浅析独立董事的独立性

由于独立董事制度本身的缺陷、我国公司制度的特点和实际国情,目前独立董事制度在我国实行的效果不够理想,独立性构成了独立董事制度的价值前提和基础。因此,本文就独立董事的独立性展开分析,并相应的对我国独立董事制度的发展完善提出建议。

Over-excavation forecast of underground opening by using Bayes discriminant analysis method

A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model,and the prior information about over-excavation of underground opening was also taken into consideration.Five parameters influencing the over-excavation of opening,including 2 groups of joints,1 group of layer surface,extension and space between structure faces were selected as geometric parameters.Engineering data in an underground opening were used as the training samples.The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained.Data in an underground engineering were used to test the discriminant ability of BDA model.The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.

Use of Otolith Shape for the Identification of Trumpeter Sillago (Sillago maculata) and Silver Sillago(Sillago sihama)

Otolith shape is species-specific in fish.In this study,161 samples of Trumpeter sillago Sillago maculata and 164 of Silver sillago Sillago sihama were collected from Beibu Gulf in July 2009.The main objective of this study was to use morphological variables and Fourier harmonics to describe otolith characteristics and use discriminant analyses to separate the two species.Otoliths were measured by traditional one and two dimensional measures(otolith length,width,diameters,area,perimeter,rectangularity and circularity),as well as by Fourier analysis to capture the finer regions of the otolith.Analysis of covariance(ANCOVA) showed that there was significant correlation between morphological variables(diameter,perimeter,otolith length,otolith width,otolith area,density,harmonics 3,harmonics 4,harmonics 5,harmonics 13,harmonics 15,and harmonics 16) and body length.To minimize size effects on the morphological variables between species,only fish with a body length between 90 and 140 mm were included in the data analysis and the variables which had significant relation with body length were transformed using the residual.The result showed that the accuracy of discriminant analysis was 97.8% and 100% for Trumpeter sillago and Silver sillago,respectively.This high accuracy indicated that otolith shape was described accurately by morphological variables and Fourier harmonics,and discriminant analysis was an effective way to identify and separate the two species.

Tensor discriminant dictionary classification method for prestack seismic reflection patterns

The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation with offset/azimuth in the prestack seismic data.In this study,a tensor discriminant dictionary learning method for classifying prestack seismic reflection patterns is proposed.The method is initially based on the tensor Tucker decomposition algorithm and uses a tensor form to characterize the prestack seismic data with multidimensional features.The tensor discriminant dictionary is then used to reduce the influence of noise on the sample features.Finally,the method uses the Pearson correlation coefficient to measure the correlation degree of the sparse representation coefficients of different types of tensors.The advantages of the new method are as follows.(1)It can retain the rich structural features in different dimensions in the prestack data.(2)It adjusts the threshold of the Pearson correlation coefficient to optimize the classification effect.(3)It fully uses drilling information and expert knowledge and performs calibration training of the sample labels.The numerical-model tests confirm that the new method is more accurate and robust than the traditional support vector machine and K-nearest neighbor classification algorithms.The application of actual data further confirms that the classification results of the new method agree with the geological patterns and are more suitable for the analysis and interpretation of sedimentary facies.

Machine learning strategies for lithostratigraphic classification based on geochemical sampling data: A case study in area of Chahanwusu River, Qinghai Province, China

Based on the complex correlation between the geochemical element distribution patterns at the surface and the types of bedrock and the powerful capabilities in capturing subtle of machine learning algorithms,four machine learning algorithms,namely,decision tree(DT),random forest(RF),XGBoost(XGB),and LightGBM(LGBM),were implemented for the lithostratigraphic classification and lithostratigraphic prediction of a quaternary coverage area based on stream sediment geochemical sampling data in the Chahanwusu River of Dulan County,Qinghai Province,China.The local Moran’s I to represent the features of spatial autocorrelations,and terrain factors to represent the features of surface geological processes,were calculated as additional features.The accuracy,precision,recall,and F1 scores were chosen as the evaluation indices and Voronoi diagrams were applied for visualization.The results indicate that XGB and LGBM models both performed well.They not only obtained relatively satisfactory classification performance but also predicted lithostratigraphic types of the Quaternary coverage area that are essentially consistent with their neighborhoods which have the known types.It is feasible to classify the lithostratigraphic types through the concentrations of geochemical elements in the sediments,and the XGB and LGBM algorithms are recommended for lithostratigraphic classification.

Differences inthe Level of Social Status of Male and Female Folk Dancers

The research was conducted in order to determine differences in the social status structure of male and female performers of folk dances. For the purpose of determining the social status structure of male and female dancers, 103 male and 145 female dancers aged 18-28 actively engaged in folk dancing were tested. For the assessment of social status, only one model that allows for true scientific approach to studying the structure of stratification dimensions has been made so far. The model was constructed by Saksida and later served as a basis for many studies conducted by other authors as well （Saksida, Caserman, Petrovic, Momirovic, and Hosek）. Designed as a phenomenological model, it has undergone several changes over time, but it is still suitable for studying social change. The INST2 questionnaire modified by Boli, Popovic, Hosek, Momirovic, and Savic （SSMIN） was applied in this study. All the data obtained in this research were processed at the Multidisciplinary Research Center of the Faculty of Sport and Physical Education, University of Pristina, through the system of data processing programs designed by D. Popovic and K Momirovic. To determine differences between the groups, a method of discriminant analysis was applied.

Multi-label dimensionality reduction based on semi-supervised discriminant analysis

Multi-label data with high dimensionality often occurs,which will produce large time and energy overheads when directly used in classification tasks.To solve this problem,a novel algorithm called multi-label dimensionality reduction via semi-supervised discriminant analysis(MSDA) was proposed.It was expected to derive an objective discriminant function as smooth as possible on the data manifold by multi-label learning and semi-supervised learning.By virtue of the latent imformation,which was provided by the graph weighted matrix of sample attributes and the similarity correlation matrix of partial sample labels,MSDA readily made the separability between different classes achieve maximization and estimated the intrinsic geometric structure in the lower manifold space by employing unlabeled data.Extensive experimental results on several real multi-label datasets show that after dimensionality reduction using MSDA,the average classification accuracy is about 9.71% higher than that of other algorithms,and several evaluation metrices like Hamming-loss are also superior to those of other dimensionality reduction methods.

Carob Origin Classification by FTIR Spectroscopy and Chemometrics

The goal of this study was to use Fourier transform mid-infrared （FTIR） spectroscopy for discrimination of samples of pods and seeds of carob from three Moroccan regions. The origin of samples Pods and seeds of carob could be distinguished from their IR spectra and this measurement was used for discriminate analysis. A multivariate analysis procedure based on the combined use of Hierarchical Cluster Aanalysis （HCA） and Partial Least Squares-Discriminant Analysis （PLS-DA） was tested and provided good classification results. Three distinctive clusters were recognised, related to the three Moroccan regions. Afterwards, PLS-DA was used for the discrimination and classification of the origin of the various Pods and seeds of carob samples. The results demonstrated that the combined use of FTIR and chemometric analysis （cluster analysis and discrimination by PLS- DA） can be used to rapidly and simply determine the origin of carob pulpe samples.

Discrimination of the rare medicinal plant Dendrobium officinale based on naringenin, bibenzyl, and polysaccharides

The aim of this study was to establish a method for discriminating Dendrobium officinale from four of its close relatives Den- drobium chrysanthum, Dendrobium erystallinum, Dendrobium aphyllum and Dendrobium devonianum based on chemical composition analysis. We analyzed 62 samples of 24 Dendrobium species. High performance liquid chromatography analysis confirmed that the four low molecular weight compounds 4＇,5,7-trihydroxyflavanone （naringenin）, 3,4-dihydroxy-4＇,5-dime- tboxybibenzyl （DDB^2）, 3＇,4-dihydroxy-3,5＇-dimethoxybibenzyl （gigantol）, and 4,4＇-dihydroxy-3,3＇,5-trimethoxybibenzyl （moscatilin）, were common in the genus. The phenol-sulfuric acid method was used to quantify polysaccharides, and the mon- osaccharide composition of the polysaccharides was determined by gas chromatography. Stepwise discriminant analysis was used to differentiate among the five closely related species based on the chemical composition analysis. This proved to be a simple and accurate approach for discriminating among these species. The results also showed that the polysaccharide content, the amounts of the four low molecular weight compounds, and the mannose to glucose ratio, were important factors for species discriminaut. Therefore, we propose that a chemical analysis based on quantification of naringenin, bibenzyl, and polysaccha- rides is effective for identifying D. officinale.