahzima (22106816)

“…<p>The functions of RNA-dependent RNA polymerases (RdRps) in RNA viruses are demonstrably modulated by native substrates of dynamic and interconvertible conformational ensembles. …”

Minimum edge cut sizes and node coverage of Leiden clusters (using CPM and modularity) on the Open Citations network. by Minhyuk Park (10783684)

The information of datasets used in this study. by Kaiyi Zhou (2553352)

“…</p><p>Methods</p><p>We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). …”

The workflow of the present study. by Kaiyi Zhou (2553352)

“…</p><p>Methods</p><p>We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). …”

Experiment condition. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Importance of the attributes of fan No.21. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Pseudo-code of MACOA. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Flow chart of the MACOA. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

The source of the fan datasets and details. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Importance of the attributes of fan No.15. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Framework of MACOA-IWKELM. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Structure chart of the IWKELM. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Flow chart of the IWKELM. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Experimental results for marginal sample sets. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

The source and details of the datasets. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Feature importance heat map of fan No.21. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”

Feature importance heat map of fan No.15. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”