Study Of Wave-Induced Scour Depth Around Group Of Piles Using Support Vector Machines

Various arrangements of pile groups are widely being used as supports of marine structures. As piles are located on erodible beds of the sea, scouring is a threat to such structures and the scour depth amounts should be considered well in their designs. Though most of these supports are constructed in form of groups of piles, majority of studies were concentrated on predictions of scouring around single piles whereas the arrangement of the piles and the spaces between them in arrangements as well as their geometry, sediment and wave characteristics should also be studied. Despite the importance of the scour hole depths, the existing prediction formulas are not capable of accurate estimations around pile groups with different arrangements. Hence, developing a robust model for the estimation of scour depth seems necessary. One of the most common approaches as an alternative to empirical ones is the soft computing methods. Artificial Neural Network as the most famous data-mining method has been successfully applied in scour studies. But there are still needs of more assessments in their applications on pile group case studies. In addition, Support Vector Machines as one of the recently applied soft computing models in scouring has scarcely been studied so far. In this study, series of large scale scouring experiments were done for various arrangements of pile groups with different pile and arrangement characteristics exposed to waves of shallow water and equilibrium scour depth around them were measured in wave basin of Ujigwa Open Laboratory of Kyoto University. Finally, by applying the provided experimental data, the applicability of data mining models were assessed in predictions of pile group scour properties. Results indicate that, data mining approaches can provide more reliable predictions of scouring properties due to waves compared to current available empirical formulae

Factors associated with incidence of type II diabetes in pre-diabetic women using Bayesian Model Averaging

Introduction: Diabetes is a chronic disease which usually begins with impaired glucose tolerance. This step is known as pre-diabetes. People with pre-diabetes are at greater risk for diabetes. Typically for the variable selection, stepwise approach is used which does not take into account model uncertainties. In this study, Bayesian Model Averaging (BMA) method was used to sort out the above shortcoming. Materials and Methods: The study population was 734 pre-diabetic women with 20 years and older participated in Tehran Lipid and Glucose Study (TLGS). In this study, the stepwise and BMA variable selection methods were employed in logistic regression. Then area under curve (AUC) for both methods was computed and compared with Delong test. All analyses was done using R version 3.1.3. Results: BMA selected the fasting plasma glucose, 2 hours’ blood glucose, and family history of diabetes, body mass index and aspirin use at baseline as risk factors for diabetic. In addition to these factors, stepwise method selected diastolic blood pressure, history of past 3 months’ hospitalization, thyroid drug use and education. Although the number of variables selected by BMA (5 variables) was less than that of stepwise (9 variables), AUC for the two methods was not significant. Conclusion: It seems that the BMA provide better model for screening of diabetes because with selecting fewer variables, prediction ability of the model is preserve