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14 results for “partial least squares”
Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models
<p>This is supporting data for the paper titled "Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models" by Leight et al. (submitted to JGR-P 11/23). Table S1 lists each spectrum used to train PLS models, its source, and which training datasets the spectrum was included in. Zip files contain the MIR and VNIR PLS model files. Model files are .asc, and can be run using the code at Ytsma, (2022), https://doi.org/10.5281/zenodo.7347345. </p>
Multivariate curve resolution -alternating least squares augmented with partial least squares baseline correction applied to mid-IR laser spectra resolves protein denaturation by reducing rotational ambiguity
<p>This record contains mid-IR spectra of a titration of beta-lactoglobulin with two detergents. Spectra are stored in the form of Matlab .mat files. More details on the data and data processing can be found in the associated publication.</p><ul><li>F_smooth: Surfactant calibration spectra</li><li>initial_estimate_4c: Intitial estimate of spectra used for MCR-ALS</li><li>protein_spectra_new: Protein spectra obtained by subtracting the background surfactant titration spectra (surftitrationnewwvcorr) from the protein</li><li>titration spectra (prtntitrationnewwvcorr)</li><li>tit_concat_new_mcr: Input for standalone MCR-ALS</li><li>true_conc_sds_nis_pr_cal: Concentrations of SDS and C12E8 as well as protein used in training data for PLS</li></ul>
Identification of glucose-6 phosphate dehydrogenase deficient patients by ATR-FTIR spectroscopy using partial least squares discriminant analysis in aqueous blood samples
<p>Dataset of associated MIR spectra collected in the titled study</p> <p>Baseline: spectrum of air</p> <p>Water: spectrum of deionised water</p> <p>P#_M/F_GD/P/C/T/U_WB/PL/RBC_D/AQ_R: sample spectrum with each section representing the following</p> <ul> <li>P#: patient number 1-30</li> <li>M (male) or F(Female)</li> <li>GD (G6PD deficient), P (Partially G6PD deficient), C & T & ET(Control), U(Unknown) <ul> <li>P10, P28, P29, & P30 were determined later to be P, C, C, P respectively</li> </ul> </li> <li>WB (whole blood), PL (plasma), RBC (red blood cells)</li> <li>D (dried sample spectrum), AQ (aqueous sample spectrum)</li> </ul>
Hyperspectral reflectance-based partial least squares regression models for predicting cotton leaf physiological traits
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Data of "Evaluation of feeding behaviour traits to predict efficiency traits in pigs using partial least square regression"
<p>This study explores the potential of using automatically recorded feeding behaviour as a proxy trait for protein and energy efficiency as well as fat gain in Swiss Large White pigs.</p> <p>behaviour.txt: A tab-delimited txt file of data used to investigate the relationship between feeding behaviour traits and protein efficiency in Swiss Large White pigs. Feeding behaviour traits taken were ‘daily feed intake’, ‘feeding rate’, ‘number of daily visits’, ‘duration at visits’, ‘feeding occupation’, and ‘feed intake per visit’.</p> <p>data_description_for_feeding_behaviour.xlsx: meta data for behaviour.txt with descriptions of variables</p>
Parkinson’s disease propagation using MRI biomarkers and partial least squares path modeling
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Data from: Compressor map regression modelling based on partial least squares
In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a Power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN). To demonstrate the potential capabilities of PLSO and PLSN for a typical interpolated prediction and extrapolated prediction, they are compared with two other classical data-driven modelling methods, namely, the look-up table and artificial neural network. PLSO and PLSN are also compared to each other. The results show that PLSO and PLSN have a better prediction performance than the look-up table and the artificial neural network, especially for the extrapolated prediction. At the same time, the computational time is also decreased sharply. Compared with PLSO, PLSN is characterized with higher prediction accuracy and shorter computational time than PLSO. It can be expected that PLSN can be time-saving and improve the accuracy of a thermodynamic model of a diesel engine.
Data from: A prediction model of compressor with variable geometry diffuser based on elliptic equation and Partial Least Squares
In order to fulfill more and more extensive intake air flow range of diesel engine, variable geometry compressor (VGC) is introduced into turbocharged diesel engine. However, due to the variable diffuser vanes angle (DVA), the prediction for the performance of VGC becomes more difficult than normal compressor. In the present study, a prediction model comprised of elliptical equation and PLS (Partial Least Squares) model was proposed to predict the performance of VGC. The speed lines of pressure ratio map and efficiency map with elliptical equation were fitted, and the coefficients of elliptical equation was introduced into PLS model to build the polynomial relationship between the coefficients and relative speed, DVA. And further, the maximal order of polynomical was detailed investigated to reduce the number of sub-coefficients and acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority in compressor performance prediction, the prediction results of this model were compared with those of look-up table and BPNN. The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than look-up table and BPNN under the same condition in the VGA performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.
Data from: Compressor map regression modelling based on partial least squares
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Data from: A prediction model of compressor with variable geometry diffuser based on elliptic equation and Partial Least Squares
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Partial Least Squares Regression Modelling for Glaucoma Detection
ClinicalTrials.gov study NCT03365245. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predicting Intraocular Lens Tilt Using OCT Measurements and Partial Least Squares Regression Modelling
ClinicalTrials.gov study NCT06305338. IPD Sharing: NO. Countries: 1. Publications: 0.
Predicting Lens Tilt Using Optical Coherence Tomography and Partial Least Squares Regression Modelling
ClinicalTrials.gov study NCT05915429. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: The mediating effect of perceived Coach's emotional support to Life satisfaction, Curiosity and Sports engagement: a Partial Least Square-Structural Equation Model
<p>This data set is from the study titled, "The mediating effect of perceived Coach’s emotional support to Life satisfaction, Curiosity and Sports engagement: a Partial Least Square-Structural Equation Model."</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.