Global Stability for a Viral Infection Model with Saturated Incidence Rate

A viral infection model with saturated incidence rate and viral infection with delay is derived and analyzed; the incidence rate is assumed to be a specific nonlinear form βxv/(1+αv). The existence and uniqueness of equilibrium are proved. The basic reproductive number R0 is given. The model is divided into two cases: with or without delay. In each case, by constructing Lyapunov functionals, necessary and sufficient conditions are given to ensure the global stability of the models

An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network

 The CO2 enhanced oil recovery (EOR) method is widely used in actual oilfields. It is extremely important to accurately predict the CO2 minimum miscibility pressure (MMP) for CO2-EOR. At present, many studies about MMP prediction are based on empirical, experimental, or numerical simulation methods, but these methods have limitations in accuracy or computation efficiency. Therefore, more work needs to be done. In this work, with the results of the slim-tube experiment and the data expansion of the multiple mixing cell methods, an improved artificial neural network (ANN) model that predicts CO2 MMP by the full composition of the crude oil and temperature is trained. To stabilize the neural network training process, L2 regularization and Dropout are used to address the issue of over-fitting in neural networks. Predicting results show that the ANN model with Dropout possesses higher prediction accuracy and stronger generalization ability. Then, based on the validation sample evaluation, the mean absolute percentage error and R-square of the ANN model are 6.99 and 0.948, respectively. Finally, the improved ANN model is tested by six samples obtained from slim-tube experiment results. The results indicate that the improved ANN model has extremely low time cost and high accuracy to predict CO2 MMP, which is of great significance for CO2-EOR.Cited as: Dong, P., Liao, X., Chen, Z., Chu, H. An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network. Advances in Geo-Energy Research, 2019, 3(4): 355-364, doi: 10.26804/ager.2019.04.0

Cross-View Graph Consistency Learning for Invariant Graph Representations

Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction. First, two complementary augmented views are derived from an incomplete graph structure through a bidirectional graph structure augmentation scheme. This augmentation scheme mitigates the potential information loss that is commonly associated with various data augmentation techniques involving raw graph data, such as edge perturbation, node removal, and attribute masking. Second, we propose a CGCL model that can learn invariant graph representations. A cross-view training scheme is proposed to train the proposed CGCL model. This scheme attempts to maximize the consistency information between one augmented view and the graph structure reconstructed from the other augmented view. Furthermore, we offer a comprehensive theoretical CGCL analysis. This paper empirically and experimentally demonstrates the effectiveness of the proposed CGCL method, achieving competitive results on graph datasets in comparisons with several state-of-the-art algorithms.Comment: 8 page

Near-Infrared Optical Absorption Enhanced in Black Silicon via Ag Nanoparticle-Induced Localized Surface Plasmon

Due to the localized surface plasmon (LSP) effect induced by Ag nanoparticles inside black silicon, the optical absorption of black silicon is enhanced dramatically in near-infrared range (1,100 to 2,500 nm). The black silicon with Ag nanoparticles shows much higher absorption than black silicon fabricated by chemical etching or reactive ion etching over ultraviolet to near-infrared (UV-VIS-NIR, 250 to 2,500 nm). The maximum absorption even increased up to 93.6% in the NIR range (820 to 2,500 nm). The high absorption in NIR range makes LSP-enhanced black silicon a potential material used for NIR-sensitive optoelectronic device. PACS 78.67.Bf; 78.30.Fs; 78.40.-q; 42.70.G

Synthesis and Characterization of Uniform Spherical Nanoporous TiO 2

The spherical nanoporous TiO2 aerogels were prepared by a simple ethanol-thermal method, using spherical cellulose alcohol-gel as the template. The morphology, crystalline structure, pore size, specific surface area, and the photocatalytic activity of obtained TiO2 aerogel were separately characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), N2 adsorption-desorption isotherms, and double beam UV-VIS spectrophotometer. The characteristics of TiO2 aerogels presented uniform sphere shape, good internal structural morphology, high specific surface area (ranging from 111.88 to 149.95 m2/g), and good crystalline anatase phase. Moreover, methyl orange dye was used as the target pollutant to characterize the photocatalytic activities and the adsorption performance. The photocatalytic experiment shows that the obtained spherical TiO2 aerogels had a higher degradation ratio of 92.9% on methyl orange dye compared with aspherical TiO2 aerogels prepared from other concentrations of tetrabutyl orthotitanate (TBOT)