scholarly journals Multispectral Analysis of IRAS Sky Flux Maps

1990 ◽  
Vol 139 ◽  
pp. 214-215
Author(s):  
L. G. Balázs ◽  
M. Kun ◽  
V. Tóth
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Clustering Algorithm ◽  
Cosmic Dust ◽  
Training Set ◽  
Single Pass ◽  
Components Analysis ◽  
Multispectral Analysis ◽  
Main Components

We have tested the performance of principal components analysis and a single-pass clustering algorithm to identify different components of the cosmic dust. Applying these techniques on a training set of 2500 points extracted from the PL51 IRAS maps we recognized two main components with temperatures of 180 K and 28 K.

scholarly journals Raman investigation of corrosion products on Roman copper-based artefacts

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 129
Author(s):  
Leila Es Sebar ◽  
Leonardo Iannucci ◽  
Yuval Goren ◽  
Peter Fabian ◽  
Emma Angelini ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Roman Period ◽  
Corrosion Products ◽  
Beer Sheva ◽  
Chemometric Technique ◽  
Components Analysis ◽  
Main Components

<p class="Abstract">This paper illustrates a case study related to the characterisation of corrosion products present on recently excavated artefacts. The archaeological findings, from the Rakafot 54 site (Beer-Sheva, Israel), consist of 23 coins and a pendant, all dating back to the Roman period. Raman spectroscopy was used to identify the corrosion products that compose the patina covering the objects. To facilitate and support their identification, spectra were then processed using principal components analysis. This chemometric technique allowed the identification of two main compounds, classified as atacamite and clinoatacamite, which formed the main components of the patinas. The results of this investigation can help in assessing the conservation state of artefacts and defining the correct restoration strategy.</p>

scholarly journals Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks

The Analyst ◽  
2016 ◽  
Vol 141 (1) ◽  
pp. 90-95 ◽  
Author(s):  
S. Van Nuffel ◽  
C. Parmenter ◽  
D. J. Scurr ◽  
N. A. Russell ◽  
M. Zelzer
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Method Validation ◽  
Cell Cultures ◽  
Principal Components ◽  
Neuronal Cell ◽  
Training Set ◽  
Neuronal Cell Cultures ◽  
Tof Sims ◽  
Components Analysis

Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures.

scholarly journals Measuring sleep in young children and their mothers: Identifying actigraphic sleep composites

2019 ◽  
Vol 43 (3) ◽  
pp. 278-285 ◽  
Author(s):  
Angela D. Staples ◽  
John E. Bates ◽  
Isaac T. Petersen ◽  
Maureen E. McQuillan ◽  
Caroline Hoyniak
Keyword(s):  
Young Children ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Sleep Latency ◽  
Community Sample ◽  
Daytime Sleep ◽  
Components Analysis ◽  
Main Components

The present study considered multiple aspects of sleep in a community sample of young children (at ages 30, 36, and 42 months) and their mothers, using both diaries and actigraphy. Through principal components analysis, 17 of 20 commonly used actigraphy variables were reduced to four main components whose variables formed composites of: Activity, night-to-night Variability, Timing, and Duration. Sleep latency and daytime sleep variables remained separate from the composites. The same components were identified at each age, and for both children and mothers. Furthermore, the sleep composites derived from the components showed greater cross-age stability than individual actigraphy variables. Finally, child and mother sleep composites were related concurrently and longitudinally. These findings demonstrate a systematic and efficient way of summarizing child and mother sleep with actigraphy variables.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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