PROTOCOL - Effects of Ashwagandha (Withania somnifera) on Physical Performance: Systematic Review and Bayesian Meta-Analysis by Diego A. Bonilla (9086201)

“…Selected publications that met all the requirements will go on to the next phase of data analysis and synthesis, where a table of their results and findings comparison will be developed and complemented by the authors considering the items mentioned before (see data items).…”

Table_1_Poor Nutritional Status and Dynapenia Are Highly Prevalent in Post-Acute COVID-19.docx by Francesco de Blasio (12763424)

“…Body composition (BC) and muscle function have also been related in such patients to poor disease outcomes.…”

DataSheet1_Global and non-Global slow oscillations differentiate in their depth profiles.docx by Sang-Cheol Seok (459143)

“…To test if the two SO types could be differentiated in their cortical-subcortical activity, we trained 30 machine learning classification algorithms to distinguish Global and non-Global SOs within each individual, and repeated this analysis for light (Stage 2, S2) and deep (slow wave sleep, SWS) NREM stages separately. …”

Skeletal_ Muscle_MRI_Registration by Lucia Fontana (9435020)

“…</p> <p>wxPython library was employed to develop the GUI, which is composed by two main windows – initial window and registration window – and 5 secondary frames for support functionalities. 3D images are presented with three views – axial, coronal and sagittal – with three sliders to adjust maximum value, minimum value, and gamma correction.…”

Dataset_Randomized Controlled Trial: Effect of isokinetic eccentric resistance training on strength, flexibility and muscle structure for the shoulder external rotator cuff muscles... by Sebastian Vetter (16670310)

“…Five days before and after the last training session, functional and structural parameters were examined for the dominant throwing shoulder using mDTI and an isokinetic dynamometer as described below. …”

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images by Shuyu Li (18401358)

“…</p><p dir="ltr">The dataset is freely accessible on IEEE DataPort, a data repository created by IEEE and can be found at the following URL: <a href="https://ieeexplore.ieee.org/document/10218394/algorithms?tabFilter=dataset" target="_blank">https://ieeexplore.ieee.org/document/10218394/algorithms?…”

a. How various statistical models account for modulation classification performance across the entire dataset. by Chris Scholes (3309477)

“…Numbered peaks indicate the significant peaks up to the lag which equals the modulation period, according to the peak picking algorithm (at <i>p</i> < .0001). Vertical lines show the peak-to-trough heights extracted for each significant peak, in order of decreasing value; <b>e.…”

Fortran & C++: design fractal-type optical diffractive element by I-Lin Ho (13768960)

“…</p> <p>(2) calculate diffraction fields for fractal and/or grid-matrix (binary) phase-holograms.</p> <p>(3) optimize the fractal and/or grid-matrix holograms for given target diffraction images, using annealing algorithms. …”

Table_2_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_1_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_3_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_6_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_5_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_4_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”

Table 1_Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents: a longitudinal study based on positive youth... by Jiahe Liu (9096353)

“…Our study aimed to examine the risk factors associated with IA among Chinese children and adolescents and leverage explainable machine learning (ML) algorithms to predict IA status at the time of assessment, based on Young’s Internet Addiction Test.…”

Supplementary file 1_CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration.docx by Jingya Xu (5572547)

“…Our multistep analytical pipeline included weighted gene coexpression network analysis (WGCNA) to map interacting genes, machine learning algorithms for robust classification, functional annotation via Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore biological pathways, CIBERSORT correlation analysis linking hub genes with immune cell states, transcriptional regulation profiling of key hubs, and single-cell sequencing to assess the functional relevance of these hubs.…”

PRODUCTIVE RESPONSES FROM BROILER CHICKENS RAISED IN DIFFERENT COMMERCIAL PRODUCTION SYSTEMS - PART I: FUZZY MODELING by Dian Lourençoni (5695376)

“…Hence, this work aimed to develop a fuzzy model for predicting the productive performance of broiler chickens as a function of the thermal environment during the various breeding phases. …”

Data_Sheet_2_Comprehensive analysis of the diagnostic and therapeutic value, immune infiltration, and drug treatment mechanisms of GTSE1 in lung adenocarcinoma.docx by Guanqiang Yan (18472116)

“…Objective<p>The aim of this investigation was to assess the diagnostic and therapeutic efficacy of G2 and S-phase expressed 1 (GTSE1) in lung adenocarcinoma (LUAD), while examining its impact on immune infiltration and drug treatment mechanisms.…”

Table 8_Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast cancer.docx by Dong Pan (1835707)

“…Additionally, DCE-MRI data from 101 patients in The Cancer Imaging Archive were used to extract radiomic features from early- and delayed-phase images. A STAT3 predictive model was developed using six machine learning algorithms. …”