Purchasing policy model based on components/parts unification

This paper presents a mathematical model for components/parts unification (CPU) policy. This model considers two components/parts that are functionally interchangeable but purchased from suppliers with different prices and quality characteristics. Because of the buyer's quality preference and suppliers' discount rates for bulky purchases, the model assists the procurement manager to determine how best to purchase the components/parts to meet its demand while minimizing the total acquisition costs.

Component azimuths of the CEArray stations estimated from P-wave particle motion

The recently built China Digital Seismic Network consists of the China National Digital Seismic Network （CNDSN）, 31 regional seismic networks and several small aperture arrays with more than 1 000 stations including 850＋ broadband stations. It forms a gigantic seismic array that provides an unprecedented opportunity to study the Earth＇s deep interior besides its routine task of seismic monitoring. Many modern seismic studies rely on rotation of vertical and horizontal components in order to separate different types of seismic waves. Knowledge of the orientations of the two horizontal components thus is important to perform a correction rotation. We analyzed particle motions of teleseismic P waves recorded by the network and used them to estimate the northcomponent azimuth of each station. An SNR-weighted-multi-event method was introduced to obtain component azimuths that best explain the P-wave particle motions of all the events recorded at a station. The method provides robust estimates including a measurement error calculated from background noise levels. We found that about one third of the stations have some sort of problems, including misorientation of the two horizontal components, mislabeling and polarity reversal in one or more components. These problems need to be taken into account for any rotation based seismic studies.

Characterizing and estimating rice brown spot disease severity using stepwise regression,principal component regression and partial least-square regression

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process

Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.

High Temperature During Rice Grain Filling Enhances Aspartate Metabolism in Grains and Results in Accumulation of AspartateFamily Amino Acids and Protein Components

Global warming causes the exacerbation of rice growing environment, which seriously affects rice growth and reproduction, and finally results in the decrease of rice yield and quality. We investigated the activities of aspartate metabolism enzymes in grains, and the contents of Aspartate-family amino acids and protein components to further understand the effects of high temperature （HT） on rice nutritional quality during rice grain filling. Under HT, the average activities of aspartate aminotransferase （AAT） and aspartokinase （AK） in grains significantly increased, the amino acid contents of aspartate （Asp）, lysine （Lys）, threonine （Thr）, methionine （Met） and isoleucine （lie） and the protein contents of albumin, globulin, prolamin and glutelin also significantly increased. The results indicated that HT enhanced Asp metabolism during rice grain filling and the enhancement of Asp metabolism might play an important role in the increase of Asp-family amino acids and protein components in grains. In case of the partial appraisal of the change of Asp-family amino acids and protein components under HT, we introduced eight indicators （amino acid or protein content, ratio of amino acid or protein, amino acid or protein content per grain and amino acid or protein content per panicle） to estimate the effects of HT. It is suggested that HT during rice grain filling was benefit for the accumulation of Asp-family amino acids and protein components. Combined with the improvement of Asp-family amino acid ratio in grains under HT, it is suggested that HT during grain filling may improve the rice nutritional quality. However, the yields of parts of Asp-family amino acids and protein components were decreased under HT during rice grain filling.

Phase Analysis and Identification Method for Multiphase Batch Processes with Partitioning Multi-way Principal Component Analysis (MPCA) Model

Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Effects of Biomineral Liquid from Spartina alterniflora on Growth,Body Color and Nutritional Components of Ricefield Eels,Monopterus albus

[Objective]The paper was to develop new fodder additives and to strengthen comprehensive utilization of Spartina alterniflora resources.[Method]The effects of biomineral liquid from S.alterniflora on growth,nutritional components of ricefield eels were studied.[Result]（1）0.5%of biomineral liquid had significant effect on weight gain rate,which was 8.37%higher than that in control group（P〈0.01）.Further supply of biomineral liquid could not increase the weight gain rate.Furthermore,biomineral liquid had no effect on the survival rate of ricefield eels.（2）Ricefield eels had certain changes in body color in the0.5%biomineral liquid group,and some ricefield eels were yellowing.（3）The contents of Cr,Fe,Zn in body of ricefield eels in treatment group were significantly higher than that in control group,but was lower than that in wild eel group,and the content of Se in treatment group was lower than that in control group and wild group（P〈0.01）.（4）The contents of K,Mg in muscle of ricefield eels in treatment group were higher than those in control group and wild eel group.The contents of ash and Ca in treatment group were close to that in control group but were higher than that in wild eel group.The content of crude fat in treatment group was close to that in wild eel group but was lower than that in control group.The contents of Zn,Se,Ni in treatment group were higher than that in control group and wild eel group,and the contents of Cr,Fe,Cu were close to that in control group and were higher than that in wild eel group（P〈0.01）.No significant differences were observed in the contents of moisture,crude fat and P among the three groups（P〉0.05）.[Conclusion]Addition of biomineral liquid from S.alterniflora could speed up the growth of ricefield eels and significantly improve their flesh quality,but had no significant effect on survival rate of ricefield eels.

HS-SPME-GC-MS Analysis on Volatile Components in Different Cultivars and Different Parts of Artemisia argyi

[Objectives] To analyze and compare the difference in volatile components of different cultivars and different parts of Artemisia argyi by using headspace solid phase microextraction( HS-SPME) and provide scientific basis for comprehensive development and rational utilization of Artemisia argyi. [Methods]Firstly,HS-SPME was used to extract the volatile components from the leaves,seeds,and stems of seven-tip Artemisia argyi,five-tip Artemisia argyi,and golden Artemisia argyi. Their chemical composition was analyzed by gas chromatographymass spectrometry( GC-MS),and the relative mass fraction of each component was determined by area normalization method. [Results] For the same variety of Artemisia argyi,the content of volatile components in leaves was higher than in seeds and stems except camphor,and the content of volatile components in seven-tip Artemisia argyi leaves was higher than in five-tip Artemisia argyi and golden Artemisia argyi leaves.HS-SPME-GC-MS analysis showed that eucalyptol,camphor,thujone,α-pinene,1-caryophyllene and caryophyllene oxide with higher content were detected in different parts of the different cultivars of Artemisia argyi. [Conclusions]Seven-tip Artemisia argyi leaves were the best medicinal parts of the three cultivars,followed by seven-tip Artemisia argyi seeds.

Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis

The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.

Performance improvement of software component with partial evaluation

To avoid the complexity and inefficiency for specific applications of the current software architecture, a novel approach using partial evaluation is proposed to improve the running performance of components. The generic program was specialized into domain-specific realization for the known knowledge and environments. The syntax and semantic(adj.) were analyzed based on byte code instruction sequences, and partial evaluation rules depicted how to perform the specialization. The partial evaluation for object-oriented programs was implemented. The experimental results show that partial evaluation is effective to speed up the running efficiency. The more generality and scalability can be obtained by the integration of partial evaluation with the favorable design mechanisms and compiler optimization technology.

Partial least squares regression and principal component analysis:similarity and differences between two popular variable reduction approaches

SUMMARY In many statistical applications,composite variables are constructed to reduce the number of variables and improve the performances of statistical analyses of these variables,especially when some of the variables are highly correlated.Principal component analysis(PCA)and factor analysis(FA)are generally used for such purposes.If the variables are used as explanatory or independent variables in linear regression analysis,partial least squares(PLS)regression is a better alternative.Unlike PCA and FA,PLS creates composite variables by also taking into account the response,or dependent variable,so that they have higher correlations with the response than composites from their PCA and FA counterparts.In this report,we provide an introduction to this useful approach and illustrate it with data from a real study.

Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis

Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.

Multi-state Information Dimension Reduction Based on Particle Swarm Optimization-Kernel Independent Component Analysis

The precision of the kernel independent component analysis( KICA) algorithm depends on the type and parameter values of kernel function. Therefore,it's of great significance to study the choice method of KICA's kernel parameters for improving its feature dimension reduction result. In this paper, a fitness function was established by use of the ideal of Fisher discrimination function firstly. Then the global optimal solution of fitness function was searched by particle swarm optimization( PSO) algorithm and a multi-state information dimension reduction algorithm based on PSO-KICA was established. Finally,the validity of this algorithm to enhance the precision of feature dimension reduction has been proven.

Analysis of Volatile Components in Different Parts of Polygala japonica Houtt.

[Objectives] To analyze the volatile components in different parts of Polygala japonica Houtt. and compare the differences of these volatile components. [Methods] The volatile components in different parts of P. japonica Houtt. were analyzed by the headspace solid-phase microextraction( HS-SPME) combined with GC-MS,and the relative percentage of each component was determined by peak area normalization. [Results] Thirty kinds of volatile components were identified from the leaves and rhizomes of P. japonica Houtt.,mainly including olefins,aromatic hydrocarbons,alkanes and esters. [Conclusions] The volatile components in different parts of P. japonica Houtt. were different,and there may be difference in the medicinal value of volatile components in different parts,thus it is required to take an overall consideration of these differences in the development and utilization of P. japonica Houtt.

Shanghai Car Parts and Components Industry

Shanghai Santana cars had produced a total of 270,000 cars by the end of 1993, and undergone SKD, CKD and localization stages. The car industry will soon become a pillar industry in Shanghai. During its development, the Shanghai car industry has always given special attention to localization, thus the localization rate of car parts and components has increased year by year. In 1988, Shanghai Santanacars produced 15,000 units, with 13,08 percent of

Performance of Incoming Solar Radiation Components in Partial Annular Solar Eclipse on June 21^st, 2020 in Helwan, Egypt

Observing and studying the solar radiation during solar eclipses is important in knowing the changes that occur to the environmental elements during this event. The main objective of this paper is the performance of the incoming variation of solar radiation components, global, direct and diffuse and their fractions during the partial annular solar eclipse on June 21st, 2020 in Helwan, Egypt (Lat. 29.866°N and Long. 31.20°E) has been made. A pyrheliometer for measuring the direct solar radiation, in three different bands;direct yellow (Y), direct red (R), direct infrared (IR), and also the total direct band (I);A pyranometers for measuring the different components of global solar radiation (G), global ultraviolet (GUV), global infrared (GIR) and a meteorological station to measure the different meteorological parameters. The duration of the solar eclipse was 01 h:59 m, and the maximum magnitude of the eclipse in this region was 0.449. The depression is clear at the solar radiation of all components due to the annular solar eclipse, while the depressions of the diffuse and global infrared solar radiation are lower. In all direct radiation compounds (I, Y, R and IR) are greatly affected by the eclipse. The diffuse fraction Kd is higher in the early time, before the partial eclipse, but during the partial annular eclipse time Kd values are suffers variation and through the day, where the values of Kd lies between Kt and KUV. The values of direct infrared solar radiation are dominant before and after the partial annular solar eclipse. The intensity of color bands (W∙m−2∙nm−1) are DIB3 > DIB2 > DIB4, and DIB1 is opposite direction with DIB3 and DIB2, the highest intensity is direct red and the lowest intensity is the direct infrared. The highest values of extinction coefficient in (GIR) solar radiation and the lowest values occur in (GUV) solar radiation, while the values of (G) solar radiation occur between them. In general trend, the values of extinction coefficient during the partial eclipse are increasing, while the minimum values of extinction coefficient occur at noon time due to the air mass is less value in the noon.

Discrimination of polysorbate 20 by high-performance liquid chromatography-charged aerosol detection and characterization for components by expanding compound database and library

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.