Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

MULTIVARIATE CLASSIFICATION BASED ON METAL CONTENTS IN HUMAN SENILE CATARACT LENSES

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.

Geochemical character and material source of sediments in the eastern Philippine Sea

Based upon analyses of grain-size, rare earth element (REE) compositions, elemental occurrence phases of REE, and U-series isotopic dating, the sediment characteristics and material sources of the study area were examined for the recently formed deep-sea clays in the eastern Philippine Sea. The analytical results are summarized as follows. (1) Low accumulation rate, poor sorting and roundness, and high contents of grains coarser than fine silt indicate relatively low sediment input, with localized material source without long distance transport. (2) The REE Contents are relatively high. Shale-normalized patterns of REE indicate weak enrichment in heavy REE (HREE), Ce-passive anomaly, and Eu-positive anomaly. (3) Elemental occurrence phases of REE between the sediments with and without crust are similar. REE mainly concentrate in residual phase and then in ferromanganese oxide phase. The light REE (LREE) enrichment, Ce-positive anomaly, and Eu-positive anomaly occur in residual phase. Ferromanganese oxide phase shows the characteristics of relatively high HREE content and Ce-passive anomaly. (4) There are differences in each above mentioned aspect between the sediments with and without ferromanganese crust. (5) Synthesizing the above characteristics and source discriminant analysis, the study sediments are deduced to mainly result from the alteration of local and nearby volcanic materials. Continental materials transported by wind and/or river (ocean) flows also have minor contributions.

改进的统计不相关最优鉴别矢量集

该文对统计不相关最优鉴别矢量集算法进行研究,在分析统计不相关最优鉴别矢量集算法的基础上提出了一种改进的方法。该方法在类内散布矩阵的特征空间中求解统计不相关最优鉴别矢量集。为了加快特征抽取速度,利用基于图像鉴别分析的维数压缩方法,对图像数据进行了压缩。在ORL和Yale人脸数据库的数值实验,验证本文所提出的方法的有效性。

广义统计不相关最优鉴别矢量集的一个理论结果

本文对统计不相关最优鉴别矢量集的理论问题进行研究 ,提出了广义统计不相关最优鉴别准则 ,并给出了广义统计不相关最佳鉴别矢量集的一个理论结果 ,研究表明 ,广义统计不相关最佳鉴别矢量集的计算公式与基于Fisher最优鉴别准则的统计不相关最佳鉴别矢量集的计算公式完全一样 ,但是以前这一点没有被认识到 .本文的研究丰富了统计不相关最优鉴别分析的特征抽取理论 .

基于模糊神经网络的人脸识别方法研究

人脸识别是模式识别研究领域的重要课题,具有广阔的应用前景。本文提出了基于模糊神 经网络的人脸识别方法。首先用最优鉴别分析方法提取人脸的最优鉴别矢量集,构成特征空间,然后在 特征空间中设计模糊神经网络分类器。在ORL人脸图象库上的实验结果表明了该方法的有效性。

渤海海域河流相油藏精细描述技术及应用研究

Aiming at the character of Bohaii Sea area and the heterogeneity of fluvial facies reservoir, litho-geophysics experiments and integrated research of geophysical technologies are carried out. To deal with practical problems in oil fields of Bohai area, such as QHD32-6, Southern BZ25-1 and NP35-2 et al., technology of reservoir description based on seismic data and reservoir geophysical methods is built. In this dissertation, three points are emphasized: ①the integration of multidiscipline; ②the application of new methods and technologies; ③the integration of quiescent and dynamic data. At last, research of geology modeling and reservoir numerical simulation based on geophysical data are integrated. There are several innovative results and conclusion in this dissertation: (1)To deal with problems in shallow sea area where seismic data is the key data, a set of technologies for fine reservoir description based on seismic data in Bohai Sea area are built. All these technologies, including technologies of stratigraphic classification, sedimentary facies identification, structure fine characterization, reservoir description, fluid recognition and integration of geological modeling& reservoir numerical simulation, play an important role in the hydrocarbon exploration and development. In the research of lithology and hydrocarbon-bearing condition, petrophysical experiment is carried out. Outdoors inspection and experiment test data are integrated in seismic forward modeling& inversion research. Through the research, the seismic reflection rules of fluid in porosity are generated. Based on all the above research, seismic data is used to classify rock association, identify sedimentary facies belts and recognition hydrocarbon-bearing condition of reservoir. In this research, the geological meaning of geophysical information is more clear and the ambiguity of geophysical information is efficiently reduced, so the reliability in hydrocarbon forecasting is improved. The methods of multi-scales are developed in microfacies research aiming at the condition of shallow sea area in Bohai Sea: ① make the transformation from seismic information to sedimentary facies reality by discriminant analysis; ②in research of planar sedimentary facies, make microfacies research on seismic scale by technologies integration of seismic multi-attributes analysis& optimization, strata slicing and seismic waveform classification; ③descript the sedimentary facies distribution on scales below seismic resolution with the method of stochastic modeling. In the research of geological modeling and reservoir numerical simulation, the way of bilateral iteration between modeling and numerical simulation is carried out in the geological model correction. This process include several steps: ①make seismic forward modeling based on the reservoir numerical simulation results and geological models; ②get trend residual of forward modeling and real seismic data; ③make dynamic correction of the model according to the above trend residual. The modern integration technology of reservoir fine description research in Bohai Sea area, which is developed in this dissertation, is successfully used in (1)the reserve volume evaluation and development research in BZ25-1 oil field and (2)the tracing while drilling research in QHD32-6 oil field. These application researches show wide application potential in hydrocarbon exploration and development research in other oil fields.

松辽盆地古龙地区葡萄花油层组沉积成岩相研究

The sediment and diagenesis process of reservoir are the key controlling factors for the formation and distribution of hydrocarbon reservoir. For quite a long time, most of the research on sediment-diagenesis facise is mainly focusing on qualitative analysis. With the further development on exploration of oil field, the qualitative analysis alone can’t meet the requirements of complicated requirements of oil and gas exploreation, so the quantitative analysis of sediment-diagenesis facise and related facies modling have become more and more important. On the basis of the research result from stratum and sediment on GuLong Area Putaohua Oil Layer Group, from the basic principles of sedimentology, and with the support from the research result from field core and mining research results, the thesis mainly makes the research on the sediment types, the space framework of sands and the evolution rules of diagenesis while mainly sticking to the research on sediment systement analysis and diagenetic deformation, and make further quantitative classification on sediment-diageneses facies qualitatively, discussed the new way to divide the sediment-diagenesis facies, and offer new basis for reservoir exploration by the research. Through using statistics theory including factor analysis, cluster analysis and discriminant analysis, the thesis devided sediment-diagenesis facies quantitatively. This research method is innovative on studying sediment-diagenesis facies. Firstly, the factor analysis could study the main mechanism of those correlative variables in geologic body, and then could draw a conclusion on the control factors of fluid and capability of reservoir in the layer of studying area. Secondly, with the selected main parameter for the cluster analysis, the classification of diagenesis is mainly based on the data analysis, thus the subjective judgement from the investigator could be eliminated, besides the results could be more quantitative, which is helpful to the correlative statistical analysis, so one could get further study on the quantitative relations of each sediment-diagenesis facies type. Finally, with the reliablities of discriminant analysis cluster results, and the adoption of discriminant probability to formulate the chart, the thesis could reflect chorisogram of sediment-diagenesis facies for planar analysis, which leads to a more dependable analytic results.According to the research, with the multi-statistics analysis methods combinations, we could get quantitative analysis on sediment-diagenesis facies of reservoir, and the final result could be more reliable and also have better operability.

陕北桥镇油田延长组长2、长1油层组沉积相及储层研究

The Ordos Basin is a large-scale craton superimposed basin locating on the west of the North China platform, which was the hotspot of interior basin exploration and development. Qiaozhen oil field located in the Ganquan region of south-central of Ordos Basin. The paper is based on the existing research data, combined with the new theory and progress of the sedimentology, sequence stratigraphy, reservoir sedimentology, petroleum geology, etc, and analyzes systematically the sedimentary and reservoir characteristics in the chang2 and chang1 oil-bearing strata group of Yanchang formation On the basis of stratigraphic classification and comparison study, the strata chang2 and chang1 were divided into five intervals. Appling the method of cartography with single factor and dominance aspect, we have drawn contour line map of sand thickness, contour line map of ratio between sand thickness and stratum thickness. We discussed distribution characteristics of reservoir sand body and evolution of sedimentary facies and microfacies. And combining the field type section , lithologic characteristics, sedimentary structures, the sedimentary facies of single oil well and particle size analysis and according to the features of different sequence, the study area was divided into one sedimentary facies、three parfacies and ten microfacies. The author chew over the characteristics of every facies, parfacies and microfacies and spatial and temporal distribution. Comprehensive research on petrologic characteristics of reservoir , diagenesis types, pore types, distribution of sand bodies, physical properties, oiliness, reservoir heterogeneities, characteristics of interlayer, eventually research on synthetic classifying evaluation of reservoir.The reservoir is classified four types: Ⅰ、Ⅱ、Ⅲ、Ⅳ and pore type, fracture-porosity type. Take reservoir's average thickness, porosity, permeability, oil saturation and shale content as parameters, by using clustering analysis and discriminant analysis, the reservoir is classified three groups. Based on the evaluation, synthetizing the reservoir quality, the sealing ability of cap rock, trap types, reservoir-forming model ,in order to analyze the disciplinarian of accumulation oil&gas. Ultimately, many favorable zones were examined for chang23，chang223，chang222，chang221，chang212，chang12，chang11 intervals. There are twenty two favorable zones in the research area. Meanwhile deploy the next disposition scheme.

社会形势预警的几种建模方法比较

Based on social survey data conducted by local research group in some counties executed in the nearly past five years in China, the author proposed and solved two kernel problems in the field of social situation forecasting: i) How can the attitudes’ data on individual level be integrated with social situation data on macrolevel; ii) How can the powers of forecasting models’ constructed by different statistic methods be compared? Five integrative statistics were applied to the research: 1) algorithm average (MEAN); 2) standard deviation (SD); 3) coefficient variability (CV); 4) mixed secondary moment (M2); 5) Tendency (TD). To solve the former problem, the five statistics were taken to synthesize the individual and mocrolevel data of social situations on the levels of counties’ regions, and form novel integrative datasets, from the basis of which, the latter problem was accomplished by the author: modeling methods such as Multiple Regression Analysis (MRA), Discriminant Analysis (DA) and Support Vector Machine (SVM) were used to construct several forecasting models. Meanwhile, on the dimensions of stepwise vs. enter, short-term vs. long-term forecasting and different integrative (statistic) models, meta-analysis and power analysis were taken to compare the predicting power of each model within and among modeling methods. Finally, it can be concluded from the research of the dissertation: 1) Exactly significant difference exists among different integrative (statistic) models, in which, tendency (TD) integrative models have the highest power, but coefficient variability (CV) ones have the lowest; 2) There is no significant difference of the power between stepwise and enter models as well as short-term and long-term forecasting models; 3) There is significant difference among models constructed by different methods, of which, support vector machine (SVM) has the highest statistic power. This research founded basis in all facets for exploring the optimal forecasting models of social situation’s more deeply, further more, it is the first time methods of meta-analysis and power analysis were immersed into the assessments of such forecasting models.

不同行业的企业形象要素体系的共性与个性

In the early part of this century, with the change from the seller's market to the buyer market, the competition between companies changed from product competition, selling competition to corporate image competition, and companies began to consciously build corporate reputation through fast developed mass media. As a result, a series of methods to build corporate image were created, such as advertising, public relations and corporate identify system(CIS), which ,in turn, promoted the development of the research of corporate image. The factors of corporate image have been the central issue of the corporate image research, for the probe of this issue is of great significance to both the development of corporate image theory and the practice of corporate image building. As far as the literature we have gathered is concerned, the exiting research on this topic either remains at the level of qualitative investigating and induction, or is limited in some particular industry. Therefore. There bean no commonly accepted corporate image theory so far. In the recent years, with the introduction of competition mechanism and the establishment of the company. As subject position in the market, the building of corporate image gas been developed quickly in our country, and the development of practice imperatively requires the guide of scientific theory. On the basis of the analysis and summarization of the research of the predecessors, the present dissertation attempts to do some investigation and research work on the common and individual characteristics of corporate image factors of the companies in different industries in our country. The method of questionnaire survey is used in the present research. The subject sample is gathered on the basis of convenience and feasibility, and at the mean time, some consideration is also given to straticulate randomization principles. The subjects are asked to select one of their most familiar companies, and determine the important of even item in the questionnaire to the selected company(i.e. the importance assessment), and then, determine the grades the selected company gains on every item(i.e. the image assessment). The discriminant analysis of corporate image of different industries. The selected sample is grouped and coded according to the standard of industry classification. The discriminant analysis is done with the selected companies as the sample and the grades of image assessment as the variables. The result indicates that industry variable is an important standard of the classification of corporate image, and the companies in the same industry are more similar in corporate image. The analysis of the common and individual characteristics of corporate image of different industries. Firstly, in every industry, the items are sieved according to the grades of importance assessment, and exploratory factor analysis is done with grades of image assessment on the selected items as the variables. Secondly, the factors drawn from every industry in arranged in order according to their importance. The result indicates that the corporate image of different industries shares some common characteristics, for there exist common factors among different industries. In the mean while, the corporate image of different industries has its individual characteristics, that is, there is some difference in the domain of the factors, and in the order of the factors(including the difference of the principle factor).

Early biomarkers to predict grade of encephalopathy following hypoxic ischaemic injury

The standard early markers for identifying and grading HIE severity, are not sufficient to ensure all children who would benefit from treatment are identified in a timely fashion. The aim of this thesis was to explore potential early biomarkers of HIE. Methods: To achieve this a cohort of infants with perinatal depression was prospectively recruited. All infants had cord blood samples drawn and biobanked, and were assessed with standardised neurological examination, and early continuous multi-channel EEG. Cord samples from a control cohort of healthy infants were used for comparison. Biomarkers studied included; multiple inflammatory proteins using multiplex assay; the metabolomics profile using LC/MS; and the miRNA profile using microarray. Results: Eighty five infants with perinatal depression were recruited. Analysis of inflammatory proteins consisted of exploratory analysis of 37 analytes conducted in a sub-population, followed by validation of all significantly altered analytes in the remaining population. IL-6 and IL-6 differed significantly in infants with a moderate/severely abnormal vs. a normal-mildly abnormal EEG in both cohorts (Exploratory: p=0.016, p=0.005: Validation: p=0.024, p=0.039; respectively). Metabolomic analysis demonstrated a perturbation in 29 metabolites. A Cross- validated Partial Least Square Discriminant Analysis model was developed, which accurately predicted HIE with an AUC of 0.92 (95% CI: 0.84-0.97). Analysis of the miRNA profile found 70 miRNA significantly altered between moderate/severely encephalopathic infants and controls. miRNA target prediction databases identified potential targets for the altered miRNA in pathways involved in cellular metabolism, cell cycle and apoptosis, cell signaling, and the inflammatory cascade. Conclusion: This thesis has demonstrated that the recruitment of a large cohortof asphyxiated infants, with cord blood carefully biobanked, and detailed early neurophysiological and clinical assessment recorded, is feasible. Additionally the results described, provide potential alternate and novel blood based biomarkers for the identification and assessment of HIE.

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Safety, tolerability, and mechanisms of antiretroviral activity of pegylated interferon Alfa-2a in HIV-1-monoinfected participants: a phase II clinical trial.

BACKGROUND: To our knowledge, the antiviral activity of pegylated interferon alfa-2a has not been studied in participants with untreated human immunodeficiency virus type 1 (HIV-1) infection but without chronic hepatitis C virus (HCV) infection. METHODS: Untreated HIV-1-infected volunteers without HCV infection received 180 microg of pegylated interferon alfa-2a weekly for 12 weeks. Changes in plasma HIV-1 RNA load, CD4(+) T cell counts, pharmacokinetics, pharmacodynamic measurements of 2',5'-oligoadenylate synthetase (OAS) activity, and induction levels of interferon-inducible genes (IFIGs) were measured. Nonparametric statistical analysis was performed. RESULTS: Eleven participants completed 12 weeks of therapy. The median plasma viral load decrease and change in CD4(+) T cell counts at week 12 were 0.61 log(10) copies/mL (90% confidence interval [CI], 0.20-1.18 log(10) copies/mL) and -44 cells/microL (90% CI, -95 to 85 cells/microL), respectively. There was no correlation between plasma viral load decreases and concurrent pegylated interferon plasma concentrations. However, participants with larger increases in OAS level exhibited greater decreases in plasma viral load at weeks 1 and 2 (r = -0.75 [90% CI, -0.93 to -0.28] and r = -0.61 [90% CI, -0.87 to -0.09], respectively; estimated Spearman rank correlation). Participants with higher baseline IFIG levels had smaller week 12 decreases in plasma viral load (0.66 log(10) copies/mL [90% CI, 0.06-0.91 log(10) copies/mL]), whereas those with larger IFIG induction levels exhibited larger decreases in plasma viral load (-0.74 log(10) copies/mL [90% CI, -0.93 to -0.21 log(10) copies/mL]). CONCLUSION: Pegylated interferon alfa-2a was well tolerated and exhibited statistically significant anti-HIV-1 activity in HIV-1-monoinfected patients. The anti-HIV-1 effect correlated with OAS protein levels (weeks 1 and 2) and IFIG induction levels (week 12) but not with pegylated interferon concentrations.

The effect of differences in methodology among some recent applications of shearing quotients.

A shearing quotient (SQ) is a way of quantitatively representing the Phase I shearing edges on a molar tooth. Ordinary or phylogenetic least squares regression is fit to data on log molar length (independent variable) and log sum of measured shearing crests (dependent variable). The derived linear equation is used to generate an 'expected' shearing crest length from molar length of included individuals or taxa. Following conversion of all variables to real space, the expected value is subtracted from the observed value for each individual or taxon. The result is then divided by the expected value and multiplied by 100. SQs have long been the metric of choice for assessing dietary adaptations in fossil primates. Not all studies using SQ have used the same tooth position or crests, nor have all computed regression equations using the same approach. Here we focus on re-analyzing the data of one recent study to investigate the magnitude of effects of variation in 1) shearing crest inclusion, and 2) details of the regression setup. We assess the significance of these effects by the degree to which they improve or degrade the association between computed SQs and diet categories. Though altering regression parameters for SQ calculation has a visible effect on plots, numerous iterations of statistical analyses vary surprisingly little in the success of the resulting variables for assigning taxa to dietary preference. This is promising for the comparability of patterns (if not casewise values) in SQ between studies. We suggest that differences in apparent dietary fidelity of recent studies are attributable principally to tooth position examined.