辽西褐土和棕壤溅蚀效应与团聚体分选特征

为了探究辽西低山丘陵区坡耕地土壤溅蚀机理,选取褐土和棕壤为研究对象,采用人工模拟降雨试验研究一定降雨能量下土壤溅蚀量与团聚体分选特征。结果表明:1)褐土和棕壤不同方向溅蚀量以及净溅蚀量与溅蚀距离呈对数递减关系。不同水平距离范围内,褐土下坡溅蚀总量是上坡溅蚀总量的4.25倍,棕壤下坡溅蚀总量是上坡溅蚀总量的4.85倍。褐土和棕壤水平距离为0~5 cm时溅蚀量和其他水平距离溅蚀量呈极显著差异。2)溅蚀对褐土和棕壤大团聚体富集作用与损耗作用的临界团聚体粒级为1 mm,当团聚体粒级>1 mm时,棕壤的富集率明显大于褐土;当团聚体的粒级<1 mm时,褐土的团聚体富集率明显大于棕壤。3)褐土和棕壤在各水平距离范围均未出现>5 mm的水稳性团聚体,>0.50~1.00 mm水稳性团聚体和>0.25~0.50 mm水稳性团聚体均出现在试验设计的水平距离范围内,褐土相比棕壤>0.25~1.00 mm粒级的团聚体,随水平距离的变化不管是增大幅度还是减小幅度,均表现为褐土大于棕壤。褐土和棕壤不同水平距离范围内各个粒级水稳性团聚体平均质量直径均<1 mm,褐土随着水平距离的增大团聚体平均质量直径表现为先增大后减小,棕壤随着水平距离的增大团聚体平均质量直径表现为逐渐减小。

不同开垦年限黑土溅蚀与团聚体分选特征

分别以开垦年限为8a、30a、50a的典型黑土耕地和未开垦的天然次生林林地表层土壤为对象,采用人工模拟降雨的方法,对比分析了不同开垦年限土壤的溅蚀规律及溅蚀过程中团聚体分选、分布特征,并通过析因分析研究了开垦对黑土溅蚀的影响机制.结果表明:不同开垦年限黑土耕地土壤的溅蚀量明显高于未开垦次生林林地土壤,且随开垦年限的延长而增加,变化范围为0.95～7.06g.cm-1;土壤溅蚀量与溅蚀距离表现出紧密的指数函数关系,而且随着水平空间距离的增加,小粒级水稳性团聚体比例逐渐增加;溅蚀对黑土团聚体产生富集作用与损耗作用的临界粒级为1.0mm;粒级>2mm、<0.25mm水稳性团聚体及有机质含量是典型黑土土壤溅蚀量及其变化特征的主要影响因子.

降雨强度和坡度对东北黑土区顺坡垄体溅蚀特征的影响

坡耕地溅蚀特征研究可揭示和反映溅蚀的发生和发展机理,而以往研究大多在无垄作坡面进行,较少涉及顺坡垄体。为此,该研究基于野外人工模拟降雨试验,设计3个降雨强度(30、60和90 mm/h)和2个坡度(3°、5°),研究降雨强度和坡度对典型黑土(Mollisol)农田顺坡垄体溅蚀量、溅蚀过程和溅蚀分选特征的影响。研究结果表明:当降雨强度由30 mm/h增加到90 mm/h时,总溅蚀量增加2.5~17.9倍。当坡度由3°增大到5°时,总溅蚀量增加30.52%~74.08%。当降雨强度为30和60 mm/h时,总溅蚀率随降雨历时呈迅速减小-缓慢减小-波动稳定的趋势。当降雨强度为90 mm/h时,总溅蚀率随降雨历时呈迅速增加-迅速减小-波动稳定的趋势。整体而言,总溅蚀量随降雨强度和坡度的增加呈幂函数关系。各试验处理下,溅蚀分选水稳性团聚体中均以<1 mm粒级的团聚体为主,平均占总量的79.01%,以0.5~1 mm粒级最多,2~5 mm粒级最少,分别占总量的32.94%和3.36%。30和60 mm/h降雨强度下,分别为<0.25和<2 mm的各粒级团聚体在降雨后期达到波动稳定,其中<0.25 mm的团聚体均呈迅速降低-缓慢降低-波动稳定的变化趋势。90 mm/h降雨强度下,1~5和<0.25 mm各粒级团聚体均呈线性平稳变化,其中<0.25 mm的团聚体呈线性减少趋势。研究可为东北黑土区水蚀防治提供科学依据。

Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data

In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.

Study on Screening of Fungi with High-resistance against Chromium and on Related Characteristics

[Objective] The aim was to separate chromium-resistant microorganism from soil contaminated by chromium.[Method] Separation and purification technique was used as follows：different concentrations of Cr^6＋ were added into medium,and chromium-resistant fungi were screened after separations and domestications.The selected fungi were under preliminary identification according to its morphological and colony characteristics.Then,related biological characteristics were studied,including measurement of growth curve,growing effects by temperature,pH value and osmotic pressure.[Result] The Cr（VI） with concentration of 1 000 mg/L was separated and selected from soils in ten different places contaminated seriously by heavy metal in adjacent region of Yulin City.Considering its morphological and colony characteristics,it was preliminarily identified as saccharomycetes,which can well grow within 15-37 ℃,and whose most suitable temperature was 28℃.Bacterial strain can grow well with pH of 4-10,and the optimum pH was 7.2;besides,it can grow well with NaCl concentration of 0.5%-5.0%.Through the experiment,the bacteria was found with resistance not only to chromium,but also to heavy metals such as Pb＋Cu,Cu＋Fe,Pb＋Fe,and Pb＋Cu＋Fe.[Conclusion] The fungi selected from the experiment were of good adaptability to natural environment,and it also had resistance to other heavy metals.

Characterization of Two Human Lung Adenocarcinoma Cell Lines by Reciprocal Chromosome Painting

Lung cancer is a leading cause of cancer death worldwide. Some lung cancer patients correlate with a gas of radon besides smoking. To search for common chromosomal aberrations in lung cancer cell lines established from patients induced by different factors, a combined approach of chromosome sorting, forward and reverse chromosome painting was used to characterize karyotypes of two lung adenocarcinoma cell lines： A549 and GLC-82 with the latter line derived from a patient who has suffered long-term exposure to environmental radon gas pollution. The chromosome painting results revealed that complex chromosomal rearrangements occurred in these two lung adenocarcinoma cell lines. Thirteen and twenty-four abnormal chromosomes were identified An A549 and GLC-82 cell lines, respectively. Almost half of abnormal chromosomes in these two cell lines were formed by non-reciprocal translocations, the others were derived from deletions and duplication/or amplification in some chromosomal regions. Furthermore, two apparently common breakpoints, HSA8q24 and 12q14 were found in these two lung cancer cell lines.

Study on Support Vector Machine Based on 1-Norm

The model of optimization problem for Support Vector Machine(SVM) is provided, which based on the definitions of the dual norm and the distance between a point and its projection onto a given plane. The model of improved Support Vector Machine based on 1-norm(1-SVM) is provided from the optimization problem, yet it is a discrete programming. With the smoothing technique and optimality knowledge, the discrete programming is changed into a continuous programming. Experimental results show that the algorithm is easy to implement and this method can select and suppress the problem features more efficiently. Illustrative examples show that the 1-SVM deal with the linear or nonlinear classification well.

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.

Modeling of fine coal flotation separation based on particle characteristics and hydrodynamic conditions

Flotation is a complex multifaceted process that is widely used for the separation of finely ground minerals. The theory of froth flotation is complex and is not completely understood. This fact has been brought many monitoring challenges in a coal processing plant. To solve those challenges, it is important to understand the effect of different parameters on the fine particle separation, and control flotation performance for a particular system. This study is going to indicate the effect of various parameters （particle Characteristics and hydrodynamic conditions） on coal flotation responses （flotation rate constant and recovery） by different modeling techniques. A comprehensive coal flotation database was prepared for the statistical and soft computing methods. Statistical factors were used for variable selections. Results were in a good agreement with recent theoretical flotation investigations. Computational models accurately can estimate flotation rate constant and coal recovery （correlation coefficient 0.85, and 0.99, respectively）. According to the results, it can be concluded that the soft computing models can overcome the complexity of process and be used as an expert system to control, and optimize parameters of coal flotation process.

Fault depth estimation using support vector classifier and features selection

Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier （SVC） method in the estimation. Using forward and nonlinear inverse techniques, detecting the depth of subsurface faults with related error is possible but it is necessary to have an initial guess for the depth and this initial guess usually comes from non-gravity data. We introduce SVC in this paper as one of the tools for estimating the depth of subsurface faults using gravity data. We can suppose that each subsurface fault depth is a class and that SVC is a classification algorithm. To better use the SVC algorithm, we select proper depth estimation features using a proper features selection （FS） algorithm. In this research, we produce a training set consisting of synthetic gravity profiles created by subsurface faults at different depths to train the SVC code to estimate the depth of real subsurface faults. Then we test our trained SVC code by a testing set consisting of other synthetic gravity profiles created by subsurface faults at different depths. We also tested our trained SVC code using real data.

A bidirectional feature selection method based on mutual information and redundancy-synergy coefficient

Feature subset selection is a fundamental problem of data mining. The mutual information of feature subset is a measure for feature subset containing class feature information. A hashing mechanism is proposed to calculate the mutual information of feature subset. The feature relevancy is defined by mutual information. Redundancy-synergy coefficient, a novel redundancy and synergy measure for features to describe the class feature, is defined. In terms of information maximization rule, a bidirectional heuristic feature subset selection method based on mutual information and redundancy-synergy coefficient is presented. This study’s experiments show the good performance of the new method.

A SEMI-OPEN-LOOP CODING MODE SELECTION ALGORITHM BASED ON EFM AND SELECTED AMR-WB+ FEATURES

To solve the problems of the AMR-WB+(Extended Adaptive Multi-Rate-WideBand) semi-open-loop coding mode selection algorithm,features for ACELP(Algebraic Code Excited Linear Prediction) and TCX(Transform Coded eXcitation) classification are investigated.11 classifying features in the AMR-WB+ codec are selected and 2 novel classifying features,i.e.,EFM(Energy Flatness Measurement) and stdEFM(standard deviation of EFM),are proposed.Consequently,a novel semi-open-loop mode selection algorithm based on EFM and selected AMR-WB+ features is proposed.The results of classifying test and listening test show that the performance of the novel algorithm is much better than that of the AMR-WB+ semi-open-loop coding mode selection algorithm.

Multi-Domain Collaborative Recommendation with Feature Selection

Collaborative f iltering, as one of the most popular techniques, plays an important role in recommendation systems. However,when the user-item rating matrix is sparse,its performance will be degenerate. Recently,domain-specific recommendation approaches have been developed to address this problem.The basic idea is to partition the users and items into overlapping domains, and then perform recommendation in each domain independently. Here, a domain means a group of users having similar preference to a group of products. However, these domain-specific methods consisting of two sequential steps ignore the mutual benefi t of domain segmentation and recommendation. Hence, a unified framework is presented to simultaneously realize recommendation and make use of the domain information underlying the rating matrix in this paper. Based on matrix factorization,the proposed model learns both user preferences of multiple domains and preference selection vectors to select relevant features for each group of products. Besides, local context information is utilized from the user-item rating matrix to enhance the new framework.Experimental results on two widely used datasets, e.g., Ciao and Epinions, demonstrate the effectiveness of our proposed model.

Optimizing crown density and volume estimation across two coniferous forest types in southern China via Boruta and Cubist methods

Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources.And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing.Based on GF-2 remote sensing data,sample plot survey data and forest resource survey data,this study used the Chinese fir(Cunninghamia lanceolata(Lamb.)Hook.)and Pinus massoniana Lamb.as research objects to tackle the key challenges in the use of remote sensing technology.The Boruta feature selection technique,together with multiple stepwise and Cubist regression models,was used to estimate crown density and volume in portions of the research area’s stands,introducing novel technological methods for estimating stand parameters.The results show that:(i)the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable,which solves the problem of data and the loss of original feature data after dimensionality reduction;(ii)using the Cubist method to build the model yields better results than using multiple stepwise regression.The Cubist regression model’s coefficient of determination(R^(2))is all more than 0.67 in the Chinese fir plots and 0.63 in the P.massoniana plots.As a result,combining the two methods can increase the estimation accuracy of stand parameters,providing a theoretical foundation and technical support for future studies.

A ROUGH SET APPROACH TO FEATURE SELECTION BASED ON SCATTER SEARCH METAHEURISTIC

Rough set theory is an effective method to feature selection, which has recently fascinated many researchers. The essence of rough set approach to feature selection is to find a subset of the original features. It is, however, an NP-hard problem finding a minimal subset of the features, and it is necessary to investigate effective and efficient heuristic algorithms. This paper presents a novel rough set approach to feature selection based on scatter search metaheuristic. The proposed method, called scatter search rough set attribute reduction （SSAR）, is illustrated by 13 well known datasets from UCI machine learning repository. The proposed heuristic strategy is compared with typical attribute reduction methods including genetic algorithm, ant colony, simulated annealing, and Tabu search. Computational results demonstrate that our algorithm can provide efficient solution to find a minimal subset of the features and show promising and competitive performance on the considered datasets.

A new feature selection method for handling redundant information in text classification

Feature selection is an important approach to dimensionality reduction in the field of text classification. Because of the difficulty in handling the problem that the selected features always contain redundant information, we propose a new simple feature selection method, which can effectively filter the redundant features. First, to calculate the relationship between two words, the definitions of word frequency based relevance and correlative redundancy are introduced. Furthermore, an optimal feature selection（OFS） method is chosen to obtain a feature subset FS1. Finally, to improve the execution speed, the redundant features in FS1 are filtered by combining a predetermined threshold, and the filtered features are memorized in the linked lists. Experiments are carried out on three datasets（Web KB, 20-Newsgroups, and Reuters-21578） where in support vector machines and na？ve Bayes are used. The results show that the classification accuracy of the proposed method is generally higher than that of typical traditional methods（information gain, improved Gini index, and improved comprehensively measured feature selection） and the OFS methods. Moreover, the proposed method runs faster than typical mutual information-based methods（improved and normalized mutual information-based feature selections, and multilabel feature selection based on maximum dependency and minimum redundancy） while simultaneously ensuring classification accuracy. Statistical results validate the effectiveness of the proposed method in handling redundant information in text classification.

Polyploidization and sexual dimorphism of floral traits in a subdioecious population of Dasiphora glabra

Aims Sexual dimorphism is a common trait in plants with sex separation,which could influence female and male functions differently.In a subdioecious population of Dasiphora glabra on the Qinghai-Tibet Plateau,we investigated sexual dimorphism of floral traits and their effects on pollinator visitation,pollen flow and seed production.We also examined differences in genome size of hermaphroditic and dioecious plants.Methods We examined sexual dimorphism in flower number,flower size,and pollen and ovule production in a subdioecious population of D.glabra.We compared pollinator visitation,pollen dispersal and seed production between sexes.We also examined the genome size of three sex morphs using flow cytometry.Important Findings The number of hermaphroditic plants was significantly more than that of male and female plants,and dioecious plants accounted for ca.40%in the study population.Hermaphroditic plants produced significantly more flowers than male and female plants.Flower size of male flowers was significantly larger than that of female and hermaphroditic flowers.Male flowers did not produce more pollen grains than hermaphroditic flowers,but female flowers produced more ovules than hermaphroditic flowers.Flies were the most frequent flower visitors and preferred large flowers,but their movements between flowers did not show any preference to large flowers.Simulated pollen flows suggested that effective pollen transfer was generally low for both hermaphroditic and male flowers,corresponding to the low seed set of naturally pollinated flowers.DNA contents of male and female plants were ca.four times than those of hermaphroditic plants.These results suggest male and female individuals have undergone polyploidy events and thus are not compatible with hermaphroditic individuals.Sexual dimorphism in floral traits in relation to pollination of dioecious plants might show an advantage in female and male functions,but this advantage is masked largely by low effectiveness of pollen transfer.