scholarly journals On the fuzziness of circulation types derived from the application of obliquely rotated principal component analysis to a T-mode climatic field

2021 ◽  
Author(s):  
Chibuike Chiedozie Ibebuchi
Keyword(s):  
Principal Component Analysis ◽  
Frequency Distribution ◽  
Principal Component ◽  
Component Analysis ◽  
Threshold Values ◽  
Study Region ◽  
Probability Of Occurrence ◽  
Circulation Types ◽  
Mean Shape ◽  
The Mean

Abstract This study examined the separability of circulation types (CTs) classified from the application of principal component analysis (PCA) to the T-mode matrix (variable is time series and observation is grid points) of a climatic field that explains atmospheric circulation; in addition to the uncertainty introduced on (i) the probability of occurrence, (ii) the mean shape of the CTs, (iii) the trend in the annual frequency of occurrence, (iv) the frequency distribution of the CTs, by using varying threshold values within the range of 0.2–0.35 to assign days to a given CT. The study region is Africa, south of the equator. Some large clusters were classified with most days in the analysis period assigned to them; these classes are interpreted as the dominant states of the atmosphere and generally, their existence results in the poor separability of the CTs since their features overlap with other CTs. Qualitatively, the choice of the threshold values within the defined range has little or no influence on the overall structure of the probability of occurrence of the CTs, the mean shape of the CTs, and the year-to-year variations in the annual occurrence of the CTs. However, it significantly impacts the frequency distribution of the CTs and the statistical significance of the trend in the annual occurrence of the CTs. Stringent threshold values within the defined range might benefit studies that aim to isolate days when specific CTs are most expressed and analyze their mechanism using composite maps, without focus on the frequency distribution and annual occurrence of the CTs. Overall, for the study region, lower threshold values within the defined range might be recommended since relatively, they do not tend to further constrain the probability of group membership, and equally seem to reveal the mechanisms that might be consistent when a given CT occurred regardless of the strength of its signal at a given time.

scholarly journals Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2342
Author(s):  
Corentin Martens ◽  
Olivier Debeir ◽  
Christine Decaestecker ◽  
Thierry Metens ◽  
Laetitia Lebrun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Added Value ◽  
Time Activity ◽  
Positron Emission ◽  
Activity Curve ◽  
The Mean ◽  
Mean Time ◽  
Met Pet

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

scholarly journals Hyperspectral data compression based upon the principal component analysis

2021 ◽  
Vol 45 (2) ◽  
pp. 235-244
Author(s):  
A.S. Minkin ◽  
O.V. Nikolaeva ◽  
A.A. Russkov
Keyword(s):  
Principal Component Analysis ◽  
Data Compression ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Gas Absorption ◽  
Absorption Bands ◽  
Spectral Bands ◽  
The Mean ◽  
Retrieval Errors

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

Relationships between sites used in the interstate wheat variety trials

1981 ◽  
Vol 32 (5) ◽  
pp. 691 ◽  
Author(s):  
PN Fox ◽  
AJ Rathjen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Variety ◽  
Principal Component ◽  
Component Analysis ◽  
Climatic Variables ◽  
Statistical Techniques ◽  
Strong Similarity ◽  
Variety Trials ◽  
Variety Trial ◽  
The Mean

A combination of statistical techniques was used to present useful information for breeders concerning the 197.5 Interstate Wheat Variety Trial. Grouping of sites was similar for all techniques, but was shown most clearly by the principal component analysis. Within three of the four groups of sites there was strong similarity between members. Some groups included widely geographically separated sites, which suggests that in the final stages of varietal testing, it might be possible to use widely separated sites as an alternative to testing over several years within a region. One group dominated the overall mean yields of the trial because it included more sites and because these sites were more uniform than sites within other groups. This domination, illustrated by regression and ranking techniques, may reduce the value to industry of the Interstate Wheat Variety Trials if these sites are not representative of extensive areas of wheat production. The differences in relative performances of varieties between sites could not be related either to differences in the mean yields at these sites or to edaphic or climatic variables. The need for such analysis of each year's data from the Interstate Wheat Variety Trials is stressed.

scholarly journals Assessment of body weight and morphological traits of two breed of grower pigs using principal component analysis

2021 ◽  
Vol 48 (5) ◽  
pp. 1-11
Author(s):  
P.O. Akporhuarho ◽  
O. Iriakpe
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Body Length ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Head Length ◽  
Large White ◽  
Poids Corporel ◽  
The Mean

The study aimed at explaining objectively the relationship between morphologic traits of two breeds of pigs (Large-white and Duroc) using principal component analysis to determine the body size of grower pigs of two different breeds with a view of identifying components that best define body conformation. Body weight and five biometric variables namely head length, body length, body girth, ham length and ear length. The descriptive statistics showed that the mean body weight of Large-white was 13.14kg while the body measurements were 24.61cm, 71.35cm, 65.12cm, 43.13cm and 21.94cm for head length, body length, body girth, ham length and ear length respectively at 5 – 24 weeks of age. The mean body weight of Duroc was 12.87kg while the body measurements were 23.70cm, 57.93cm, 47.93cm, 22.90cm, 19.26cm for head length, body length, body girth, ham length and ear length respectively. The coefficient of correlation ranges from 0.08-0.424 and 0.01-0.402 for Large-white and Duroc respectively. The association between and were the highest for Duroc, body length r=0.402 and Large-white, body girth 0.424. Two components were identified for Large-white while those of Duroc were three components. The ratios of variance were 53.55 and 71.07% for Large-white and Duroc, respectively. The first factor in each case accounted for the biggest percentage of the total variation, and was designated the general size, the other factors (indices of body shape) offer forms of variation independent of the general size. The principal component based regression models which were chosen for selecting animals for optimal balance accounted for 58 and 76% of the variation in the body weight for Large-white and Duroc respectively. The study concluded that the use of principal component analysis techniques tends to explore the interdependence in the original five parameters measured: head length, body length, body girth, ham length and ear length of Large-white and Duroc     L'étude explique objectivement la relation entre les traits morphologiques de deux races de porcs (gros blanc et de Duroc) à l'aide d'une analyse de composants principaux afin de déterminer la taille du corps des porcs de producteurs de deux races différentes en vue d'identifier les composants qui définissent le mieux la conformation corporelle. Poids corporel et cinq variables biométriques, nommément longueur de la tête, longueur du corps, circonférence du corps, longueur du jambon et longueur de l'oreille. Les statistiques descriptives ont montré que le poids corporel moyen de gros blanc était de 13,14 kg tandis que les mesures du corps étaient de 24,61 cm, 71,35 cm, 65,12 cm, 43,13 cm et 21,94 cm pour la longueur de la tête, la longueur du corps, la circonférence du corps, la longueur du jambon et la longueur de l'oreille respectivement à 5 - 24 semaines. Le poids corporel moyen de Duroc était de 12,87 kg tandis que les mesures du corps étaient de 23,70 cm, 57,93 cm, 47,93 cm, 22,90 cm, 19,26 cm pour la longueur de la tête, la longueur du corps, la circonférence du corps, la longueur du jambon et la longueur de l'oreille respectivement. Le coefficient de corrélation varie de 0,08 à 0,424 et de 0,01 à 0,402 pour les gros blancs et Duroc respectivement. L'association entre et étaient les plus élevées pour Duroc, la longueur du corps R = 0,402 et de gros blancs, la circonférence du corps 0,424. Deux composants ont été identifiés pour les gros blancs tandis que ceux de Duroc étaient trois composants. Les ratios de variance étaient respectivement de 53,55 et 71,07% pour les gros blancs et Duroc. Le premier facteur de chaque cas représentait le plus gros pourcentage de la variation totale et a été désigné la taille générale, les autres facteurs (indices de la forme du corps) offrent des formes de variation indépendantes de la taille générale. Les principaux modèles de régression basés sur les composants choisis pour sélectionner des animaux pour un solde optimal représentaient 58 et 76% de la variation du poids corporel pour les grands blancs et Duroc respectivement. L'étude a conclu que l'utilisation de techniques d'analyse des composants principaux a tendance à explorer l'interdépendance dans les cinq paramètres d'origines mesurées: longueur de la tête, longueur du corps, circonférence corporelle, longueur du jambon et longueur de l'oreille de grosse blanc et de Duroc

Post-traumatic headaches correlate with migraine symptoms in youth with concussion

Cephalalgia ◽  
2015 ◽  
Vol 36 (4) ◽  
pp. 309-316 ◽  
Author(s):  
Geoffrey L Heyer ◽  
Julie A Young ◽  
Sean C Rose ◽  
Kelly A McNally ◽  
Anastasia N Fischer
Keyword(s):  
Principal Component Analysis ◽  
Neck Pain ◽  
Retrospective Cohort ◽  
Pediatric Patients ◽  
Principal Component ◽  
Component Analysis ◽  
Full Cohort ◽  
Post Traumatic ◽  
The Mean ◽  
Promax Rotation

Objective The term “post-traumatic migraine” (PTM) has been used to describe post-traumatic headaches (PTHs) that have associated migraine features, but studies of this relationship are lacking. The objective of the present study was to determine whether PTH correlates strongly with migraine symptoms among youth with concussion. Methods Twenty-three symptoms were analyzed from a retrospective cohort of 1953 pediatric patients with concussion. A principal component analysis (PCA) with oblique Promax rotation was conducted to explore underlying symptom relationships in the full cohort and in subcohorts stratified by the presence ( n = 414) or absence ( n = 1526) of premorbid headache. Results The mean patient age was 14.1 years; 63% were male. Headache was the most common postconcussion symptom, acknowledged by 69.4% of patients. When considering the full cohort, the PCA demonstrated clustering of headache with photophobia, phonophobia, nausea, dizziness, and neck pain. Similar clustering was present among patients without premorbid headaches. Repeating the analysis in the patients with preconcussion headaches led to elimination of neck pain from the cluster. Conclusions PTH correlates strongly with other migraine symptoms among youth with concussion, regardless of premorbid headaches. This clustering of migraine symptoms supports the existence of PTM as a distinct clinical entity in some patients.

scholarly journals Karl Fischer Water Titration—Principal Component Analysis Approach on Bread Products

2020 ◽  
Vol 10 (18) ◽  
pp. 6518
Author(s):  
Gabriela Popescu ◽  
Isidora Radulov ◽  
Olimpia A. Iordănescu ◽  
Manuela D. Orboi ◽  
Laura Rădulescu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Water Content ◽  
Principal Component ◽  
Component Analysis ◽  
Reaction Rates ◽  
Multivariate Statistical ◽  
Karl Fischer ◽  
Core Samples ◽  
High Fraction ◽  
The Mean

(1) Background: The water content and the way of bonding in the food matrices, including bread, can be easily and simply evaluated by Karl Fischer titration (KFT). The goal was to identify the main KFT parameters that influence the similarity/dissimilarity of commercial bread products, using multivariate statistical analysis. (2) Methods: Various commercial bread samples were analyzed by volumetric KFT and the water content, parameters from titration process and KFT kinetics were used as input for principal component analysis (PCA). (3) Results: The KFT water content was in the range of 35.1–44.2% for core samples and 19.4–22.9% for shell samples. The storage and transportation conditions consistently influence the water content of bread. The type of water molecules can be evaluated by means of KFT water reaction rates. The mean water reaction rates up to 2 min are consistently higher for bread core samples, which indicates a high fraction of “surface” water. PCA reveals the similarity of core samples and various bread types, as well as dissimilarity between bread parts, mainly based on KFT kinetic parameters. (4) Conclusions: KFT kinetics can be a useful tool for a rapid and simple differentiations between various types of bread products.

scholarly journals A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis

2017 ◽  
Vol 33 (1) ◽  
pp. 15-41 ◽  
Author(s):  
Aida Calviño
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Synthetic Data ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Simple Method ◽  
Processing Times ◽  
The Mean ◽  
Mean Vector

Abstract In this article we propose a simple and versatile method for limiting disclosure in continuous microdata based on Principal Component Analysis (PCA). Instead of perturbing the original variables, we propose to alter the principal components, as they contain the same information but are uncorrelated, which permits working on each component separately, reducing processing times. The number and weight of the perturbed components determine the level of protection and distortion of the masked data. The method provides preservation of the mean vector and the variance-covariance matrix. Furthermore, depending on the technique chosen to perturb the principal components, the proposed method can provide masked, hybrid or fully synthetic data sets. Some examples of application and comparison with other methods previously proposed in the literature (in terms of disclosure risk and data utility) are also included.

scholarly journals Machine learning techniques to derive bioclimatic classifications for Colombia

2021 ◽  
Author(s):  
Richard Rios ◽  
Elkin A. Noguera-Urbano ◽  
Jairo Espinosa ◽  
Jose Manuael Ochoa
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Logistic Regression ◽  
Regression Models ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Techniques ◽  
Study Region ◽  
Logistic Regression Models ◽  
Learning Techniques

Bioclimatic classifications seek to divide a study region into geographic areas with similar bioclimatic characteristics. In this study we proposed two bioclimatic classifications for Colombia using machine learning techniques. We firstly characterized the precipitation space of Colombia using principal component analysis. Based on Lang classification, we then projected all background sites in the precipitation space with their corresponding categories. We sequentially fit logistic regression models to re-classify all background sites in the precipitation space with six redefined Lang categories. New categories were the used to define a new modified Lang and Caldas-Lang classifications.

scholarly journals Denoising Swarm Geomagnetic Virtual Observatories using principal component analysis

2020 ◽  
Author(s):  
Grace Cox ◽  
Will Brown ◽  
Ciaran Beggan ◽  
Magnus Hammer ◽  
Chris Finlay
Keyword(s):  
Principal Component Analysis ◽  
Local Time ◽  
Principal Component ◽  
Component Analysis ◽  
Small Subset ◽  
Time Sampling ◽  
Quiet Time ◽  
Data Set ◽  
The Mean ◽  
Virtual Observatories

<p>Geomagnetic Virtual Observatories (GVOs) use satellite measurements to provide estimates of the mean internally-generated magnetic field (MF) over a specified period (usually one or four months) at a fixed location in space, mimicking the mean values obtained at ground-based observatories (GOs). These permit secular variation (SV) estimates anywhere on the globe, thereby mitigating the effects of uneven GO coverage. Current GVO estimates suffer from two key contamination sources: first, local time sampling biases due to satellite orbital dynamics, and second, MFs generated in regions external to the Earth such as the magnetosphere and ionosphere. Current methods to alleviate this contamination have drawbacks:Averaging over four months removes the local time sampling bias at the cost of reduced temporal resolution</p><ol><li>Stringent data selection criteria such as night-time, quiet-time only data greatly reduce, but do not entirely remove, external MF contamination and result in a small subset (<5%) of the available data being used</li> <li>Removing model predictions for external MFs from the measurements also reduces noise, however such parameterisations cannot fully describe these physical systems and some of their signal remains in the data.</li> </ol><p>Here we present an alternative approach to denoising GVOs that uses principal component analysis (PCA). This method retains monthly resolution, uses all available vector satellite data and removes contamination from orbital effects and external MFs. We present an application of PCA, implemented in an open-source Python package called MagPySV, to new GVOs calculated as part of a Swarm DISC project.  The denoised data will be incorporated into a new GVO data set that will be available to the geomagnetism community as an official Swarm product.  </p>

Commodity Testing and Sub-Sample Homogeneity System for the Meatspec Analyser

1998 ◽  
Vol 6 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Anthony M.C. Davies ◽  
Ian A. Cowe ◽  
Robin P. Withey ◽  
Colin G. Eddison ◽  
Tom Fearn
Keyword(s):  
Principal Component Analysis ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Component Analysis ◽  
The Mean ◽  
Sample Homogeneity ◽  
Pork Samples

A system based on the use of principal component analysis has been devised for testing the identity and the homogeneity of the sample being analysed by a Meatspec analyser. The system checks that the spectral characteristics of the sample are consistent with the calibration in use and rejects any sub-sample which shows a deviation from the mean of all sub-samples greater than a previously defined specification. The use of principal component distances enabled the design of a system that can accommodate the development of new calibrations for different commodities or different analytes with little additional effort by the calibration developer. Results of the application of the system when analysing for fat, moisture and protein in beef and pork samples are presented for normal beef or pork samples and test samples containing deliberately generated non-homogenous characteristics.

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