Complex Network Model Research Articles

Research on Safety Risk Transfer in Subway Shield Construction Based on Text Mining and Complex Networks

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction being more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks (SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules, and complex networks to deep mine subway construction safety incident reports and explore the risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risks. Then, association rules are introduced to explore the causal relationships among safety risks. Finally, the key safety risks and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risks in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key safety risk or cutting off the transfer paths. This paper provides new ideas and methods for SCSR identification and influence element mining, and the results of the study help safety managers propose accurate subway construction safety risk control measures.

An approach to co-medication mechanism mining of Chinese Materia Medica and western medicines based on complex networks with the multi-source heterogeneous information

The co-medication strategy of Chinese Materia Medica (CMM) and western medicines under the holistic integrative medicine (HIM) model offers the advantages of increased efficacy and reduced toxicity, particularly in clinical practice for treating complex and challenging diseases. However, due to the mutual fragmentation of basic theories and principles of medication decision-making between traditional Chinese medicine (TCM) and western medicine, research on the mechanisms for the combined use of CMM and western medicines has become a challenging problem in modern clinical medicine research. Traditional drug combination mining models encounter numerous bottlenecks when dealing with this complex decision-making problem with multi-source information characteristics. This paper aims to mine the optimal drug combination strategy from a real-life decision-making scenario of co-medication between CMM and western medicines. It abstracts this realistic problem as a class of complex network optimal overlapping community detection problem, focusing on solving key difficulties such as the inability of existing drug complex network models to handle the multi-source information of drugs and the randomness in the community detection process. We integrate co-medication prescription information with attribute information to construct the speaker-listener label propagation algorithm based on semantic attribute features (SA-SLPA) and develop a new medicine combination identification strategy based on weighted complex networks. Using 6671 clinically real prescriptions, we conclude that the detection quality of the SA-SLPA method proposed in this paper is improved by up to 78% compared to existing methods. This improvement illustrates the scientificity and effectiveness of the model in this paper and provides new ideas for the application of theoretical models and methods of interdisciplinary disciplines such as decision science, data science, and computational science in the field of modern medicine.

Research on resilience model of UAV swarm based on complex network dynamics

The resilience of UAV swarms mainly revolves around ensuring stable and uninterrupted operations. Malicious attacks can implement the adverse impacts of potential threats through swarm communication links. In this context, the SIS (Susceptible → Infected → Susceptible) method is suitable for describing the information transmission within UAV swarms. An enhanced resilience model of the UAV swarm is proposed in this study, which incorporates the factors of self-dynamics, dynamics of topology, dynamics of information transmission, and SIS into the complex network model. The model proposed in this paper has the capability to effectively capture changes in the network topology as well as the dynamics of the system. The average number of susceptible drones is utilized as the metric to evaluate the resilience of the swarm. Furthermore, an experiment is conducted where a UAV swarm successfully carries out a surveillance mission. The proposed model not only enables the support of mission planning but also facilitates the design enhancements of UAV swarms.

Collar recognition and matching of clothing style drawings based on complex networks

Seeking a fast and effective algorithm for describing and classifying clothing styles is currently a hot topic in the textile and clothing industry. The study first constructs a sample library of collar styles, preprocesses clothing images, and uses complex network models to describe and extract features; Implement clothing image classification based on support vector machines, and finally use image matching algorithms to match the image to be matched with the target object. It was indicated that the complex network model established by the research institute can effectively depict the characteristics of various collar styles, and the complex network model can effectively depict the characteristics of various collar styles. In 10 experiments, the overall average overall accuracy was 98%; Regarding different types of collars, the average accuracy of 10 experiments is above 96%, indicating that the collar classification accuracy based on support vector machine method is very high. The classifying results of the 10 categories obtained by the research method have little change and are relatively stable. For each category, the research method is higher than the other two methods. The results can indicate that the research method has effect and is more suitable for collar recognition and classification. Compared with traditional Hu invariant moments and HOG feature extraction algorithms, the research algorithm has stronger noise suppression ability, and using Euclidean distance as a similarity measure has great advantages.

Synchronization of multi-weighted complex networks with mixed variable delays and uncertainties via impulsive pinning control

In this paper, the synchronization problem in nonlinearly-coupled multi-weighted complex networks with parameter uncertainties and multiple delays, where both internal time-varying delays and multiple coupling time-varying delays are included, is investigated by adopting a pinning impulsive control scheme, which combines the merits of pinning control and impulsive control. Impulsive pinning control ratio has been applied to select controlled nodes at each impulsive instant. Based on the Lyapunov function method, sufficient conditions for achieving complete synchronization of multi-weighted complex networks have been derived. And numerical simulations are presented to demonstrate the validity and efficacy of our derived theoretical results, which show that under the criteria proposed in this paper, multi-weighted complex networks with parameter uncertainties and multiple delays, could achieve complete synchronization by applying pinning impulsive control. Besides, taking bus lines as the network nodes, a public traffic roads network model with multi-weights is established and analyzed numerically as an application of the proposed multi-weighted complex network model.

Robustness Analysis of an Urban Public Traffic Network Based on a Multi-Subnet Composite Complex Network Model.

An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public traffic networks contribute to the safe maintenance and operation of urban traffic systems. In this paper, a new cascading failure model for urban public traffic networks is constructed based on a multi-subnet composite complex network model. In order to better simulate the actual traffic flow in the composite network, the concept of traffic function is proposed in the model. Considering the different effects of various relationships on nodes in the composite network, the traditional cascading failure model has been improved and a deliberate attack strategy and a random attack strategy have been adopted to study the robustness of the composite network. In the experiment, the urban bus-subway composite network in Qingdao, China, was used as an example for simulation. The experimental results showed that under two attack strategies, the network robustness did not increase with the increase in capacity, and the proportion of multiple relationships had a significant impact on the network robustness.

A Resilience Approach for Diagnosing and Predicting HBV-Related Diseases Based on Blood Tests

Chronic hepatitis B virus (HBV) infection, which threatens global public health, is a major contributor to liver-related morbidity and mortality. Examinations for liver diseases related to chronic HBV infection—including laboratory tests, ultrasounds, computed tomography (CT), and liver biopsies—may take up medical resources, particularly since they overlap in most instances. Thus, there is an urgent need to establish an economical and effective diagnosis method in order to streamline the medical process for HBV-related diseases. Using complex network models constructed based on clinical blood tests, we provide such a method by defining the novel measure of functional resilience to assess patients’ liver conditions. By combining network models and dynamics, we discovered the pivotal items and their corresponding thresholds, which can guide further research on preventing disease deterioration in critical states of these diseases. The macro-averaged precision of our method, functional resilience, is 84.74%, whereas the macro-averaged precision of physicians’ experience without assistance from imaging or biopsy is 55.63%. From an economic perspective, our approach could save the equivalent of at least 30 USD per visit for most Chinese patients and at least 400 USD per visit for most US patients, compared with general diagnostic methods. Globally, this will add to savings of at least 10.5 billion USD annually. Our method can comprehensively evaluate the condition of patients’ livers and help avert the waste of medical resources during the diagnosis of liver disease by reducing excessive imaging exams.

Combination of bioaffinity ultrafiltration-UFLC-ESI-Q/TOF-MS/MS, in silico docking and multiple complex networks to explore antitumor mechanism of topoisomerase I inhibitors from Artemisiae Scopariae Herba

BackgroundArtemisiae Scopariae Herba (ASH) has been widely used as plant medicine in East Asia with remarkable antitumor activity. However, the underlying mechanisms have not been fully elucidated.MethodsThis study aimed to construct a multi-disciplinary approach to screen topoisomerase I (topo I) inhibitors from ASH extract, and explore the antitumor mechanisms. Bioaffinity ultrafiltration-UFLC-ESI-Q/TOF-MS/MS was used to identify chemical constitution of ASH extract as well as the topo I inhibitors, and in silico docking coupled with multiple complex networks was applied to interpret the molecular mechanisms.ResultsCrude ASH extract exhibited toxicogenetic and antiproliferative activities on A549 cells. A series of 34 ingredients were identified from the extract, and 6 compounds were screened as potential topo I inhibitors. Docking results showed that the formation of hydrogen bond and π-π stacking contributed most to their binding with topo I. Interrelationships among the 6 compounds, related targets and pathways were analyzed by multiple complex networks model. These networks displayed power-law degree distribution and small-world property. Statistical analysis indicated that isorhamnetin and quercetin were main active ingredients, and that chemical carcinogenesis-reactive oxygen species was the critical pathway. Electrophoretic results showed a therapeutic effect of ASH extract on the conversion of supercoiled DNA to relaxed forms, as well as potential synergistic effect of isorhamnetin and quercetin.ConclusionsThe results improved current understanding of Artemisiae Scopariae Herba on the treatment of tumor. Moreover, the combination of multi-disciplinary methods provided a new strategy for the study of bioactive constituents in medicinal plants.

Artificial nanophotonic neuron with internal memory for biologically inspired and reservoir network computing

Neurons with internal memory have been proposed for biological and bio-inspired neural networks, adding important functionality. We introduce an internal time-limited charge-based memory into a III–V nanowire (NW) based optoelectronic neural node circuit designed for handling optical signals in a neural network. The new circuit can receive inhibiting and exciting light signals, store them, perform a non-linear evaluation, and emit a light signal. Using experimental values from the performance of individual III–V NWs we create a realistic computational model of the complete artificial neural node circuit. We then create a flexible neural network simulation that uses these circuits as neuronal nodes and light for communication between the nodes. This model can simulate combinations of nodes with different hardware derived memory properties and variable interconnects. Using the full model, we simulate the hardware implementation for two types of neural networks. First, we show that intentional variations in the memory decay time of the nodes can significantly improve the performance of a reservoir network. Second, we simulate the implementation in an anatomically constrained functioning model of the central complex network of the insect brain and find that it resolves an important functionality of the network even with significant variations in the node performance. Our work demonstrates the advantages of an internal memory in a concrete, nanophotonic neural node. The use of variable memory time constants in neural nodes is a general hardware derived feature and could be used in a broad range of implementations.

Investigation of the Global Stock Trading Based on Visibility Graph and Entropy Weight Method

The increasing complexity and dynamics of the stock trading market are major challenges for the financial industry and are primary dilemmas for all countries nowadays. In addition, the stock trading market has a considerable impact on the global economy, and its importance is self-evident. To cope with the complexity and dynamics of a stock trading market, this paper applies complex network theory and model to explore the topology of the global stock trading network. First, this paper collects stock trading data from 74 countries from 1999 to 2020. It converts the collected stock trading data of these countries into a complex network using a type of algorithm based on the time series visibility graph (VG) algorithm. Then, the data are analyzed by a complex network model, and six analytical metrics are obtained. Finally, the six metrics are analyzed by the entropy weight method to identify the key nodes in the network and to obtain the ranking of each country’s stock trading data. This paper is an effective application of complex network and entropy weight method in stock trend analysis, which mainly includes two contributions. First, the VG algorithm provides a novel research perspective for modeling the global stock trading trend. Second, key nodes in the network are analyzed and identified based on the entropy weight method, and the ranking of key nodes in the stock trading network is obtained, which provides a new method for further research on the stock trading trend, investment portfolio, and stock return forecasting.

Statistical models of complex brain networks: a maximum entropy approach.

The brain is a highly complex system. Most of such complexity stems from the intermingled connections between its parts, which give rise to rich dynamics and to the emergence of high-level cognitive functions. Disentangling the underlying network structure is crucial to understand the brain functioning under both healthy and pathological conditions. Yet, analyzing brain networks is challenging, in part because their structure represents only one possible realization of a generative stochastic process which is in general unknown. Having a formal way to cope with such intrinsic variability is therefore central for the characterization of brain network properties. Addressing this issue entails the development of appropriate tools mostly adapted from network science and statistics. Here, we focus on a particular class of maximum entropy models for networks, i.e. exponential random graph models, as a parsimonious approach to identify the local connection mechanisms behind observed global network structure. Efforts are reviewed on the quest for basic organizational properties of human brain networks, as well as on the identification of predictive biomarkers of neurological diseases such as stroke. We conclude with a discussion on how emerging results and tools from statistical graph modeling, associated with forthcoming improvements in experimental data acquisition, could lead to a finer probabilistic description of complex systems in network neuroscience.

Simulating the CCUS technology diffusion in thermal power plants: An agent-based evolutionary game model in complex networks

The utilization of carbon capture, utilization, and storage (CCUS) technology is critical for achieving carbon neutrality and establishing a circular carbon economy. Thermal power plants represent a significant contributor to carbon emissions, and facilitating the spread of CCUS technology throughout the power industry is a crucial means of advancing this technology. The diffusion of CCUS technology would be influenced by policy incentives, market environment, enterprises' individual characteristics as well as complex social network relationships. To study the impacts of these factors on the diffusion of CCUS technology, this study develops an evolutionary game model of CCUS technology diffusion in thermal power plants based on a small-world network. This model includes agents as the network nodes with different production capacities and decision preferences. Through numerical simulations, the study examined the impact of market prices, carbon taxes, and subsidies as incentive measures under different diffusion environments. The results demonstrate that those with higher production capabilities show a higher level of sensitivity to incentives for technology diffusion. To reduce fiscal expenditure, the government can adopt differentiated incentive measures for enterprises with varying production capacities. In addition, the synergistic combination of various incentive measures can effectively decrease the government's expenses in promoting CCUS technology. The incentive mechanisms for promoting CCUS diffusion should be implemented preferably during the early stage of low-carbon transition in the power sector. As the operating hours of thermal power plants decrease, the cost of promoting technology diffusion increases.

Entropy of the ensemble of acyclic graphs

The structural phase transition in complex network models is known to yield knowledge relevant for several problems of practical application. Examples include resilience of artificial networks, dynamics of epidemic spreading, among others. The model of random graphs is probably the simplest model of complex networks, and the solution of this model falls into the universality class mean field percolation. In this work, we concentrate on a similar problem, namely, the structural phase transition in the ensemble of random acyclic graphs. It should be noted that several approaches to the problem of random graphs, such as generating functions or the Molloy–Reed criterion, rely on the fact that before the critical point, cycles should not be present in random graphs. In this way, up to the critical point our solution should produce results that are equivalent to these other methods. Our approach takes advantage of the fact that acyclic graphs allow for an exact combinatorial enumeration of the whole ensemble, what leads to an exact expression for the entropy of this system. With this definition of entropy we can determine the onset of the critical transition as well as the critical exponents associated with the transition. Our results are illustrated with Monte-Carlo results and are discussed within the context of general random graphs, as well as in comparison with another model of acyclic graphs.

Assessing the Degradation of Safety Management Performance in Large Construction Projects: An Investigation and Decision Model Based on Complex Network Modeling

Modern safety control theory suggests that the accumulation of safety management defects at the organizational level can lead to a degradation in the overall safety management performance. This problem is exacerbated by the increasing complexity of safety management in large construction projects. The theoretical frameworks proposed by existing studies can provide generalized guidance for identifying safety management defects, but they are not flexible enough to address the complexity of a safety management system (SMS) in specific large construction projects. This study proposed an investigation and decision model based on a complex network model of SMSs. The main purpose was to accurately assess the degradation of safety management performance through the comprehensive identification of safety management defects for large construction projects. The functional components and their interactions in SMSs were graphically represented in a complex network using the fuzzy DEMATEL technique. Based on this, deep-seated safety management defects were identified by tracing the path of influence between the functional components and their roots. Furthermore, the results of this identification were used to support the assessment of the degradation of the safety performance of the overall SMS. The proposed model was verified with a large-scale wastewater treatment plant construction project in Lanzhou City, China. The degradation of the functional components could be presented in a complex visual network map to facilitate an understanding of the weak points or risk-sensitive areas throughout the SMS. Especially in the case of false safety perceptions, deep-seated safety management defects can be identified in time to prevent a sudden collapse of the SMS through early warnings. In addition, it also facilitates timely short-term improvement strategies and systematic long-term improvement strategies for long-term sustainability and increased resilience.

Inter-Organizational Partnering Strategies in Disaster Response: A Complex Network Perspective

Establishing appropriate inter-organizational partnership in disaster response is of great help to the improvement of disaster relief performance. However, the selection of proper disaster response organizations for government agencies to build partnerships among the numerous relief organizations remains to be addressed. To fill the gap, this paper adopted a complex network modeling method to explain the formation mechanism of inter-organizational partnership in disaster response. Then, based on a numerical simulation method, the paper compared different inter-organizational partnering strategies, and explored the optimal inter-organizational partnering strategy in the disaster response process. Results demonstrate that the optimal partnering strategy is contingent on disaster response conditions, with accurate disaster information and abundant relief materials to choose the material difference principle, and vague disaster information to choose the organizational influence principle. At the same time, frequent information communication and material cooperation allocation can be transformed into low-frequency information communication and government-led material allocation. This study provides practical guidance for disaster relief organizations to choose inter-organizational partnering strategies, and enriches the disaster organization management theory.

Passivity Analysis and Complete Synchronization of Fractional Order for Both Delayed and Non-Delayed Complex Dynamical Networks with Couplings in the Derivative

This manuscript explores the analysis of passivity and synchronization criteria for a complex fractional-order dynamical network model with derivative couplings and time-varying delays. The passivity problem of the proposed network model is deduced using various inequality methods and presented as a linear matrix inequality. To ensure complete synchronization for a fractional-order complex dynamical network with derivative couplings (CDNMDC), we derive suitable criteria using an adaptive feedback control method. Additionally, we investigate the synchronization criterion of these complex networks while accounting for parameter uncertainties. Finally, we provide an example to demonstrate the effectiveness of the proposed solutions.

Hardware Implementation for Convolutional Neural Networks in Artificial Intelligence

Artificial Intelligence (AI) has brought great convenience and help to human society by improving efficiency, increasing productivity and reducing cost. As a part of deep learning, convolutional neural networks (CNNs) have been widely concerned by researchers in recent years. In this paper, five parts of the CNN structure, including input layer, convolutional layer, pooling layer, fully connected layer, activation function and output layer are going to be elaborated. Besides, four different kinds of hardware, which can be used in AI implementation, including graphics processing unit (GPU), field programmable gate array (FPGA), application-specific integrated circuit (ASIC) and brain-like chips will be discussed in this paper. Based on the different characteristics of these four groups of hardware, this paper will analyze their feasibility to implement artificial intelligence algorithm. After contrasting their cost, flexibility and power consumption, it is concluded that different hardware has different advantages to implement AI under different circumstances. GPU performs better to handle with parallel operations or construct complex network models of AI. FPGA is able to achieve flexible AI model programming. On the other hand, ASIC is preferred considering its low power consumption and cost to implement AI. Although brain-like chip is not as well developed as the other three chips, it is promising to implement AI in the future.

Numerical coupling of 0D and 1D models in networks of vessels including transonic flow conditions. Application to short-term transient and stationary hemodynamic simulation of postural changes.

When modeling complex fluid networks using one-dimensional (1D) approaches, boundary conditions can be imposed using zero-dimensional (0D) models. An application case is the modeling of the entire human circulation using closed-loop models. These models can be considered as a tool to investigate short-term transient and stationary hemodynamic responses to postural changes. The first shortcoming of existing 1D modeling methods in simulating these sudden maneuvers is their inability to deal with rapid variations in flow conditions, as they are limited to the subsonic case. On the other hand, numerical modeling of 0D models representing microvascular beds, venous valves or heart chambers is also currently modeled assuming subsonic flow conditions in 1D connecting vessels, failing when transonic and supersonic flow conditions appear. Therefore, if numerical simulation of sudden maneuvers is a goal in closed-loop models, it is necessary to reformulate the current methodologies used when coupling 0D and 1D models, allowing the correct handling of flow evolution for both subsonic and transonic conditions. This work focuses on the extension of the general methodology for the Junction Riemann Problem (JRP) when coupling 0D and 1D models. As an example of application, the short-term transient response to head-up tilt (HUT) from supine to upright position of a closed-loop model is shown, demonstrating the potential, capability and necessity of the presented numerical models when dealing with sudden maneuvers.

Cocoa beans classification using enhanced image feature extraction techniques and a regularized Artificial Neural Network model

Cut-Test technique employs visual inspection of interior coloration, compartmentalization, and defects of beans for effective classification of cocoa beans. However, due to its subjective nature and natural variations in visual perception, it is intrinsically limited, resulting in disparities in verdicts, imprecision, discordance, and time-consuming and labor-intensive classification procedures. Machine Learning (ML) techniques have been proposed to address these challenges with significant results, but there is still a need for improvement. In this paper, we propose a color and texture extraction technique for image representation, as well as a generalized, less complex Neural Network model, to help improve the performance of machine classification of Cut-Test cocoa beans. A total of 1400 beans were classified into 14 grades. Experimental results on the equal cocoa cut-test dataset, which is the standard publicly available cut-test dataset, show that the novel extraction method combined with the developed Artificial Neural Networks provides a more homogeneous classification rate for all grades, obtaining 85.36%, 85%, 83%, and 83% for accuracy, precision, recall, and F1 measure, respectively. The proposed model outperforms other ML models, such as Support Vector Machines, Decision Trees, Random Forests, and Naïve Bayes, on the same dataset. Additionally, the proposed ANN model demonstrates relatively better generalization when compared with earlier work by Santos on the same dataset. The proposed techniques in this work are robust on the cut-test dataset and can serve as an accurate Computer-Aided Diagnostic tool for cocoa bean classification.

Global trade networks bring targeted opportunity for energy-related CH4 emission mitigation.

Recent literature highlights the contributions the global energy sector has made to anthropogenic CH4 emissions, calling for immediate action. However, extant studies have failed to reveal the energy-related CH4 emissions induced by global trades of intermediate and final commodities or services. This paper traces fugitive CH4 emissions via global trade networks using the multi-regional input-output and complex network models. Results show that approximately four-fifths of global fugitive CH4 emissions in 2014 were associated with international trade, of which 83.07% and 16.93% were embodied in the intermediate and final trades, respectively. Japan, India, the USA, South Korea, and Germany were the world's five largest net importers of embodied fugitive CH4 emissions, while Indonesia, Russia, Nigeria, Qatar, and Iran were the five largest net exporters. Gas-related embodied emission transfers were the largest in both the intermediate and the final trade networks. The fugitive CH4 emissions embodied within the intermediate and final trade networks were all characterized by five trading communities. The virtual fugitive CH4 emission transfers via intermediate trade were largely determined by global energy trade patterns, especially the trade in regionally integrated crude oil and natural gas. Significant heterogeneity was revealed by the coexistence of numerous loosely linked economies and several hub economies (e.g., China, Germany, the USA, and South Africa). Interventions on the demand side of interregional and intraregional trade partners in different communities and hub economies will bring targeted opportunities for global energy-related CH4 emission reduction.