Sonographic differentiation of diffuse liver cancer from portal cirrhosis

Objective: To differentiate diffuse liver cancer from portal cirrhosis by using ultrasonography. Methods: We analyzed the sonographic images of 15 patients with diffuse liver cancer and 30 patients with portal cirrhosis. Results: The patients with diffuse liver cancer showed enlarged liver and obvious echo of nodules. The rate of portal embolism and swelling of lymph nodes a- round the porta hepatis was high. The patients with portal cirrhosis showed diminished liver and the obvi- ous echo of fiber proliferation. The rates of spleen enlargement and ascites as well as gallbladder edema were high. Conclusions: To identify sonographic characteristics inside and outside of the liver. It is helpful in diffe- rentiating diffuse liver cancer from portal cirrhosis. The sonographic characteristics inside the liver in- clude surface and size, node echo, echo of fibrous tissue hyperplasia. They are difficult to identify when diffuse liver cancer merges with considerable cirrho- sis. The acoustic image characteristics of the two di- seases overlap. Hence attention should be paid to the size of the liver, proliferation of cells of diffuse liver carcinoma. In sonographic characteristics outside the liver, embolism of the portal vein and swelling of lymph nodes in the porta hepatis are particularly use- ful to identify diffuse liver cancer or diffuse liver cancer combined with liver cirrhosis in particular.

Cascading failures of overload behaviors using a new coupled network model between edges

With the development of network science,the coupling between networks has become the focus of complex network research.However,previous studies mainly focused on the coupling between nodes,while ignored the coupling between edges.We propose a novel cascading failure model of two-layer networks.The model considers the different loads and capacities of edges,as well as the elastic and coupling relationship between edges.In addition,a more flexible load-capacity strategy is adopted to verify the model.The simulation results show that the model is feasible.Different networks have different behaviors for the same parameters.By changing the load parameters,capacity parameters,overload parameters,and distribution parameters reasonably,the robustness of the model can be significantly improved.

A novel method for identifying influential nodes in complex networks based on gravity model

How to identify influential nodes in complex networks is an essential issue in the study of network characteristics.A number of methods have been proposed to address this problem,but most of them focus on only one aspect.Based on the gravity model,a novel method is proposed for identifying influential nodes in terms of the local topology and the global location.This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes,replaces the shortest distance with a probabilistically motivated effective distance,and fully considers the influence of nodes and their neighbors from the aspect of gravity.On eight real-world networks from different fields,the monotonicity index,susceptible-infected-recovered(SIR)model,and Kendall’s tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods.The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.