Small-world Characteristics Research Articles

Examining embodied carbon emission flow relationships among different industrial sectors in China

Similar to the carbon transfer among regions and countries, the embodied carbon emissions among industries also have serious carbon leakage problems. It is important to determine the embodied carbon flow relationships among industrial sectors, to clarify the responsibilities of each sector, and to adjust the sectoral structure accordingly. Given that, an embodied carbon emission flow relationships network among industrial sectors (ECEFRN) is developed in this paper. In detail, an environmentally extended input-output (EEIO) model is applied to quantitatively estimate the embodied carbon emission flow relationships among industrial sectors in China. The roles and functions of each industrial sector in the network and the evolution characteristics of the embodied carbon emission flow relationships are determined by the social network analysis (SNA) method. Results show that ECEFRN has the small-world characteristics, and key industrial sectors in the network are identified based on the analysis of degree centrality and betweenness centrality. During the study period, in the ECEFRN, “Processing of petroleum, coking, and nuclear fuel”, “Smelting and pressing of metals”, “Production and supply of electricity and heat” are important “suppliers”. “Construction”, “Other service” and “Manufacture of transport equipment” are important “importers”. “Manufacture of foods and tobacco”, “Manufacture of transport equipment”, “Manufacture of computers, communication and other electronic equipment” are important “intermediaries”. Based on the aforementioned research results, this paper proposes corresponding policy suggestions. Network-based research has contributed more insights to the understanding of carbon emission reduction responsibilities among industrial sectors. These findings will provide certain theoretical support and policy references for policymakers when formulating policies for carbon emission reduction and industrial structure adjustment.

Human Brain Networks in Physiological and Pathological Aging: Reproducibility of Electroencephalogram Graph Theoretical Analysis in Cortical Connectivity.

Background: Physiological and pathological brain aging plays a central role in brain network modulation. The aim of the present article was to assess the stability of a proposed method for evaluation of small-world (SW) characteristics for the study of the human connectome. Subjects and Methods: Eighty subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease (AD). Electroencephalograms (EEGs) were recorded during six separate sessions (480 recordings) at an average intersession interval of 3.8 ± 0.2 days. We applied graph theory functions to the weighted and undirected networks obtained by the lagged linear coherence estimated by exact low-resolution electromagnetic tomography (eLORETA). We explored the following frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Results: The proposed method for evaluation of SW characteristics exhibited good reproducibility and stability. Furthermore, results showed the pattern, Young>Elderly>AD, in low-frequency delta and theta bands and vice versa in the higher alpha band. Finally, a correlation with age was confirmed in healthy subjects, showing that the older the age, the higher the SW values for alpha 2. Discussion: Evidences from the present study confirm the stability of the SW index and suggest that the analysis of connectivity patterns evaluated from EEGs can be supported by the graph theory. The proposed method for evaluation of SW characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on administration of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions. Impact statement The graph analysis tools described in this study represent an interesting approach to study the distinctive characteristics of physiological aging by focusing on functional connectivity networks. The proposed method for evaluation of small-world characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on delivery of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions.

Could network structures generated with simple rules imposed on a cubic lattice reproduce the structural descriptors of globular proteins?

Abstract A direct way to spot structural features that are universally shared among proteins is to find analogues from simpler condensed matter systems. In the current study, the feasibility of creating ensembles of artificial structures that can automatically reproduce a large number of geometrical and topological descriptors of globular proteins is investigated. Towards this aim, a simple cubic (SC) arrangement is shown to provide the best background lattice after a careful analysis of the residue packing trends from 210 globular proteins. It is shown that a minimalistic set of rules imposed on this lattice is sufficient to generate structures that can mimic real proteins. In the proposed method, 210 such structures are generated by randomly removing residues (beads) from clusters that have a SC lattice arrangement such that all the generated structures have single connected components. Two additional sets are prepared from the initial structures via random relaxation and a reverse Monte Carlo simulated annealing algorithm, which targets the average radial distribution function (RDF) of 210 globular proteins. The initial and relaxed structures are compared to real proteins via RDF, bond orientational order parameters and several descriptors of network topology. Based on these features, results indicate that the structures generated with 40% occupancy closely resemble real residue networks. The structure generation mechanism automatically produces networks that are in the same topological class as globular proteins and reproduce small-world characteristics of high clustering and small shortest path lengths. Most notably, the established correspondence rules out icosahedral order as a relevant structural feature for residue networks in contrast to other amorphous systems where it is an inherent characteristic. The close correspondence is also observed in the vibrational characteristics as computed from the Anisotropic Network Model, therefore hinting at a non-superficial link between the proteins and the defect laden cubic crystalline order.

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics.

With the continuous deepening of Artificial Neural Network (ANN) research, ANN model structure and function are improving towards diversification and intelligence. However, the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough. Hence, a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper. Firstly, four classical neural network models are illustrated: Back Propagation (BP) network, Deep Belief Network (DBN), LeNet5 network, and olfactory bionic model (KIII model), and the neuron transmission mode and equation, network structure, and weight updating principle of the models are analyzed qualitatively. The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models, and the LeNet5 network simulates the nervous system in depth. Secondly, evaluation indexes of ANN are constructed from the perspective of bionics in this paper: small-world, synchronous, and chaotic characteristics. Finally, the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics. The experimental results show that the DBN network, LeNet5 network, and BP network have synchronous characteristics. And the DBN network and LeNet5 network have certain chaotic characteristics, but there is still a certain distance between the three classical neural networks and actual biological neural networks. The KIII model has certain small-world characteristics in structure, and its network also exhibits synchronization characteristics and chaotic characteristics. Compared with the DBN network, LeNet5 network, and the BP network, the KIII model is closer to the real biological neural network.

Research on Network Model of Dentate Gyrus Based on Bionics.

As an important part of the brain, the dentate gyrus has an irreplaceable effect in the process of memory generation. Therefore, the study of the dentate gyrus model has important significance in the study of brain function. This paper, combined with the real anatomical structure of the dentate gyrus, is based on the existing calculation model for studying the pathological state of the dentate gyrus, a network model of dentate gyrus based on bionics. Then, a simulation experiment on the normal dentate gyrus model is performed on the NEURON platform, the output of each neuron in the model is observed, and a conclusion that the improved model can respond to stimuli, generate action potentials, and transmit them along with the neural network is made. At the same time, the output results are compared with the existing pathological models, and the characteristics of the stimulus response between neurons in the dentate gyrus under normal physiological conditions are obtained. Finally, according to the semiquantitative classification definition and quantitative classification definition of the small-world network, the model is analyzed, and it is concluded that the improved dentate gyrus network model has small-world characteristics. Therefore, the neurons in the improved dentate gyrus model are tightly connected and can simulate the real dentate gyrus to a certain extent.

Cyber‐physical description and CPS‐based pinning approach of mixed traffic

In this paper, a cyber-physical description of mixed traffic that can reflect the interaction between vehicles is first proposed. By treating vehicles as nodes and the cyber-physical interaction between vehicles as directed edges between nodes, the network of vehicles in traffic is obtained. The small-world and scale-free characteristics of mixed traffic are found under this description. Then the CPS-based pinning approach for achieving indirect control of HVs is proposed. Besides the control algorithm of pinning nodes, the impact of the spatial distribution of automated vehicles on traffic is studied. The approach is compatible with the existing control of automated vehicle in mixed vehicle platoon. Numerical simulation results under three-lane scenario illustrated the effectiveness of the proposed pinning approach.

Analyses of Contact Networks of Community Dogs on a University Campus in Nakhon Pathom, Thailand.

Free-roaming dogs have been identified as an important reservoir of rabies in many countries including Thailand. There is a need for novel insights to improve current rabies control strategies in these countries. Network analysis is commonly used to study the interactions between individuals or organizations and has been applied in preventive veterinary medicine. However, contact networks of domestic free-roaming dogs are mostly unexplored. The objective of this study was to explore the contact network of free-roaming dogs residing on a university campus. Three one-mode networks were created using co-appearances of dogs as edges. A two-mode network was created by associating the dog with the pre-defined area it was seen in. The average number of contacts a dog had was 6.74. The normalized degree for the weekend network was significantly higher compared to the weekday network. All one-mode networks displayed small-world network characteristics. Most dogs were observed in only one area. The average number of dogs which shared an area was 8.67. In this study, we demonstrated the potential of observational methods to create networks of contacts. The network information acquired can be further used in network modeling and designing targeted disease control programs.

Small-world Structure in Children’s Featured Semantic Networks

Background: Knowing the development pattern of children’s language is applicable in developmental psychology. Network models of language are helpful for the identification of these patterns. Objectives: We examined the small-world properties of featured semantic networks of developing children. Materials & Methods: In this longitudinal study, the featured semantic networks of children aged 18-30 months were obtained using R software version 3.5.2 and the igraph software package. The data of 2000 English (British)-speaking children, half boy and half girls, were gathered from existing databases of MCDI (between 2000 and 2007) and McRae feature norms. The growth pattern of these networks was illustrated by graph measures. Comparing these measures with those of the reference random networks, the small-world structure can be examined. Results: To have a comparison between path length and clustering coefficient of featured semantic networks with those of random networks, we computed the Q quotient. The results showed that the values of the Q quotient at 18, 22, 26, and 30 months of age were all more than 1, which confirms the small-world characteristic of the networks. Conclusion: Featured semantic networks of children exhibited a small-world structure, in which there was a local structure in the form of clusters of words. For global access, some words act as bridges connecting semantically distant clusters. These networks possess small-world property from the early months of age. The small-world structure cannot be seen in the less dense networks built with a higher cut-off threshold.

Energy Network Embodied in Trade along the Belt and Road: Spatiotemporal Evolution and Influencing Factors

As an important part of trade in the Belt and Road Initiative (BRI) area, significant research attention has been devoted to direct energy transfer, whereas studies on energy embodied in non-energy products have largely been neglected. To present an overview of energy trade for the BRI members, this study combined multi-regional input-output (MRIO) analysis with complex network analysis to model energy use flows within the BRI’s intermediate and final trade network during 2000–2015. Results showed that intermediate energy trade volume is about 7.29-fold larger than that of final trade. Russia and Mainland China were found to be the main net exporter and net importer in intermediate energy trade, respectively, but in final energy trade their roles are reversed. In intermediate energy trade, resource exploitation and heavy industry are the leading intermediate exporter and importer respectively, whereas household consumption is the largest importer (accounting for about three-fifths of the total) in final energy trade. Based on the complex network analysis, the BRI countries were found to trade widely in the final network while cooperating deeply in the intermediate network, with obvious small-world features. Mainland China and Russia were identified as key economies in both intermediate and final trade networks. In addition, quadratic assignment procedure (QAP) analysis was adopted to explore the determinants of the BRI energy trade from 2000 to 2015. It was found that geographic distance, land adjacency, and culture and language have a consistently significant impact on intermediate trade. Closer geographic distance, being adjacent to land, a higher level of economic development, and a larger size of population can promote final trade. This study aimed to supplement existing studies on direct energy trade and provides implications for understanding the sustainable energy development in the BRI area.

Altered Functional Topological Organization in Type-2 Diabetes Mellitus With and Without Microvascular Complications.

Microvascular complications can accelerate cognitive impairment in patients with type 2 diabetes mellitus (T2DM) and have a high impact on their quality of life; however, the underlying mechanism is still unclear. The complex network in the human brain is the physiological basis for information processing and cognitive expression. Therefore, this study explored the relationship between the functional network topological properties and cognitive function in T2DM patients with and without microvascular complications (T2DM-C and T2DM-NC, respectively). Sixty-seven T2DM patients and 41 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological assessment. Then, graph theoretical network analysis was performed to explore the global and nodal topological alterations in the functional whole brain networks of T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. The T2DM-C group exhibited significantly higher local efficiency (Eloc), normalized cluster coefficient (γ), and small-world characteristics (σ) than the HCs. Patients with T2DM at different clinical stages (T2DM-C and T2DM-NC) showed varying degrees of abnormalities in node properties. In addition, compared with T2DM-NC patients, T2DM-C patients showed nodal properties disorders in the occipital visual network, cerebellum and middle temporal gyrus. The Eloc metrics were positively correlated with HbA1c level (P = 0.001, r = 0.515) and the NE values in the right paracentral lobule were negatively related with serum creatinine values (P = 0.001, r = −0.517) in T2DM-C patients. This study found that T2DM-C patients displayed more extensive changes at different network topology scales. The visual network and cerebellar may be the central vulnerable regions of T2DM-C patients.

Characteristics of Complex Networks in Neural Networks

The complex network characteristics of the neuronal functional network composing of individual neurons have been rarely studied, within even fewer recording the number of neurons. In this paper, we use a multi-electrode recording spike trains datasets recorded from slice cultures of rodent somatosensory cortex in monkeys. The number of neurons in a single recording process reached several hundred. The entire recording time was divided into non-overlapping time segments to construct a functional network over different times. We analyze the small world characteristics, the modular network and community structures of neuronal functional networks. The network characteristics are analyzed to determine the changing process with the evolution of different neuronal functional networks. We proposed a new similarity coefficient S to compare the similarity indexes between the community structures. We found that neuronal networks have small world and modularity characteristics in common, when compared to a random network. The network characteristics are stronger when the networks retain a small number of edges. These findings contribute to the study of the neuronal network organization of individual neurons.

Fault Evolution Model of Manufacturing System Based on Small-world Networks

Manufacturing system is a multi-level complex dynamic system composed of different types of machines, and machine fault is a major factor of manufacturing system’s reliability. Hence, identification of fault evolution path and intensity is the prerequisite of fault prevention for complex manufacturing system. In this paper, a new multi-level model of fault evolution in manufacturing system is proposed. Based on the multi levels of manufacturing system, fault evolution path of manufacturing system has two types.One is physical evolutionpath within a single machine, and the other is flow evolutionpath during manufacturing process. Physical evolution has small-world characteristics, its intensity is the product of fault evolution probability and fault-load between fault nodes. Flow evolutionis judged by production relationships between machines, its intensity is production loss of manufacturing system due to unexpected shutdown of machines. Therefore, the integrated intensity of fault evolution within manufacturing system isthe comprehensive result of physical evolution and flow evolution.Then, ant colony algorithm isapplied to search fault evolution path with maximum intensity, sorting steps of fault evolution intensity is given to support decision making of fault prevention in manufacturing system. In the end, a case study of headstock manufacturing system is provided to verify the efficiency of the proposed method.

Power grid partition with improved biogeography-based optimization algorithm

With the continuous expansion of the scale of the interconnected power grid and the large amount of new energy access, the power grid suffers from a wider range of faults. Unlocking the electromagnetic ring network and performing layered and partitioned operation of the power grid is the future development trend of the power grid. Grid partition operation can effectively enhance the controllability within each sub-partition of the power grid, the decoupling between the partitions and the robustness of cascade failure. Depending on the power grid topology and electrical characteristics, this paper proposes a new power grid partition algorithm based on the improved biogeography-based optimization (BBO) algorithm, which improves the algorithm's calculation speed and partition quality. The algorithm, first, analyzes the physical and operational characteristics of the power network, and determines the zoning principle based on the actual situation; next combined with the definition of relevant parameters of the complex network theory, constructs a topology model that conforms to the characteristics of the power grid; at the same time, considering the small-world characteristics of the power grid topology, the original biogeography is improved the update method of algorithm node information exchange reduces the time complexity of the algorithm and makes it more suitable for modern power grids. Then, according to the value of the improved modularity function and the grid zoning principle, the optimal zoning plan is output. Finally, the IEEE-39 node system and the IEEE-118 node system are designated as calculation examples. The extensive calculation example results demonstrate that the method of community network analysis considering the physical characteristics of the power grid is reasonable to a certain extent. The calculation method is simple and fast, which meets the needs of complex power grid analysis and engineering calculation.

Research on the dynamic evolution and its influencing factors of stock correlation network in the Chinese new energy market

Based on the correlation of stock price volatility, this paper constructs a stock correlation network model in the Chinese new energy market, focusing on the dynamic evolution of the stock correlation network and its influencing factors. The results show that: (i) the stock correlation network displays the small world feature in the dynamic evolution process. (ii) the network entropy effectively depicts the change direction of network structure and describes the new energy market volatility. (iii) the network centrality ranking in the new energy market is mainly affected by new energy enterprises’ internal characteristic variables.

Research on the Structure and Characteristics of the Overall Social Network of Professional Athletes

This study chooses Chinese athletes as the research object and constructs the overall network of its social support network and discussion network. From the micro-, meso-, and macrolevels of the social network structure, the structure and characteristics of the athlete’s overall social network are analyzed. Through research, we found that there is embeddedness, that is, the relevance, between society support networks, between society discussion networks, and between society support networks and society discussion networks. At the same time, in the athletes’ social support network and social discussion network, some athletes have no contact with other players; they have no “power” in the group as well, so it is difficult to obtain network resources. We also found that there are small-world characteristics in the social network of Chinese professional athletes. The above findings will provide a deeper understanding of the peculiarities of athlete groups and have certain practical significance for improving athletes’ daily training and life management conditions.

Brain function evaluation in the minimally conscious state using cross-sample entropy based on brain network measure under the spinal cord stimulation

Although spinal cord stimulation (SCS), as an effective method for consciousness modulation, has been applied in MCS, the underlying mechanism is still not clear. In this study, the resting-state EEG signals recorded from healthy volunteers were employed as a control group for comparison. The sample entropy, as well as the brain networks characteristics derived from the cross-sample entropy, were used to investigate the neural responses between the states of the pre-SCS and post-SCS. The results showed that the complexity of EEG signals increased in the frontal and central regions after the SCS. The SCS also enhanced the coupling mode in intra-region of pairwise channels within the frontal region and central region, as well as the inter-region channels between the frontal region with other regions in high frequency bands ($\\alpha$ 8$\\sim$13 Hz, $\\beta$ 13$\\sim$30 Hz, $\\gamma$ 30$\\sim$45 Hz). This indicated that the information integration increased after SCS. Also, compared with the pre-SCS state, the average clustering coefficient increased and the average path length decreased in the post-SCS state in high frequency band (from $\\alpha$ to $\\gamma$). Also, the small-world network characteristics of the patients in the high frequency band are significantly enhanced after SCS ($p<0.001$ in $\\alpha,~\\beta,$ and $\\gamma$). Compared with the healthy control group, the change directions of these indices tend to the brain function indices of resting states in normal subjects. There was no significant difference between pre-SCS in MCS patient state and the resting state in healthy control subjects in the sample entropy and cross sample entropy of some regions, such as the sample entropy of the $\\beta$ and $\\gamma$ frequency bands in the frontal and the central regions($p>0.05$). However, the parameters of brain networks still exist significant differences between the post-SCS and resting state. Therefore, we think that these indices changes after SCS may be induced from the “short-range effect of SCS. We speculate that the SCS has the ability to remodeling brain function through the “short-range effect. This study provided a new interpretation of the underlying mechanism of the SCS, and also provided a new perspective for assessing the brain function of the patients with MCS.

From the Perspective of Jurisprudence View the Application of Urban Image Monitoring Technology and the Application and Improvement of the Information Collection System in This Field

As times go by, social management faces new challenges. This article examines the application of urban image surveillance technology and the methods of information collection and processing from a legal perspective, and explains the necessity of creating image surveillance. This article introduces the application of the system to the construction of legal systems in countries where urban image surveillance has been applied earlier and with more advanced legal systems at home and abroad, from the construction of the image surveillance legal system, the protection of personal privacy rights, and the protection of communication data. Explain the legislative principles to be followed in the legislative process, and put forward the principles of human rights and freedom, the principle of public interest, the principle of rule of law, and the principle of information security. Finally, I put forward a point of view on how to formulate a legal and fair legal system. It is clear that in the field of legislation, it is necessary to seek constitutional support, use civil law to regulate, use personal information protection law to regulate, and use urban image monitoring system legislation to manage. This paper proposes a “peer-to-peer tree” architecture of a two-tier distributed indexing service system based on service types. Its joining and leaving algorithms create and maintain the framework, cascading organizations related to service interests into a tree structure. Learning the neighbor search algorithm can slowly evolve the peer layer composed of many cascaded trees into an overlay network with small-world characteristics, thereby ensuring a higher search efficiency. Research shows that through functional testing and performance testing, it is found that when the number of supernodes is 200, the success rate is the highest.

Chongqing Transportation Network Simulation and Research Based on Floyd Algorithm

In recent years, research on complex networks is in full swing, and transportation networks are also an important part of the research on complex networks. Since the 21st century, people’s living standards have increased substantially, transportation plays a key role in our lives, and public transportation is an indispensable product of transportation in modern cities, and it greatly facilitates people’s life and travel. This time, the Floyd algorithm is used to improve and apply the complex network, and MATLAB and pajek software are used to simulate, and the empirical research on Chongqing’s bus network is carried out. It is concluded that Chongqing’s bus network has small-world characteristics. With certain help, it can provide reference for the construction plan for relevant people.

Research on Differential Brain Networks before and after WM Training under Different Frequency Band Oscillations

Previous studies have shown that different frequency band oscillations are associated with cognitive processing such as working memory (WM). Electroencephalogram (EEG) coherence and graph theory can be used to measure functional connections between different brain regions and information interaction between different clusters of neurons. At the same time, it was found that better cognitive performance of individuals indicated stronger small-world characteristics of resting-state WM networks. However, little is known about the neural synchronization of the retention stage during ongoing WM tasks (i.e., online WM) by training on the whole-brain network level. Therefore, combining EEG coherence and graph theory analysis, the present study examined the topological changes of WM networks before and after training based on the whole brain and constructed differential networks with different frequency band oscillations (i.e., theta, alpha, and beta). The results showed that after WM training, the subjects' WM networks had higher clustering coefficients and shorter optimal path lengths than before training during the retention period. Moreover, the increased synchronization of the frontal theta oscillations seemed to reflect the improved executive ability of WM and the more mature resource deployment; the enhanced alpha oscillatory synchronization in the frontoparietal and fronto-occipital regions may reflect the enhanced ability to suppress irrelevant information during the delay and pay attention to memory guidance; the enhanced beta oscillatory synchronization in the temporoparietal and frontoparietal regions may indicate active memory maintenance and preparation for memory-guided attention. The findings may add new evidence to understand the neural mechanisms of WM on the changes of network topological attributes in the task-related mode.

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane.

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub‐state where integration predominates, and a predominantly segregated sub‐state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting‐state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small‐world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst‐suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub‐state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.