Betel quid chewing has been associated with several human cancers. However, the role of betel quid in carcinogenesis remains uncertain. Piper betel contains high concentrations of safrole (an inducer of DNA oxidative damage). Safrole may be metabolized by hepatic sulfotransferase 1A1 (SULT1A1), or glutathione S-transferases (GSTM1, GSTT1, and GSTP1). Thus, we investigated the association of genetic polymorphisms of SULT1A1, GSTM1, GSTT1, and GSTP1 with DNA oxidative damage among betel quid chewers. A biomarker for oxidative stress, urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) level, was analyzed using isotope-dilution LC-MS/MS in 64 betel quid chewers and 129 non-betel quid chewers. Data on demographics and habits (smoking, alcohol drinking, and betel quid chewing) were obtained from questionnaires. Our results revealed that urinary 8-OHdG level was higher in chewers with SULT1A1 Arg-His genotype than in chewers with SULT1A1 Arg-Arg genotype. Urinary 8-OHdG level was also higher in chewers with GSTP1 Ile-Ile genotype. Furthermore, the combined effect of SULT1A1 and GSTP1 genotypes on urinary 8-OHdG was evaluated. Non-chewers with both SULT1A1 Arg-Arg and GSTP1 Val-Val/Ile-Val (reference group) had the lowest mean level (3.6 ng/mg creatinine), whereas chewers with either SULT1A1 Arg-His or GSTP1 Ile-Ile had the highest 8-OHdG mean level (6.2 ng/mg creatinine; vs. reference group, P = 0.04). Chewers with both of SULT1A1 Arg-Arg and GSTP1 Val-Val/Ile-Val (4.6 ng/mg creatinine), and non-chewers with either SULT1A1 Arg-His or GSTP1 Ile-Ile (4.7 ng/mg creatinine) had a moderately increased 8-OHdG level. Thus, the susceptible SULT1A1 and GSTP1 genotypes may modulate increased DNA oxidative stress elicited by betel-quid chewing.

Euclidean distance function based fuzzy clustering algorithms can only be used to detect spherical structural clusters. The purpose of this study is improved Fuzzy C-Means algorithm based on Mahalanobis distance to identify concept structure for Linear Algebra. In addition, Concept structure analysis (CSA) could provide individualized knowledge structure. CSA algorithm is the major methodology and it is based on fuzzy logic model of perception (FLMP) and interpretive structural modeling (ISM). CSA could display individualized knowledge structure and clearly represent hierarchies and linkage among concepts for each examinee. Each cluster of data can easily describe features of knowledge structures. The results show that there are five clusters and each cluster has its own cognitive characteristics. In this study, the author provide the empirical data for concepts of linear algebra from university students. To sum up, the methodology can improve knowledge management in classroom more feasible. Finally, the result shows that Algorithm based on Mahalanobis distance has better performance than Fuzzy C-Means algorithm.

The popular fuzzy c-means algorithm based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, Gustafson-Kessel clustering algorithm needs added constraint of fuzzy covariance matrix, Gath-Geva clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Mahalanobis Clustering Algorithm Based on FCM by taking a new threshold value and a new convergent process is proposed. The experimental results of real data sets show that our proposed new algorithm has the best performance. Not only replacing the common covariance matrix with the correlation matrix in the objective function in the Normalized Mahalanobis Clustering Algorithm Based on FCM.

Currently, cognitive psychologists and mathematics educators are looking again at conceptual and procedural knowledge in mathematics learning. Building relationship between conceptual knowledge and the procedures of mathematics contributes to long-term memory of procedures and to their effective use. So we know that symbols could enhance concept and procedures apply concepts to solve problem efficiently. Sketch the graph of exponential function and logarithmic function. Find the inverse exponential function, logarithmic function and so on. The lack of other concrete example in general exponential and logarithmic function prevented the development of application about general exponential and logarithm. Numerical fundamental mathematic problems are provided with complicated number frequently. The lack of other concrete example in general exponential and logarithmic function prevented the development of application about general exponential and logarithm. Numerical fundamental mathematic problems are provided with complicated number frequently The conceptual knowledge, not mechanical algorithms, need more study and thought to identify. But there is a limitation for students use some materials to clarify complicated mathematic concepts perfectly. In order to insight the misconception of learning fundamental mathematics and progress teaching. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Applying the method of the cluster of fuzzy c-mean, we could distinguish characteristics of six groups. We analysis the whole data and discuss the relationship between knowledge structures of the sample. The result and discoveries from the research can offer pupils' misconception of learning fundamental mathematics with reference of diagnosis.

Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.