Network Structure Model Research Articles

Analysis of E-commerce Marketing Strategy of Power Enterprises in Jilin Province from the Perspective of Economic Transition

As a new technical means, e-commerce provides a new idea for the reform of power supply enterprises. At present, the power company is in the context of economic transformation. To create a world-class power supply enterprise, it is necessary to keep track of the world’s advanced management ideas, methods and means, and integrate the various management services of the enterprise into the increasingly becoming an Internet society. Taking the power supply company of Jilin Province as an example, this paper proposes some improvement schemes for the operation mode of the existing power supply system according to the requirements of the power market for power supply enterprises and the advantages of e-commerce. According to the actual situation of the power supply enterprises in Jilin Province, the network model, structure model and database model of the power supply enterprise, as well as the website design scheme of the e-commerce of the power supply enterprise are designed.

Research on the Supernetwork Equalization Model of Multilayer Attributive Regional Logistics Integration

Based on the multi-level structure attributes of regional logistics, regional logistics integration presents complex features. Therefore, according to the operational requirements of regional logistics integration, we built a regional logistics network structure model consisting of infrastructure, information resources, and organizational networks; selected the level of relationship between different layers and information dissemination, and transport flow as decision variables. We built a supernetwork mathematical model of regional logistics integration with multiple layers of attributes. Considering the constraints of marginal cost changes and overall revenue, we found an equilibrium solution for the operation of regional logistics integration supernetwork. That is, the optimal match between the level of relationship, the level of information dissemination and the transportation flow, provides a reference basis for the formulation of relevant policies and implementation strategies.

A hybrid model for the patent citation network structure

Percolation theory on the patent citation network is studied and the percolation threshold points are identified. The results show that there is a significant change of the threshold throughout our dataset years, implying changes in the formation process of the patent citation network. There is a first shift at around 2001, and a very delayed transition point after 2008. Giant component formation in such networks is an indication of the existence of inter-disciplinary patents. In order to explain the changes observed, a hybrid model for creating networks is suggested here. The model is based on a combination of random networks and preferential attachment. It is also compared with results from the well-known configuration model. The hybrid model fits better the data of the patent citation network, rather than a single scale-free or a single Erdős–Rényi network, and explains the increase in preferential attachment in later years. Both the degree distribution and the results of the analysis through percolation theory agree well with real data. This enables the formation of a plausible explanation for the structural changes of the patent citation network’s evolution.

Atheism, Social Networks and Health: A Review and Theoretical Model

Despite accumulating evidence of the importance of social capital in predicting health outcomes, no work has yet systematically investigated the structural differences between the social networks of god-believers and atheists. This is an especially important gap in the religion/secularism research because religiosity appears to be declining throughout the Western world (Zuckerman, Galen & Pasquale, 2016). Despite stereotypes of atheists as atomized, psychologically unhealthy and anti-social (e.g., Bainbridge, 2005), a growing body of evidence suggests that strongly-identified atheists are more likely to join secular social clubs as well as benefit from better mental and physical health compared to less affirmatively-identified secular individuals. As a step toward developing this line of research, the present article operationalizes social network structure within the study of secularism, discusses the available research with a focus on atheism in particular, and integrates this research into a schematic theoretical model of atheist self-identity, network structure and health.

Reliability of time-constrained multi-state network susceptible to correlated component faults

Correlation can seriously degrade reliability and capacity due to the simultaneous failure of multiple components, which lowers the probability that a system can execute its required functions with acceptable levels of confidence. The high cost of fault in time-critical systems necessitates methods to explicitly consider the influence of correlation on reliability. This paper constructs a network-structured model, namely time-constrained multi-state network (TCMSN), to investigate the capacity of a computer network. In the TCMSN, the physical lines comprising the edges of the computer network experience correlated faults. Our approach quantifies the probability that d units of data can be sent from source to sink in no more than T units of time. This probability that the computer network delivers a specified level of data before the deadline is referred to as the system reliability. Experimental results indicate that the negative influence of correlation on reliability could be significant, especially when the data amount is close to network bandwidth and the time constraint is tight. The modeling approach will subsequently promote design and optimization studies to mitigate the vulnerability of networks to correlated faults.

Evaluating the oil production and wastewater treatment efficiency by an extended two-stage network structure model with feedback variables

Oil is an indispensable and important energy source in modern society, and oil production plays a vital role in economic development. However, there is no denying that oil production has a very bad impact on the environment. To realize the sustainable development of oil production, the environmental problems caused by oil production need to be controlled and managed strictly. Aiming at the practical problems of insufficient recoverable reserves of high quality oil and aggravating environmental pollution, efficient oil production and wastewater treatment become more and more important. Therefore, the whole system is divided into two stages. The stage 1 is oil development, and the stage 2 is wastewater treatment. Considering that the model needs to solve the undesirable output, an extended two-stage Slacks-Based Measure (SBM) Data Envelopment Analysis (DEA) network structure model with a feedback variable is established. The efficiency value of each stage can be obtained, and the weakness of each stage can be identified, so that the efficiency value of the whole system is more accurate. And then thirteen oilfields are selected for the numerical analysis to verify the validity and accuracy of the proposed model, the results demonstrate that the overall system is efficient only if the two stages are efficient; the oil production has higher efficiencies than the oilfield wastewater treatment; There is a stronger relationship between the efficiency of oilfield wastewater treatment and the whole system. A comparison with a traditional model demonstrated that the proposed model has a more scientific, stable and practical evaluation methods.

Reliability Analysis of Complex Systems with Failure Propagation

Failure propagation is a critical factor for the reliability and safety of complex systems. To recognise and identify failure propagation of systems, a deep fusion model based on deep belief network (DBN) and Bayesian structural equation model (BSEM) is proposed. The deep belief network is applied to extract features between status monitoring data and the performance degradation in different failure components. To calculate the path weight of failure propagation, the Bayesian structural equation model is proposed to study the relationship among different fault modes. After getting the performance degradation of each fault through DBN and calculating the path weight of fault propagation by BSEM, it is available to get the overall reliability of the system. The aircraft landing gear system with 19 fault patterns is selected to evaluate the feasibility of the proposed deep fusion model. The results demonstrate that the overall reliability of the system can be obtained by analysing the fault propagation of multiple fault patterns, and the proposed model has a lower deviation than traditional back propagation neural network.

Design and Implementation of Soil Environmental Quality Visualization System Based on WebGIS

Multi-scale soil environmental quality survey and social production practice have accumulated a large number of soil data, which is the valuable essential data for practice and research. It is urgent to rely on information technology to strengthen the management and application of soil information. This paper designs a WebGIS system for visualizing soil environmental quality based on the B/S model of three-tier network structure. The system uses the map engine ArcGIS API for JavaScript and HTML5 canvas dynamic rendering technology as the core of the technology, and combines ArcGIS Server and spatial database technology to achieve a series of functions such as soil environmental quality data management, soil pollution visualization and statistical analysis. The system deployment is applied to the Yunnan Provincial Environmental Monitoring Center Station, which enables rapid and high-performance visual display of soil environmental quality information and enhances the rapid information sharing capability between the environmental monitoring departments of Yunnan Province.

Reduction of the complexity of models of network structures and systems

Reduction of the complexity of models of network structures and systems

Structural network inference from time-series data using a generative model and transfer entropy

In this paper we develop a novel framework for inferring a generative model of network structure representing the causal relations between data for a set of objects characterized in terms of time series. To do this we make use of transfer entropy as a means of inferring directed information transfer between the time-series data. Transfer entropy allows us to infer directed edges representing the causal relations between pairs of time series, and has thus been used to infer directed graph representations of causal networks for time-series data. We use the expectation maximization algorithm to learn a generative model which captures variations in the causal network over time. We conduct experiments on fMRI brain connectivity data for subjects in different stages of the development of Alzheimer’s disease (AD). Here we use the technique to learn class exemplars for different stages in the development of the disease, together with a normal control class, and demonstrate its utility in both graph multi-class and binary classifications. These experiments are showing the effectiveness of our proposed framework when the amounts of training data are relatively small.

An integrated two-layer network model for designing a resilient green-closed loop supply chain of perishable products under disruption

Disruption in the electric power system network can have dramatic effects on supply chain performance in terms of economic and environmental objectives, especially for perishable products. This paper addresses the design of a resilient green-closed loop supply chain network for perishable products under the risk of electric power network disruption. The research has presented an integrated model for the interdependent two-layer network structure as a strategy to mitigate the power disruption risk in order to minimize the expected total network cost and the expected total amount of carbon emissions. Integration of two networks on both objectives along with the effect of products’ lifetime has been analyzed in a real case study of the dairy industry. The results designate that the integration of the two networks leads to improvements in both objective functions. The average total network cost and total carbon emissions have been reduced by 21% and 25% compared to the non-integrated layers, respectively. Furthermore, by increasing the products lifetime, the projected model enjoys higher performance being compared with the non-integrated layers model. This paper concludes that the integrated decision making about the infrastructure networks e.g. power network with the supply chain enjoys economic and environmental benefits for both networks layers under power network disruptions.

Differential Markov random field analysis with an application to detecting differential microbial community networks.

Micro-organisms such as bacteria form complex ecological community networks that can be greatly influenced by diet and other environmental factors. Differential analysis of microbial community structures aims to elucidate systematic changes during an adaptive response to changes in environment. In this paper, we propose a flexible Markov random field model for microbial network structure and introduce a hypothesis testing framework for detecting differences between networks, also known as differential network analysis. Our global test for differential networks is particularly powerful against sparse alternatives. In addition, we develop a multiple testing procedure with false discovery rate control to identify the structure of the differential network. The proposed method is applied to data from a gut microbiome study on U.K. twins to evaluate how age affects the microbial community network.

Evolving dynamic networks: An underlying mechanism of drug resistance in epilepsy?

At least one-third of all people with epilepsy have seizures that remain poorly controlled despite an increasing number of available anti-epileptic drugs (AEDs). Often, there is an initial good response to a newly introduced AED, which may last up to months, eventually followed by the return of seizures thought to be due to the development of tolerance. We introduce a framework within which the interplay between AED response and brain networks can be explored to understand the development of tolerance. We use a computer model for seizure generation in the context of dynamic networks, which allows us to generate an ‘in silico’ electroencephalogram (EEG). This allows us to study the effect of changes in excitability network structure and intrinsic model properties on the overall seizure likelihood. Within this framework, tolerance to AEDs – return of seizure-like activity – may occur in 3 different scenarios: 1) the efficacy of the drug diminishes while the brain network remains relatively constant; 2) the efficacy of the drug remains constant, but connections between brain regions change; 3) the efficacy of the drug remains constant, but the intrinsic excitability within brain regions varies dynamically. We argue that these latter scenarios may contribute to a deeper understanding of how drug resistance to AEDs may occur.

A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective

In the digital era, cooperation has become a new way of firm operation, but only rarely have studies investigated how internal cooperative mechanisms among departments affect firm performance evaluation. Grounded on resource-based view theory, a two-stage network structure model of a hotel (i.e. operations stage and marketing stage) is presented, illustrating how a hotel consumes internal resources and obtains competitive advantages and market profits. A DEA-based bi-objective model is put forward to evaluate the hotel performance from the perspective that the two internal stages cooperate with each other. To solve the proposed model, an extended augmented weighted Tchebycheff metric and a new algorithm are used. An empirical analysis using data from 68 Taiwanese international tourist hotels in 2011 was conducted, and the results corroborated the theorems and remarks about the proposed model. This study offers a comprehensive benchmarking tool to improve hotel decision-making.

Locating the source of large-scale outbreaks of foodborne disease.

In today's globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. We study the problem of identifying the source of emerging large-scale outbreaks of foodborne disease; a crucial step in mitigating their proliferation. To solve the source identification problem, we formulate a probabilistic model of the contamination diffusion process as a random walk on a network and derive the maximum-likelihood estimator for the source location. By modelling the transmission process as a random walk, we are able to develop a novel, computationally tractable solution that accounts for all possible paths of travel through the network. This is in contrast to existing approaches to network source identification, which assume that the contamination travels along either the shortest or highest probability paths. We demonstrate the benefits of the multiple-paths approach through application to different network topologies, including stylized models of food supply network structure and real data from the 2011 Shiga toxin-producing Escherichia coli outbreak in Germany. We show significant improvements in accuracy and reliability compared with the relevant state-of-the-art approach to source identification. Beyond foodborne disease, these methods should find application in identifying the source of spread in network-based diffusion processes more generally, including in networks not well approximated by tree-like structure.

Benchmarking time-series data discretization on inference methods.

The rapid development in quantitatively measuring DNA, RNA and protein has generated a great interest in the development of reverse-engineering methods, that is, data-driven approaches to infer the network structure or dynamical model of the system. Many reverse-engineering methods require discrete quantitative data as input, while many experimental data are continuous. Some studies have started to reveal the impact that the choice of data discretization has on the performance of reverse-engineering methods. However, more comprehensive studies are still greatly needed to systematically and quantitatively understand the impact that discretization methods have on inference methods. Furthermore, there is an urgent need for systematic comparative methods that can help select between discretization methods. In this work, we consider four published intracellular networks inferred with their respective time-series datasets. We discretized the data using different discretization methods. Across all datasets, changing the data discretization to a more appropriate one improved the reverse-engineering methods' performance. We observed no universal best discretization method across different time-series datasets. Thus, we propose DiscreeTest, a two-step evaluation metric for ranking discretization methods for time-series data. The underlying assumption of DiscreeTest is that an optimal discretization method should preserve the dynamic patterns observed in the original data across all variables. We used the same datasets and networks to show that DiscreeTest is able to identify an appropriate discretization among several candidate methods. To our knowledge, this is the first time that a method for benchmarking and selecting an appropriate discretization method for time-series data has been proposed. All the datasets, reverse-engineering methods and source code used in this paper are available in Vera-Licona's lab Github repository: https://github.com/VeraLiconaResearchGroup/Benchmarking_TSDiscretizations. Supplementary data are available at Bioinformatics online.

Intrusion Detection Classification Model on an Improved k-Dependence Bayesian Network

Edge computing extends traditional cloud services to the edge of the network, and the highly dynamic and heterogeneous environment at the edge of the network makes the network security situation facing severe challenges. Therefore, it is of great theoretical and practical significance to study and build a high-precision Intrusion Detection Classification Model (IDCM) for network security in the emerging edge computing mode. This paper studies an improved k-dependency Bayesian network (KDBN) structural model that can accurately describe the dependence relationships among system variables and reduce the complexity of the Bayesian network structure by reducing the directed edges of weak dependence. On this basis, this paper constructs an IDCM based on improved KDBN by introducing the maximum a posterior criterion and a virtual augmentation method for samples of small category. The experiments use the KDDCup99 (10%) intrusion detection data set for verification, which show that the IDCM based on improved KDBN has high efficiency, high detection accuracy and high stability, which optimally addresses the issues discussed in many references, such as low detection accuracy and poor stability, for small categories (U2R and R2L) in the KDDCup99 (10%) intrusion detection data set.

An Energy-Optimization Clustering Routing Protocol Based on Dynamic Hierarchical Clustering in 3D WSNs

An important issue in the three-dimensional (3D) wireless sensor networks (WSNs) is sensor energy optimization. To alleviate this issue, we introduce a new 3D spherical network structure model, and by combining the original energy consumption model to construct a new method to determine the optimal number of clusters and balance the total energy consumption. Since the different sizes of the clusters are generated by traditional hierarchical clustering, it causes unbalanced energy consumption in the network. To alleviate this problem, we adopt an improved dynamic hierarchical clustering method and implement two strategies that include the following three contributions: the introduction of the distance similarity index to get a better clustering, a double cluster head (CH) strategy to reduce the load of a cluster head in a large cluster, and a node dormancy mechanism to balance the energy consumption of the network. In addition, we also propose the optimal cluster-head function to select the CH of each cluster in each round, and the optimal cluster-head function is constructed based on the residual energy and positions of the nodes. Finally, to optimize the CH election strategy, several parameters of the optimal cluster-head function are determined according to the network structure. The simulation results show that our routing protocol is more robust compared with four other protocols, which is of great significance for the application in 3D environment monitoring.

Control of thermal conduction and rectification in a model of complex networks with two asymmetric parts

The controlling of heat flux is an important topic in the field of heat conduction and has potential applications in thermal devices. We here design a model of asymmetric network structure to study this problem in complex networks, which is composed of two parts with different topologies. We find that the heat conduction and rectification can be significantly changed by adjusting two parameters: the network rewiring parameter $\ensuremath{\delta}$ and coupling parameter $\ensuremath{\lambda}$. Two typical network structures are considered. One is composed of a random network and a two-dimensional regular lattice with half by half. Another is composed of two random networks with opposite coupling parameter $\ifmmode\pm\else\textpm\fi{}\ensuremath{\lambda}$. Our numerical simulations show that for the first case, both the network flux and rectification coefficient decrease monotonically with the increase of $\ensuremath{\delta}$ and there is an optimal $\ensuremath{\lambda}$ for the maximum flux. For the second case, the network flux will decrease monotonically while the rectification coefficient increases monotonically, with the increase of $\ensuremath{\lambda}$. A brief theory of phonon spectra is applied to explain the numerical results.

An Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on AGNES with Balanced Energy Consumption Optimization.

To further prolong the lifetime of wireless sensor network (WSN), researchers from various countries have proposed many clustering routing protocols. However, the total network energy consumption of most protocols is not well minimized and balanced. To alleviate this problem, this paper proposes an energy-efficient clustering routing protocol in WSNs. To begin with, this paper introduces a new network structure model and combines the original energy consumption model to construct a new method to determine the optimal number of clusters for the total energy consumption minimization. Based on the balanced energy consumption, then we optimize the AGglomerative NESting (AGNES) algorithm, including: (1) introduction of distance variance, (2) the dual-cluster heads (D-CHs) division of the energy balance strategy, and (3) the node dormancy mechanism. In addition, the CHs priority function is constructed based on the residual energy and position of the node. Finally, we simulated this protocol in homogeneous networks (the initial energy = 0.4 J, 0.6 J and 0.8 J) and heterogeneous networks (the initial energy = 0.4–0.8 J). Simulation results show that our proposed protocol can reduce the network energy consumption decay rate, prolong the network lifetime, and improve the network throughput in the above two networks.