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This study aims to determine the seasonal variations and composition of suspended particulate matter in different sizes PM 1.0 , PM 2.5 , PM 10 and the total suspended particles (TSP) emitted at major intra-urban traffic intersections (TIs) of Ilorin metropolis. The concentration levels of PM (PM 1.0 , PM 2.5 , PM 10 ) obtained at the TIs during the rush hours (45.1, 77.9, and 513 μg/m 3 ) are higher than the levels obtained for the non-rush hour periods (42.3, 62.7, and 390 μg/m 3 ). The average on-road respiratory deposition dose (RDD) rates of PM 1.0 , PM 2.5 and PM 10 during the dry period at TIs types was found to be about 24%, 9% and 25% higher than those obtained during the wet period. Based on EF values calculated, Pb and Zn were anthropogenically-derived while Fe, Mn, Cr, Cu and Mg were of crustal source. Principal component analysis (PCA) has been applied to a set of PM data in order to determine the contribution of different sources. It was found that the main principal factors extracted from particulate emission data were related to exhaust and non-exhaust emissions such as tyre wears, oil and fuel combustion sources. [ABSTRACT FROM AUTHOR]

We proposed a population structure correction for genome-wide selection based on covariance analysis via eigenvector (EVG) decomposition. The agreement between the predicted and true breeding values was evaluated by independent cross-validation data sets. Other correction methods such as correction via principal components, best linear unbiased prediction, and deregressed phenotype were also evaluated. Based on different simulation scenarios, the proposed EVG out performed the other methods in the prediction of accuracy. [ABSTRACT FROM AUTHOR]

In this paper we estimate nonlinear Taylor rules over the 1986-2008 sample time period and augment the traditional Taylor rule by including principal components to better model Federal Reserve policy. Including principal components is useful in that they extract information about the overall economy from multiple economic indicators in a statistically optimal way. Additionally, given that uncertainty may influence Federal Reserve decisions, we incorporate an uncertainty index in the reaction function of the Federal Reserve. We find substantial evidence that the Federal Reserve responded to increases in macroeconomic uncertainty by cutting the Federal Funds rate over the sample period. We also find evidence that the Federal Reserve responded aggressively to increases in capacity utilization, especially when the inflation rate was above 2%. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the quality of surfacewater sources in the Jia Bharali river basin and adjoining areas of the Himalayan foothills with respect to heavy elements viz. (As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) by hydrochemical and multivariate statistical techniques, such as cluster analysis (CA) and principal component analysis (PCA). This study presents the first ever systematic analysis on toxic elements of water samples collected from 35 different surface water sources in both the dry and wet seasons for a duration of 2 hydrological years (2009-2011). Varimax factors extracted by principal component analysis indicates anthropogenic (domestic and agricultural run-off) and geogenic influences on the trace elements. Hierarchical cluster analysis grouped 35 surfacewater sources into three statistically significant clusters based on the similarity of water quality characteristics. This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective surfacewater quality management. [ABSTRACT FROM AUTHOR]

We extend the literature on female autonomy along two dimensions. We first develop a new, direct female autonomy index using survey responses of spouses to a variety of household decision-making questions. We then examine the effects of a mother's autonomy on her child's secondary enrollment. We find that our measure is consistent with some of the existing autonomy measures, and that higher autonomy of mothers is correlated with higher secondary enrollment for boys, but not girls. Our results are robust to a range of sensitivity tests. [ABSTRACT FROM AUTHOR]

In this article, we are concerned with detecting the true structure of a functional polynomial regression with autoregressive (AR) errors. The first issue is to detect which orders of the polynomial are significant in functional polynomial regression. The second issue is to detect which orders of the AR process in the AR errors are significant. We propose a shrinkage method to deal with the two problems:polynomial order selection and autoregressive order selection. Simulation studies demonstrate that the new method can identify the true structure. One empirical example is also presented to illustrate the usefulness of our method. [ABSTRACT FROM AUTHOR]

This paper examines the technical and economic feasibilities of the sequential optimization technique, i.e., Taguchi coupled with principal component analysis (PCA). Fenton oxidation with four operating parameters, such as [Dye]ini, Dye/Feþ2 (wt/wt), H2O2/Fe+2 (wt/wt), and pH, was selected as a case study. COD (%), TOC (%), and color removal efficiency were chosen as responses. Total principal component index (TPCI) values, obtained as an output of sequential optimization, were used to determine the optimized conditions for Fenton oxidation. [Dye]: 100 mg/L, Dye/Fe+2(wt/wt): 50, H2O2/Fe+2(wt/wt): 25 and pH: 3 were obtained as the optimized values. In addition, ANOVA and the interaction of factors showed that pH was the most significant factor with the percent contribution of 90.86%. Economic evaluation with simultaneous comparison with Taguchi and central composite design (CCD) showed that Taguchi coupled with PCA is the most economic and time-efficient optimization technique. In this study, experimental cost was reduced to $54/m3, which showed 95% reduction relative to CCD. Moreover, chemical consumption and experimental time were also reduced by approximately 95-97% and 68%, respectively. Thus, it can be concluded that Taguchi coupled with PCA is the most cost-effective and less-laborious way of optimizing multi-response processes such as Fenton oxidation. [ABSTRACT FROM AUTHOR]

Linear principal component analysis (PCA) is a widely used approach to reduce the dimension of gene or miRNA expression data sets. This method relies on the linearity assumption, which often fails to capture the patterns and relationships inherent in the data. Thus, a nonlinear approach such as kernel PCA might be optimal. We develop a copula-based simulation algorithm that takes into account the degree of dependence and nonlinearity observed in these data sets. Using this algorithm, we conduct an extensive simulation to compare the performance of linear and kernel principal component analysis methods towards data integration and death classification.We also compare these methods using a real data set with gene and miRNA expression of lung cancer patients. First few kernel principal components show poor performance compared to the linear principal components in this occasion. Reducing dimensions using linear PCA and a logistic regression model for classification seems to be adequate for this purpose. Integrating information from multiple data sets using either of these two approaches leads to an improved classification accuracy for the outcome. [ABSTRACT FROM AUTHOR]

When the number of assets (N) exceeds the number of time periods (T), the sample covariance matrix is singular, and the portfolio optimization problem cannot be solved via traditional mean-variance algebra. In such a case, the Moore–Penrose (MP) generalized inverse becomes handy: In this paper, we critically examine the MP solution of the portfolio optimization problem. Our findings include: i) the MP solution leads to a portfolio of “pseudo-riskfree composite assets”; ii) it is orthogonal to principal components, iii) most importantly, it is poorly diversified. We illustrate our findings using equity market data. [ABSTRACT FROM AUTHOR]

Mining activities and resulting wastes can be considered as one of the most important sources of hazardous elements in the environment. Knowledge of the spatial distribution of toxic elements in waste dump systems is necessary to assess environmental hazard and strategy. To achieve this goal, this paper investigates spatial distribution of toxic elements using statistical and geostatistical analysis. A total of 58 soil samples were collected, and the amount of As, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb and Zn was then determined at 'Sarcheshmeh' copper mine waste dumps. In order to evaluate the presence of multivariate outliers, Mahalanobis distance technique ( D ) was applied and the multivariate outlier samples were removed. This resulted in an increase in correlation coefficient. To reduce dimension of data set, principal component analysis was applied and four principal components were determined which indicate 83.463% of the total variance of data set. Estimated PCs together with the toxic elements maps based on the ordinary kriging display aggregation of toxic elements in some parts, and validity of predictions was evaluated using the leave-one-out cross-validation method. The regression coefficients of estimated and observed values presented the reliability of the kriging estimates. Sequential Gaussian simulation method was applied for principal components due to similar results of estimated principal components and toxic elements. The results of simulation maps are almost identical to estimated outcomes. [ABSTRACT FROM AUTHOR]

The dominant mode of July-August seasonal variability of Indian summer monsoon rainfall obtained by performing an empirical orthogonal function analysis over India, excluding northeast India, for the period 1979-2014 is highly correlated to the quasi-stationary midlatitude Eurasian Rossby wave train, known as the 'Silk Road' pattern. A new hypothesis has been proposed and tested using observed Indian rainfall and the ERA-Interim dataset, by which the positive feedback between the midlatitude Rossby wave and the Indian summer monsoon circulation has been established. A Rossby wave train features successive upper-tropospheric negative and positive geopotential height anomalies over the northeast of the Mediterranean and northwest of India, respectively. The negative height anomaly decreases the sum of the vertical integral of potential and internal energy, while this increases in the region of positive geopotential height anomalies. This is associated with middle to lower tropospheric midlatitude descent and ascent anomalies at 40°E and 50°E, which cause negative surface temperature anomalies over Syria, Iraq, Jordan and Saudi Arabia, and a positive surface temperature anomaly over Iran, respectively. The negative and positive surface temperature anomalies are associated with positive and negative surface pressure anomalies over Saudi Arabia and Iran, respectively, which generate anomalous northwesterly winds over eastern Saudi Arabia and the Persian Gulf. Further downstream this anomalous northwesterly wind combines with the climatological background cross-equatorial southwesterly flow in the Arabian Sea. The flow further converges towards central and northwest India. The flow also acts as a source of moisture supply from the warm Arabian Sea and Persian Gulf for the deep convection over the Western Ghats, northwest and central India and hence reinforces active Indian summer monsoon conditions. [ABSTRACT FROM AUTHOR]

This paper describes the second part of a series of investigation to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from the future hyperspectral and geostationary satellite sensors such as Tropospheric Emissions: Monitoring of POllution (TEMPO). The information content in these hyperspectral measurements is analyzed for 6 principal components (PCs) of surface spectra and a total of 14 aerosol parameters that describe the columnar aerosol volume V total , fine-mode aerosol volume fraction, and the size distribution and wavelength-dependent index of refraction in both coarse and fine mode aerosols. Forward simulations of atmospheric radiative transfer are conducted for 5 surface types (green vegetation, bare soil, rangeland, concrete and mixed surface case) and a wide range of aerosol mixtures. It is shown that the PCs of surface spectra in the atmospheric window channel could be derived from the top-of-the-atmosphere reflectance in the conditions of low aerosol optical depth (AOD ≤ 0.2 at 550 nm), with a relative error of 1%. With degree freedom for signal analysis and the sequential forward selection method, the common bands for different aerosol mixture types and surface types can be selected for aerosol retrieval. The first 20% of our selected bands accounts for more than 90% of information content for aerosols, and only 4 PCs are needed to reconstruct surface reflectance. However, the information content in these common bands from each TEMPO individual observation is insufficient for the simultaneous retrieval of surface’s PC weight coefficients and multiple aerosol parameters (other than V total ). In contrast, with multiple observations for the same location from TEMPO in multiple consecutive days, 1–3 additional aerosol parameters could be retrieved. Consequently, a self-adjustable aerosol retrieval algorithm to account for surface types, AOD conditions, and multiple-consecutive observations is recommended to derive aerosol parameters and surface reflectance simultaneously from TEMPO. [ABSTRACT FROM AUTHOR]

Survey sampling with model-assisted estimation has recently gained popularity in forest inventory. Another option for utilizing the auxiliary information is to use poststratification, which is a special case of model-assisted estimation with class variables as explanatory variables. In this study, we compared the efficiency of poststratification with an increasing number of strata with model-assisted estimation. We carried out a study based on a simulated population. We considered four different types of poststratifications, namely ( i) stratification based on predictions of a linear model, ( ii) stratification based on a regression tree model, ( iii) stratification based on the first principal component of the explanatory variables, and ( iv) stratification based on the regression tree model with the first principal component as the only explanatory variable. Furthermore, we examined both the traditional poststratification mean and variance estimators and the difference estimator and its variance estimator for poststratification. Within the recommended range of number of strata, the model-assisted approach was more efficient than poststratification. With a large number of strata, poststratification produced smaller standard error of estimates, but problems such as empty strata were encountered with small sample sizes. Using the first principal component directly for stratification or as an explanatory variable was the most efficient approach. [ABSTRACT FROM AUTHOR]

High-resolution satellite images contain a huge amount of information. Shadows in such images generate real problems in classifying and extracting the required information. Although signals recorded in shadow area are weak, it is still possible to recover them. Significant work is already done in shadow detection direction but, classifying shadow pixels from vegetation pixels correctly is still an issue as dark vegetation areas are still misclassified as shadow in some cases. In this letter, a new image index is developed for shadow detection employing multiple bands. Shadow pixels are classified from the index histogram by an automatic threshold identification procedure. The whole approach is applied on different study areas and high accuracies are achieved (average of 97%). The linear correlation method is then applied to compensate the classified shadow pixels. Two standard approaches of shadow detection are then applied to the same study areas to validate the proposed approach. The results show that the proposed approach achieves the best results. It also gives robust shadow detection results in classifying shadow from vegetation pixels comparable to the other two considered standard approaches. [ABSTRACT FROM PUBLISHER]

Freezing stress is an important abiotic stress that limiting the yield and the spatial distribution of many important crops. This study was undertaken to screen 136 doubled haploid (DH) lines of camelina (Camelina sativa L.) along with four canola (Brassica napus) cultivars (Hyola 401, Lord, Roska and Cascade) as experimental control under freezing stress conditions (-14 °C for 6 h) to identify lines with high or low level of tolerance to freezing stress for further studies. First, a protocol was developed for large scale screening of camelina germplasm under freezing stress conditions. For this purpose, an experiment with different freezing temperatures (-5, -10, -15 and -20 °C) was conducted to find an appropriate temperature that discriminated best between genotypes (i.e. the LT50 temperature). The LT50 values for camelina lines were varied between -10.2 and -17.1 °C with an average of -13.94 °C for all of the camelina lines. Therefore, we selected the -14 °C exposure for 6 h as an appropriate temperature to screening of camelina lines. The principal components of measured parameters (LT50, survival percentage, relative conductivity and scoring) was using principal component analysis that determine freezing-tolerant and freezing-sensitive lines. Among 136 doubled haploid lines, some lines (58, 62 and 101) had higher level of freezing tolerance and some of them (8, 16, 32, 91 and 107) were freezing sensitive. The selected lines in a preliminary freezing screening are useful for further evaluations. Stres mržnjenja je važan abiotički stres koji ograničava prinos i prostornu distribuciju mnogih važnih useva. Cilj istrživanja je skrining 136 DH linija (Camelina sativa L. zajedno sa četiri kultivara uljane repice (Brassica napus) (Hyola 401, Lord, Roska i Cascade) kao kontrola u uslovima mraza (-14 °C for 6 h) da se identifikuju linije sa visokim ili niskim nivoom toleratnosti na stres mraza za buduća ispitivanja. Prvo, razvijen je protocol za skrining većeg broja genotipova pri niskim temparaturama. Za to ogled sa različitim temparaturama (-5, -10, - 15 i -20 °C) je sproveden da se pronađu odgovarajuće temparture koje najbolje razdvajaju genotipove (t.j. LT50 temperatura). Vrednost LT50 je varirala između -10.2 i -17.1 °C sa prosekom -13.94 °C za sve linije. Zato smo izabrali -14 °C izlaganje tokom 6 h kao odgovarajću temparaturu za skrining linija. PCA je korišćena za merenje parametara (LT50, procenat preživljavanja, relativne provodljivosti) da se odrede linije koje su tolertne i osetljive na mraz. Između 136 DH linija, neke linije (58, 62 i 101) su imale viši nivo toleratnosti a neke od njih (8, 16, 32, 91 i 107) su bile senzitivne. Izabrane linije na osnovu primarnog skrininga su korisne za dalju evaluaciju. [ABSTRACT FROM AUTHOR]

Dasypyrum is a genus with two species, an annual diploid Dasypyrum villosum and a perennial diploid or tetraploid D. breviaristatum. It is difficult to distinguish between the two, especially for a non-taxonomist. In this paper we demonstrate how to distinguish between the two species and between the two ploidy levels of D. breviaristatum. We achieve this using RAPD analysis to identify potential candidates for marker development and after carrying out principal component analyses and partitioning of genetic variance, developed SCARs and an identification key. [ABSTRACT FROM AUTHOR]

Soils around coal mining are important reservoir of hazardous elements (HEs), nanominerals, and ultrafine compounds. This research reports and discusses the soil concentrations of HEs (As, Cd, Cr, Cu, Ni, Pb, and Zn) in coal residues of abandoned mines. To assess differences regarding environmental impact and risk assessment between coal abandoned mines from the Santa Catarina state, eighteen coal cleaning rejects with different mineralogical and chemical composition, from eight abandoned mines were collected. Nanominerals and ultra-fine minerals from mining-contaminated areas were analyzed by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and high-resolution transmission electron microscope (HR-TEM), providing new information on the mineralogy and nano-mineralogy of these coal residues. The total contents of 57 elements (HEs, alkali metals, and rare earth elements) were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The calculation of NWACs (Normalized Average Weighted Concentration), together with the chemometric analysis by Principal component analysis (PCA) confirmed the variability of the samples regarding their city and their mine of origin. Moreover, the results confirmed the existence of hotspots in mines near urban areas. [ABSTRACT FROM AUTHOR]

Because eigen-analysis method can’t work when the input sequence is too long or environment is nonstationary for blind estimate of PN sequence synchronous direct sequence code-division multiple access (DS-CDMA) signals with different power level. This paper introduced an on-line unsupervised learning neural network (NN) for adaptive feature extraction via principal component analysis (LEAP) method. Firstly,it chose the period segmented DS-CDMA signals as NN input and used the symlxil function of each weight vector to represent the PN sequence of each user. Then through the continuous input signal. it trained the weight vectors of NN repeatedly until convergence. Finally. the PN sequence of each user could he rebuilt by the symbolic function of each weight vector. Furthermore, this paper introduced variable step to improve the convergence rate. Theoretical analysis and simulation experiments show that LEAP NN method is efficiency in condition with -20 dB signal to noise rations (SNR) and 10 users of synchronous DS-CDMA signals with different power level. Besides, its convergence rate is better than traditional Sanger NN method. [ABSTRACT FROM AUTHOR]

The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown to be (a) bounded from below by the smallest error variance in a variable from the analyzed set and (b) bounded from above by the largest error variance in a variable from that set. In the case of a unidimensional set of analyzed measures, it is pointed out that the reliability and criterion validity of any principal component are bounded from above by these respective coefficients of the optimal linear combination with maximal reliability and criterion validity (for a criterion unrelated to the error terms in the individual measures). The discussed psychometric features of principal components are illustrated on a numerical data set. [ABSTRACT FROM AUTHOR]

The objective of the work was to describe and group units of fattening of commercial Zebu bulls grazing with low supplementation. We studied 44 bovine meat production units belonging to credit and service cooperatives by private farmers, in the municipality Sibanicú, Camagüey province, Cuba. Visits to each of the units were carried out for three years and took the official records of each producer for the variables: the resources, animals, management, production and economics. The major statisticians for this variables were calculated. The average initial weight, final weight, the weight gain and the production by hectare were 202,6; 371,6; 0,501 and 326,2 kg 339,7 days of fattening. Five principal components rotated (Varimax) were obtained. The Principal Analysis Components (PCA) showed a higher than 0,66 Kaiser-Meyer-Olkin coefficient, a coefficient of sphericity of significant Bartlett (P < 0,01) and eigenvalues greater than 1. Pooled units in two groups using two cluster analysis. [ABSTRACT FROM AUTHOR]

The dominant mode of July-August (JA) seasonal variability of Indian summer monsoon rainfall (ISMR) are obtained by performing empirical orthogonal function (EOF) analysis. The first dominant mode of ISMR and its relationships with the sea surface temperature (SST), pressure level wind and geopotential height (GPH) fields are examined using gridded datasets for the period 1979-2014. The principal component of the first leading mode (PC1) obtained in the EOF analysis of JA rainfall over Indian landmass is highly correlated with north-west and central India rainfall, and anti-correlated with east-equatorial Atlantic SST (EEASST). The positive EEASST anomaly intensifies the inter-tropical convergence zone over Atlantic and west equatorial Africa which generates stationary wave meridionally, as meridional transfer of energy is strong, as the influence of background jet-streams are minimal over North Africa and Europe. The anomalous positive and negative GPH are generated over sub-tropics and extra-tropics, respectively, due to the stationary wave. This increases the climatological background steep pressure gradient between sub-tropics and extra-tropics consisting of anomalous negative GPH field over north-west (NW) Europe and vice versa for negative EEASST anomaly. The anomalous positive GPH over NW Europe acts as center of action for the propagation of a Rossby wave train to NW India via Europe consisting of anomalous high over NW of India. This intensifies the Tibetan High westward which reinforces the outbreak of monsoon activities over central and NW India. [ABSTRACT FROM AUTHOR]

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We give an explicit algorithm and source code for computing optimal weights for combining a large number N of alphas. This algorithm does not cost $${\mathcal {O}}(N^3)$$ or even $${\mathcal {O}}(N^2)$$ operations but is much cheaper, in fact, the number of required operations scales linearly with N. We discuss how in the absence of binary or quasi-binary 'clustering' of alphas, which is not observed in practice, the optimization problem simplifies when N is large. Our algorithm does not require computing principal components or inverting large matrices, nor does it require iterations. The number of risk factors it employs, which typically is limited by the number of historical observations, can be sizably enlarged via using position data for the underlying tradables. [ABSTRACT FROM AUTHOR]

Sorghum is the most popular crop in arid and semi-arid areas, especially in Sub-Saharan African countries. Genotype effects, environmental and the interaction of genotype by environmental factors have an influence on phenotypic traits. The aim of the study is to identify the relationship between grain yield and other yield-related traits and select the genotypes which perform better in grain yield as well as to examine the association between the uncorrelated phenotypic traits and grain yield via mixed model. The data was generated using a lattice square design. Principal component analysis was used to generate uncorrelated variables for the mixed model. The study revealed that there was a difference in grain yield due to the treatment and there was a pairwise relationship among the phenotypic variables. 77.12% of the total variance of the original phenotypic variables was explained by the first three principal components and decided to use PCAs as input variables for the mixed model. All PCs had significant effects on grain yield as well as grain yield variability due to random effects associated with genotypes, genotype interaction by treatment, and replication within the treatment. The variability of grain yield due to genotype effect was explained about 45.73%, the variation of grain yield due to the interaction of genotype by the treatment was also explained about 39.06% and 1.55% of the variation of grain was explained by replication within treatment. The best performer genotypes recommended for mass production were G40 (Genotype 40), G186 (Genotype 186) and G196 (Genotype 196) without any constraint of environment. The genotypes recommended for mass production under irrigation conditions were G40 (Genotype 40), G62 (Genotype 62) and G192 (Genotype 192). G26 (Genotype 26), G55 (Genotype 55) and G49 (Genotype 49) were the genotypes recommended for mass production under stress conditions. Overall, the study recommends using a mixed model to fit the grain yield, and future work will focus on to evaluate the performance of genotypes under different environments and years of production., Competing Interests: We have no known competing financial interest or personal relationships., (© 2023 The Authors.)

Inference of population structure from genetic data plays an important role in population and medical genetics studies. With the advancement and decreasing cost of sequencing technology, the increasingly available whole genome sequencing data provide much richer information about the underlying population structure. The traditional method originally developed for array-based genotype data for computing and selecting top principal components (PCs) that capture population structure may not perform well on sequencing data for two reasons. First, the number of genetic variants p is much larger than the sample size n in sequencing data such that the sample-to-marker ratio n / p $n/p$ is nearly zero, violating the assumption of the Tracy-Widom test used in their method. Second, their method might not be able to handle the linkage disequilibrium well in sequencing data. To resolve those two practical issues, we propose a new method called ERStruct to determine the number of top informative PCs based on sequencing data. More specifically, we propose to use the ratio of consecutive eigenvalues as a more robust test statistic, and then we approximate its null distribution using modern random matrix theory. Both simulation studies and applications to two public data sets from the HapMap 3 and the 1000 Genomes Projects demonstrate the empirical performance of our ERStruct method., (© 2022 The International Biometric Society.)

With traditional driving cycle predictive model, the state point in vehicle-acceleration projection plane couldn’t cover the real driving state completely. And date-missing caused by this lead to interruption of the prediction process. So in this paper, a real-time prediction model with variable horizon is proposed to solve the problem. Real driving data is used to reconstruct the driving cycle and the accuracy of the real time prediction model could be estimated based on historical information. By using principal component analysis and cluster analysis, an online prediction model with variable horizon based on Marco Chain is established. The correctness of this method is verified by experiment of Hardware-in-loop simulation. And the result shows that the accuracy of variable time prediction model is 8.203km/h, which has been improved by 20% comparing with fixed time prediction model. [ABSTRACT FROM AUTHOR]

Informed by recent work on tensor singular value decomposition and circulant algebra matrices, this paper presents a new theoretical bridge that unifies the hypercomplex and tensor-based approaches to singular value decomposition and robust principal component analysis. We begin our work by extending the principal component pursuit to Olariu's polar $n$-complex numbers as well as their bicomplex counterparts. In doing so, we have derived the polar $n$ -complex and $n$ -bicomplex proximity operators for both the $\ell _1$ - and trace-norm regularizers, which can be used by proximal optimization methods such as the alternating direction method of multipliers. Experimental results on two sets of audio data show that our algebraically informed formulation outperforms tensor robust principal component analysis. We conclude with the message that an informed definition of the trace norm can bridge the gap between the hypercomplex and tensor-based approaches. Our approach can be seen as a general methodology for generating other principal component pursuit algorithms with proper algebraic structures. [ABSTRACT FROM PUBLISHER]

We study the impact on return and volatility of 8 capital markets (Bulgaria, Czech Republic, France, Germany, Hungary, Poland, Romania and US) during three crisis (subprime crisis, global financial crisis and Brexit), that occurred in January, 1st 2007 - August, 26th, 2016 time frame. In order to reduce the initial causal space represented by the returns on CEE markets, we used the Principal Component Analysis. One principal component was identified and placed within a AR(2)-GARCH(1,1) model for return and associated volatility. The results show that the volatility during the global crisis and that related to the Brexit moment are statistically significant, but they present different coefficients: the volatility boosted during the global financial crisis and lowered after the Brexit announcement. This late result can be explained by the prudent stance of the investors who are concerned to evaluate the impact of the Brexit and the consequences for the financial markets of the associated measures. [ABSTRACT FROM AUTHOR]

Operational transfer path analysis (OTPA) calculates contributions of reference points to response point vibration by using only operational data. Through OTPA, effective interior noise and vibration reduction are achieved by applying intensive countermeasure to the high contributing part. However, it becomes difficult occasionally when many reference points have similar contributions by a vibration mode. In this case, obtaining high contributing vibration mode and considering how to reduce the mode become important information. In this study, we attempted to calculate the vibration mode contribution by modifying OTPA. Principal component calculated in OTPA procedure is composed of correlated vibration factors among reference points. We then considered the relationship between the principal component and the vibration mode, and associated the principal components with the vibration modes of a test structure. As a result, high contributing vibration modes to the response point could be found. In addition, information about which side of the structure (response or reference side) had better to be measured intensively was also obtained by evaluating the influence of each principal component to the response point (principal component transfer function). Finally, Several countermeasures were applied to the structure considering the principal component and vibration mode contributions. The result shows effective vibration reduction at the response point could be carried out. Through these procedures, the modified OTPA became more useful tool for applying effective countermeasure.

The paper reports the results of the study of hydrogen bonding complexes between secondary amides and various aromatic hydrocarbons. The possibility of using chemometric methods was investigated in order to characterize N-H•••π hydrogen bonded complexes. Hierarchical clustering and Principal Component Analysis (PCA) have been applied on infrared spectroscopic and Taft parameters of 43 N-substituted amide complexes with different aromatic hydrocarbons. Results obtained in this report are in good agreement with conclusions of other spectroscopic and thermodynamic analysis.

The aim of this paper was the application of multivariate statistical techniques to evaluate spatial and temporal variations in the water quality of Potrero de los Funes River using physical, chemical and bacteriological parameters and select the most significant parameters of organic pollution in the river in order to implement in the future water quality monitoring. The river was monitored regularly at three sites: RP1, RP2 and RP3, over the period 2008–2009, for 16 parameters. The complex data matrix was treated with three multivariate statistical techniques: cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA). CA generated three groups of sites, cluster 1 (RP1), cluster 2 (RP2) and cluster 3 (RP3) according to relatively low, very high and moderate pollution regions, respectively. PCA identified two components, which were responsible for the data structure explaining 73% of the total variance of the data matrix. Temporal DA (Wet season and Dry season) showed that turbidity, NO3- and COD were the discriminant variables. Spatial DA shows that there were significant differences between the three categorical classes, 1 (RP1, low pollution region), 2 (RP2, strongly polluted zone) and 3 (RP3, moderate polluted site) .The discriminating functions contained only eight parameters (EC, NO3-, turbidity, DO, BOD, COD, total coliform and fecal coliform) to discriminate between sites. The application of these techniques has achieved meaningful classification of physical, chemical and bacteriological variables and of river water samples, based on seasonal and spatial criteria. This study is essential for the future design of fast and effective monitoring programs of river water quality. That would include only parameters that are indicative of organic pollution.

O objetivo deste trabalho foi avaliar a repetibilidade da produção de sementes 27 clones guaraná (Paullinia cupana). Os clones foram avaliados em dez ensaios instalados em blocos ao acaso com duas repetições e três plantas por parcela, com espaçamento de 5x5 m. A produção de sementes foi avaliada por quatro anos (1998, 1999, 2000 e 2001) a partir do início da fase produtiva, após dois anos do plantio. O coeficiente de repetibilidade foi estimado pelos métodos: análise de variância (ANOVA), componentes principais com base em matriz de correlações (CPCOR), componentes principais com base em matriz de variâncias e covariâncias fenotípicas (CPCOV) e análise estrutural com base na matriz de correlações (AECOR). Foram estimados também o número mínimo de observações e o coeficiente de determinação. As estimativas dos coeficientes de repetibilidade com base nos quatro anos, nos dez ambientes, foram relativamente baixas - 0,2618 (AECOR), 0,3064 (CPCOR), 0,3183 (ANOVA) e 0,5308 (CPCOV) - e variaram de acordo com o método e o ambiente. São necessários, no mínimo, quatro anos de avaliação para a obtenção de valores genotípicos dos clones com coeficiente de determinação de 80%.The aim of this work was to evaluate the seed yield repeatability of 27 guarana clones. Ten assays were carried out for clone evaluation using a completely randomized block design, with two replicates and three plants per plot with a 5x5 m spacing. The seed yield was measured during four years (1998, 1999, 2000 and 2001) after the beginning of the production phase, two years after planting. Four methods were used for repeatability coefficient estimation: analysis of variance (ANOVA), principal components analysis based on correlation matrix (CPCOR), principal components analysis based on phenotypical variance and covariance matrix (CPCOV), and structural analysis based on correlation matrix (AECOR). Minimum observations number and determination coefficient were also evaluated. The repeatability coefficient estimates based on these four years, in all ten environments, were relatively low - 0.2618 (AECOR), 0.3064 (CPCOR), 0.3183 (ANOVA) and 0.5308 (CPCOV) - and varied according to method and environment. At least four years of evaluation are necessary for obtaining the real genotypical value of the clones for an 80% determination coefficient.

Starting from the recent UNICEF publications on child poverty in the developed countries, which received a wide audience in the political and scientific world, in this paper we further analyze the UNICEF study data base and present three composite indices that are multidimensional and quantitative measures of child well-being. While the original UNICEF studies simply added together the ranks on different measurement scales, we present a much more sophisticated approach, with the first of our indicators being a non-parametric measure, while the remaining two are parametric. In the non-parametric index of child welfare, the well-being indicators are given the same weights in their aggregation to form different components from which an overall index is being constructed. Two different forms of the parametric index are estimated by using principal component analysis. The first model uses a pool of all indicators without classification of the indicators by type of well-being, while the second model estimates first the sub-components separately and then uses the share of variance explained by each principal component to compute the weighted average of each component and their aggregation into an index of overall child well-being. The indices indicate which countries have the best system of child welfare and show how child well-being varies across countries and regions. The indices are composed of six well-being components including material, health and safety, educational well-being, family and peer relationships, behaviours and risks and subjective well-being. Each of the components is generated from a number of well-being sub-indicators.