scholarly journals Competition Strategies of Metritic and Healthy Transition Cows

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 854
Author(s):  
Borbala Foris ◽  
Marina A. G. von Keyserlingk ◽  
Daniel M. Weary
Keyword(s):  
Principal Component Analysis ◽  
Health Status ◽  
Individual Variation ◽  
Agonistic Behavior ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Social Competition ◽  
Transition Cows ◽  
Strategy Change

Our study aimed to characterize social competition strategies in transition cows, and determine how these varied with health status. We retrospectively followed 52 cows during 3 periods (PRE: d −6 to −1 prepartum, POST1: d 1 to 3 postpartum, POST2: d 4 to 6 postpartum). Cows diagnosed with metritis on d 6 postpartum (n = 26) were match paired with healthy cows (n = 26). Measures of agonistic behavior (i.e., replacements at the feeder) and feeding synchrony were determined by an algorithm based on electronic feed bin data, and used to calculate competition strategies via principal component analysis. We found consistent strategies, defined by two components (asynchrony and competitiveness; explaining 82% of the total variance). We observed no differences in strategies when comparing healthy and metritic cows, but metritic cows tended to change their strategies more between PRE and POST1, and between POST1 and POST2, indicating that strategies change in association with parturition and metritis. We conclude that cows show individual variation in competition strategies, and that automated measures of strategy change may help in detecting metritis.

Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

scholarly journals Spatial and temporal variation of total electron content as revealed by principal component analysis

2016 ◽  
Vol 34 (12) ◽  
pp. 1109-1117 ◽  
Author(s):  
Elsayed R. Talaat ◽  
Xun Zhu
Keyword(s):  
Principal Component Analysis ◽  
Total Electron Content ◽  
General Circulation ◽  
Circulation Model ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Spatial And Temporal Variation ◽  
Electron Content ◽  
Total Electron

Abstract. Eleven years of global total electron content (TEC) data derived from the assimilated thermosphere–ionosphere electrodynamics general circulation model are analyzed using empirical orthogonal function (EOF) decomposition and the corresponding principal component analysis (PCA) technique. For the daily averaged TEC field, the first EOF explains more than 89 % and the first four EOFs explain more than 98 % of the total variance of the TEC field, indicating an effective data compression and clear separation of different physical processes. The effectiveness of the PCA technique for TEC is nearly insensitive to the horizontal resolution and the length of the data records. When the PCA is applied to global TEC including local-time variations, the rich spatial and temporal variations of field can be represented by the first three EOFs that explain 88 % of the total variance. The spectral analysis of the time series of the EOF coefficients reveals how different mechanisms such as solar flux variation, change in the orbital declination, nonlinear mode coupling and geomagnetic activity are separated and expressed in different EOFs. This work demonstrates the usefulness of using the PCA technique to assimilate and monitor the global TEC field.

scholarly journals Data Mining to Select Relevant Variables Influencing External and Internal Workload of Elite Blind 5-a-Side Soccer

2021 ◽  
Vol 18 (6) ◽  
pp. 3155
Author(s):  
José M. Gamonales ◽  
Kiko León ◽  
Daniel Rojas-Valverde ◽  
Braulio Sánchez-Ureña ◽  
Jesús Muñoz-Jiménez
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Technique ◽  
Total Variance ◽  
Data Mining Technique ◽  
Internal Load ◽  
Relevant Variables ◽  
Data Reduction Method

(1) Background: Data mining has turned essential when exploring a large amount of information in performance analysis in sports. This study aimed to select the most relevant variables influencing the external and internal load in top-elite 5-a-side soccer (Sa5) using a data mining model considering some contextual indicators as match result, body mass index (BMI), scoring rate and age. (2) Methods: A total of 50 top-elite visually impaired soccer players (age 30.86 ± 11.2 years, weight 77.64 ± 9.78 kg, height 178.48 ± 7.9 cm) were monitored using magnetic, angular and rate gyroscope (MARG) sensors during an international Sa5 congested fixture tournament.; (3) Results: Fifteen external and internal load variables were extracted from a total of 49 time-related and peak variables derived from the MARG sensors using a principal component analysis as the most used data mining technique. The principal component analysis (PCA) model explained 80% of total variance using seven principal components. In contrast, the first principal component of the match was defined by jumps, take off by 24.8% of the total variance. Blind players usually performed a higher number of accelerations per min when losing a match. Scoring players execute higher DistanceExplosive and Distance21–24 km/h. And the younger players presented higher HRAVG and AccMax. (4) Conclusions: The influence of some contextual variables on external and internal load during top elite Sa5 official matches should be addressed by coaches, athletes, and medical staff. The PCA seems to be a useful statistical technique to select those relevant variables representing the team’s external and internal load. Besides, as a data reduction method, PCA allows administrating individualized training loads considering those relevant variables defining team load behavior.

scholarly journals Principal Component Analysis of Categorized Polytomous Variable-Based Classification of Diabetes and Other Chronic Diseases

2019 ◽  
Vol 16 (19) ◽  
pp. 3593 ◽  
Author(s):  
Musa Uba Muhammad ◽  
Ren Jiadong ◽  
Noman Sohail Muhammad ◽  
Munawar Hussain ◽  
Irshad Muhammad
Keyword(s):  
Diabetes Mellitus ◽  
Principal Component Analysis ◽  
Chronic Diseases ◽  
Economic Status ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Classification Techniques ◽  
Polytomous Variable

A chronic disease diabetes mellitus is assuming pestilence proportion worldwide. Therefore prevalence is important in all aspects. Researchers have introduced various methods, but still, the improvement is a need for classification techniques. This paper considers data mining approach and principal component analysis (PCA) techniques, on a single platform to approaches on the polytomous variable-based classification of diabetes mellitus and some selected chronic diseases. The PCA result shows eigenvalues, and the total variance is explained for the principal components (PCs) solution. Total of twelve attributes was analyzed with the intention to precise the pattern of the correlation with minimum factors as possible. Usually, factors with large eigenvalues retained. The first five components have their eigenvalues large enough to be retained. Their variances are 18.9%, 14.0%, 13.6%, 10.3%, and 8.6%, respectively. That explains ~65.3% of the total variance. We further applied K-means clustering with the aid of the first two PCs. As well, correlation results between diabetes mellitus and selected diseases; it has revealed that diabetes patients are more likely to have kidney and hypertension. Therefore, the study validates the proposed polytomous method for classification techniques. Such a study is important in better assessment on low socio-economic status zone regions around the globe.

scholarly journals Evaluation of structure in Pinus halepensis M. stands in North Greece

1997 ◽  
Vol 62 ◽  
Author(s):  
D. Karamanolis ◽  
G. Stamatelos ◽  
P. Gkanatsas
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Stand Structure ◽  
Pinus Halepensis ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Multivariate Technique ◽  
New Variables

The  Principal Component Analysis (P.C.A.) is a multivariate technique useful in  the description and    the revealing of relations between variables in a great number of data. The  structure of Pinus    halepensis forests by P.C.A. was studied. The  method was applied in silvicultural data of Pinus    halepensis forests in Kassandra Peninsula.  Sampling was done on 49 plots spreaded over of the    peninsula. By the analysis of a total of 12 initial variables it was found  that the first 6 principal    components, new variables, interpret almost 83% of the total variance. It  was also found that the    first component, which explains 29.6%, affects the configuration of stand  structure.

scholarly journals Comparing global seismic tomography models using varimax principal component analysis

Solid Earth ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1601-1634
Author(s):  
Olivier de Viron ◽  
Michel Van Camp ◽  
Alexia Grabkowiak ◽  
Ana M. G. Ferreira
Keyword(s):  
Principal Component Analysis ◽  
Seismic Tomography ◽  
Lower Mantle ◽  
Model Comparison ◽  
Principal Component ◽  
Shear Velocity ◽  
Component Analysis ◽  
Total Variance ◽  
Low Velocity ◽  
Additional Tool

Abstract. Global seismic tomography has greatly progressed in the past decades, with many global Earth models being produced by different research groups. Objective, statistical methods are crucial for the quantitative interpretation of the large amount of information encapsulated by the models and for unbiased model comparisons. Here we propose using a rotated version of principal component analysis (PCA) to compress the information in order to ease the geological interpretation and model comparison. The method generates between 7 and 15 principal components (PCs) for each of the seven tested global tomography models, capturing more than 97 % of the total variance of the model. Each PC consists of a vertical profile, with which a horizontal pattern is associated by projection. The depth profiles and the horizontal patterns enable examining the key characteristics of the main components of the models. Most of the information in the models is associated with a few features: large low-shear-velocity provinces (LLSVPs) in the lowermost mantle, subduction signals and low-velocity anomalies likely associated with mantle plumes in the upper and lower mantle, and ridges and cratons in the uppermost mantle. Importantly, all models highlight several independent components in the lower mantle that make between 36 % and 69 % of the total variance, depending on the model, which suggests that the lower mantle is more complex than traditionally assumed. Overall, we find that varimax PCA is a useful additional tool for the quantitative comparison and interpretation of tomography models.

scholarly journals Rice Odours’ Readings Investigation Using Principal Component Analysis

2018 ◽  
Vol 7 (2.29) ◽  
pp. 488
Author(s):  
Nurul Aini Abdul Wahab ◽  
Shamshuritawati Sharif
Keyword(s):  
Principal Component Analysis ◽  
High Dimension ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
The Other ◽  
Total Variance ◽  
Scree Plot ◽  
Cumulative Proportion ◽  
Principle Component

The use of electronic nose (e-nose) devices plus principal component analysis can help the process of categorizing the 16 different rice into its type. Generally, the physical feature of an e-nose own more than one hole to capture the odour of rice. For example, the portable e-nose so-called Insniff does have 10 holes (or variables). In this situations, we will have a dataset that consist high-dimension dataset where lead to the presence of interdependencies between all variables under study. Therefore, this study is presented to investigate the odour of rice for identifying the most important variables contributing to the rice odour readings. The principal component analysis (PCA) is implemented to determine the component that best represent the all 10 variables in order to eliminate the interdependency problem, and (2) to identify which variable is considered as important and influential to the newly-formed principle component (PC). The results from PCA suggested that the first two principle components is chosen. It is based on three assessments which are Kaiser’s criterion larger than 1, cumulative proportion of total variance, and scree plot. These two principle components explained 89% of total variance. Results showed that sensor 1 (0.931) and sensor 2 (0.966) are the two important variables that highly contribute to PC1. On the other hand, for PC2, the highest contribution is from sensor 8 (0.828). This study demonstrate that PCA is effective for investigating rice odour readings.  

Pattern discovery of health curves using an ordered probit model with Bayesian smoothing and functional principal component analysis

2020 ◽  
pp. 096228022095183
Author(s):  
Shijia Wang ◽  
Yunlong Nie ◽  
Jason M Sutherland ◽  
Liangliang Wang
Keyword(s):  
Principal Component Analysis ◽  
Health Status ◽  
Principal Component ◽  
Probit Model ◽  
Component Analysis ◽  
Ordered Probit ◽  
Functional Principal Component Analysis ◽  
Ordered Probit Model ◽  
Functional Principal Component ◽  
Bayesian Smoothing

This article is motivated by the need for discovering patterns of patients’ health based on their daily settings of care to aid the health policy-makers to improve the effectiveness of distributing funding for health services. The hidden process of one’s health status is assumed to be a continuous smooth function, called the health curve, ranging from perfectly healthy to dead. The health curves are linked to the categorical setting of care using an ordered probit model and are inferred through Bayesian smoothing. The challenges include the nontrivial constraints on the lower bound of the health status (death) and on the model parameters to ensure model identifiability. We use the Markov chain Monte Carlo method to estimate the parameters and health curves. The functional principal component analysis is applied to the patients’ estimated health curves to discover common health patterns. The proposed method is demonstrated through an application to patients hospitalized from strokes in Ontario. Whilst this paper focuses on the method’s application to a health care problem, the proposed model and its implementation have the potential to be applied to many application domains in which the response variable is ordinal and there is a hidden process. Our implementation is available at https://github.com/liangliangwangsfu/healthCurveCode .

Assessment of water chemistry in a segment of Qua Iboe River Estuary by Principal component analysis

2021 ◽  
Vol 36 (1) ◽  
pp. 1-11
Author(s):  
I.I. Akpan
Keyword(s):  
Principal Component Analysis ◽  
Water Chemistry ◽  
River Estuary ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Total Alkalinity ◽  
Mineral Nutrient ◽  
Data Set ◽  
Physico Chemical

Assessment of water chemistry in a segment of Qua Iboe River Estuary, Niger Delta Region of Nigeria was carried out from January to December 2018 at three sampling stations. Seventeen physico-chemical parameters were analyzed using standards procedure. A total of 12 samples were collected from each station. Principal Component Analysis (PCA) was employed in the assessment of the study area. Three principal components, accounting for 99.59%, 100.01% and 100% of the total variance of information contained in the original data set for dry season were obtained. In the wet season, the components accounted for 66.0%, 69.97% and 67.50% of the total variance respectively. Results revealed that the most loading factor in the PCA when considering all the sampling stations in different seasons together in PC1, PC2 and PC3 axes were mainly sulphate, phosphate-phosphorus, calcium, potassium, temperature, total dissolved solids, total alkalinity, total hardness, dissolved oxygen, sodium, electrical conductivity, biological oxygen demand, pH, total suspended solids, A and magnesium. These loadings could be grouped into mineral/nutrient, physico-chemical, organic and domestic factors. General assessment of the study area did not indicate much deviation from prescribed standards, but sufficient to maintain a varied aquatic biodiversity.

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