Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

The recent development of channel technology has promised to reduce the transaction verification time in blockchain operations.When transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each other.If one party refuses to do so,the channel is unstable.A stable channel is thus required.Because nodes may show uncooperative behavior,they may have a negative impact on the stability of such channels.In order to address this issue,this work proposes a dynamic evolutionary game model based on node behavior.This model considers various defense strategies'cost and attack success ratio under them.Nodes can dynamically adjust their strategies according to the behavior of attackers to achieve their effective defense.The equilibrium stability of the proposed model can be achieved.The proposed model can be applied to general channel networks.It is compared with two state-of-the-art blockchain channels:Lightning network and Spirit channels.The experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable state.Thus its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

Water level updating model for flow calculation of river networks

Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.

Review of permeability evolution model for fractured porous media

The ability to capture permeability of fractured porous media plays a significant role in several engineering applications, including reservoir, mining, petroleum and geotechnical engineering. In order to solve fluid flow and coupled flow-deformation problems encountered in these engineering applications,both empirical and theoretical models had been proposed in the past few decades. Some of them are simple but still work in certain circumstances; others are complex but also need some modifications to be applicable. Thus, the understanding of state-of-the-art permeability evolution model would help researchers and engineers solve engineering problems through an appropriate approach. This paper summarizes permeability evolution models proposed by earlier and recent researchers with emphasis on their characteristics and limitations.

A COUPLED 1-D AND 2-D CHANNEL NETWORK MATHEMATICAL MODEL USED FOR FLOW CALCULATIONS IN THE MIDDLE REACHES OF THE YANGTZE RIVER

A coupled one-dimensional （1-D） and two-dimensional （2-D） channel network mathematical model is proposed for flow calculations at nodes in a channel network system in this article. For the 1-D model, the finite difference method is used to discretize the Saint-Venant equations in all channels of a looped network. The Alternating Direction Implicit （ADI） method is adopted for the 2-D model at the nodes. In the coupled model, the 1-D model provides a good approximation with small computational effort, while the 2-D model is applied for complex topography to achieve a high accuracy. An Artificial Neural Network （ANN.） method is used for the data exchange and the connectivity between the 1-D and 2-D models. The coupled model is applied to the Jingjiang-Dongting Lake region, to simulate the tremendous looped channel network system, and the results are compared with field data. The good agreement shows that the coupled hydraulic model is more effective than the conventional 1-D model.

Base communication model of IP covert timing channels

IP covert timing channel （IPCTC） is an unconventional communication channel which attaches time information to the packets of an overt channel as messages carders, e.g., using different inter-packet delays to transmit messages in a packet-switched network. Although the IPCTCs have many different communication methods, based on the concept of time, we categorized the base communication model of the IPCTCs into three types and then utilized the signal processing theory to build their mathematical models. As a result, the basic characteristics of the IPCTCs＇ base model were formally derived. Hence, the characteristics of any IPCTC can be derived from the base models that consist of the IPCTC. Furthermore, a set of approaches was devised to implement the base model of the IPCTCs in a TCP/IP network. Experimental results show the correctness of the pro- posed base model of the IPCTCs in this paper.

Reference Image Guided Super-Resolution via Progressive Channel Attention Networks

In recent years,the convolutional neural networks(CNNs)for single image super-resolution(SISR)are becoming more and more complex,and it is more challenging to improve the SISR performance.In contrast,the reference image guided super-resolution(RefSR)is an effective strategy to boost the SR(super-resolution)performance.In RefSR,the introduced high-resolution(HR)references can facilitate the high-frequency residual prediction process.According to the best of our knowledge,the existing CNN-based RefSR methods treat the features from the references and the low-resolution(LR)input equally by simply concatenating them together.However,the HR references and the LR inputs contribute differently to the final SR results.Therefore,we propose a progressive channel attention network(PCANet)for RefSR.There are two technical contributions in this paper.First,we propose a novel channel attention module(CAM),which estimates the channel weighting parameter by weightedly averaging the spatial features instead of using global averaging.Second,considering that the residual prediction process can be improved when the LR input is enriched with more details,we perform super-resolution progressively,which can take advantage of the reference images in multi-scales.Extensive quantitative and qualitative evaluations on three benchmark datasets,which represent three typical scenarios for RefSR,demonstrate that our method is superior to the state-of-the-art SISR and RefSR methods in terms of PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity).

Generous or Selfish? Weighing Transaction Forwarding Against Malicious Attacks in Payment Channel Networks

Scalability has long been a major challenge of cryptocurrency systems,which is mainly caused by the delay in reaching consensus when processing transactions on-chain.As an effective mitigation approach,the payment channel networks(PCNs)enable private channels among blockchain nodes to process transactions off-chain,relieving long-time waiting for the online transaction confirmation.The state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing,scheduling,and initial deposits,as well as preventing the system from security and privacy attacks.However,the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely neglected.To fill this gap,we employ the game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion attacks.Our study is progressive,as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of PCN.Our analysis is complementary,since we utilize not only the classic game theory with the complete rationality assumption,but also the evolutionary game theory considering the limited rationality of players to portray the evolution of PCN.The results of numerous simulation experiments verify the effectiveness of our analysis.

Modelling soil erodibility in mountain rangelands of southern Kyrgyzstan

Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in the south of Kyrgyzstan. The main objective of this study was to map soil erodibility in the mountainous rangelands of Kyrgyzstan. The results of this effort are expected to contribute to the development of soil erodibility modelling approaches for mountainous areas. In this study, we mapped soil erodibility at two sites, both representing grazing rangelands in the mountains of Kyrgyzstan and having potentially different levels of grazing pressure. We collected a total of 232 soil samples evenly distributed in geographical space and feature space. Then we analyzed the samples in laboratory for grain size distribution and calculated soil erodibility values from these data using the Revised Universal Soil Loss Equation (RUSLE) K-factor formula. After that, we derived different terrain indices and ratios of frequency bands from ASTER GDEM and LANDSAT images to use as auxiliary data because they are among the main soil forming factors and widely used for prediction of various soil properties. Soil erodibility was significantly correlated with channel network base level (geographically extrapolated altitude of water channels), remotely sensed indices of short-wave infrared spectral bands, exposition, and slope degree. We applied multiple regression analysis to predict soil erodibility from spatially explicit terrain and remotely sensed indices. The final soil erodibility model was developed using the spatially explicit predictors and the regression equation and then improved by adding the residuals. The spatial resolution of the model was 30 m, and the estimated mean adjusted coefficient of determination was 0.47. The two sites indicated different estimated and predicted means of soil erodibility values (0.035 and 0.039) with a 0.05 significance level, which is attributed mainly to the considerable difference in elevation.