Development and evaluation of the Directive and Nondirective Support Scale for Patients with Type 2 Diabetes

Objectives:This study aims to develop the Directive and Nondirective Support Scale for Patients with Type 2 Diabetes(DNSS-T2DM)to measure diabetes-specific support and patients’preference as well as evaluate the construct validity and reliability of the DNSS-T2DM.Methods:A cross-sectional study was conducted in Tongzhou District,Beijing,China from July to September 2015.A total of 474 participants who had been diagnosed as type 2 diabetes by physicians and completed the DNSS-T2DM were included.The original 11-item DNSS-T2DM contains five items on nondirective support(Items 1-5)and six items on directive support(Items 6-11).There were two parallel questions for each item with one to measure the preference for support(Preference part)and the other to measure the perception of support in reality(Reality part).The final DNSS-T2DM was determined based on the results of the exploratory factor analysis(EFA).The construct validity of the final DNSS-T2DM was evaluated by the confirmatory factor analysis(CFA).The reliability was evaluated by internal consistency with Cronbach’sαcoefficients.Results:A final 7-item DNSS-T2DM loaded on 2 factors with four items representing nondirective support and three items representing directive support was determined based on the EFA.The CFA indicated a satisfactory construct validity.The internal consistency of the 7-item DNSS-T2DM as well as the nondirective support items was satisfactory with Cronbach’sα≥7.00.70.Conclusions:Our study supported the validity and reliability of the 7-item DNSS-T2DM.Further studies on the application of the DNSS-T2DM in different settings and population are needed.

Electromagnetic side-channel attack based on PSO directed acyclic graph SVM

Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.