The information of datasets used in this study. حسب 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. حسب 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). …"

PLOS One data. حسب Ayşenur Eser (21713911)

"…<div><p>Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainment. Within this advancing field, the aim of this study was to test the feasibility of a functional near-infrared spectroscopy (fNIRS)-based BCI system for accurate prediction and objective identification of three fundamental emotional states that involved positive, negative and neutral conditions. …"

Surround Suppression mechanism. حسب Elnaz Nemati (21402730)

Experiment condition. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. …"

Data Sheet 1_Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease.pdf حسب Everest Castaneda (21758900)

"…Node and edge arrangements within gene networks impact comparisons between classes of disease, and connectivity metrics, such as those focused on degrees, betweenness, and centrality, do not yield sufficient discernment of disease network classification. …"

MD5 signatures from combined file regions حسب Paul A. Gagniuc (1818325)

Orbital-Based Correlated Electron–Nuclear Dynamics for Extended Systems with Exact Factorization حسب Daeho Han (10579081)

"…In this work, we introduce a practical orbital-based framework for simulating correlated electron–nuclear dynamics in extended systems within the exact factorization (XF) formalism. Building on our earlier derivation of time-dependent Kohn–Sham (TDKS) equations that merge real-time time-dependent density functional theory with XF, we apply the classical path approximation and incorporate pairwise XF-derived decoherence corrections in the Kohn–Sham basis. …"