scholarly journals Principal Component Score Modeling for the Rapid Description of Chromatographic Separations

2008 ◽  
Vol 24 (1) ◽  
pp. 202-208 ◽  
Author(s):  
S. Edwards-Parton ◽  
N.F. Thornhill ◽  
D.G. Bracewell ◽  
J.M. Liddell ◽  
N.J. Titchener-Hooker
Keyword(s):  
Principal Component ◽  
Principal Component Score ◽  
Chromatographic Separations ◽  
Component Score

scholarly journals Effects of quantitative and qualitative principal component score strategies on the structure of coffee, rubber tree, rice and sorghum core collections

1998 ◽  
Vol 30 (Suppl 1) ◽  
pp. S237 ◽  
Author(s):  
Serge Hamon ◽  
Stéphane Dussert ◽  
Monique Deu ◽  
Perla Hamon ◽  
Marc Seguin ◽  
...  
Keyword(s):  
Rubber Tree ◽  
Principal Component ◽  
Principal Component Score ◽  
Core Collections ◽  
Component Score

Stock BBS Factor Model Using Principal Component Score

2014 ◽  
pp. 3-15 ◽  
Author(s):  
Hirohiko Suwa ◽  
Eiichi Umehara ◽  
Toshizumi Ohta
Keyword(s):  
Factor Model ◽  
Principal Component ◽  
Principal Component Score ◽  
Component Score

Integrating principal component score strategy with power core method for development of core collection in Indian soybean germplasm

2015 ◽  
Vol 15 (3) ◽  
pp. 230-238 ◽  
Author(s):  
C. Gireesh ◽  
S. M. Husain ◽  
M. Shivakumar ◽  
G. K. Satpute ◽  
Giriraj Kumawat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Core Collection ◽  
Principal Component ◽  
Oilseed Crop ◽  
Principal Component Score ◽  
Core Collections ◽  
Component Score ◽  
Soybean Germplasm ◽  
Mini Core Collection ◽  
Core Method

Soybean is a leading oilseed crop in India, which contains about 40% of protein and 20% of oil. Core collection will accelerate the management and utilization of soybean genetic resources in breeding programmes. In the present study, eight agromorphological traits of 3443 soybean germplasm were analysed for the development of core collection using the principal component score (PCS) strategy and the power core method. The PCS strategy yielded core collection (CC1) of 576 accessions, which accounted for 16.72% of the entire collection (EC). The analysis based on the power core programme resulted in CC2 of 402 accessions, which accounted for 11.67% of the EC. Statistical analysis showed similar trends for the mean and range estimated in both core collections and EC. In addition, the variance, standard deviation and coefficient of variance were in general higher in core collections than in the EC. The correlations observed in the EC in general were preserved in core collections. A total of 311 and 137 unique accessions were found in CC1 and CC2 in addition to 265 accessions that were found to be common in both core collections. These 265 common accessions were the most diverse core sets, which accounted for 7.64% of the EC. We proposed to constitute an integrated core collection (ICC) by integrating both common and unique accessions. The ICC comprised 713 accessions, which accounted for about 20.62% of the EC. Statistical analysis indicated that the ICC captured maximum variation than CC1 and CC2. Therefore, the ICC can be extensively evaluated for a large number of economically important traits for the identification of desirable genotypes and for the development of mini core collection in soybean.

Short-term wind speed interval prediction by improved regularized extreme learning machine based on attribute reduction

2019 ◽  
Vol 44 (6) ◽  
pp. 631-644
Author(s):  
Chao Pan ◽  
Dian Wang ◽  
Qide Tan
Keyword(s):  
Wind Speed ◽  
Extreme Learning Machine ◽  
Attribute Reduction ◽  
Prediction Method ◽  
Principal Component ◽  
Stable Operation ◽  
Principal Component Score ◽  
Component Score ◽  
Interval Prediction ◽  
Learning Machine

Accurate wind speed forecasting is important for stable operation of the power system when large-scale wind power is connected to the grid. According to the randomness of wind speed caused by the interaction of weather attributes, this article presents a new wind speed interval prediction method by improved regularized extreme learning machine based on attribute reduction. First, the principal component analysis is used to extract the principal component score sequences of multi-dimensional meteorological attribute factors, and the principal component score sequences are weighted by the variance contribution rate. Then, the original wind speed series is processed by fuzzy information granulation to obtain three components, which represent the minimum value, maximum value, and variation trend of the wind speed interval. The weighted principal component score sequence and the wind speed fuzzy granulation component are used as the input model of the prediction model, and the gradient prediction is performed using the improved regularized extreme learning machine of the gravity search algorithm. Finally, the prediction effect of the proposed method is simulated and analyzed based on the measured data of the wind farm. The results show that the combination prediction method can effectively improve the operational efficiency and accuracy of wind speed prediction, and has strong generalization ability.

scholarly journals Novel ideas for the comprehensive evaluation of varus knee osteoarthritis: radiological measurements of the morphology of the lateral knee joint

2021 ◽  
Author(s):  
Wenru Ma ◽  
Fengkun Wang ◽  
Shengnan Sun ◽  
Zengshuai Han ◽  
Lei Ding ◽  
...  
Keyword(s):  
Linear Regression ◽  
Knee Joint ◽  
Morphological Characteristics ◽  
Principal Component ◽  
Varus Deformity ◽  
Principal Component Score ◽  
Varus Knee ◽  
Proximal Fibula ◽  
Component Score ◽  
Knee Oa

Abstract BackgroundThe lateral anatomical and morphological characteristics of knees with varus knee osteoarthritis (OA) have not received sufficient attention. This study used several radiological parameters to describe the morphological characteristics of the lateral knee with OA to determine whether there are relationships between varus knee OA and parameters such as lateral plateau widening (LPW), proximal fibula curvature (PFC), and fibula height (FH).MethodsThe study retrospectively analyzed 1072 subjects [376 males, 696 females; mean age 66.84 ± 7.04 (range 46–83) years; mean body mass index (BMI) 26.98 ± 3.22 kg/m²] who underwent standard radiography for diagnosing or evaluating symptomatic knee joint disease. The 163 Kellgren and Lawrence (K-L) grades 0 and I knees were categorized into the no-knee-OA group, and the 909 K-L grades II–IV knees were classified into the knee-OA group. The medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), and hip-knee-ankle angle (HKAA) were measured to investigate varus knee deformity. The LPW, PFC, and FH were measured. T-tests and chi-square tests were used to compare each index between the two groups. Binary logistic regression was performed to examine the correlation between indexes and knee OA occurrence. Principal component analysis was used to calculate the comprehensive principal component score of varus deformity, which was used to comprehensively evaluate the knee varus deformity degree by reducing the original data's dimension. Multiple logistic and linear regression analyses were performed to examine the correlations between the three parameters and K-L grades and the comprehensive principal component score of varus deformit.ResultsLPW and PFC were significantly greater and FH was significantly smaller in the knee-OA group than in the no-knee-OA group. LPW, PFC, and FH were correlated with knee OA occurrence. One principal component, named the comprehensive principal component score of varus deformity, was extracted from the three indexes, and the total variance of the principal component interpretation was 76.60%. Multivariate logistics and linear regression analysis showed that after adjusting for age and BMI, LPW and PFC were positively correlated with K-L grading and varus deformity. FH was significantly and negatively associatedwith K-L grading and varus deformity (all P < 0.05).ConclusionsRegular morphological changes take place in the lateral knee with varus OA, including lateral dislocation of the tibial plateau, proximal fibula bending, and upward movement of the fibular head. Changes in LPW, PFC, and FH could enable a more comprehensive assessment of varus knee OA occurrence, severity, and deformity.Level of EvidenceRetrospective Study Level III

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