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Abstract: In this paper, we consider the dynamical behavior of delayed Cohen–Grossberg neural networks with discontinuous activation functions. Some sufficient conditions are derived to guarantee the existence, uniqueness and global stability of the equilibrium point of the neural network. Convergence behavior for both state and output is discussed. The constraints imposed on the interconnection matrices, which concern the theory of M-matrices, are easily verifiable and independent of the delay parameter. The obtained results improve and extend the previous results. Finally, we give an numerical example to illustrate the effectiveness of the theoretical results. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the convergence dynamics of Cohen–Grossberg neural networks (CGNNs) with continuously distributed delays. Without assuming the differentiability and monotonicity of activation functions, the differentiability of amplification functions and the symmetry of synaptic interconnection weights, we construct suitable Lyapunov functionals and employ inequality technique to establish some sufficient conditions ensuring existence, uniqueness, global asymptotic stability, global exponential convergence, and even global exponential stability of equilibria. Our results are not only presented in terms of system parameters and can be easily verified and also less restrictive than previously known criteria and can be applied to neural networks including Hopfield neural networks, bidirectional association memory neural networks and cellular neural networks. [Copyright &y& Elsevier]

The traditional molecular sieve membrane for gas separation is mostly prepared by zeolite, MOFs and other materials. Nano-carbon materials such as graphene and carbon nanotubes have been studied in the field of gas separation in recent years which have large specific surface area and high porosity. The performance of carbon nanotubes and graphene can be effectively improved by adding corresponding groups. A novel molecular sieve membrane was synthesized by sulfhydryl and amino-modified graphene oxide/oxidized single-walled carbon nanotubes (NH 2 -SH-GO/SWCNTs). The composite material was characterized by transmission electron microscopy, field emission scanning electron microscopy, X-ray diffraction, thermo-gravimetric analysis, Fourier transform infrared spectroscopy, and Brunauer-Emmett-Teller specific surface area analyzers. The results reveal that laminated graphene oxide and tubular single-walled carbon nanotubes are intertwined and carbon nanotubes attach onto the surface of GO or intercalate into the GO sheets to form a spatial stereoscopic structure. TGA results demonstrate that the loading amount of amino and sulfydryl groups in NH 2 -SH-GO/SWCNTs is about 11.91 wt%. When the intake pressure of N 2 is 0.10 MPa, the permeability coefficient for N 2 passing through NH 2 -SH-GO/SWCNTs is 1829 Barrer. With the increase of temperature, permeability coefficient for N 2 decreases, while it increases as the intake pressure rises. The experimental results for mixed gases passing through NH 2 -SH-GO/SWCNTs show that, when P 0 = 0.10 MPa and T = 323 K, the permeability coefficient for CO 2 is 1896 Barrer, while that for CO is only 116 Barrer. At 323 K, selectivity for N 2 /CO, CO 2 /CO and CO 2 /N 2 is 39.2, 46.3, and 9.3, respectively. These results indicate that NH 2 -SH-GO/SWCNTs may be a promising molecular sieve membrane for gas separation. Image 1 • Simultaneously separate CO 2 and N 2 from blast furnace gas. • Preparation of new style NH 2 -SH-GO/SWCNTs. • The permeability coefficient for N 2 is 1829 Barrer. • Selectivity for N 2 /CO, CO 2 /CO and CO 2 /N 2 is 39.2, 46.3, and 9.3, respectively. [ABSTRACT FROM AUTHOR]

Abstract: In this work, we consider a system of delay difference equations with McCulloch-Pitts nonlinearity, which describes the dynamics of a network of three neurons with no internal decays. It is shown that every solution is either truncated periodic or unbounded. Our results have potential applications in neural networks. [Copyright &y& Elsevier]

Graphene oxide (GO) was prepared using a modified Hummers' method, and titanium dioxide nanotubes (TiO 2 NT) were prepared by a hydrothermal method. The gas sensing material (graphene oxide-titanium dioxide nanotubes) was obtained by self-assembling composite GO and TiO 2 NT.Then the material was coated onto a gold electrode to prepare the device. This resultant device exhibits excellent response to low concentrations of acetone and high selectivity over other gases. Through SEM test analysis, TiO 2 NT was tiled on a sheet of GO to form a physically bonded heterojunction semiconductor composite. XRD analysis confirms that the anatase TiO 2 was successfully produced and there was no impurity peak in the composite. During the gas sensing test, it was proved that the device exhibited an excellent response to 0.3 ppm of acetone at 250 °C. The heterojunction material still exhibits the resistance characteristics of the n-type semiconductor, and the material resistance is reduced in a gas environment by carrier conduction. The increase of sensitivity arises from the fact that the holes of GO and the carriers of TiO 2 NT are moved towards each other to form a space charge region, which causes the material to form a larger resistance difference in the test gas environment. [ABSTRACT FROM AUTHOR]