Introduction:Diabetes is a lifestyle disease which requires a multipronged approach for its management, wherein patient has an important role to play in terms of self-care practices, which can be taught to them by educational programs. To develop such an educational program, a baseline assessment of knowledge and self-care practices of patients, needs to be made. The two objectives of the study were to estimate the knowledge of diabetic patients regarding the disease and its complications, and to estimate the knowledge and adherence to self-care practices concerned with Type 2 diabetes mellitus.Methods:The study was conducted in rural Sullia, Karnataka, from January 2014 to May 2015. The sample size was calculated to be 400, and the sampling method was probability proportionate to sampling size.Result:Majority of them were married males of Hindu religion and belonged to upper middle class. Only 24.25% of them had good knowledge. Among the self-care practices, foot care was the most neglected area.Conclusion:Only one-fourth of the study population had a good knowledge toward diabetes. Adherence to some of the self-care practices was also poor. Government policies may help in creating guidelines on diabetes management, funding community programs for public awareness, availability of medicines, and diagnostic services to all sections of the community. Continuing education programs for health-care providers and utilization of mass media to the fullest potential may also help in creating awareness.

Successful automation of a machining process system requires an effective and efficient tool condition monitoring system to ensure high productivity, products of desired dimensions, and long machine tool life. As such the component's processing quality and increased system reliability will be guaranteed. This paper presents a classification of healthy and faulty conditions of the face milling tool by using the Naïve Bayes technique. A set of descriptive statistical parameters is extracted from the vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features are fed to the Naïve Bayes algorithm. The output of the algorithm is used to study and classify the milling tool condition and it is found that the Naïve Bayes model is able to give 96.9% classification accuracy. Also the performances of the different classifiers are compared. Based on the results obtained, the Naïve Bayes technique can be recommended for online monitoring and fault diagnosis of the face milling tool.

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This paper presents the use of multilayer perceptron (MLP) for fault diagnosis through a histogram feature extracted from vibration signals of healthy and faulty conditions of single point cutting tools. The features were extracted from the vibration signals, which were acquired while machining with healthy and different worn-out tool conditions. Principle component analysis (PCA) used to select important extracted features. The artificial neural network (ANN) algorithm was applied as a fault classifier in order to know the status of cutting tool conditions. The accuracy of classification with MLP was found to be 82.5 %, which validates that the proposed approach is an effective method for fault diagnosis of single point cutting tools.