FollowMe: One Social Importance-Based Collaborative Scheme in MONs

The performance of mobile opportunistic networks mainly relies on collaboration among nodes. Thus far, researchers have ignored the influence of node sociality on the incentive process, leading to poor network performance. Considering the fact that followers always imitate the behavior of superstars, this paper proposes FollowMe, which integrates the social importance of nodes with evolutionary game theory to improve the collaborative behavior of nodes. First, we use the prisoner’s dilemma model to establish the matrix of game gains between nodes. Second, we introduce the signal reference as a game rule between nodes. The number of nodes choosing different strategies in a game round is used to calculate the cumulative income of the node in combination with the probability formula. Finally, the Fermi function is used to determine whether the node updates the strategy. The simulation results show that, compared with the random update rule, the proposed strategy is more capable of promoting cooperative behavior between nodes to improve the delivery rate of data packets.

Microscopic model of optical potential for testing the 12,14Be+p elastic scattering at 700 Mev

The data on the 12,14Be + p elastic scattering cross sections at 700 Mev are compared with those obtained by solving the relativistic wave equation with the microscopic optical potentials calculated as folding of the NN amplitude of scattering with densities of these nuclei in the form of the symmetrized Fermi function with the fitted radius and diffuseness parameters, and also with the densities obtained in two microscopic models, based on the Generator Coordinate Method (GCM) and the other one – on the Variational Method of Calculations (VMC). For 12Be, above models turn out to be in a small disagreement with the data at "large" angles of scattering θ ≥ 9°, while for the 14Be one sees some inconsistence at smaller angles, too.

Strong absorption model with antisymmetric S-matrix

We present a strong absorption model with an [Formula: see text]-matrix in a new form of an antisymmetrized Fermi distribution for describing diffraction patterns of neutron–nucleus scattering at intermediate energies. The proposed antisymmetric [Formula: see text]-matrix has a form close to the conventional one and allows to obtain exact analytical expressions for the damping factor that determines the elastic scattering amplitude and the total cross-sections for interaction, reactions and elastic scattering. The use of an antisymmetrized Fermi function instead of the usual Fermi function in successful strong absorption models basically does not notably affect the calculated nucleon–nucleus elastic scattering differential cross-sections in the intermediate energy region.

Simulation Study on Quantum Capacitances of Graphene Nanoribbon VLSI Interconnects

In this paper, study on the capacitive effects of Graphene nanoribbon (GNR) in VLSI interconnect has been studied as a function of GNR width, Fermi function and gate voltage. The quantum capacitance of GNR has been simulated in terms of Fermi function for three different values of insulator thickness — 1.5[Formula: see text]nm, 2[Formula: see text]nm and 2.5[Formula: see text]nm. After that, quantum capacitance is studied in both degenerate and nondegenerate region with respect to Fermi function and gate voltage of range 1–5[Formula: see text]V. Then, the total capacitance of GNR is studied as a function of gate voltage of [Formula: see text][Formula: see text]–5[Formula: see text]V range at degenerate and nondegenerate regions, where width of GNR is considered 4[Formula: see text]nm. Finally, the total capacitance of GNR is studied in both regions with varying GNR width, considering fixed gate voltage of 3[Formula: see text]V. After analyzing these simulations, it has been found that GNR in degenerate region shows nearly steady capacitance under a certain applied gate voltage.

Artificial Neural Network Based CNTFETs Modeling

Based on artificial neural network (ANN), a new method of modeling carbon nanotube field effect transistors (CNTFETs) is developed. This paper presents two ANN CNTFET models, including P-type CNTFET (PCNTFET) and N-type CNTFET (NCNTFET). In order to describe the devices more accurately, a segmentation voltage of the voltage between gate and source is defined for each type of CNTFET to segment the workspace of CNTFET. With the smooth connection by a quasi-Fermi function for, the two segmented networks of CNTFET are integrated into a whole device model and implemented by Verilog-A. To validate the ANN CNTFET models, quantitative test with different device intrinsic parameters are done. Furthermore, a complementary CNTFET inverter is designed using these NCNTFET and PCNTFET ANN models. The performances of the inverter show that our models are both efficient and accurate for simulation of nanometer scale circuits.