scholarly journals Discriminant analysis of the relationship between estimated spinal level of posterior superior iliac spine (PSIS) and spinal-pelvic parameters

2019 ◽  
Author(s):  
Xi Luo ◽  
Yuan Wang ◽  
Ximing Xu ◽  
Kaiqiang Sun ◽  
Jian Zhu ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Sacral Slope ◽  
Posterior Superior Iliac Spine ◽  
Spinal Level ◽  
Linear Discriminant ◽  
Factors Affecting ◽  
Pelvic Parameters ◽  
Imaging Characteristics ◽  
Different Populations ◽  
The Relationship

Abstract Background: The spinal level determined by reference of posterior superior iliac spine (PSIS) will be different because of the various sagittal posture of spine-pelvis complex. The study aimed at investigating the anatomical factors affecting the estimated spinal level of PSIS from the standpoint of spine-pelvis paraments, and provided a basis for improving the accuracy of positioning. Methods: The lumbar X-ray images of 76 patients were retrospectively analyzed. The population was classified according to the estimated level of PSIS. lumbar lordosis (LL), sacral slope (SS), pelvic incidence (PI), pelvic tilt (PT) and other parameters were measured. Then, the latent factors affecting the estimated level were filtered and obtained by One-way ANOVA and Fisher linear discriminant analysis to further summarize the imaging characteristics of different populations. Results: Three different levels of L5 (10 cases), S1 (46 cases) and S2 (20 cases) were observed. ANOVA analysis showed that LL, SS, PT, PI, SS-PT, LL-SS and lordosis of L1-L5 (LL L1-L5 ) were significantly different among the three groups ( P < 0.05). Discriminant analysis showed that LL, SS, SS-PT and LL L1-L5 were the main factors affecting the estimated level of PSIS (P < 0.05). Conclusions: The variations of morphological parameters such as LL, SS, SS-PT and LL L1-L5 can affect the estimated level of PSIS, and the level can be predicted by the discriminant function. In the study, the function is D=-4.458+0.13×LL-0.115×SS+0.45× （SS-PT）+0.39×LL L1-L5 , which proved 71.1％ of the discriminant accuracy rate.

Algorithm Study of Face Recognition on Improved 2DLDA

2011 ◽  
Vol 128-129 ◽  
pp. 58-61
Author(s):  
Shi Ping Li ◽  
Yu Cheng ◽  
Hui Bin Liu ◽  
Lin Mu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Small Sample ◽  
New Method ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Face Representation ◽  
Paired Class ◽  
The Relationship

Linear Discriminant Analysis (LDA) [1] is a well-known method for face recognition in feature extraction and dimension reduction. To solve the “small sample” effect of LDA, Two-Dimensional Linear Discriminant Analysis (2DLDA) [2] has been used for face recognition recently，but its could hardly take use of the relationship between the adjacent scatter matrix. In this paper, I improved the between-class scatter matrix, proposed paired-class scatter matrix for face representation and recognition. In this new method, a paired between-class scatter matrix distance metric is used to measure the distance between random paired between-class scatter matrix. To test this new method, ORL face database is used and the results show that the paired between-class scatter matrix based 2DLDA method （N2DLDA） outperforms the 2DLDA method and achieves higher classification accuracy than the 2DLDA algorithm.

scholarly journals Research on Consumer Behavior of Wine in HoChiMinh City

2020 ◽  
Author(s):  
Ha Nam Khanh Giao ◽  
Pham Ngoc Duong ◽  
Tran Ngoc Tu
Keyword(s):  
Consumer Behavior ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Ho Chi Minh City ◽  
Analysis Method ◽  
Factors Affecting ◽  
Discriminant Analysis Method ◽  
The Relationship

This study was conducted to find out the factors affecting the consumers’ choice of wine in HoChiMinh City, Vietnam. The multiple regression model was not statistically significant for finding the relationship between the factors and the Money spent on wine, so discriminant analysis method was used to evaluate the contribution of factors to the differentiation between consumers’ group presented by average bottle consumed per month. The Symbolic benefit factor turned out to be the strongest, followed by Enjoyment benefit factor and Utilitarian &amp; Experiental benefit factor. The findings were used to provide suggestions for wine marketers in Ho Chi Minh City market.

Application of Multivariate Analyses to NIR Spectra of Gelatinized Starch

1992 ◽  
Vol 46 (9) ◽  
pp. 1420-1425 ◽  
Author(s):  
D. Bertrand ◽  
C. N. G. Scotter
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Multivariate Analyses ◽  
Principal Component Regression ◽  
Principal Component ◽  
Effect Of Temperature ◽  
Linear Discriminant ◽  
Starch Gelatinization ◽  
Gelatinized Starch ◽  
The Relationship

This paper describes an approach for studying collections of near-infrared spectra by using multivariate analyses. The method is illustrated with the use of two sets of spectra of gelatinized starch, recorded in the transmission mode between 650 and 1235 nm. The first set consisted of 99 spectra of partly gelatinized samples (from 24.5 to 100% gelatinization). Application of principal component analysis (PCA) made it possible to identify an outlying sample and to identify the importance of spectral variations due to the effect of scattering. Hence, it was possible to eliminate the scatter variations. From principal component regression (PCR), it was shown that the relationship between corrected spectra and gelatinization was not linear. Discriminant analysis was applied to seven classes of starch gelatinization. Only five samples out of 98 were incorrectly identified. The second set of samples was designed for studying the effect of temperature variation on the spectra of fully gelatinized starch samples. It was possible to show from PCR that the relationship between the spectra and temperature was linear. The “spectral patterns” assessed from discriminant analysis of starch gelatinization and from the PCR of temperature were compared.

Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia

Landslides ◽  
2015 ◽  
Vol 13 (4) ◽  
pp. 671-681 ◽  
Author(s):  
Alfonso Mariano Ramos-Cañón ◽  
Luis Felipe Prada-Sarmiento ◽  
Mario Germán Trujillo-Vela ◽  
Juan Pablo Macías ◽  
Ana Carolina Santos-R
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant ◽  
The Relationship

scholarly journals Association between back muscle degeneration and spinal-pelvic parameters in patients with degenerative spinal kyphosis

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Weiwei Xia ◽  
Han Fu ◽  
Zhenqi Zhu ◽  
Chenjun Liu ◽  
Kaifeng Wang ◽  
...  
Keyword(s):  
Erector Spinae ◽  
Sacral Slope ◽  
Muscle Degeneration ◽  
Imaging Data ◽  
Spinal Level ◽  
Sagittal Vertical Axis ◽  
Cross Sectional ◽  
Pelvic Parameters ◽  
Lumbar Level ◽  
Psoas Muscles

Abstract Background The paraspinal and psoas muscles have been considered to be essentially important for stabilizing the spinal column, and the muscle degeneration was found to exist in degenerative spinal kyphosis (DSK) patients. However, it is still not clear the relationship between muscle degeneration and spinal-pelvic alignment. The purpose of this study was to determine the correlations between the individual muscle degeneration at each lumbar spinal level and spinal-pelvic parameters in DSK patients. Methods The imaging data of 32 patients with DSK were retrospectively analyzed. The fat infiltration (FI) and relative cross-sectional area of muscle (RCSA) were quantitatively measured for multifidus (MF), erector spinae (ES) and psoas (PS) at each spinal level from L1/2 to L5/S1. The correlations were analyzed between RCSA and the sagittal vertical axis (SVA), thoracic kyphosis (TK), thoracolumbar kyphosis (TLK), lumbar lordosis (LL), sacral slope (SS), pelvic tilt (PT) and pelvic incidence (PI). Results The FI of MF and ES at L3/4, L4/5 and L5/S1 were higher than that at L1/2 and L2/3. The FI of PS at L4/5 and L5/S1 were lower than that of L1/2, L2/3 and L3/4. The RCSA of ES and PS from L1/2 to L5/S1 gradually increased, whereas the RCSA of ES from L1/2 to S5/S1 gradually decreased. The RCSA of MF at the L1/2 level was negatively correlated SVA (r = − 0.397,p = 0.024); the RCSA at L3/4, L4/5 and L5/S1 levels were negatively correlated with TK (r = − 0.364, p = 0.04; r = − 0.38, p = 0.032; r = − 0.432, p = 0.014); the RCSA at L4/5 level was positively correlated with LL (r = 0.528, p = 0.002). The RCSA of ES at L3/4 and L4/5 levels were positively correlated with PI (r = 0.377, p = 0.037) and SS (r = 0.420, p = 0.019). Conclusions FI of MF and ES at lower lumbar level is higher than that at upper level, but FI of PS at upper lumbar level is higher than that at lower level. MF and ES have different roles for maintaining the sagittal spinal-pelvic balance.

The number of employed as a macroeconomic indicator of working time dynamics

2020 ◽  
Vol 16 (5) ◽  
pp. 935-945
Author(s):  
I.A. Zaikova
Keyword(s):  
Comparative Analysis ◽  
Manufacturing Sector ◽  
Working Hours ◽  
Working Time ◽  
Labor Costs ◽  
Factors Affecting ◽  
Time Dynamics ◽  
Developed Economies ◽  
The Relationship ◽  
Macroeconomic Indicator

Subject. The working time of workers at any stage of economic development is a value reflecting the level of labor productivity. Any progress in productivity contributes to changes in the volume of labor costs and the number of employed. Depending on the relationship between the total volume of labor costs and the number of employed, the duration of working time per one worker may change (it may increase, decrease, or remain unchanged). Objectives. The study aims to confirm the importance of such a macroeconomic indicator as the number of employed in varying working hours. Methods. The study rests on the comparative analysis of countries with developed economies based on some indicators like dynamics of the working time fund, dynamics of the number of employed, average number of hours worked during the year per employee, etc. The analyzed timespan is 25 years (from 1991 to 2016). Results. The comparative analysis revealed that in the non-production sphere and the economy as a whole the macroeconomic determinants correlate so that the length of working time per worker reduces. When considering the analysis results for the manufacturing sector, no single trend was identified. Conclusions. One of the key factors affecting the change in working hours is the number of employed. The relationship between the working time fund and the number of employed directly determines the dynamics of working time per worker.

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