ALLIED FUZZY c-MEANS CLUSTERING MODEL

A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive to initializations and often generates coincident clusters. AFCM overcomes this shortcoming and it is an ex tension of PCM. Membership and typicality values can be simultaneously produced in AFCM. Experimental re- suits show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better.

Fuzzy c-means text clustering based on topic concept sub-space

To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.

XCAP-C-like Protein Existing in Nuclei and Chromosomes of Allium sativa

In studying the XCAP-C-like protein in the root meristematic cells of Allium sativa L., the nuclei were isolated from the cells and the nuclear matrices prepared. A 165 kD polypeptide, which is equivalent to XCAP-C in molecular weight, was demonstrated in the nuclei by SDS-PAGE, and was then proved to be an XCAP-C-like protein by Western blot using an anti-XCAP-C antiserum, but neither the polypeptide nor the XCAP-C-like protein was detected in die nuclear matrix. The nuclei, Chromosomes and chromosome scaffolds were observed to emanate strong, specific fluorescence after labeled with the anti-XCAP-C antiserum and an FITC-conjugated secondary antibody, indicating their containment of the XCAP-C-like protein. It was confirmed by viewing with immunoelectron microscopy that the gold particles representing the localization of the XCAP-C-like protein were found to be mainly distributed in the condensed chromatin regions of the nuclei and chromosomes.

A new species in the genus Hedotettix Bolivar(Orthoptera: Tetrigidae), including chromosome karyotype, from the western Yunnan Province in China

A new species, Hedotettix nujiangensis Zheng sp. nov., is described. The chromosome complement of H. nujiangensis consists of 2n （♂） = 13. Sex determination is XO. All chromosomes are telocentric （T） and the sex chromosome is the fourth element in size. Type specimens are deposited at Southwest Forestry University.

C-means-based ant colony algorithm for TSP

To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.

A Fully Integrated Low Power Transmitter in an 802.11b Transceiver System

A fully integrated low power transmitter for an IEEE 802. llb transceiver system is implemented in SMIC 0.18μm technology. The direct-conversion transmitter includes two Chebyshev I low pass filters,two PGAs,a SSB mixer, and a PA driver. The transmitter provides a gain control of 32dB in 3dB steps. The maximum output power is - 3.4dBm and the EVM is 6. 8%. The power consumption of the transmitter is only 57.6mW with a 1.8V power supply. The chip area of the transmitter is 1.6mm × 1.6mm.

NEW SHADOWED C-MEANS CLUSTERING WITH FEATURE WEIGHTS

Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.

Watershed classification by remote sensing indices： A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.

Antibodies against the C-terminal peptide of rabbit oviductin inhibit mouse early embryo development to pass 2-cell stage

A full-length rabbit oviductin cDNA(1909bp) was cloned. It consists of a 5’-UTR of 52bp, an open reading frame (ORF) of 1374bp and a 3’-UTR of 483bp and has more than 80% homology with that of other mammal oviductins. N-terminal peptide (NTP) (384 residues) and C-terminal peptide (CTP) (73 residues) of deduced protein precursor has about 80% and 50% identity with that of other mammals respectively. Fusion proteins GST-NTP 368(1R-368N)and GST-CTP73 (369F-441A) were expressed and purified. NH2-terminal of CTP sequencing reveals that the purified protein is consistent with the deduced one. In order to study the function of NTP and CTP the mouse anti-NTP and rabbit anti-CTP antisera were prepared. Tissue-specific (skeleton muscle, oviduct, uterus, ovary, liver, heart and brain) analysis indicated that rabbit oviductin was only found in oviduct. The conditioned medium derived from the rabbit oviduct mucosa epithelial cells has a function of overcoming the early embryonic development block of Kunming mous e cultured in vitro. Anti-CTP antiserum could totally inhibit the early embryo development at 2-cell stage cultured in the conditioned culture medium, but anti-NTP antiserum couldn’t. There was a positive relationship between the ratio of early embryos at development block and the dosage of anti-CTP antiserum added in the conditioned culture medium. These results suggest that oviductin has a function not only on fertilization, but also on the release of early embryonic development block, and the later function domain of rabbit oviductin may be situate in its C-terminal.

Correlation between the expression of vascular endothelial growth factor c and C-erbB-2 in human breast cancer

Objective: We aimed to study the transcription level of VEGF-C in human breast cancer tissue, and explore the correlations with the expression of C-erbB-2. Methods: The expression of VEGF-C mRNA in 51 cases of human breast cancer was assessed by hybridization in situ. The expressions of C-erbB-2 was assessed by immunohistochemistry. Results: The positive rate of VEGF-C mRNA was 54.9% in 51 cases of breast cancer. The transcription level had correlation with tumor size and status of lymph nodes(P < 0.05). The expression of VEGF-C mRNA had a positive correlation with the expression of C-erbB-2(P < 0.05). Conclusion: The up-expression of VEGF-C has a significant correlation with the malignancy level and clinical stage of breast cancer. The combined detection of VEGF-C, C-erbB-2 may help to estimate the prognosis of patients with breast cancer and study on thetherapeutic implications.

Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction

In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.

A product module mining method for PLM database

Modular technology can effectively support the rapid design of products, and it is one of the key technologies to realize mass customization design. With the application of product lifecycle management(PLM) system in enterprises, the product lifecycle data have been effectively managed. However, these data have not been fully utilized in module division, especially for complex machinery products. To solve this problem, a product module mining method for the PLM database is proposed to improve the effect of module division. Firstly, product data are extracted from the PLM database by data extraction algorithm. Then, data normalization and structure logical inspection are used to preprocess the extracted defective data. The preprocessed product data are analyzed and expressed in a matrix for module mining. Finally, the fuzzy c-means clustering(FCM) algorithm is used to generate product modules, which are stored in product module library after module marking and post-processing. The feasibility and effectiveness of the proposed method are verified by a case study of high pressure valve.

Atherosclerosis： from biology to pharmacological treatment

A recent explosion in the amount of cardiovascular risk has swept across the globe. Primary prevention is the preferred method to lower cardiovascular risk. Lowering the prevalence of obesity is the most urgent matter, and is pleiotropic since it affects blood pressure, lipid profiles, glucose metabolism, inflammation, and atherothrombotic disease progression. Given the current obstacles, success of primary prevention remains uncertain. At the same time, the consequences of delay and inaction will inevitably be disastrous, and the sense of urgency mounts. Pathological and epidemiological data confirm that atherosclerosis begins in early childhood, and advances seamlessly and inexorably throughout life. Risk factors in childhood are similar to those in adults, and track between stages of life. When indicated, aggressive treatment should begin at the earliest indication, and be continued for many years. For those patients at intermediate risk according to global risk scores, C-reactive protein, coronary artery calcium, and carotid intima-media thickness are available for further stratification. Using statins for primary prevention is recommended by guidelines, is prevalent, but remains under prescribed. Statin drugs are unrivaled, evidence-based, major weapons to lower cardiovascular risk. Even when low density lipoprotein cholesterol targets are attained, over half of patients continue to have disease progression and clinical events. Though clinical evidence is incomplete, altering or raising the blood high density lipoprotein cholesterol level continues to be pursued. The aim of this review is to point out the attention of key aspects of vulnerable plaques regarding their pathogenesis and treatment.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

Motion feature descriptor based moving objects segmentation

A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search （3DRS） algorithm is used in order to obtain motion information accurately. A motion feature descriptor （MFD） is designed to describe motion feature of each block in a picture based on motion intensity, motion in occlusion areas, and motion correlation among neighbouring blocks. Then, a fuzzy C-means clustering algorithm （FCM） is implemented based on those MFDs so as to segment moving objects. Moreover, a new parameter named as gathering degree is used to distinguish foreground moving objects and background motion. Experimental results demonstrate the effectiveness of the proposed method.

Seroprevalence of Human Brucellosis in Kuku Dairy Scheme, Khartoum State, Sudan

A seroprevalence investigation of human brucellosis was carried out in Kuku Dairy Scheme, Sudan. A total of 176 serum samples were collected and screened by Rose Bengal Plate Test （RBPT）. The positive sera vqere further examined using Tube Agglutination Test （TAT） and c-Elisa. The seropositivity was 15.9%, 14.8% and 11.4% using RBPT, TAT and c-Elisa respectively. Whereas, the active infection based on seropositivity and clinical signs were 4.6%, 4.6% and 2.3% in case of RBPT, TAT and c-Elisa respectively. Based on c-Elisa result the infected individuals were further subjected to clinical examination and treated with streptomycin and doxocycline for six weeks.

The relationship between nNOS and Cyt-c in mitochondria of glioma

Objective： The aim of this study was to investigate the association between neuronal nitric oxide synthase （nNOS） and the expression level of Cytochrome C （Cyt-c） in mitochondria. Methods： The pathological diagnosis of glioma and tumor classification was by HE staining, and we use immunohistochemistry method to analyse the level of nNOS in different pathological grade glioma and the expression level of Cyt-c in mitochondria meanwhile. Results： The levels of nNOS were highest in grade Ⅲ tumors, moderate in grade Ⅱ tumors, and lowest different in grade I tumors. There was significant difference of the nNOS levels among different pathological grade tumors （P 〈 0.05）. Furthermore, the similar phenomenon was observed in the expression level of Cyt-c in mitochondria （P 〈 0.05）. Conclusion： The expression level of nNOS and Cyt-c in mitochondria was significantly related to the pathological grade of glioma.

Study on the ultraviolet-visible spectral feature of tobacco leaves by pattern recognition

In order to differentiate regions, varieties, and parts of tobacco leaves, two pattern recognition methods through pattern classification modeling were developed based on the comprehensive information of ultraviolet-visible spectroscopy （UV-VIS） by employing one-way analysis of variance （ANOVA1） and wave range random combination （WRRC） technology from MATLAB. This proposed classification method has never been reported previously and the instrument and operation for this method is much more convenient and efficient than previous reported classification methods. The result of this paper demonstrated that the spectral features extracted by ANOVAI and WRRC methods could be used to differentiate tobacco leaves with different patterns. The ANOVAI method had a training recognition rate range of 75.00-87.50%,4 and a validation recognition rate range of 57.14-100%. The WRRC method had a training recognition rate range of 75.00-94.12% and a validation recognition rate range of 66.67-100%. The ANOVAI method is more convenient and efficient in model developing, while the WRRC method utilizes fewer model variables and is more robust.

3-Phase Fault Finding in Oil Field MV Distribution Network Using Fuzzy Clustering Techniques

This paper studies an existing 13.8 kilovolt distribution network which, serves an oil production field spread over an area of approximately 60 kilometers square, in order to locate any fault that may occur anywhere in the network using fuzzy c-mean classification techniques. In addition, Sections 5 and 6 introduce two different methods for normalizing data and selecting the optimum number of clusters in order to classify data. Results and conclusions are given to show the feasibility for the suggested fault location method. Suggestion for future related research has been provided in Section 8.

Research on Image Segmentation Algorithm based on Fuzzy C-mean Clustering

This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the search clustering center has small amount of calculation according to density, so it can greatly improve the calculation speed of fuzzy C- means algorithm. The experimental results show that, this method can make the fuzzy clustering to obviously improve the speed, so it can achieve fast image segmentation.