Search

Understanding the spatial interactions of human mobility is crucial for urban planning, traffic engineering, as well as for the prevention and control of infectious diseases. Although many models have been developed to model human mobility, it is not clear whether such models could also capture the traveling mechanisms across different time periods (e.g. workdays, weekends or holidays). With one-year long nationwide location-based service (LBS) data in China, we investigate the spatiotemporal characteristics of population movements during different time periods, and make thorough comparisons for the applicability of five state-of-the-art human mobility models. We find that population flows show significant periodicity and strong inequality across temporal and spatial distribution. A strong “backflow” effect is found for cross-city movements before and after holidays. Parameter fitting of gravity models reveals that travels in different type of days consider the attractiveness of destinations and cost of distance differently. Surprisingly, the comparison indicates that the parameter-free opportunity priority selection (OPS) model outperforms other models and is the best to characterize human mobility in China across all six different types of days. However, there is still an urgent need for development of more dedicated models for human mobility on weekends and different types of holidays.

2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others., This article gives an overview of commuting patterns in China in 2020, offering the most comprehensive understanding on national mobility at fine resolution across various scenarios in China.

The problem of network disintegration, which aims at identifying the critical nodes or edges whose removal will lead to a network collapse, has attracted much attention due to its wide applications. This paper focuses on the disintegration of directed networks. We propose a disintegration strategy based on tabu search. Experiments show that the disintegration effect of our strategy is obviously better than those of typical disintegration strategies based on local structural properties. Moreover, we find that the critical nodes identified to remove in directed networks are not those nodes with large degree or betweenness centrality that always are the crucial properties in undirected network.

Evaluating the significance of nodes or links has always been an important issue in complex networks, and the definition of significance varies with different perspectives. The significance of nodes or links in maintaining the network connectivity is widely discussed due to its application in targeted attacks and immunization. In this paper, inspired by the weak tie phenomenon, we define the links’ significance by the dissimilarity of their endpoints. Some link prediction algorithms are introduced to define the dissimilarity of nodes based solely on the network topology. Experiments in synthetic and real networks demonstrate that the method is especially effective in the networks with higher clustering coefficients.

In this study, we focus on the efficient disintegration method in spatial networks. First, we present an optimization model for the disintegration strategy in the spatial network and introduce a heuristic algorithm to seek the efficient disintegration strategy. Experiment in American fiber network indicates that our method could seek the efficient disintegration strategy with the tabu search. Also, the disintegration effect based on tabu search substantially exceeds the degree-core strategy and betweenness-core strategy.

As the robustness of network is mainly determined by the number of redundant paths in the network, many networks perform well in some robustness metrics just because of their large network sizes. It is well-known that edge addition can remarkably enhance the robustness of complex networks. However, more edges lead to more costs. Thus, it is an interesting and significant problem to obtain a cost-efficient network for robustness. In our recent work, the natural connectivity has been proposed as a spectral measure of robustness in complex networks as it has an intuitive physical meaning and a simple mathematical formulation, which has proved reliably in measuring structure robustness. In this paper, we propose to measure the cost-effectiveness ratio of network robustness using the concept of relative natural connectivity, which is defined as the ratio of the natural connectivity of a network to the natural connectivity of the referential Erdos-Renyi random graph with the same network size and edge density. Extensive numerical simulations confirm our method.

Population migration is an essential medium for the spread of epidemic, which can accelerate localized outbreaks of disease into widespread epidemic. Large-scale population movements between different areas increase the risk of cross-infection and bring great challenges to epidemic prevention and control. As COVID-19 can spread rapidly through human-to-human transmission, understanding its migration patterns is essential to modeling its spreading and evaluating the efficiency of mitigation policies applied to COVID-19. Using nationwide mobile phone data to track population flows throughout China at prefecture-level, we use the temporal network analysis to compare topological metrics of population mobility network during two consecutive months between before and after the outbreak, i.e. January 1st to February 29th. To detect the regions which are closely connected with population movements, we propose a Spatial-Louvain algorithm through adapting a gravity attenuation factor. Moreover, our proposed algorithm achieves an improvement of 14% in modularity compared with the Louvain algorithm. Additionally, we divide the period into four stages, i.e. normal time, Chunyun migration, epidemic interventions, and recovery time, to describe the patterns of mobility network’s evolution. Through the above methods, we explore the evolution pattern and spatial mechanism of the population mobility from the perspective of spatiotemporal big data and acquire some meaningful findings. Firstly, we find that after the lockdown of Wuhan and effective epidemic interventions, a substantial reduction in mobility lasted until mid-February. Secondly, based on the economic interaction and geographic location, China has formed an urban agglomeration structure with core cities centering and radiating toward the surroundings. Thirdly, in the extreme cases, the dominant factor of population mobility in remote areas is geographic location rather than economy. Fourthly, the urban agglomeration structure of cities is robust so that when the epidemic weakens or disappears, the city clusters can quickly recover into their original patterns.

Many real-world systems can be described by scale-free networks with power-law degree distributions. Scale-free networks show a “robust yet fragile” feature due to their heterogeneous degree distributions. We propose to enhance the structural robustness of scale-free networks against intentional attacks by changing the displayed network structure information rather than modifying the network structure itself. We first introduce a simple mathematical model for attack information and investigate the impact of attack information on the structural robustness of scale-free networks. Both analytical and numerical results show that decreasing slightly the attack information perfection by information disturbance can dramatically enhance the structural robustness of scale-free networks. Then we propose an optimization model of disturbance strategies in which the cost constraint is considered. We analyze the optimal disturbance strategies and show an interesting but counterintuitive finding that disturbing “poor nodes” with low degrees preferentially is more effective than disturbing “rich nodes” with high degrees preferentially. We demonstrate the efficiency of our method by comparison with edge addition method and validate the feasibility of our method in two real-world critical infrastructure networks.

Robustness and small-world effect are two crucial structural features of complex networks and have attracted increasing attention. However, little is known about the relation between them. Here we demonstrate that, there is a conflicting relation between robustness and small-world effect for a given degree sequence. We suggest that the robustness-oriented optimization will weaken the small-world effect and vice versa. Then, we propose a multi-objective trade-off optimization model and develop a heuristic algorithm to obtain the optimal trade-off topology for robustness and small-world effect. We show that the optimal network topology exhibits a pronounced core-periphery structure and investigate the structural properties of the optimized networks in detail.

The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the “comic effect” of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized.

The energy utilization is crucial for electric or hybrid vehicles to solve the problem of braking energy recovery, especially of low speed energy recovery. In this paper, the energy transform model of electric vehicle in braking state is analyzed, and the half bridge control main circuit was selected. The energy recovery strategies and methods on the breaking process of low speed were researched. The concrete realization of parameters decision and system control was given. The results show that the energy recovery strategy and electric vehicle model can improve the energy transform efficiency in low speed state.

Dealing with the protection of critical infrastructures, many game-theoretic methods have been developed to study the strategic interactions between defenders and attackers. However, most game models ignore the interrelationship between different components within a certain system. In this paper, we propose a simultaneous-move attacker-defender game model, which is a two-player zero-sum static game with complete information. The strategies and payoffs of this game are defined on the basis of the topology structure of the infrastructure system, which is represented by a complex network. Due to the complexity of strategies, the attack and defense strategies are confined by two typical strategies, namely, targeted strategy and random strategy. The simulation results indicate that in a scale-free network, the attacker virtually always attacks randomly in the Nash equilibrium. With a small cost-sensitive parameter, representing the degree to which costs increase with the importance of a target, the defender protects the hub targets with large degrees preferentially. When the cost-sensitive parameter exceeds a threshold, the defender switches to protecting nodes randomly. Our work provides a new theoretical framework to analyze the confrontations between the attacker and the defender on critical infrastructures and deserves further study.

It has been recently proposed that natural connectivity can be used to efficiently characterize the robustness of complex networks. The natural connectivity has an intuitive physical meaning and a simple mathematical formulation, which corresponds to an average eigenvalue calculated from the graph spectrum. However, as a network model close to the real-world system that widely exists, the scale-free network is found difficult to obtain its spectrum analytically. In this article, we investigate the approximation of natural connectivity based on the largest eigenvalue in both random and correlated scale-free networks. It is demonstrated that the natural connectivity of scale-free networks can be dominated by the largest eigenvalue, which can be expressed asymptotically and analytically to approximate natural connectivity with small errors. Then we show that the natural connectivity of random scale-free networks increases linearly with the average degree given the scaling exponent and decreases monotonically with the scaling exponent given the average degree. Moreover, it is found that, given the degree distribution, the more assortative a scale-free network is, the more robust it is. Experiments in real networks validate our methods and results.

In order to make use of the resources of total flavanone in pericarp of longan.The flavonids were extracted with ethanol. Used spectrophotometry to check the flavanone.The total flavanone in the pericarp was 1.101 mg/ml and the rate of recovery was 99.98%.The extraction and purifying methods in this experiment can get the outcome and the purity of the flavanone very high. This method is a purely physical process and has not any pollution. It is an ideal way to extract the flavanone of longan skin.

CEL I, extracted from celery, is the first known eukaryotic nuclease that cleaves DNA with high specificity at sites of base-substitution mismatch and DNA distortion. It is a key enzyme for TILLING research. Here we reported a crude extraction method and activity assay of CEL I. Incision at mismatches of single nucleotide suggested that CEL I can effectively detect DNA at G-->A base substitution and the result can be obtained from an ABI377 Sequencer. Therefore, the extracted enzyme can be used in TILLING.