Spatial coherence and persistence in annual streamflow in the western United States [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): Four broad regions of the western United States within which annual streamflows exhibit strong spatial coherence are identified using principal component analysis with a varimax rotation. Geographically, the four regions encompass the Pacific Northwest, Far West-Great Basin, Central Rockies-High Plains, and Northern Great Plains. These regions are really consistent with previously documented, descriptively derived streamflow regimes as well as with general atmospheric circulation and precipitation modes of variation. Collectively, the four regional components account for nearly 63 percent of the total annual variation in western U.S. streamflow. The time history of most principal component patterns exhibit little or no persistence.

Surface temperature and precipitation variations in the western U.S. and their relation to large scale circulation indices [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): An analysis of the principal components of surface temperature and precipitation in the western U.S. is presented. Data consist of monthly mean temperature and total precipitation for 66 climate divisions west of the Continental Divide, for the years 1931-1984. The analysis is repeated for three separate combinations of months - the water year (Oct - Sept), the cool season (Oct - Mar) and the warm season (Apr - Sept). Inspection of monthly precipitation climatology indicates that selection of these combinations of months results in very few awkward splittings of the natural precipitation seasons found in the West.

PCB-integrated optical waveguide sensors: An ammonia gas sensor

This paper presents a novel platform for the formation of cost-effective PCB-integrated optical waveguide sensors. The sensor design relies on the use of multimode polymer waveguides that can be formed directly on standard PCBs and commercially-available chemical dyes, enabling the integration of all essential sensor components (electronic, photonic, chemical) on low-cost substrates. Moreover, it enables the detection of multiple analytes from a single device by employing waveguide arrays functionalised with different chemical dyes. The devices can be manufactured with conventional methods of the PCB industry, such as solder-reflow processes and pick-and-place assembly techniques. As a proof of principle, a PCB-integrated ammonia gas sensor is fabricated on a FR4 substrate. The sensor operation relies on the change of the optical transmission characteristics of chemically functionalised optical waveguides in the presence of ammonia molecules. The fabrication and assembly of the sensor unit, as well as fundamental simulation and characterisation studies, are presented. The device achieves a sensitivity of approximately 30 ppm and a linear response up to 600 ppm at room temperature. Finally, the potential to detect multiple analytes from a single device is demonstrated using principal-component analysis. © 1983-2012 IEEE.

Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.

Mobility and profiles of polychlorinated dibenzo-p-dioxins and dibenzofurans in sediment of Ya-Er Lake, China

The sediment of Ya-Er Lake had been heavily polluted by polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) from the former chloralkali industry. The total amounts of PCDD/Fs and I-TEQ decreased along the water flow direction and also decreased from top to bottom layers of sediment cores. Sediment of Pond 1 was dominated by PCDF, especially TCDF. In contrast, in the other four ponds, PCDD dominated in all layers and octachlorinated dibenzo-p-dioxin (OCDD) predominated in all of the homologues. When homologue profiles from sediments and water samples were compared using principal component analysis (PCA), the first two principal components represented 95.2% of the variance in the data. The first component explained 75.9% of the variance and the second one 19.3%. Two clusters were most distinct, presenting a shift in PCDD/Fs composition from PCDF to heptachlorinated dibenzo-p-dioxin (HpCDD) and OCDD in sediments and water from Pond I to Ponds 2-5. The pattern variation between Pond 1 and Ponds 2-5 in Ya-Er Lake was most likely due to the change of process in the chemical plant after the dams between the ponds were built. The results of the present study also showed that log K-oc of PCDD/Fs calculated from data of sediment and water in the field were comparable with theoretical log K-oc. The results also implied that the concentrations of PCDD/Fs in water and sediments could be predicted from each other by log K-oc. (C) 2001 Elsevier Science Ltd. All rights reserved.

Fast Discrimination of Commerical Corn Varieties Based on Near Infrared Spectra

The existing methods for the discrimination of varieties of commodity corn seed are unable to process batch data and speed up identification, and very time consuming and costly. The present paper developed a new approach to the fast discrimination of varieties of commodity corn by means of near infrared spectral data. Firstly, the experiment obtained spectral data of 37 varieties of commodity corn seed with the Fourier transform near infrared spectrometer in the wavenurnber range from 4 000 to 12 000 cm (1). Secondly, the original data were pretreated using statistics method of normalization in order to eliminate noise and improve the efficiency of models. Thirdly, a new way based on sample standard deviation was used to select the characteristic spectral regions, and it can search very different wavenumbers among all wavenumbers and reduce the amount of data in part. Fourthly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 99.98%. Finally, according to the first ten components, recognition models were established based on BPR. For every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 900 samples of the other varieties were used as the second testing set. Calculation results showed that the average correctness recognition rate of the 37 varieties of corn seed was 94.3%. Testing results indicate that the discrimination method had higher precision than the discrimination of various kinds of commodity corn seed. In short, it is feasible to discriminate various varieties of commodity corn seed based on near infrared spectroscopy and BPR.

A New Discrimination Method of Maize Seed Varieties Based on Near-Infrared Spectroscopy

A new discrimination method for the maize seed varieties based on the near-infrared spectroscopy was proposed. The reflectance spectra of maize seeds were obtained by a FT-NIR spectrometer (12 000-4 000 cm(-1)). The original spectra data were preprocessed by first derivative method. Then the principal component analysis (PCA) was used to compress the spectra data. The principal components with the cumulate reliabilities more than 80% were used to build the discrimination models. The model was established by Psi-3 neuron based on biomimetic pattern recognition (BPR). Especially, the parameter of the covering index was proposed to assist to discriminating the variety of a seed sample. The authors tested the discrimination capability of the model through four groups of experiments. There were 10, 18, 26 and 34 varieties training the discrimination models in these experiments, respectively. Additionally, another seven maize varieties and nine wheat varieties were used to test the capability of the models to reject the varieties not participating in training the models. Each group of the experiment was repeated three times by selecting different training samples at random. The correct classification rates of the models in the four-group experiments were above 91. 8%. The correct rejection rates for the varieties not participating in training the models all attained above 95%. Furthermore, the performance of the discrimination models did not change obviously when using the different training samples. The results showed that this discrimination method can not only effectively recognize the maize seed varieties, but also reject the varieties not participating in training the model. It may be practical in the discrimination of maize seed varieties.

环境因素影响草地柠条生长的主成分及典型相关分析

Principal Component and Canonical Correlation Analysis of the Environmental Factors Influencing the Growth of Caragana korshinskii Kom. in Grassland

A QUANTITATIVE ASSESSMENT OF SOURCE CONTRIBUTIONS TO INHALABLE PARTICULATE MATTER POLLUTION IN METROPOLITAN BOSTON

In this paper, source apportionment techniques are employed to identify and quantify the major particle pollution source classes affecting a monitoring site in metropolitan Boston, MA. A Principal Component Analysis (PCA) of paniculate elemental data allows the estimation of mass contributions for five fine mass panicle source classes (soil, motor vehicle, coal related, oil and salt aerosols), and six coarse panicle source classes (soil, motor vehicle, refuse incineration, residual oil, salt and sulfate aerosols). Also derived are the elemental characteristics of those source aerosols and their contributions to the total recorded elemental concentrations (i.e. an elemental mass balance). These are estimated by applying a new approach to apportioning mass among various PCA source components: the calculation of Absolute Principal Component Scores, and the subsequent regression of daily mass and elemental concentrations on these scores.

Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters

Empirical Orthogonal Function (EOF) analysis is used in this study to generate main eigenvector fields of historical temperature for the China Seas (here referring to Chinese marine territories) and adjacent waters from 1930 to 2002 (510 143 profiles). A good temperature profile is reconstructed based on several subsurface in situ temperature observations and the thermocline was estimated using the model. The results show that: 1) For the study area, the former four principal components can explain 95% of the overall variance, and the vertical distribution of temperature is most stable using the in situ temperature observations near the surface. 2) The model verifications based on the observed CTD data from the East China Sea (ECS), South China Sea (SCS) and the areas around Taiwan Island show that the reconstructed profiles have high correlation with the observed ones with the confidence level > 95%, especially to describe the characteristics of the thermocline well. The average errors between the reconstructed and observed profiles in these three areas are 0.69A degrees C, 0.52A degrees C and 1.18A degrees C respectively. It also shows the model RMS error is less than or close to the climatological error. The statistical model can be used to well estimate the temperature profile vertical structure. 3) Comparing the thermocline characteristics between the reconstructed and observed profiles, the results in the ECS show that the average absolute errors are 1.5m, 1.4 m and 0.17A degrees C/m, and the average relative errors are 24.7%, 8.9% and 22.6% for the upper, lower thermocline boundaries and the gradient, respectively. Although the relative errors are obvious, the absolute error is small. In the SCS, the average absolute errors are 4.1 m, 27.7 m and 0.007A degrees C/m, and the average relative errors are 16.1%, 16.8% and 9.5% for the upper, lower thermocline boundaries and the gradient, respectively. The average relative errors are all < 20%. Although the average absolute error of the lower thermocline boundary is considerable, but contrast to the spatial scale of average depth of the lower thermocline boundary (165 m), the average relative error is small (16.8%). Therefore the model can be used to well estimate the thermocline.

基于改进的OFA-MPCA的监控方法

多向主元分析(MPCA)是利用多变量统计方法从纷杂的海量数据信息中提取出能够准确表征数据信息的几个主元,并通过投影法来降低数据的维数,主要应用于间歇生产过程中.在实际的间歇生产过程中,由于各种原因导致各批次异步造成它们运行时间的不一致,而无法直接建立有效的统计模型,正交函数近似(OFA)是一种基于正交基的投影变换技术,通过对原始数据进行OFA处理后,可以用投影系数来描述原始数据所具有的特征,并且可以达到轨迹同步化和压缩数据量的目的.对OFA法进行了部分改进,并结合MPCA法对典型的间歇过程——青霉素发酵过程进行了仿真研究.结果表明,改进的OFA计算速度有了极大的提高,且改进的OFA-MPCA法能完好地对各批次进行同步、建模并得出准确的监视结果.

内蒙古龙头山Ag-Pb-Zn多金属矿床成矿模式及找矿模型

The recent years research indicated that middle-south section of Da Hinggan Mountains metallogenic belt has two periods(Hercynian and Yanshanian) characteristics of metallogenesis, as well as the most of ore deposits in the area closely relate to Permian strata. Longtoushan ore deposit discovered in 2004 is an Ag-Pb-Zn polymetallic ore deposit born in Permian and located in the east hillside of the metallogenic belt, which has considerable resources potentials. It has important research value for its good metallogenic location and blank research history. Base on the detail field geology studies, the geology characteristics of "two stages and three kinds of metallogensis" has established. According to further work through geochemistry research including trace element, REE, S, Pb and Sr isotope, as well as petrography, microtemperature measurement, Laser Raman analysis and thermodynamics calculation of fluid inclusion, origin and characteristic of the ore-forming material and fluid has been discussed. And a new technology of single pellet Rb-Sr isochrones has been tried for dating its born time. Bae on above work, study of ore deposit comparison has been carried out, and metallogesis controlling factor and geological prospecting symbol have been summarized. Finally, metallogenic model and prospecting model have been established. According to above, the next step work direction has been proposed. Main achievement of the paper are listed as follow: 1.Longtoushan ore deposit has experienced two metallogenic periods including hot-water sedimentation period and hydrothermal reformation period. There are three kinds of metallizing phase: bedded(or near-bedded) phase, vein-shaped phase and pipe-shaped phase. The mian metallogenic period is hot-water sedimentation period. 2.Ore deposit geochemistry research indicated that the metal sulfides have charcateristic of hot-water sedimentation metallogensis, but generally suffered later hydrothermal transformation. The barite mineral isotope content is homogenous, showing the seabed hot-water sedimentation origin characteristic. Wall rock, such as tuff is one of metallogenic material origins. Both of Pb model age and Rb-Sr isochrone research older age value than that of strata, possibly for been influenced by hydrothermal transformation, and interfusion of ancient basis material. 3.There are two kinds of main metallogenic fluid inclusion in barite of the Longtoushan ore deposit, which are rich gas phase( C type) and liquid phase (D type). Their size is 2～7um, and principal components is H2O. Both kinds of fluid inclusion have freezing point temperature -7.1～-2.4℃ and -5.5～-0.3℃, salinity 4.0～10.6wt% and 0.5～8.5wt%, homogeneous temperature 176.8～361.6℃ and 101.4～279.9℃, which peak value around 270℃ and 170℃, respectively. Density of the ore-forming fluid is 0.73～0.97g/cm3, and metallogenic pressure is 62.3×105～377.9×105Pa. Above characteristic of the fluid inclusion are well geared to that of ore deposit originated in seabed hot-water sedimentation. 4.Through the comparison research, that Longtoushan ore deposit has main characteristic of hot-water sedimentation ore deposit has been indicated. Ore-forming control factor and prospecting symbol of it has been summarized, as well as metallogenic model and prospecting model. Next step work direction about prospecting has also been proposed finally.

O comportamento do consumidor de software de gestão e contabilidade das PMEs em Portugal, França e Espanha

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Empresariais.

Contributo para o desenvolvimento de um Instrumento de Investigação Neuropsicológica

Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais, especialidade em Psicologia

Liquidity risk and the performance of UK mutual funds

We examine the role of liquidity risk, both as a stock characteristic as well as systematic liquidity risk, in UK mutual fund performance for the first time. Using four alternative measures of stock liquidity we extract principal components across stocks in order to construct systematic or market liquidity factors. We find that on average UK mutual funds are tilted towards liquid stocks (except for small stock funds as might be expected) but that, counter-intuitively, liquidity as a stock characteristic is positively priced in the cross-section of fund performance. We find that systematic liquidity risk is positively priced in the cross-section of fund performance. Overall, our results reveal a strong role for stock liquidity level and systematic liquidity risk in fund performance evaluation models.