Average Path Length Research Articles

Impacts of the Middle Route of China's South-to-North Water Diversion Project on the water network structure in the receiving basin.

The Middle Route of South-to-North Water Diversion Project (MRSNWD) is the main skeleton of China's National Water Network, its construction has changed the structure of the original water network, and analyzing the topological change of the water network in context with MRSNWD is significant for water network planning and management. In this study, the overall network characteristics of the water network in 2010 and 2020 were analyzed based on the small-world and scale-free characteristics of complex network theory. The topological changes of the water network from a node perspective were examined using three network centrality indexes: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC), while assessing the important nodes of the water network and recognizing functional areas of cold-hot spots. The results show that the water network's centrality in the study area improved after the project construction, with the average degree of the water network increasing from 2.39 to 2.42 and the average path length decreasing from 111.81 to 97.08. The propagation efficiency and network stability also increased, with a rise in important node proportion from 9.8 to 14.4%. The nodes in the DC hotspot zone along the project route have increased by 1.5%, implying an increase in the connectivity of the water network, while MRSNWD optimizes its north-south hub propagation path. "Small-worldness" indicates that most nodes of a network can be accessed and connected over shorter paths. The water network has a significant "small-worldness" and has been enhanced by the MRSNWD's construction. Approximating the water network as a scale-free network can impact its security by identifying critical nodes. The results of this research can provide the necessary technical support and reference significance for China's National Water Network.

Three Decades of Oasis Transition and Its Driving Factors in Turpan–Hami Basin in Xinjiang, China: A Complex Network Approach

As a predominant ecosystem-providing area and distinctive landscape in arid regions, an oasis plays an important role in maintaining land stability, human production, and daily activities. Studying the dynamics of oasis and its driving factors is vital to supporting arid regions’ sustainable development. As a typical mountain–desert–oasis landscape, the Turpan–Hami (Tuha) Basin, located in Xinjiang, China, includes complex interactions among different land types. For this study, we revealed the spatio-temporal patterns and transition processes of the oasis using a complex network method between 1990 and 2020 in the Tuha Basin. In the oasis transition network, the degree value, betweenness centrality, and average path length were calculated to express the transition relationship, key oasis type, and oasis structural stability, respectively. Six factors related to climate change and human actives were selected to investigate the driving forces behind oasis transitions, including the average temperature and precipitation in the growing season, the total power of agricultural machinery (TAMP), the production of raw coal (PRC), the total output value of the plantation industry (TPI), and the population (Pop). Our results show that the oasis area of the Tuha Basin, including the natural oasis and artificial oasis, all grew from 1990 to 2020, with the natural oasis expanding more than the artificial oasis. The transitions between oasis types became more frequent as the area of oasis land types increased throughout the study period. Grassland acted as the most important oasis type in the network, with the highest betweenness centrality, but its importance declined due to the increasing complexity of the oasis transition network from 1990 to 2020. The transitions between oasis types became simpler, and the oasis structural stability decreased. Through driving force analysis, the oasis changes showed a positive correlation with the temperature (p-value < 0.05, r = 0.88), and urbanization and industrialization factors prompted transitions to built-up areas and cropland from grassland and shrubland. In summary, our results suggest that to create a harmonious symbiotic relationship between the natural environment in dryland and human activities, preventing grassland degradation and excessive reclamation of land cover is an available way. Meanwhile, the protection of shrubland and water resources is also important. This study provided reference and theory support for promoting the sustainable development of oases.

Research on the urban rail transit disaster chain: Critical nodes, edge vulnerability and breaking strategy

The rising number of disasters poses significant challenges that are influencing the operation safety of urban rail transit systems. However, most studies to date have focused on individual disasters or types of disasters of urban rail transit, while few have focused on urban rail transit disaster chains. This paper collects 562 urban rail transit disaster cases and builds a disaster chain network model of urban rail transit systems based on complex network theory. The vulnerability of the disaster chain network is analyzed in terms of nodes and edges, and targeted chain-breaking disaster mitigation strategies are proposed, based on the nodes and edges found to have more substantial intermediary roles. The main results of this study show that: (1) disaster nodes have larger clustering coefficients and smaller average path lengths. The disasters “power failure”, “fire”, “thick smoke and poisonous gas”, and “signal and communications system problem” are of high importance and cause a greater loss of network efficiency when removed. (2) The edges “power failure - terrorist attack”, “thick smoke and poisonous gas - passenger uncivilized behavior”, and “signal and communications system problems - operation error” have higher vulnerability and cause a greater loss of network efficiency when removed. (3) Developing breaking strategies for these disaster nodes and edges in advance can achieve faster chain disconnection and disaster reduction.

On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

Abstract There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine (SVM). We found that both approaches could detect changes in the brain’s automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers’ emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.

Spatial Optimization and Temporal Changes in the Ecological Network: A Case Study of Wanning City, China

Ecological networks serve as vital tools for safeguarding biodiversity and ensuring regional ecological stability. This study, conducted in Wanning City, employs minimum-area threshold analysis to pinpoint crucial ecological sources while extracting potential ecological corridors using the minimum cumulative resistance model. Our investigation delves into the ecological network’s elements and structural transformations within Wanning City, spanning the period from 2000 to 2020, and assesses the priorities for ecological network preservation. The findings of our research reveal noteworthy spatial disparities in the distribution of ecological sources across Wanning City. Furthermore, the ecological corridors display sparse patterns in the north and denser patterns in the south. Over the two decades from 2000 to 2020, Wanning’s ecological resources exhibited a discernible trend of contraction and fragmentation, accompanied by an uneven spatial distribution. The average path length of the ecological corridors has increased, indicative of reduced biological flow efficiency. Correspondingly, the structural accessibility of the ecological network has decreased, signifying a decline in landscape connectivity. Based on our analysis, we propose an ecological protection and restoration framework denoted as “One Belt, Four Sources, Eight districts, multiple corridors, and multiple points”. Therefore, with the Shangxi–Jianling, Liulianling, Nanlin, and Jiexin nature reserves as the core area, and Houan Town, Damao Town, Changfeng Town, and Liji Town as the key restoration areas, we have proposed an ecological protection and restoration pattern.

Intelligent path planning by an improved RRT algorithm with dual grid map

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.

Partitioning the effects of coffee-Urochloa intercropping on soil microbial properties at a centimeter-scale

Conservation farming and crop diversity increase soil health, organic matter content, and soil microbial activity. However, the design and implementation of sustainable practices often lack a detailed understanding of their impacts on microbial communities in soil. Here, we studied the effects of an Arabica coffee (Coffea arabica) – Brachiaria (Urochloa spp.) intercropping system on soil microbial properties by partitioning at the centimeter-scale the topsoil layers. In particular, we collected and analyzed four soil layers in a vertical stratification gradient of 0–2.5 cm (layer 1), 2.5–5.0 cm (layer 2), 5–10 cm (layer 3), and 10–15 cm (layer 4). Soil samples were subjected to chemical analysis, bacterial community profiling, and quantification of microbial enzymatic activity for β-glucosidase and acid-phosphatase, in addition to the quantification of N-fixation and P-solubilization gene abundances. We found that intercropping increased acid-phosphatase activity at layer 1, β-glucosidase activity at layers 2 and 3, and the amount of soil organic matter and total magnesium at layers 2 and 3. Intercropping increased the relative abundance of the N-fixation gene at layer 3 and bacterial diversity at layers 1 and 3. Overall, intercropping significantly changed the soil bacterial community structure and resulted in a more interconnected co-occurrence network (i.e., greater node connectivity, network density, and lower edges average path length). Taken together, this study provides evidence for the positive impact of intercropping in the soil at a centimeter-scale vertical stratification. It corroborates the notion that plant diversity stimulates microbial activity and species interactions in soil.

Dynamic Path Planning for Mobile Robots by Integrating Improved Sparrow Search Algorithm and Dynamic Window Approach

To overcome the limitations of the sparrow search algorithm and the challenges of dynamic obstacle avoidance in mobile robots, an integrated method combining the enhanced sparrow search algorithm with the dynamic window approach is introduced. First, logistic–tent chaotic mapping is utilized for the initialization of the sparrow population, thereby achieving a uniform distribution of the sparrow population and simultaneously enhancing the exploratory capability of the algorithm. The implementation of the elite reverse learning strategy aims to diversify the sparrow population, thus improving the quality of initial solutions and the algorithm’s search accuracy. Additionally, the position update dynamic self-adaptive adjustment strategy is adopted to enhance the optimization capability of the algorithm by refining the position update formulas for both producers and scroungers. By combining the Lévy flight strategy and the optimal position perturbation strategy, the algorithm’s efficacy in escaping local optima can be improved. Second, an adaptive velocity adjustment strategy is presented for the dynamic window approach and optimized for its evaluation function to enhance the safety of the path. Third, the enhanced sparrow search algorithm is integrated with the dynamic window approach to tackle the problems of the non-smooth global path and inadequate dynamic obstacle avoidance capability. Both simulation and experimental results show the superiority of the enhanced sparrow search algorithm in comparison to other algorithms in terms of the path length, total rotation angle, and algorithm execution time. Notably, in comparison to the basic sparrow search algorithm, there is a decrease in average path lengths by 15.31% and 11.92% in the improved sparrow search algorithm. The integrated algorithm not only crafts local paths rooted in global paths but also adeptly facilitates real-time dynamic obstacle evasion, ensuring the robot’s safe arrival at its destination.

Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances

Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology.

Topological characterization of the microstructure of magnesium alloy materials based on complex networks

Abstract Magnesium alloys, as the lightest commercial metal engineering structural materials, have good application prospects in the automotive, communication equipment, aerospace, and military industries because of their light specific gravity, high strength properties such as specific strength and stiffness, shielding from electromagnetic radiation, and easy recycling. In this paper, starting from the density and microstructure of magnesium alloy, the mechanical properties and corrosion resistance of magnesium alloy are proposed to measure the indexes, and the corresponding prediction model is constructed. Then, fine-crystal magnesium alloys are prepared sequentially by ingot casting and isometric channel extrusion, and then the microstructure of magnesium alloys is observed by scanning electron microscope and transmission electron microscope to extract the micro-parameters of magnesium alloys. A martensitic phase transition topology model was introduced to determine the macroscopic shape strain of magnesium alloy crystals. Complex network analysis is used to quantify the topological structure parameters of a magnesium alloy network in terms of degree, clustering coefficient, and average path length. Finally, the correlation between the macroscopic features and microstructure of magnesium alloy is explored with the obtained data, and the mechanical and corrosion resistance properties of magnesium alloy are analyzed through simulation experiments by combining them with the constructed prediction model. The microstructural topological characteristic parameters of magnesium alloys show that when the pressure is 50 kPa, the average path length after weighting decreases from 36.012 × 10−3 to 34.015 × 10–3, and the force transfer efficiency is gradually increasing. The AZ31 alloy samples obtained from casting in this paper have a capacitive arc diameter of about 1600 Ω-cm² and the best corrosion resistance among the tested samples.

A CLUSTERED FRACTAL DISCRETE FRACTURE NETWORK MODEL FOR FRACTURED COAL

The fracture network in fractured coal is the main channel of coal seam gas flow. Not only the geometric topology properties (such as fractal characteristics) of a single fracture but also the connection topology properties (interconnection characteristics between fractures) of the fracture network have an important impact on the fluid flow in fracture networks. In this study, the connection topology properties of the fracture network in the fractured coal are explored based on the complex network theory for the first time. The property parameters such as the fracture node degree, the clustering coefficient, and the average path length are analyzed. It shows that the average clustering coefficient of the fracture network in fractured coal is larger, and the average path length is smaller. The connection property of the fracture network in the fractured coal presents a typical “small-world” clustering model. Further, by considering the fractal characteristics of the single fracture and the clustering characteristics of the fracture network, an improved clustered fractal discrete fracture network (DFN) model is developed. Then, based on the lattice Boltzmann method, the permeability properties of the generated clustered fractal DFNs are analyzed. The results show that the permeability of DFNs is positively correlated with the average clustering coefficient of fracture network, and negatively correlated with the fractal dimension of fracture. Therefore, the topological clustering characteristics of fracture networks and the fractal characteristics of fractures cannot be ignored in describing the fluid flow in the fracture network, and our clustered fractal DFN model provides a new idea for guiding the optimization design in DFN engineering.

Unveiling the adaptation strategies of woody plants in remnant forest patches to spatiotemporal urban expansion through leaf trait networks

BackgroundWith the expansion of urban areas, the remnants of forested areas play a crucial role in preserving biodiversity in urban environments. This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches, thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity. MethodsOur study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang, China. We constructed leaf trait networks (LTNs) based on 26 anatomical, structural, and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs. Results and conclusionsOur results indicate that shrubs within these patches have greater average path lengths and diameters than trees. With increasing urban expansion intensity, we observed a rise in the edge density of the LTN-shrubs. Additionally, modularity within the networks of shrubs decreased as road density and urban expansion intensity increased, and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index. Notably, patches subjected to ‘leapfrog’ expansion exhibited greater average patch length and diameter than those experiencing edge growth. Stomatal traits were found to have high degree centrality within these networks, signifying their substantial contribution to multiple functions. In urban remnant forests, shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.

Dominance of rock exposure and soil depth in leaf trait networks outweighs soil quality in karst limestone and dolomite habitats

Leaf trait networks (LTNs) visualize the intricate linkages reflecting plant trait-functional coordination. Typical karst vegetation, developed from lithological dolomite and limestone, generally exhibits differential communities, possibly due to habitat rock exposure, soil depth, and soil physicochemical properties variations, leading to a shift from plant trait variation to functional linkages. However, how soil and habitat quality affect the differentiation of leaf trait networks remains unclear. LTNs were constructed for typical dolomite and limestone habitats by analyzing twenty-one woody plant leaf traits across fifty-six forest subplots in karst mountains. The differences between dolomite and limestone LTNs were compared using network parameters. The network association of soil and habitat quality was analyzed using redundancy analysis (RDA), Mantle's test, and a random forest model. The limestone LTN exhibited significantly higher edge density with lower diameter and average path length when compared to the dolomite LTN. It indicates LTN differentiation, with the limestone network displaying a more compact architecture and higher connectivity than the dolomite network. The specific leaf phosphorus and leaf nitrogen contents of dolomite LTN, as well as the leaf mass and leaf carbon contents of limestone LTN, significantly contributed to network degree and closeness, serving as crucial node traits regulating LTN connectedness. Additionally, both habitat LTNs significantly correlated with soil nitrogen and phosphorus, stoichiometric ratios, pH, and organic carbon, as well as soil depth and rock exposure rates, with soil depth and rock exposure showing greater relative importance. Soil depth and rock exposure dominate trait network differentiation, with the limestone habitat exhibiting a more compact network architecture than the dolomite habitat.

THREE PROPERTIES OF FRACTAL NETWORKS BASED ON BEDFORD–MCMULLEN CARPET

In this paper, we consider the networks modeled by several self-affine sets based on the Bedford–Mcmullen carpet. We calculate three properties of the networks, including the cumulative degree distribution, the average clustering coefficient and the average path length. We show that such networks have scale-free and small-world effects.

Resting state connectivity biomarkers of seizure freedom after epilepsy surgery

Alterations in brain networks may cause the lowering of the seizure threshold and hypersynchronization that underlie the recurrence of unprovoked seizures in epilepsy. The aim of this work is to estimate functional network characteristics, which may help predicting outcome of epilepsy surgery.Twenty patients were studied (11 females, 9 males, mean age 33 years) with scalp-recorded HD-EEG in resting state (eyes closed, no interictal discharges) before intracranial evaluation, which allowed the precise determination of the epileptogenic zone. Dipole source time courses in the brain were estimated using Weighted Minimum Norm Estimate based on HD-EEG signals. Information inflow and outflow of atlas-based brain regions were computed using partial directed connectivity. A set of graph measures for pairwise connections in standard EEG frequency bands was calculated.After epilepsy surgery 10 patients were seizure-free (Engel 1a) and 10 patients continued suffering from seizures (Engel outcome worse than 1a). Inflow of the regions containing the epileptogenic zone in the beta and delta frequency bands was significantly lower in patients who achieved seizure-freedom after surgery, compared with patients who continued to have seizures (p = 0.012, and p = 0.026, respectively). Average path length in the beta frequency band was significantly higher in patients who achieved seizure freedom (p = 0.012). In the delta frequency band, local efficiency and clustering coefficient were significantly higher in patients who achieved seizure freedom (0.033, 0.046).In patients who achieved seizure freedom after surgery, the preoperative analysis of the epileptic network exhibited stronger separation of the region containing the seizure onset zone, with less inflow of information. In contrast, shorter paths within the epileptic network may facilitate hypersynchronous neuronal activity and thus the recurrence of seizures in non-seizure free patients. This study supports the hypothesis that epileptic network properties might help to define suitable candidates for epilepsy surgery.

Multiple sclerosis is associated with differences in semantic memory structure.

Although language is often considered to be largely intact in multiple sclerosis (MS), word-finding difficulties are a common complaint. Recent work suggests that declines in language are not solely the result of motoric and cognitive slowing that is most strongly associated with MS. Network science approaches have been effectively used to examine network structure as it relates to clinical conditions, aging, and language. The present study utilizes a network science approach to investigate whether individuals with MS exhibit less interconnected and resilient semantic networks compared to age-matched neurotypical peers. We used semantic fluency data from 89 participants with MS and 88 neurotypical participants to estimate and analyze the semantic network structure for each participant group. Additionally, we conducted a percolation analysis to examine the resilience of each network. Network measures showed that individuals with MS had lower local and global clustering coefficients, longer average shortest path lengths, and higher modularity values compared to neurotypical peers. Small-worldness, network portrait divergence measures, and community detection analyses were consistent with these results and indicated that macroscopic properties of the two networks differed and that the semantic network for individuals with MS was more fractured than the neurotypical peer network. Moreover, a spreading activation simulation and percolation analysis suggested that the semantic networks of individuals with MS are less flexible and activation degrades faster than those of age-matched neurotypical participants. These differing semantic network structures suggest that language retrieval difficulties in MS partially result from decline in language-specific factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimation of Photon Path Length and Penetration Depth in Articular Cartilage Zonal Architecture Over the Therapeutic Window.

The key characteristics of light propagation are the average penetration depth, average maximum penetration depth, average maximum lateral spread, and average path length of photons. These parameters depend on tissue optical properties and, thus, on the pathological state of the tissue. Hence, they could provide diagnostic information on tissue integrity. This study investigates these parameters for articular cartilage which has a complex structure. We utilize Monte Carlo simulation to simulate photon trajectories in articular cartilage and estimate the average values of the light propagation parameters (penetration depth, maximum penetration depth, maximum lateral spread, and path length) in the spectral band of 400-1400 nm based on the optical properties of articular cartilage zonal layers and bulk tissue. Our findings suggest that photons in the visible band probe a localized small volume of articular cartilage superficial and middle zones, while those in the NIR band penetrate deeper into the tissue and have larger lateral spread. In addition, we demonstrate that a simple model of articular cartilage tissue, based on the optical properties of the bulk tissue, is capable to provide an accurate description of the light-tissue interaction in articular cartilage. The results indicate that as the photons in the spectral band of 400-1400 nm can reach the full depth of articular cartilage matrix, they can provide viable information on its pathological state. Therefore, diffuse optical spectroscopy holds significant importance for objectively assessing articular cartilage health. In this study, for the first time, we estimate the light propagation parameters in articular cartilage.

Analysis of tourism destination centrality and structural properties of tourism system: Complex network perspective

ourism has become a new way of living with the living standard development. This study analyses the tourism destination centrality and spatial patterns of the tourism system using complex network analysis. An analysis of 245 destinations in the South of Thailand has found that the network has a low network density, large average path length and low clustering coefficient. Some a small number of high-degree destinations connect to each other, while most connect to others with a low degree. The network comprises 18 subnetworks that destinations densely connect to others in the same subnetwork but sparsely connect to others in different ones. Destinations play different roles in the network based on which a centrality measure is used, degree, betweenness and closeness centrality. 31 destinations with high hub and authority centrality are the centers playing as hubs of the network. The study’s findings draw implications for the sector.

COMPLEX NETWORKS GENERATED BY A SELF-SIMILAR PLANAR FRACTAL

Many complex networks have scale-free and small-world effects. In this paper, a family of evolving networks is constructed modeled by a non-symmetric self-similar planar fractal, using the encoding method in fractal geometry. Based on the self-similar structure, we study the degree distribution, clustering coefficient and average path length of our evolving network to verify their scale-free and small-world characteristics.

New Vocational Education Law on Artificial Intelligence Helps Build a Skilled Society

Abstract Under the guidance of the new vocational education method of artificial intelligence, this paper divides the skill-based society into skill-based organizations, and constructs a knowledge dynamic equilibrium model based on superimposed knowledge fields. The knowledge-explicit module of skill-based talent flow and the external dynamic equilibrium module are integrated to achieve the evolutionary modeling of knowledge resources in skill-based organizations. Based on the discussion of the new vocational education law, the characteristic models of intellectual resource development, knowledge dynamic growth, group evolution and talent motivation in skill-based organizations are proposed respectively. The simulation analysis of the knowledge dynamic equilibrium network is combined with the analysis of influencing factors to inspire the education of higher vocational colleges and universities under the new vocational education method. As measured by the data, the average path length mean values of the intra-organizational knowledge network when N=160, 260, 330 are 2.2761, 2.4437 and 2.5394, respectively, which indicates that skilled talents have a strong willingness to interact with each other and share knowledge. The construction of skill-based organizations should fully create a strong environment for talent exchange and focus on cultivating students’ willingness and ability to communicate in vocational education.