Search

The paper investigates the resilience of an agriculture supply chain through the lens of complex network perspective. Given the susceptibility of these supply chains to bothrandom events (rainfall and yield uncertainty), and targeted events (income tax raids on millers, strikes, and lockouts), as well as spillover effects of disruptions; we propose a supply chain architecture that helps in achieving a balanced performance against these disruptions. For this, we propose a new attachment rule—'price/cost-based attachment rule', which we use along with degree-based and distance-based attachment rules in generating a resilient supply chain topology. We call the proposed supply chain network—'Balanced Supply Network' whose performance is compared with a scale-free network (BA Network) and a random network (ER Network). The comparison is based on critical performance indicators such as availability (demand and supply availability rate) and connectivity (size of largest all-role connected component). The findings, on expected lines, reveal that our proposed network exhibits a performance trade-off between BA Network and ER Network when subjected to targeted disruptions and disruption propagation scenarios. However, in case of random disruptions, it ensures maximum resilience and even outperforms BA Network due to its construction properties. Further, to optimize resilience, we introduce weights to the attachment rules of our proposed network. These weight assignments enable us to identify the most effective configuration among the three attachment rules for enhancing the network's ability to withstand disruptions. [ABSTRACT FROM AUTHOR]

In this study, we develop a graph-theoretic network model for genealogical data. It is based on the naming system used by the Galo tribe in the Indian state of Arunachal Pradesh. P-graphs are used to visually represent the genealogy. According to the analysis of the network on social network metrics, the genealogical network is a sparsely connected, scale-free network with an average path length of 11.615 and a graph density of 0.002. Only a small portion of its nodes have high centrality values. The network has three communities, twenty authority nodes, two hub nodes, and observed no relinking marriages. [ABSTRACT FROM AUTHOR]

The purpose of this work is to examine the nature of the historically observed and empirically described by the Pareto law inequality in the distribution of wealth and income in an economic system. This inequality is presumed to be the result of unequal opportunity by its members. An analytical model of the economic system consisting of a large number of actors, all having equal access to its total wealth (or income) has been developed that is formally represented by a scale-free network comprised of nodes (actors) and links (states of wealth or income). The dynamic evolution of the complex network can be mapped in turn, as is known, into a system of quantum particles (links) distributed among various energy levels (nodes) in thermodynamic equilibrium. The distribution of quantum particles (photons) at different energy levels in the physical system is then derived based on statistical thermodynamics with the attainment of maximal entropy for the system to be in a dynamic equilibrium. The resulting Planck-type distribution of the physical system mapped into a scale-free network leads naturally into the Pareto law distribution of the economic system. The conclusions of the scale-free complex network model leading to the analytical derivation of the empirical Pareto law are multifold. First, any complex economic system behaves akin to a scale-free complex network. Second, equal access or opportunity leads to unequal outcomes. Third, the optimal value for the Pareto index is obtained that ensures the optimal, albeit unequal, outcome of wealth and income distribution. Fourth, the optimal value for the Gini coefficient can then be calculated and be compared to the empirical values of that coefficient for wealth and income to ascertain how close an economic system is to its optimal distribution of income and wealth among its members. Fifth, in an economic system with equal opportunity for all its members there should be no difference between the resulting income and wealth distributions. Examination of the wealth and income distributions described by the Gini coefficient of national economies suggests that income and particularly wealth are far off from their optimal value. We conclude that the equality of opportunity should be the fundamental guiding principle of any economic system for the optimal distribution of wealth and income. The practical application of this conclusion is that societies ought to shift focus from policies such as taxation and payment transfers purporting to produce equal outcomes for all, a goal which is unattainable and wasteful, to policies advancing among others education, health care, and affordable housing for all as well as the re-evaluation of rules and institutions such that all members in the economic system have equal opportunity for the optimal utilization of resources and the distribution of wealth and income. Future research efforts should develop the scale-free complex network model of the economy as a complement to the current standard models. [ABSTRACT FROM AUTHOR]

The interaction and diversification of different behavior patterns of individual communication sources in cyberspace make the crisis evolution increasingly complex. Based on the activity and attitude of individual communication sources, a nonlinear dynamic system is obtained, and numerical simulation is carried out in combination with model threshold, and a co performance model of strong relationship and weak relationship social platform is constructed. The theoretical analysis is verified by numerical simulation on scale-free networks, and the propagation and evolution characteristics of strong and weak social platforms under different settings are discussed from the aspects of the heterogeneity of positive information dissemination sources' propagation ability, information dissemination sources' enthusiasm, information dissemination sources' heterogeneity and inter layer activity heterogeneity. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs), with a special emphasis on the Internet of Health Things (IoHT) network. SFNs' vulnerabilities to malicious assaults while remaining resilient to random attacks. The proposed ICRS overcomes this issue by offering a novel reconnection approach that employs separate edges between network centers. Destructive assaults that have a significant impact on network connectivity, emphasizing the importance of a robust network that can resist a variety of attacks. ICRS is positioned as a solution that optimizes the network via reconnection techniques, changing it into an onion‐like structure with increased robustness. The simulation results depict that ICRS outperforms the existing algorithms in terms of robustness enhancement. The results show that ICRS performs 48%, 29%, 22%, and 16% better than Barabasi Albert (BA), Hill Climbing (HC), Simulated Annealing (SA), Random Edge Swap Mechanism (RESM), and Robustness Strategy (ROSE), respectively. [ABSTRACT FROM AUTHOR]

Objective: Surveillance is critical for the rapid implementation of control measures for diseases caused by aerially dispersed plant pathogens, but such programs can be resource-intensive, especially for epidemics caused by long-distance dispersed pathogens. The current cucurbit downy mildew platform for monitoring, predicting and communicating the risk of disease spread in the United States is expensive to maintain. In this study, we focused on identifying sites critical for surveillance and treatment in an attempt to reduce disease monitoring costs and determine where control may be applied to mitigate the risk of disease spread. Methods: Static networks were constructed based on the distance between fields, while dynamic networks were constructed based on the distance between fields and wind speed and direction, using disease data collected from 2008 to 2016. Three strategies were used to identify highly connected field sites. First, the probability of pathogen transmission between nodes and the probability of node infection were modeled over a discrete weekly time step within an epidemic year. Second, nodes identified as important were selectively removed from networks and the probability of node infection was recalculated in each epidemic year. Third, the recurring patterns of node infection were analyzed across epidemic years. Results: Static networks exhibited scale-free properties where the node degree followed a power-law distribution. Betweenness centrality was the most useful metric for identifying important nodes within the networks that were associated with disease transmission and prediction. Based on betweenness centrality, field sites in Maryland, North Carolina, Ohio, South Carolina and Virginia were the most central in the disease network across epidemic years. Removing field sites identified as important limited the predicted risk of disease spread based on the dynamic network model. Conclusions: Combining the dynamic network model and centrality metrics facilitated the identification of highly connected fields in the southeastern United States and the mid-Atlantic region. These highly connected sites may be used to inform surveillance and strategies for controlling cucurbit downy mildew in the eastern United States. [ABSTRACT FROM AUTHOR]

In this study, a Cournot duopoly model describing Caputo fractional-order differential equations with piecewise constant arguments is discussed. We have obtained two-dimensional discrete dynamical system as a result of applying the discretization process to the model. By using the center manifold theory and the bifurcation theory, it is shown that the discrete dynamical system undergoes flip bifurcation about the Nash equilibrium point. Phase portraits, bifurcation diagrams, and Lyapunov exponents show the existence of many complex dynamical behaviors in the model such as the stable equilibrium point, period-2 orbit, period-4 orbit, period-8 orbit, period-16 orbit, and chaos according to changing the speed of the adjustment parameter v 1 . The discrete Cournot duopoly game model is also considered on two scale-free networks with different numbers of nodes. It is observed that the complex dynamical networks exhibit similar dynamical behaviors such as the stable equilibrium point, flip bifurcation, and chaos depending on changing the coupling strength parameter c s . Moreover, flip bifurcation and transition chaos take place earlier in more heterogeneous networks. Calculating the largest Lyapunov exponents guarantees the transition from nonchaotic to chaotic states in complex dynamical networks. [ABSTRACT FROM AUTHOR]

Introduction: Acute respiratory distress syndrome (ARDS) presents a significant clinical challenge, with ventilator-induced lung injury (VILI) being a critical complication arising from life-saving mechanical ventilation. Understanding the spatial and temporal dynamics of VILI can inform therapeutic strategies to mitigate lung damage and improve outcomes. Methods: Histological sections from initially healthy mice and pulmonary lavageinjured mice subjected to a second hit of VILI were segmented with Ilastik to define regions of lung injury. A scale-free network approach was applied to assess the correlation between injury regions, with regions of injury represented as 'nodes' in the network and 'edges' quantifying the degree of correlation between nodes. A simulated time series analysis was conducted to emulate the temporal sequence of injury events. Results: Automated segmentation identified different lung regions in good agreement with manual scoring, achieving a sensitivity of 78% and a specificity of 85% across 'injury' pixels. Overall accuracy across 'injury', 'air', and 'other' pixels was 81%. The size of injured regions followed a power-law distribution, suggesting a 'rich-get-richer' phenomenon in the distribution of lung injury. Network analysis revealed a scale-free distribution of injury correlations, highlighting hubs of injury that could serve as focal points for therapeutic intervention. Simulated time series analysis further supported the concept of secondary injury events following an initial insult, with patterns resembling those observed in seismological studies of aftershocks. Conclusion: The size distribution of injured regions underscores the spatially heterogeneous nature of acute and ventilator-induced lung injury. The application of network theory demonstrates the emergence of injury 'hubs' that are consistent with a 'rich-get-richer' dynamic. Simulated time series analysis demonstrates that the progression of injury events in the lung could follow spatiotemporal patterns similar to the progression of aftershocks in seismology, providing new insights into the mechanisms of injury distribution and propagation. Both phenomena suggest a potential for interventions targeting these injury 'hubs' to reduce the impact of VILI in ARDS management. [ABSTRACT FROM AUTHOR]

Copyright of Teacher Education Research is the property of Teacher Education Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyses the characteristics and laws of information dissemination in social networks, proposes a propensity index based on the propensity of individuals in the network to spread information about events, and studies the influence of the propensity index on information spreading. This study is based on a scale-free network and takes the SI model as the base model and improves it. The experimental results show that the propagation process and infection density of information is affected by the propensity index, and the stronger the individual's propensity for information spreading, the larger the propensity index owned, which promotes the propagation of information. Moreover, the information spreading model based on propensity index is more in line with the reality of information spreading. [ABSTRACT FROM AUTHOR]

How to accurately model and effectively suppress the spread of network viruses has been a major concern in the field of complex networks and cybersecurity. Most existing work often considers the transmission between the infected and uninfected nodes (i.e., horizontal transmission) and assumes that all new nodes connected to the Internet are susceptible, but the nodes might have been implanted with a backdoor or virus by attackers or infected nodes before connecting to the Internet. This vertical transmission also provides an important route for virus propagation. In this paper, we investigate the propagation of network viruses under the combined influence of network topology and hybrid transmission (i.e., horizontal and vertical transmissions). Through rigorous qualitative analysis, we identify the propagation threshold R 0 which determines whether viruses in the network tend to become extinct or persist, and explore the impacts of vertical transmission on the viral spread. Furthermore, we consider the problem of how to dynamically contain the hybrid spread of network viruses with limited resources. By utilizing optimal control theory, we prove the existence of an optimal control strategy. Finally, a group of representative simulation experiments verify the validity of the theoretical findings. Specifically, the simulation results show that the optimal control strategy proposed in this paper reduces the value of the target generic function J by 67.69% compared with no control. [ABSTRACT FROM AUTHOR]

The Sun is an external object that significantly impacts the earth's atmosphere and space weather conditions. Flares and coronal mass ejections are large-scale solar atmospheric features that mainly emerge at active regions above the sunspots and have influenced the earth. The sunspots, considered signatures of solar activity, are fascinating features related to the internal dynamics and activity of the Sun. The appearance of the sunspots in the photosphere shows the complexity of the magnetic field on the Sun. The frequency and size of sunspots change over time which show periods (e.g., eleven years periodicity) that may be a sign of the complex Sun. The time series of the sunspot numbers have been recorded for several centuries, and this time series is significantly changed over time. The complex network approach is a way to investigate the inherent property of complex time series, such as the sunspots time series. In this study, the growing complex network with the visibility condition is constructed using the time series of the sunspots (time and numbers) for 1922 to 2016 collected by SILSO. We compute the complex network parameters such degree of nodes, shortest path length, and clustering coefficients. We examine the sunspot complex network's scale-free, small-world, and assortative properties. We show that the degree distribution of the complex network for the time series of the sunspots obeys a power-law distribution function. We applied a method via maximum likelihood estimation in the Bayesian framework to obtain the power indicated. Therefore, the degree exponent is obtained larger than three, so the complex network for the time series of the sunspots is small- world and scale-free. The power-low behavior is an essential characteristic of self-organized or self-organized criticality systems. Limited productivity is a crucial property for these complex systems. The small word behavior indicates that the large sunspot numbers in the time series are clustered with several small values neighbors and linked with distinct large values in the time series. The small-world network represents a small characteristic path length with a high clustering coefficient. The scale-free and small-word behavior for the network of the sunspots time series may imply that the sunspots and sunspot groups forming via complex non-linear dynamics. Changing the magnetic polarity of the sunspots during the solar cycle can be a characteristic of such complex systems. The limited predictability in sunspots' time series, e.g., the intensity of activity within a solar cycle, may also be another sign of the complex Sun. The behavior of the degree of node distribution, clustering coefficient, and shortest path length indicates that the time series of sunspots is a non-random system. We showed that the degree of correlation is a function of the network size and can be considered as an assortative, dis-assortative, or neutral network. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study takes diffusion capacity, absorptive capacity, and relationship strength as the main influencing factors, constructs models of knowledge flow in small‐world networks and scale‐free networks, and uses numerical simulation to observe the flow characteristics of explicit knowledge and tacit knowledge in different networks. The knowledge of explicit flow in different networks exhibits the phenomenon of knowledge emergence, but this phenomenon is more obvious in a small‐world network. The flow of tacit knowledge in a small‐world network has a better effect. In a scale‐free network, the quantity and frequency of knowledge flow are significantly higher than those in a small‐world network. The reason for this phenomenon is the differences in the response, connection and structure of different networks. The quantity and frequency of the flow of explicit knowledge in the same network are significantly higher than those of tacit knowledge. The reason for this phenomenon is the different types of knowledge flow in different modes, with different levels of flow difficulty and flow sustainability. First, this study visually compares the differences in flow between the two types of knowledge. Second, flow models of the two types of knowledge are constructed, and the flow characteristics of the two types of knowledge in different networks are simulated. Finally, the reasons for the differences in flow between the two types of knowledge are explained by using loosely coupled theory. [ABSTRACT FROM AUTHOR]

It is well known that in many real networks, such as brain networks and scientific collaboration networks, there exist higher order nonpairwise relations among nodes, i.e. interactions between more than two nodes at a time. This simplicial structure can be described by simplicial complexes and has an important effect on topological and dynamical properties of networks involving such group interactions. In this paper, we study analytically resistance distances in iteratively growing networks with higher order interactions characterized by the simplicial structure that is controlled by a parameter |$q$|⁠. We derive exact formulas for interesting quantities about resistance distances, including Kirchhoff index, additive degree-Kirchhoff index, multiplicative degree-Kirchhoff index, as well as average resistance distance, which have found applications in various areas elsewhere. We show that the average resistance distance tends to a |$q$| -dependent constant, indicating the impact of simplicial organization on the structural robustness measured by average resistance distance. [ABSTRACT FROM AUTHOR]

A reasonable definition of nodes load and capacity is essential for improving the robustness of scale-free networks against cascading failure, which has gained significant attention over recent years. This paper presents two methods for defining the load-capacity model: a degree-based method and a betweenness-based method. In these methods, the initial load and capacity of nodes were determined by considering the degrees and betweenness centrality of nodes and their neighbors. These values could be adjusted using both global and local parameters. This paper achieved load redistribution during cascading failures through targeted attacks on network nodes. In addition, this study applied load redistribution to cascading failure processes in networks by targeting network nodes. In order to evaluate the effectiveness of the proposed approach, this paper examines the impact of adjusting two parameters on the minimum critical tolerance coefficient and network robustness. Computer-generated scale-free networks and a real network were used for evaluation purposes. The findings indicated that higher global parameters resulted in a lower average robustness index. Moreover, our degree-based method demonstrated a smaller minimum critical tolerance coefficient and average robustness index compared to existing load definition methods. Therefore, the proposed methods enhanced the robustness and integrity of scale-free networks against attacks. [ABSTRACT FROM AUTHOR]

Enterprises in the prefabricated building supply chain (PBSC) only share information according to their interests, which is bound to cause conflicts of interest and reduce the efficiency of supply chain operations. To promote information sharing (IS) in PBSC, it is necessary to construct an evolutionary game model that fits the realistic network. In this paper, based on the integration of existing research, 13 influencing factors of IS in PBSC are analyzed comprehensively from the perspective of information ecology theory. In addition, due to the complexity and uncertainty of the PBSC, enterprise interaction and supply chain network structure affect the IS decision. Therefore, this paper builds an evolutionary game model of IS in PBSC under a scale-free network, and conducts numerical simulation analysis with MATLAB 2017 software to analyze the evolution law of enterprise IS under different situations. The results show that (1) when the network scale is large, the density of information sharers generally increases, and the speed of network evolution to a steady state generally slows down; (2) eight factors can promote the increase in information sharers' density, and five factors can inhibit it, but factors have no significant effect on the speed of network evolution to reach the steady state. Based on the simulation results, this paper proposes countermeasures and suggestions such as strengthening the support of the policy environment and social environment, setting up the demonstration benchmark of leading construction enterprises, establishing a directional information resource database, and improving information technologies and risk management systems to provide the scientific basis for government supervision and enterprise decision making. [ABSTRACT FROM AUTHOR]

The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) is conducive to the advance of brain-like intelligence. However, the current brain-like model is insufficient in biological rationality. In addition, its evaluation method for anti-disturbance performance is inadequate. To explore the self-adaptive regulation performance of a brain-like model with more biological rationality under external noise, a scale-free spiking neural network(SFSNN) is constructed in this study. Then, the anti-disturbance ability of the SFSNN against impulse noise is investigated, and the anti-disturbance mechanism is further discussed. Our simulation results indicate that: (i) our SFSNN has anti-disturbance ability against impulse noise, and the high-clustering SFSNN outperforms the low-clustering SFSNN in terms of anti-disturbance performance. (ii) The neural information processing in the SFSNN under external noise is clarified, which is a dynamic chain effect of the neuron firing, the synaptic weight, and the topological characteristic. (iii) Our discussion hints that an intrinsic factor of the anti-disturbance ability is the synaptic plasticity, and the network topology is a factor that affects the anti-disturbance ability at the level of performance. [ABSTRACT FROM AUTHOR]

Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks, this paper employs a potential game for the vertex cover problem, designs a novel cost function for network vertices, and proves that the solutions to the minimum value of the potential function are the minimum vertex covering (MVC) states of a general complex network. To achieve the optimal (minimum) covering states, we propose a novel distributed time-variant binary log-linear learning algorithm, and prove that the MVC state of a general complex network is attained under the proposed optimization algorithm. Furthermore, we estimate the upper bound of the convergence rate of the proposed algorithm, and show its effectiveness and superiority using numerical examples with representative complex networks and optimization algorithms. [ABSTRACT FROM AUTHOR]

In this article, we study the mechanism of generating a scale-free network using strong dependency between nodes. It is found that dependency should be aided by two key factors, namely, the reduced structure of network connections and small zero inflation. Our finding provides key conditions bridging the gap between the two groups having different opinions on scale-free networks. These groups include a camp of network scientists that consider scale-free networks as ideal objects in the large-size limit and the others that consider them as concrete objects in the real world. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The spread of rumors in the era of new media poses a serious challenge to sustaining social order. Models regarding rumor propagation should be proposed in order to prevent them. Taking the cooling-off period into account in this paper, a modified ISCR model with saturated incidence and time delay on a scale-free network is introduced. The basic reproduction number R 0 , which does not depend on time delay τ , is given by simple calculation. The stability of the rumor-free and rumor-endemic equilibrium points is proved by constructing proper Lyapunov functions. The study of the ISCR rumor-spreading process acquires an understanding of the impact of many factors on the prevalence of rumors. Then, the optimal control strategy for restraining rumors is studied. Numerous sensitivity studies and numerical simulations are carried out. Based on the saturated incidence and time delay, results indicate that the effect of time delay plays a significant part in rumor propagation on a scale-free network. [ABSTRACT FROM AUTHOR]

The interaction and diversification of different behavior patterns of individual communication sources in cyberspace make the crisis evolution increasingly complex. Based on the activity and attitude of individual communication sources, a nonlinear dynamic system is obtained, and numerical simulation is carried out in combination with model threshold, and a co performance model of strong relationship and weak relationship social platform is constructed. The theoretical analysis is verified by numerical simulation on scale-free networks, and the propagation and evolution characteristics of strong and weak social platforms under different settings are discussed from the aspects of the heterogeneity of positive information dissemination sources' propagation ability, information dissemination sources' enthusiasm, information dissemination sources' heterogeneity and inter layer activity heterogeneity. [ABSTRACT FROM AUTHOR]

A striking discovery in the field of network science is that the majority of real networked systems have some universal structural properties. In general, they are simultaneously sparse, scale-free, small-world, and loopy. In this article, we investigate the second-order consensus of dynamic networks with such universal structures subject to white noise at vertices. We focus on the network coherence HSO characterized in terms of the $\mathcal {H}_{2}$ -norm of the vertex systems, which measures the mean deviation of vertex states from their average value. We first study numerically the coherence of some representative real-world networks. We find that their coherence HSO scales sublinearly with the vertex number $N$. We then study analytically HSO for a class of iteratively growing networks—pseudofractal scale-free webs (PSFWs), and obtain an exact solution to HSO, which also increases sublinearly in $N$ , with an exponent much smaller than 1. To explain the reasons for this sublinear behavior, we finally study HSO for Sierpinśki gaskets, for which HSO grows superlinearly in $N$ , with a power exponent much larger than 1. Sierpinśki gaskets have the same number of vertices and edges as the PSFWs but do not display the scale-free and small-world properties. We thus conclude that the scale-free, small-world, and loopy topologies are jointly responsible for the observed sublinear scaling of HSO. [ABSTRACT FROM AUTHOR]

In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm intelligence methods have been introduced to identify epistasis in recent years. However, the existing methods still have some limitations, such as high-consumption and premature convergence. In this study, we proposed a multi-objective artificial bee colony (ABC) algorithm based on the scale-free network (SFMOABC). The SFMOABC incorporates the scale-free network into the ABC algorithm to guide the update and selection of solutions. In addition, the SFMOABC uses mutual information and the K2-Score of the Bayesian network as objective functions, and the opposition-based learning strategy is used to improve the search ability. Experiments were performed on both simulation datasets and a real dataset of age-related macular degeneration (AMD). The results of the simulation experiments showed that the SFMOABC has better detection power and efficiency than seven other epistasis detection methods. In the real AMD data experiment, most of the single nucleotide polymorphism combinations detected by the SFMOABC have been shown to be associated with AMD disease. Therefore, SFMOABC is a promising method for epistasis detection. [ABSTRACT FROM AUTHOR]

Copyright of Revista Científica is the property of Revista Cientifica, Universidad Distrital Francisco Jose de Caldas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to study the impact of random environments during the spread of the disease, we propose a novel stochastic SIQS model on scale-free networks, which introduces stochastic perturbations to the infected rates. We first obtain the existence of global positive solutions. Moreover, by constructing appropriate stochastic Lyapunov functions, we prove sufficient conditions for extinction and persistence of the disease. Finally, we verify the analysis results through numerical simulations. In addition, the results of previous studies are also improved in our research. [ABSTRACT FROM AUTHOR]

Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes. [ABSTRACT FROM AUTHOR]

The properties of a scale-free network are little known; its node degree following a power-law distribution is among its few known properties. By selecting real-field scale-free networks from a network dataset and comparing them to other networks, such as random and non-scale-free networks, the topology characteristics of scale-free networks are identified. The assortative coefficient is identified as a key metric of a scale-free network. It is also identified that most scale-free networks have negative assortative coefficients. Traditional generation models of scale-free networks are evaluated based on the identified topology characteristics. Most representative models, such as BA and Holme&Kim, are not effective in generating real-field scale-free networks. A link-rewiring method is suggested that can control the assortative coefficient while preserving the node degree sequence. Our analysis reveals that it is possible to effectively reproduce the assortative coefficients of real-field scale-free networks through link-rewiring. [ABSTRACT FROM AUTHOR]

As a fundamental research object, the minimum edge dominating set (MEDS) problem is of both theoretical and practical interest. However, determining the size of a MEDS and the number of all MEDSs in a general graph is NP-hard, and it thus makes sense to find special graphs for which the MEDS problem can be exactly solved. In this paper, we study analytically the MEDS problem in the pseudofractal scale-free web and the Sierpiński gasket with the same number of vertices and edges. For both graphs, we obtain exact expressions for the edge domination number, as well as recursive solutions to the number of distinct MEDSs. In the pseudofractal scale-free web, the edge domination number is one-ninth of the number of edges, which is three-fifths of the edge domination number of the Sierpiński gasket. Moreover, the number of all MEDSs in the pseudofractal scale-free web is also less than that corresponding to the Sierpiński gasket. We argue that the difference of the size and number of MEDSs between the two studied graphs lies in the scale-free topology. [ABSTRACT FROM AUTHOR]

This paper investigates the evolutionary public goods game on a network and studies the effect of the leaders rewiring mechanism (LRM) on the evolution of cooperation. A trust mechanism is introduced to give information to the leader about the sincerity of the group. The network dynamics is driven by the LRM, allowing leaders to change their game groups if these groups are not trusted anymore. We investigate how the emergence of the network guided by LRM affects the transformation of individuals' strategies and empowers them to cooperate. We find that LRM plays a crucial role in the emergence of cooperation, by clustering the graph into regions with high clusters of cooperators and small one of defectors. LRM enables cooperators to form compact big clusters, thus reducing exploitation by defectors. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the average path length for a class of scale-free modular networks with deterministic growth. To facilitate the analysis, we define the sum of distances from all nodes to the nearest hub nodes and the nearest peripheral nodes. For the unweighted network, we find that whether the scale-free modular network is single-hub or multiple-hub, the average path length grows logarithmically with the increase of nodes number. For the weighted network, we deduce that when the network iteration t tends to infinity, the average weighted shortest path length is bounded, and the result is independent of the connection method of network. [ABSTRACT FROM AUTHOR]

The development of the wireless sensor networks (WSNs) in the recent years is mainly due to the readily obtainable low-cost and short-range sensors. The objective of the WSN system is to both sense and transmit the real-time sense contents belonging to a particular monitoring environment for the back-end system for performing the necessary processing and analysis tasks. One of the most significant tasks of prime consideration is to ensure the safety and privacy aspects of the WSN in a wireless channel. The ultimate idea of the wireless sensor network is to preserve the limited node energy and thereby process the huge volume of data without affecting the robustness. The main objective of this work is to strengthen the robustness of the wireless sensor network that particularly relies on a scale-free type of network. The ability of the Scale-free WSNs in tolerating the random attacks efficiently have made them appear significant; on the other hand, they may be vulnerable to certain malicious attacks, that specifically targets the significant nodes in the system. This limitation has been addressed in this work by means of introducing a new modelling strategy called sequential probability ratio test (SPRT) that assists in the generation of scale-free network topologies. SPRT, a unique robustness improvising algorithm for the scale free type of WSNs, has been designed in this work and also the proposed SPRT is compared with the existing EROSE technique. The wide range of experimental results thus ensure that the introduced SPRT modelling methodology can achieves robustness and enhances the security of WSN. [ABSTRACT FROM AUTHOR]

Simultaneous use of data repeated levelling measurements and continuous GNSS observations allows increasing the spatial resolution of geodynamics models. For this purpose, it is necessary to create a single network, a so-called hybrid network. This paper aims at examining the possibility of using scale-free network theory to determine the most relevant common points in hybrid networks using the distance criterion. Used on European network points: UELN (United European Levelling Network) and EPN (European Permanent GPS Network) and the regional network. In the hybrid network (UELN + EPN), 18 pseudo-nodal points with the highest number of links were identified. The accepted distance criterion shows that about 90 % of the EPN points can be used as common points. The application of the scale-free network theory allows determining the significance of points in a hybrid network. [ABSTRACT FROM AUTHOR]

The complexity and changeability of electromagnetic environment make the deficiency of traditional methods of electromagnetic protection increasingly prominent. The organisms with the regulation of nervous system have advantages of self-adaptive, self-organizing, and self-repairing. Therefore, it is necessary to explore a new thought on electromagnetic protection by drawing from the biological self-adaptive advantage. In this study, two kinds of scale-free spiking neural networks (sfSNNs) with different clustering coefficients are constructed. Then, the antiinterference function of the sfSNNs under the ac magnetic field stimulation is evaluated and compared. Finally, the antiinterference mechanism is analyzed. The experimental results show that both sfSNNs have a certain antiinterference function, and the performance of the sfSNN with high clustering coefficient is better than that with low clustering coefficient in the antiinterference function; the dynamic evolution of neural information processing in the sfSNN is clarified; and the dynamic regulation of synaptic plasticity is the intrinsic factor of the antiinterference function of the sfSNNs. This study lays a theoretical foundation for the electromagnetic protection of electronic system based on adaptive bionic mechanism. [ABSTRACT FROM AUTHOR]

Strategic emerging industrial cluster network as an important form of the current industrial development, with an intensive technological innovation, prominent economies of scale and knowledge spillovers, and other characteristics, is a symbol of strategic emerging industry formation and an effective model for its development. In this paper, the Logistic model is used to characterize the strategic emerging industry cluster's life cycle. Then, the scale-free evolution model being optimized based on the growth rate of strategic emerging industrial clusters and gray number being introduced, an evolution model of the strategic emerging industrial network is constructed considering the growth of innovators flow, the evolution process, and the stability state of the strategic emerging industrial cluster network are analyzed. Finally, an example is given to simulate and comparatively analyze the characteristics of the inflection point with the change of relevant parameters in the evolution process of the cluster network. The research results can provide some theoretical reference and guidance for the cultivation of strategic emerging industries. [ABSTRACT FROM AUTHOR]

Featured Application: The Climate conditions as factors in the equations and random values will be incorporated into the featured application for future research model. The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the probability of wildfire propagation in certain areas. In this study, we firstly demonstrate that the landscapes of wildfire propagation can be represented as a scale-free network, where the wildfire is modeled as the scale-free network whose degree follows the power law. By establishing the state-related concepts and modifying the Binary-Addition-Tree (BAT) together with the PageRank, we propose a new methodology to serve as a reliable tool in predicting the probability of wildfire propagation in certain areas. Furthermore, we demonstrate that the proposed maximum-state PageRank used in the methodology can be implemented separately as a fast, simple, and effective tool in identifying the areas that require immediate protection. The proposed methodology and maximum-state PageRank are validated in the example generated from the Barabási-Albert model in the study. [ABSTRACT FROM AUTHOR]

In transmission problem of many complex networks, there are some restrictions on the routing path between a pair of sources and destinations, especially for the transmission process that has a segment distance limit, which has a wide range of applications in real transportation, information transmission, etc. on large real networks. In this work, we constructed a complex network transmission model, defined the transmission distance limit to L and randomly selected a part of the nodes in the scale-free network as the station. Based on the shortest path algorithm and a weighted routing algorithm, we observed the results under the transmission distance limit. The results show that the setting of L is important, which can be used to guide and control the fuel carrying capacity of vehicles on the transportation network. Finally, we selected two real road network for simulation and the results demonstrate the effectiveness of the algorithm in scale-free networks and real networks. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have been successfully applied to solve numerous optimization problems. Neighborhood field optimization algorithm (NFO) has been proposed to integrate the neighborhood field in EAs, which utilizes local cooperation behaviors to explore new solutions. In this paper, certain new NFO variants are proposed based on the cooperation of descendent neighbors. The competitive and cooperative behaviors of NFO variants provide a remarkable ability to accelerate information exchanges and achieve global search. Experimental results show that NFO variants perform better than basic and other state-of-the-art EAs under different benchmark functions. For NFO and other EAs, it is difficult to quantify benefits of local cooperation in the optimization process. For this purpose, the cooperation behaviors are analyzed in a new network approach in this paper. In the proposed NFO variants, population graph shows a scale-free network with power-law distribution. Network characteristics, i.e., degree distribution, cluster coefficient and average degree, are used to quantify the cooperation behaviors. Experimental results show that network characteristics can effectively indicate the optimization performance of NFO variants in terms of convergence rate and population diversity. NFO variants with large cluster coefficients and significant heterogeneous characteristics can achieve a significant performance improvement on numerous problems. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) epidemic met coincidentally with massive migration before Lunar New Year in China in early 2020. This study is to investigate the relationship between the massive migration and the coronavirus disease 2019 (COVID-19) epidemic in China. Methods: The epidemic data between January 25th and February 15th and migration data between Jan 1st and Jan 24th were collected from the official websites. Using the R package WGCNA, we established a scale-free network of the selected cities. Correlation analysis was applied to describe the correlation between the Spring Migration and COVID-19 epidemic. Results: The epidemic seriousness in Hubei (except the city of Wuhan) was closely correlated with the migration from Wuhan between January 10 and January 24, 2020. The epidemic seriousness in the other provinces, municipalities and autonomous regions was largely affected by the immigration from Wuhan. By establishing a scale-free network of the regions, we divided the regions into two modules. The regions in the brown module consisted of three municipalities, nine provincial capitals and other 12 cities. The COVID-19 epidemics in these regions were more likely to be aggravated by migration. Conclusions: The migration from Wuhan could partly explain the epidemic seriousness in Hubei Province and other regions. The scale-free network we have established can better evaluate the epidemic. Three municipalities (Beijing, Shanghai and Tianjin), eight provincial capitals (including Nanjing, Changsha et al.) and 12 other cities (including Qingdao, Zhongshan, Shenzhen et al.) were hub cities in the spread of COVID-19 in China. [ABSTRACT FROM AUTHOR]

Recently, [T. M. Gwizdałła, The swarm-like update scheme for opinion formation, in Computational Collective Intelligence, eds. N. T. Nguyen, G. A. Papadopoulos, P. Jedrzejowciz, B. Trawinski and G. Vossen (Springer International Publishing, Cham, 2017), pp. 66–75.] we have proposed the scheme of performing the opinion formation simulation based on popular global optimization mechanism — the Particle Swarm Optimization (PSO). The basic idea was to use the interaction between two potential directions of agents' heading: those forced by the global opinion and those forced by the opinion of neighbors/colleagues. In the proposed paper, some enhancement of the proposed model is shown. We assume that, when performing the binary PSO-like update of system, we use the generalized version of logistic function. The results are promising in the sense that the introduced change increases explicitly, the number of possible solutions. [ABSTRACT FROM AUTHOR]

Knowledge about the molecular basis of SARS-CoV-2 infection is incipient. However, recent experimental results about the virus interactome have shown that this single-positive stranded RNA virus produces a set of about 28 specific proteins grouped into 16 non-structural proteins (Nsp1 to Nsp16), four structural proteins (E, M, N, and S), and eight accessory proteins (orf3a, orf6, orf7a, orf7b, orf8, orf9b, orf9c, and orf10). In this brief communication, the network model of the interactome of these viral proteins with the host proteins is analyzed. The statistical analysis of this network shows that it has a modular scale-free topology in which the virus proteins orf8, M, and Nsp7 are the three nodes with the most connections (links). This result suggests the possibility that a simultaneous pharmacological attack on these hubs could assure the destruction of the network and the elimination of the virus. [ABSTRACT FROM AUTHOR]

The investigation of earthquake recurrence networks that were constructed from two aftershock sequences in Greece, is performed, aiming to detect whether the structure of networks became distinct (non-random) before the occurrence of either the main shock or a major (strong) aftershock. The network nodes are the time series observations, which are the aftershocks magnitudes. Their binary connections are given by the arbitrary threshold ε on the recurrence matrix, which is computed with the Heaviside function. Two aftershock sequences are the 2014 Kefalonia doublet, main shocks ( M = 6.1 and M = 6.0 ), and the 2015 Lefkada main shock ( M = 6.5 ) sequence. The earthquake networks are formed for three different thresholds of ε , for monitoring eight basic network measures and non-trivial properties such as the small world and scale free, and examining their structure during the evolution of the sequences. To assess whether the values of the eight network measures are statistically significant and the network gets non-trivial properties are present, the construction of randomized networks is required and then the comparison of the randomized network values with the ones from the original recurrence networks. The monitoring of network measures reveals that their original values diverge from the statistical significance, i.e., the structure of networks is random, immediately after the main shocks and shortly before the occurrence of the strongest aftershocks. On the contrary, in the intervening time, between the occurrences of main shocks and strongest aftershocks, their original values are statistically significant, which means that reveal the distinct network structure. The small-world and scale-free properties are sought but not revealed. [ABSTRACT FROM AUTHOR]

The frequent global financial crises in recent years show that it is necessary to implement macroprudential regulation for the banking system. At present, quantitative research on the macroprudential regulation for the dynamic Chinese banking network system is lacking, while the related studies in other countries have not considered the interbank network structure. Therefore, in the present paper, we construct a dynamic banking network model with a scale-free network and a dynamic macroprudential regulation model under four risk allocation mechanisms (CVaR, Incremental VaR, Shapley value EL, and ΔCoVaR) for the dynamic Chinese banking network system. Then, we conduct empirical research to study the effect of the macroprudential regulation model on the Chinese banking network system. Our results show that the Chinese banking network system was the most unstable in 2010 and that the average default probability decreased every year after the macroprudential regulation, indicating the effectiveness of the macroprudential regulation model. From the perspective of the scale-free network structure, we find that the intrinsic mechanism of macroprudential regulation is to rewire the interbank linkages from small banks to large banks with more interbank lending to prevent contagious risk, thereby improving the stability of the entire banking system. Moreover, the regulation effects of ΔCoVaR and CVaR mechanisms are found to be better than those of the other mechanisms. The regulation effect of ΔCoVaR is the most significant. [ABSTRACT FROM AUTHOR]

We consider a simple preferential attachment graph process, which begins with a finite graph and in which a new (t + 1)st vertex is added at each subsequent time step t that is connected to each previous vertex u ≤ t with probability d u (t)/ t , where d u (t) is the degree of u at time t. We analyse the graph obtained as the infinite limit of this process, and we show that, as long as the initial finite graph is neither edgeless nor complete, with probability 1 the outcome will be a copy of the Rado graph augmented with a finite number of either isolated or universal vertices. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A network is scale-free if its connectivity density function is proportional to a power-law distribution. It has been suggested that scale-free networks may provide an explanation for the robustness observed in certain physical and biological phenomena, since the presence of a few highly connected hub nodes and a large number of small-degree nodes may provide alternate paths between any two nodes on average—such robustness has been suggested in studies of metabolic networks, gene interaction networks and protein folding. A theoretical justification for why many networks appear to be scale-free has been provided by Barabási and Albert, who argue that expanding networks, in which new nodes are preferentially attached to highly connected nodes, tend to be scale-free. In this paper, we provide the first efficient algorithm to compute the connectivity density function for the ensemble of all homopolymer secondary structures of a user-specified length—a highly nontrivial result, since the exponential size of such networks precludes their enumeration. Since existent power-law fitting software, such as powerlaw, cannot be used to determine a power-law fit for our exponentially large RNA connectivity data, we also implement efficient code to compute the maximum likelihood estimate for the power-law scaling factor and associated Kolmogorov–Smirnov p value. Hypothesis tests strongly indicate that homopolymer RNA secondary structure networks are not scale-free; moreover, this appears to be the case for real (non-homopolymer) RNA networks. [ABSTRACT FROM AUTHOR]

We study the effects of the reciprocal links on the dynamics of direct Boolean networks with scale-free topology (SFRBNs). By means of the method of the Derrida Plot, we have investigated the SFRBNs characterized by different values of average degree and different values of reciprocity in order to test the behavioral regimes of the system. The following step was to perform numerical simulation with the quenched Kauffman model to study the dynamical properties of critical SFRBNs with 〈 k 〉 c = 2. The distribution of the number of different attractors, the period of the cyclic attractors, the transient duration and the fraction of the frozen nodes, have been studied as a function of the reciprocity and network size. The results presented reveal that reciprocity seems to have no direct effect on the changing of the behavioral regime of SFRBNs with given value of 〈 k 〉. On the contrary, we observed that reciprocal links have a profound effect on the dynamic of critical SFRBNs. [ABSTRACT FROM AUTHOR]