Influence Maximization based on Simplicial Contagion Model in Hypergraph

In recent years, the issue of node centrality has been actively and extensively explored due to its applications in product recommendations, opinion propagation, disease spread, and other areas involving maximizing node influence. This paper focuses on the problem of influence maximization on the Simplicial Contagion Model, using the susceptible-infectedrecovered (SIR) model as an example. To find practical solutions to this optimization problem, we have developed a theoretical framework based on message passing processes and conducted stability analysis of equilibrium solutions for the self-consistent equations. Furthermore, we introduce a metric called collective influence and propose an adaptive algorithm, known as the Collective Influence Adaptive (CIA), to identify influential propagators in the spreading process. This method has been validated on both synthetic hypergraphs and real hypergraphs, outperforming other competing heuristic methods.Comment: 19 pages,16 figure

Performance of Networked Control Systems

Data packet dropout is a special kind of time delay problem. In this paper, predictive controllers for networked control systems (NCSs) with dual-network are designed by model predictive control method. The contributions are as follows. (1) The predictive control problem of the dual-network is considered. (2) The predictive performance of the dual-network is evaluated. (3) Compared to the popular networked control systems, the optimal controller of the new NCSs with data packets dropout is designed, which can minimize infinite performance index at each sampling time and guarantee the closed-loop system stability. Finally, the simulation results show the feasibility and effectiveness of the controllers designed

Determining the Solution Space of Vertex-Cover by Interactions and Backbones

To solve the combinatorial optimization problems especially the minimal Vertex-cover problem with high efficiency, is a significant task in theoretical computer science and many other subjects. Aiming at detecting the solution space of Vertex-cover, a new structure named interaction between nodes is defined and discovered for random graph, which results in the emergence of the frustration and long-range correlation phenomenon. Based on the backbones and interactions with a node adding process, we propose an Interaction and Backbone Evolution Algorithm to achieve the reduced solution graph, which has a direct correspondence to the solution space of Vertex-cover. By this algorithm, the whole solution space can be obtained strictly when there is no leaf-removal core on the graph and the odd cycles of unfrozen nodes bring great obstacles to its efficiency. Besides, this algorithm possesses favorable exactness and has good performance on random instances even with high average degrees. The interaction with the algorithm provides a new viewpoint to solve Vertex-cover, which will have a wide range of applications to different types of graphs, better usage of which can lower the computational complexity for solving Vertex-cover

Analysis on the evolution process of BFW-like model with explosive percolation of multiple giant components

Recently, the modified BFW model on random graph [Phys. Rev. Lett., 106, 115701 (2011)], which shows a strongly discontinuous percolation transition with multiple giant components, has attracted much attention from physicists, statisticians and materials scientists. In this paper, by establishing the theoretical expression of evolution equations on the modified BFW model, the steady-state and evolution process are analyzed and a close correspondence is built between the values of parameter \alpha and the number of giant components in steady-states, which fits very well with the numerical simulations. In fact, with the value of \alpha decreasing to 0.25, the error between theoretical and numerical results is smaller than 4% and trends to 0 rapidly. Furthermore, the sizes of giant components for different evolution strategies can also be obtained by solving some constraints derived from the evolution equations. The analysis of the steady-state and evolution process is of great help to explain why the percolation of modified BFW model is explosive and how explosive it is.Comment: 12 pages, 5 figure

Diagnostic Significance of Serum IgG Galactosylation in CA19-9-Negative Pancreatic Carcinoma Patients

Background: Although Carbohydrate antigen 19-9 (CA19-9) is considered clinically useful and informative for pancreatic carcinoma (PC), false positive results, and false negative results have restricted its clinical use. Especially missed or delayed diagnosis of PC patients with negative CA19-9 value limited the utility. To improve prognosis of PC patients, the discovery of reliable biomarkers to assist CA19-9 is desired. Serum IgG galactosylation based on our previous report was altered in PC patients comparing to healthy controls. The objective of this study was to explore the diagnostic significance of IgG galactosylation in assisting CA19-9 for PC in a comprehensive way.Methods: Serum IgG galactosylation profiles were analyzed by MALDI-MS in cohort 1 (n = 252) and cohort 2 in which all CA19-9 levels were negative (n = 133). In each cohort, not only healthy controls and PC patients but also benign pancreatic disease (BPD) patients were enrolled. Peaks were acquired by the software of MALDI-MS sample acquisition, followed by being processed and analyzed by the software of Progenesis MALDI. IgG Gal-ratio, which was calculated from the relative intensity of peaks G0, G1, and G2 according to the formula (G0/(G1+G2×2)), was employed as an index for indicating the distribution of IgG galactosylation.Results: The Gal-ratio was elevated in PC comparing with that in non-cancer group (healthy controls and BPD). The area under the receiver operating characteristic curve (AUC) of IgG Gal-ratio was higher than that of CA19-9 (0.912 vs. 0.814). The performance was further improved when Gal-ratio and CA19-9 were combined (AUC: 0.928). Meanwhile, Gal-ratio also had great diagnostic value with a sensitivity of 92.31% (AUC: 0.883) in detection of PC at early stage. Notably, IgG Gal-ratio has great sensitivity (90.63%) and specificity (76.81%) in CA19-9-negative PC patients.Conclusions: IgG Gal-ratio had a great performance in detection of PC and could be used to assist CA19-9 in improving diagnosis performance through early stage detection, differentiation from BPD, and PC diagnosis with CA19-9-negative level