Vol. 1, No. 5 [October 2015]:

For a wide variety of problems in structures, linear and nonlinear solid mechanics, dynamics, and submarines structural stability problems FEM can be used, based on the development in computer technology. As to arch dam stress analysis, the conventional trial-load method has got lots of successful experience, but also has some disadvantages. For instance, it is hard to deal with complicated foundation, topography feature etc. At these aspects, the Finite Element Methods (FEM) has large advantage. Finite element method is an essential and powerful tool for solving structural problems not only in the marine field but also in the design of most industrial products and even in non-structural fields. This paper mainly focuses on the location of major stress concentration points and deflections of the dam under dynamic loads and also to evaluate the fatigue life of the structure by using ANSYS.
KEYWORDS: Double curved arch dams, Dynamic analysis, fatigue analysis, Maximum Credible Earthquake (MCE) ,

As the technology advances and telephone, cable and data networks converges into a single network, MAC (medium access control) protocols play momentous role in WSNs. MAC protocols define rules for accessing the shared medium efficiently. MAC protocols are used for energy conservation and to avoid collisions. In this paper we describe different MAC protocols for WSN. This paper presents the comparative study of different MAC protocols. It shows which protocol will be suitable for specific application and environment. The aim of this paper is to analyze the most energy efficient protocol so that MAC protocols can be categorized and their performance can be compared. There are some problems in existing MAC protocols that is in cellular systems QOS (quality of service) is not good, bandwidth efficiency issues and network synchronisation is difficult. Bluetooth is also having short range issue.
Keywords- Wireless sensor networks, energy efficient MAC protocols, CDT(continual data scheme)

Data mining is the process of analyzing large sets of data and then extracting the meaning of the data. It helps in predicting future trends and patterns, allowing business in decision making. Data mining applications can answer business questions that take much time to resolve traditionally. Large amount of data which is generated for the prediction of heart disease is analyzed traditionally and is too complicated and voluminous to be processed. Data mining provides the techniques and methods for the transformation of data into useful information for decision making. These techniques make the process fast and it takes less time for the prediction system to predict the heart disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. Result from using neural networks is 99.62% in one paper [6] . By Applying data mining techniques to heart disease data which needs to be processed, we can get effective results and achieve reliable performance which will help in decision making in healthcare industry. It will help the medical practitioners to diagnose the disease in less time and predict the probable complications well in advance. Identifying the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as high blood cholesterol, diabetes, smoking, poor diet, obesity, hyper tension, stress, etc. Data mining functions and techniques are used to identify the level of risk factors to help the patients in taking precautions in advance to save their life.
Keywords: heart disease ,data mining, data mining techniques, data mining tools, data mining applications, methodology