Numerical solution of two dimensional stagnation flows of Micropolar fluids towards a shrinking sheet by using SOR Iterative Procedure

In this paper, the problem of two dimensional stagnation flows of micropolar fluids towards a shrinking sheet has been solved numerically by SOR iterative procedure. The similarity transformations have been used to reduce the highly nonlinear partial differential equations of motion to ordinary differential equations. The resulting equations are then integrated by using appropriate numerical techniques. The results have been calculated on three different grid sizes to check the accuracy of the results. The numerical results have been obtained for various values of the parametera. For, the problem relates to the stagnation flow towards a stretching sheet. For, the problem relates to the flow towards a shrinking sheet. Moreover, the results computed for micropolar case are found in good agreement with those obtained with the Newtonian results

Use of On-line Taxonomies in Creating Global Schemas

Two approaches for integrating heterogeneous database schemas based on the concept of a global schema are surveyed. The first approach relies heavily on manual resolution of various incompatibilities, particularly semantic, that exist among different local database schemas. The second handles this resolution automatically. In this work, usage of an on-line general taxonomy as a tool for resolving various incompatibilities among component schemas is introduced. This tool is a part of an on-going research for developing a new methodology for integrating heterogeneous database schemas using an on-line general taxonomy. Many advantages offered by the tool are discussed

A Simple Study on Weight and Height of Students

This study was conducted on a group of male and female students of age range of 18-25 years. In this paper it is tried to find out a correlation between height and weight of male and female students. Then the simple regression equations of weight on height are fitted for both for male and female students. A total of 639 students of different departments of BRAC University, Dhaka, Bangladesh in the spring semester of 2016 are participated in this survey. Body Mass Index (BMI) of the students was calculated to compare the health status of male and females students. It is found that that the most of the students (males and females) have the normal weight. It is interestingly noticed that the higher percentage (34.18%) of males are overweight than the females; whereas the females (13.33%) are more than double in underweight than their male’s counterpart (5.93%). The correlation between height and weight of male students is calculated as 0.435 (Pearson’s coefficient of correlation). On the other hand the correlation between height and weight of female students was 0.319. From the t tests, it is proved that the both the coefficients of correlation are highly statistically significant (p-value<0.01). From the simple regression equations of weight on height, it is found that the both for male and female students the effect of height on weight is almost same. It is also found that the effect of height on weight both for male and female students is highly significant (p-value<0.01)

Use of machine learning algorithms for prediction of fetal risk using cardiotocographic data

Background: A major contributor to under-five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor.Aim: The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses.Methods: CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Ten different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. Model with best performance on specified metrics was then identified.Results: Determined by obstetricians\u27 interpretation of CTGs as gold standard, 70% of them were normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification models generated by XGBoost, decision tree, and random forest had high precision (\u3e96%) to predict the suspect and pathological state of the fetus based on the CTG tracings. However, on testing data, XGBoost model had the highest precision to predict a pathological fetal state (\u3e92%).Conclusion: The classification model developed using XGBoost technique had the highest prediction accuracy for an adverse fetal outcome. Lay health-care workers in low- and middle-income countries can use this model to triage pregnant women in remote areas for early referral and further management