Factorial Design of Cement Slurries Containing Limestone Powder for Self-Consolidating Slurry-Infiltrated Fiber Concrete

Slurries with high penetrability for production of Self-consolidating Slurry Infiltrated Fiber Concrete (SIFCON) were investigated in this study. Factorial experimental design was adopted in this investigation to assess the combined effects of five independent variables on mini-slump test, plate cohesion meter, induced bleeding test, J-fiber penetration test and compressive strength at 7 and 28 days. The independent variables investigated were the proportions of limestone powder (LSP) and sand, the dosages of superplasticiser (SP) and viscosity agent (VA), and water-to-binder ratio (w/b). A two-level fractional factorial statistical method was used to model the influence of key parameters on properties affecting the behaviour of fresh cement slurry and compressive strength. The models are valid for mixes with 10 to 50% LSP as replacement of cement, 0.02 to 0.06% VA by mass of cement, 0.6 to 1.2% SP and 50 to 150% sand (% mass of binder) and 0.42 to 0.48 w/b. The influences of LSP, SP, VA, sand and W/B were characterised and analysed using polynomial regression which identifies the primary factors and their interactions on the measured properties. Mathematical polynomials were developed for mini-slump, plate cohesion meter, J-fiber penetration test, induced bleeding and compressive strength as functions of LSP, SP, VA, sand and w/b. The estimated results of mini-slump, induced bleeding test and compressive strength from the derived models are compared with results obtained from previously proposed models that were developed for cement paste. The proposed response models of the self-consolidating SIFCON offer useful information regarding the mix optimization to secure a highly penetration of slurry with low compressive strength

Gene selection by cooperative competition clustering

Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Glycemic index, carbohydrate and fiber intakes and risk of reflux esophagitis, Barrett’s esophagus, and esophageal adenocarcinoma

Objective: To examine the association between dietary glycemic index (GI), glycemic load (GL), total carbohydrate, sugars, starch, and fiber intakes and the risk of reflux esophagitis, Barrett’s esophagus, and esophageal adenocarcinoma.

Methods: In an all-Ireland study, dietary information was collected from patients with esophageal adenocarcinoma (n = 224), long-segment Barrett’s esophagus (n = 220), reflux esophagitis (n = 219), and population-based controls (n = 256). Multiple logistic regression analysis examined the association between dietary variables and disease risk by tertiles of intake and as continuous variables, while adjusting for potential confounders.

Results: Reflux esophagitis risk was positively associated with starch intake and negatively associated with sugar intake. Barrett’s esophagus risk was significantly reduced in people in the highest versus the lowest tertile of fiber intake (OR 0.44 95%CI 0.25–0.80). Fiber intake was also associated with a reduced risk of esophageal adenocarcinoma, as was total carbohydrate intake (OR 0.45 95%CI 0.33–0.61 per 50 g/d increase). However, an increased esophageal adenocarcinoma risk was detected per 10 unit increase in GI intake (OR 1.42 95%CI 1.07–1.89).

Conclusions: Our findings suggest that fiber intake is inversely associated with Barrett’s esophagus and esophageal adenocarcinoma risk. Esophageal adenocarcinoma risk is inversely associated with total carbohydrate consumption but positively associated with high GI intakes.

Abnormal integrity of association fiber tracts in amnestic mild cognitive impairment.

The association fiber tracts integrity of the inter-hemispheric and within-hemispheric communication was poor understood in amnestic type mild cognitive impairment (aMCI) patients by diffusion tensor imaging (DTI). A region of interest-based DTI approach was applied to explore fiber tract differences between 22 aMCI patients and 22 well-matched normal aging. Correlations were also sought between fractional anisotropy (FA) values and the cognitive performance scores in the aMCI patients. Extensive impairment of association fiber tracts integrity was observed in aMCI patients, including bilateral inferior fronto-occipital fascicles, the genu of corpus callosum, bilateral cingulate bundles and bilateral superior longitudinal fascicles II (SLE II) subcomponent. In addition, the FA value of right SLE II was significantly negatively correlated to the performance of Trail Making Test A and B, whilst the values of right posterior cingulate bundle was significantly positive correlation with MMSE score. As aMCI is a putative prodromal syndrome to Alzheimer's disease (AD), this study suggested that investigation of association fiber tracts between remote cortexes may yield important new data to predict whether a patient will eventually develop AD.