In this paper the nano conical shell model is developed based on modified strain gradient theory. The governing equations of the nano truncated conical shell are derived using the FSDT, and the size parameters through modified strain gradient theory have been taken into account. Hamilton’s principle is used to obtain the governing equations, and the shell’s equations of motion are derived with partial differentials along with the classical and non-classical boundary conditions. Galerkin’s method and the Generalized Differential Quadrature (GDQ) approach are applied to obtain the linear free vibrations of the carbon nano cone (CNC). The CNC is studied with simply supported boundary condition. The results of the new model are compared with those of the classical and couple stress theories, which point to the conclusion that the classical and couple stress models are special cases of modified strain gradient theory. Results also reveal that rigidity of the nano truncated conical shell in the strain gradient theory is greater than that in the modified couple stress and classical theories respectively, which leads to an increase in dimensionless natural frequency ratio. Moreover, the study investigates the effect of the size parameters on nano shell vibration for different lengths and vertex angles. Manuscript profile

The current study uses heart rate variability (HRV) signal processing to investigate changes in the multifractal dimension in congestive heart failure (CHF) patients and predict sudden cardiac death (SCD). In this regard, HRV signals are first extracted, and their four sub-signals are determined using the Local Characteristic Decomposition (LCD) method. In the next step, using the Teager Energy method, the instant amplitudes of each sub-signal obtained in the previous step are calculated; thus, new signals are generated based on these instant amplitudes. Employing multifractal detrended fluctuation analysis (MF-DFA), the modified fractal dimensions of each new signal are then obtained. With the t-test method, appropriate features are selected and input into the support vector machine (SVM) classifier. By detecting subtle changes in HRV signals, this method can detect SCD in CHF patients. The results indicate that the proposed algorithm can distinguish the signals of SCD subjects with an accuracy of 84.76% 26 minutes before the event. In addition, after passing each 5-minute interval, the proposed method can update and determine how much time is left before SCD occurs Manuscript profile