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This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.

This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively.

Recently, the increasing demand for telemedicine services has raised interest in the use of medical image protection technology. Conventional block ciphers are poorly suited to image protection due to the size of image data and increasing demand for real-time teleradiology and other online telehealth applications. To meet this challenge, this paper presents a novel chaos-based medical image encryption scheme. To address the efficiency problem encountered by many existing permutation–substitution type image ciphers, the proposed scheme introduces a substitution mechanism in the permutation process through a bit-level shuffling algorithm. As the pixel value mixing effect is contributed by both the improved permutation process and the original substitution process, the same level of security can be achieved in a fewer number of overall rounds. The results indicate that the proposed approach provides an efficient method for real-time secure medical image transmission over public networks.

This paper presents a systematic topological study to derive all possible basic and non-isolated three-port converters (TPCs) using power flow diagrams. Unlike most reported TPCs with one bidirectional port, this paper considers up to two bidirectional ports and provides a comprehensive analytical tool. This tool acts as a framework for all power flow combinations, selection, and design. Some viable converter configurations have been identified and selected for further analysis.

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70-90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks.

It has been studied that a few informed individuals in a group of interacting dynamic agents can influence the majority to follow the position and velocity of a virtual leader. Previously it has been shown that a cluster-based selection of informed agents can drive more agents to follow the virtual leader compared to a random selection. However, a practical question is: How many informed agents to select? In order to address this, here we propose a novel method for selecting informed agents based on community structures in the initial spatial distribution of agents. The number of informed agents are decided based on the strongest community structure. We test and analyze the performance of the proposed method against random and cluster-based selections of informed agents using extensive computer simulations. Results of our study show that community-based selection can be useful in deciding an optimum number of informed agents such that a majority of the group can achieve their common objective.

[This corrects the article DOI: 10.1371/journal.pone.0156756.].

Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local (\"microscopic\") level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level--fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks.

The power grid in a typical micro distribution system is non-ideal, presenting itself as a voltage source with significant impedance. Thus, grid-connected converters interact with each other via the non-ideal grid. In this study, we consider the practical scenario of voltage-source converters connected to a three-phase voltage source with significant impedance. We show that stability can be compromised in the interacting converters. Specifically, the stable operating regions in selected parameter space may be reduced when grid-connected converters interact under certain conditions. In this paper, we develop bifurcation boundaries in the parameter space with respect to Hopf-type instability. A small-signal model in the dq -frame is adopted to analyze the system using an impedance-based approach. Moreover, results are presented in design-oriented forms so as to facilitate the identification of variation trends of the parameter ranges that guarantee stable operation.

Co-occurrence networks of Chinese characters are constructed from collections of essays in different periods of China： the ancient Chinese language, the Chinese language in Wei, Jin, and Southern-Northern Dynasties, the recent Chinese language, and the modern Chinese language, and their statistical parameters are studied. It has been found that 99.6% networks have the scale-free feature and 95.0% networks have the small- world effect. This study reveals some commonalities and differences among articles in different periods of China from a complex network perspective. There has been a controversial question as to whether the literatures in Wei, Jin, and Southern-Northern Dynasties should belong to the ancient Chinese language or the recent Chinese language in the linguistic study. Our work shows that the statistical parameters of networks in Wei, Jin, and Southern-Northern Dynasties are clearly different from those of networks in the other periods of China, and it seems more reasonable that the literatures in Wei, Jin, and Southern-Northern Dynasties belong to the recent Chinese language.