Name and Maintain Topological Faces in Rotating and Scanning Features

Gao Xue-Yao,Li Jia-Qi,Guo Hao,Gao Yun-Feng,Liu Yu-Hong 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.3

Features created in rotating and scanning operations are very complex. Naming and identifying their topological faces is an important problem in CAD fields. In this paper, a new method of coding topological faces in rotating and scanning features is proposed. Firstly, contour segments are numbered. Secondly, an angle between contour segment and rotating axis is computed. Thirdly, all topological faces are named based on contour segments’ numbers, rotating axis and other information. When a face splits and several subfaces merge, a method of processing their codes is given. The proposed method is applied to HUST-CAID feature modeling system. Experimental results show that it can name and identify topological faces effectively in operations.

Retrieve CAD Model Based on Face Matching Sequence

Gao Xue-Yao,Li Hui-Nan,Hu Ru,Zhang Chun-Xiang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.3

A new model retrieval method based on face matching sequence is proposed in this paper. Attribute adjacent graph is used to describe two faces’ geometry similarity and topological relationship in CAD model. According to the difference of edge numbers, similarities between two models’ faces are computed and face similarity matrix is constructed. Ant colony algorithm (ACA) is applied to obtain an optimal sequence of matching faces between two models. Accumulate similarity values of optimal matching faces to calculate two models’ similarity. Experimental results show that this method can evaluate two CAD models’ shape difference effectively.

2D Geometric Constraint Optimum Solving Based on Problem Decomposition

Xue-Yao Gao,Chun-Xiang Zhang,Zhi-Mao Lu 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.1

Constraint solving is widely applied to many fields including computer aided design, 2 dimension (2D) model design and computer aided manufacturing. Geometric constraint solution is a difficult problem because there are a large number of entities and related parameters in 2D sketches. In this paper, a new method which decomposes geometric constraint relations based on entity-parameter graphs is proposed for reducing the size of constraint solution. A geometric constraint problem is decomposed into many independent sub-problems. Then, particle swarm optimization algorithm is used to solve constraint equations in each sub-problem. Solutions of all sub-problems are integrated to obtain the original problem’s solution. In experiments, the proposed method is applied to HUST-CAID system. Experimental results show that the method can effectively solve 2 dimension geometric constraints.

Compute Similarity of CAD Models Based on Bipartite Graph

Xue-Yao Gao,Chun-Xiang Zhang,Zhi-Mao Lu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.2

Model retrieval is widely applied to many fields including computer aided design, feature modeling and computer aided manufacturing. In order to retrieve a satisfied CAD model from a large model library, a new method to compute the similarity between two CAD models is presented in which a bipartite graph is used to match source faces with target faces. The number of edges in faces is extracted from CAD models. Based on the number of edges in faces, the similarity between source face and target face is computed. The maximum matching weight is calculated by KM algorithm. Then, the similarity between two CAD models is gotten. Several CAD models are given and their similarities are computed. Experimental results show that the method can evaluate the similarity between source CAD model and target CAD model efficiently.

Disambiguate Chinese Word Sense Based on Linguistics Knowledge

Chun-Xiang Zhang,Long Deng,Xue-Yao Gao,Zhi-Mao Lu 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.6

Word sense disambiguation (WSD) is important to many application problems in natural language processing fields, such as machine translation, parsing analysis and information retrieval. In this paper, we propose a new method to determine correct sense categories of Chinese words based on linguistics knowledge. The left word string and the right word string around the ambiguous word are respectively analyzed. Their syntactic structures are obtained for determining its intended sense. Syntactic category and part of speech are extracted as disambiguation features. A naive bayesian model is used as the classifier. Experimental results showed that the accuracy rate of classification arrives at 64%. The performance of disambiguation is improved.

Tea Consumption, Alcohol Drinking and Physical Activity Associations with Breast Cancer Risk among Chinese Females: a Systematic Review and Meta-analysis

Gao, Ying,Huang, Yu-Bei,Liu, Xue-Ou,Chen, Chuan,Dai, Hong-Ji,Song, Feng-Ju,Wang, Jing,Chen, Ke-Xin,Wang, Yao-Gang Asian Pacific Journal of Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.12

Objective: To evaluate associations between tea consumption, alcohol drinking and physical activity and breast cancer risk among Chinese females. Methods: Three English databases (PubMed, ScienceDirect and Wiley) and three Chinese databases (CNKI, WanFang and VIP) were independently searched by 2 reviewers up to December 2012, complemented by manual searches. The quality of included studies was assessed with the Newcastle-Ottawa Scale items. Random-effects models were used to estimate the pooled odds ratios (ORs) and 95% confidence intervals (CIs). Potential publication bias was estimated through Egger's and Begg's tests. Heterogeneity between studies was evaluated with $I^2$ statistics. Results: Thirty-nine studies involving 13,204 breast cancer cases and 87,248 controls were identified. Compared with non-drinkers, regular tea drinkers had decreased risk (OR=0.79, 95%CIs: 0.65-0.95; $I^2$=84.9%; N=16). An inverse association was also found between regular physical activity and breast cancer risk (OR=0.73, 95%CIs: 0.63-0.85; $I^2$=77.3%; N=15). However, there was no significant association between alcohol drinking and breast cancer risk (OR=0.85, 95%CIs: 0.72-1.02; $I^2$=63.8%; N=26). Most of the results from the subgroup analysis were consistent with the main results. Conclusion: Tea consumption and physical activity are significantly associated with a decreased risk of breast cancer in Chinese females. However, alcohol drinking may not be associated with any elevation of risk.

Association between Dietary Factors and Breast Cancer Risk among Chinese Females: Systematic Review and Meta-analysis

Liu, Xue-Ou,Huang, Yu-Bei,Gao, Ying,Chen, Chuan,Yan, Ye,Dai, Hong-Ji,Song, Feng-Ju,Wang, Yao-Gang,Wang, Pei-Shan,Chen, Ke-Xin Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.3

Background: Evidence for associations between dietary factors and breast cancer risk is inconclusive among Chinese females. To evaluate this question, we conducted a systematic review of relevant case-control and cohort studies. Methods: Studies were systematically searched among 5 English databases (PudMed, ScienceDirect, Wiley, Clinicaltrials.gov, and Cochrane) and 3 Chinese databases (CNKI, WanFang, and VIP) until November 2012. Random effects models were used to estimate summary odds ratios (ORs) and the corresponding 95% confidence intervals (CIs). Results: Thirty one case-control studies and two cohort studies involving 9,299 cases and 11,413 controls were included. Consumption of both soy and fruit was significantly associated with decreased risk of breast cancer, with summary ORs of 0.65 (95% CIs: 0.43-0.99; I2=88.9%, P<0.001; N=13) and 0.66 (95% CIs: 0.47-0.91; $I^2$=76.7%, P<0.001; N=7), respectively. Consumption of fat was significantly associated with increased risk of breast cancer (OR=1.36; 95% CIs: 1.13-1.63; $I^2$=47.9%, P=0.088; N=6). There was nonsignificant association between consumption of vegetables and breast cancer risk (OR=0.72; 95% CIs: 0.51-1.02; $I^2$= 74.4%, P<0.001; N=9). However, sensitivity analysis based on adjusted ORs showed decreased risk of breast cancer was also associated with consumption of vegetables (OR=0.49; 95% CIs: 0.30-0.67). Conclusion: Both soy food and fruit are significantly associated with decreased risk of breast cancer among Chinese females, and vegetables also seems to be protective while dietary fatexerts a promoting influence.

Colorectal Cancer Screening in High-risk Populations: a Survey of Cognition among Medical Professionals in Jiangsu, China

Chen, Yao-Sheng,Xu, Song-Xin,Ding, Yan-Bing,Huang, Xin-En,Deng, Bin,Gao, Xue-Feng,Wu, Da-Cheng Asian Pacific Journal of Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.11

To investigate the cognition of medical professionals when following screening guidelines for colorectal cancer (CRC) and barriers to CRC screening. Between February 2012 and December 2012, an anonymous survey with 19-questions based on several CRC screening guidelines was randomly administered to gastroenterologists, oncologists, general surgeons, and general practitioners in Jiangsu, a developed area in China where the incidence of CRC is relatively high. The average cognitive score was 26.4% among 924 respondents. Gastroenterologists and oncologists had higher scores compared with others (p<0.01 and p<0.01, respectively); doctor of medicine (M.D.) with or without doctor of philosophy (Ph.D.) or holders with bachelor of medical science (BMS) achieved higher scores than other lower degree holders (P<0.05). More importantly, doctors who finished CRC related education in the past year achieved higher scores than the others (p<0.001). The most commonly listed barriers to referring high-risk patients for CRC screening were "anxiety about colonoscopy without anesthesia", "lack of awareness of the current guidelines" and "lack of insurance reimbursement". Lack of cognition was detected among doctors when following CRC screening guidelines for high-risk populations. Educational programs should be recommended to improve their cognition and reduce barriers to CRC screening.

Refolding and Purification of Recombinant Human Interferon-g Expressed as Inclusion Bodies in Escherichia coli Using Size Exclusion Chromatography

Shan-Jing Yao,Yi-Xin Guan,Hai-Xue Pan,Yong-Gui Gao,Man-Gi Cho 한국생물공학회 2005 Biotechnology and Bioprocess Engineering Vol.10 No.2

A size exclusion chromatography (SEC) process, in the presence of denaturant in the refolding buffer was developed to refold recombinant human interferon- (rhIFN-) at a high concentration. The rhIFN- was overexpressed in E. coli, resulting in the formation of inactive inclusion bodies (IBs). The IBs were first solubilized in 8 M urea as the denaturant, and then the refolding process performed by decreasing the urea concentration on the SEC column to suppress protein aggregation. The effects of the urea concentration, protein loading mode and column height during the refolding step were investigated. The combination of the buffer-exchange effect of SEC and a moderate urea concentration in the refolding buffer resulted in an efficient route for producing correctly folded rhIFN-, with protein recovery of 67.1% and specific activity up to 1.2 107 IU/mg.

Word Sense Disambiguation Based on Perceptron Model

Zhang Chun-Xiang,Gao Xue-Yao,Lu Zhi-Mao 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5

Word sense disambiguation (WSD) is an important research topic in natural language processing field, which is very useful for machine translation and information retrieval. In this paper, a linear combination model based on multiple discriminative features is proposed to determine correct sense of an ambiguous word, in which morphology and part of speech in left and right words around ambiguous word are used as features. Then, perceptron algorithm is applied to optimize the WSD model. Experiments show that the WSD performance is improved after the proposed method is applied.