Average Degree Of Network Research Articles

Superradiant phase transitions in ultrastrong coupling regime

Abstract One of the long-standing problems in quantum optics and laser physics is the observation of both equilibrium and non-equilibrium (Dicke) superradiant states in two-level (qubit) systems interacting with the quantized electromagnetic (EM) field. We demonstrate that the superradiant phase transition (PT) at thermal equilibrium in such a system is close to ultrastrong light–matter coupling (USC) condition, which is currently available for superconducting devices. We examine the USC regime for EM fields interacting with two-level systems located in highly connected graph nodes of photonic networks beyond the rotating wave approximation. We find that a critical matter–field coupling parameter reduces ⟨ k ⟩ times due to network peculiarities providing suitable conditions for equilibrium PT observation; ⟨ k ⟩ is a network average node degree. We study the non-equilibrium (Dicke) superradiance for the same network system and show that superradiance appears as a non-equilibrium PT to the superradiant laser operating within a bad cavity limit. We show that population inversion in this case plays the same role as the reciprocal temperature inherent to the equilibrium superradiant PT. Our results open new perspectives for experimental observation of superradiance in cavity-free systems in the gigahertz photonic regime and beyond.

Diversity and interactions of rhizobacteria determine multinutrient traits in tomato host plants under nitrogen and water disturbances

Abstract Coevolution within the plant holobiont extends the capacity of host plants for nutrient acquisition and stress resistance. However, the role of the rhizospheric microbiota in maintaining multinutrient utilization (i.e., multinutrient traits) in the host remains to be elucidated. Multinutrient cycling index (MNC), analogous to the widely used multifunctionality index, provides a straightforward and interpretable measure of the multinutrient traits in host plants. Using tomato as a model plant, we characterized MNC (based on multiple aboveground nutrient contents) in host plants under different nitrogen and water supply regimes and explored the associations between rhizospheric bacterial community assemblages and host-plant multinutrient profiles. Rhizosphere bacterial community diversity, quantitative abundance, predicted function, and key topological features of the co-occurrence network were more sensitive to water supply than to nitrogen supply. A core bacteriome comprising 61 genera, such as Candidatus Koribacter and Streptomyces, persisted across different habitats and served as a key predictor of host-plant nutrient uptake. The MNC index increased with greater diversity and higher core taxon abundance in the rhizobacterial community, while decreasing with higher average degree and graph density of rhizobacterial co-occurrence network. Multinutrient absorption by host plants was primarily regulated by community diversity and rhizobacterial network complexity under the interaction of nitrogen and water. The high biodiversity and complex species interactions of the rhizospheric bacteriome play crucial roles in host-plant performance. This study supports the development of rhizosphere microbiome engineering, facilitating effective manipulation of the microbiome for enhanced plant benefits, which supports sustainable agricultural practices and plant health.

Characteristics and Driving Factors of Benthic Animal Communities in Different Water Functional Zones of the Jiangsu Section of the Yangtze River

This study explored the characteristics of benthic animal communities in different water functional areas and the driving factors affecting changes in the community structure of four water functional zones of the Jiangsu section of the Yangtze River: the protection, buffer, reserve, and development and utilization zones. The results showed that the alpha diversity of the benthic animal communities in the protected and reserved zones was significantly higher than that of the buffer and development and utilization zones, and the benthic animal community structure differed significantly across different water functional zones. These zones indirectly affected the community of benthic fauna due to their environmental heterogeneity. Furthermore, the average degree, map density, and average clustering coefficient of the molecular ecological network were highest in the protected zone. The average path length was shorter, and there were more types and numbers of key species in the benthic animal community in the protected zone, indicating high levels of connectivity and efficiency in transferring substances, energy, and information between benthic animals. These results will provide a scientific basis for studying the characteristics and driving factors of benthic animal communities in the Yangtze River and have important significance for assessing and restoring aquatic ecology in the Yangtze River Basin.

The coupled awareness-epidemic dynamics with individualized self-initiated awareness in multiplex networks

The outbreak of an epidemic often stimulates the generation of public awareness about epidemic prevention. This heightened awareness encourages individuals to take proactive protective measures, thereby curbing the transmission of the epidemic. Previous research commonly adopts an assumption that each individual has the same probability of awakening self-protection awareness after infection. However, in the real-world process, different individuals may generate varying awareness responses due to the differences in the amount of information received. Therefore, in this study, we first propose a coupled awareness-epidemic spreading model, where the self-initiated awareness of each individual can be influenced by the number of aware neighbors. Subsequently, we develop a Micro Markov Chain Approach to analyze the proposed model and explore the effects of different dynamic and structural parameters on the coupled dynamics. Findings indicate that individual awareness awakening can effectively promote awareness diffusion within the proposed coupled dynamics and inhibit epidemic transmission. Moreover, the influence of awareness diffusion on epidemic transmission exhibits a metacritical point, from which the epidemic threshold increases with the increase in the awareness diffusion probability. The research findings also suggest that the increase in the average degree of virtual-contact networks can reduce the value of the metacritical point, while the change in the average degree of the physical-contact networks does not affect the metacritical point. Finally, we conduct extensive experiments on four real networks and obtain results consistent with the above conclusions. The systematic research findings of this study provide new insights for exploring the interaction between individual awareness and epidemic transmission in the real world.

Simulation Analysis of Artificial Intelligence Technology Diffusion under Market Competition and Policy Incentives Based on Complex Network Evolutionary Game Models

The relationship network between enterprises will change their adoption behavior of AI technology and this micro-decision-making mechanism will eventually decide whether AI technology can diffuse and the extent of diffusion on the macro level. However, the existing AI technology diffusion research mostly focuses on the integration of AI technology with other industries from the industrial level, ignoring the complexity of the micro-complex game process and interactions within the enterprise network on the macro technology diffusion. In this regard, this paper builds a game model of AI technology diffusion in core and non-core enterprises from the levels of market competition and policy incentives based on complex network evolutionary game theory. It does this through simulation analysis that examines the mechanism of key factors affecting the diffusion of AI technology, as well as the influence and combination effects of pertinent policies. The study shows that (1) AI technology diffuses more effectively in non-core enterprises than it does in core enterprises; (2) changes in parameters like technology cost and policy regimes have a more evident impact on core enterprises than non-core ones; (3) in market competition, increasing the network average degree, the proportion of AI technology products in the mainstream market, the opportunity cost, the cost reduction factor, or decreasing the cost of AI technology can all promote the diffusion of AI technology; (4) under policy incentives, increasing the proportion of AI technology subsidies and the influence of high-tech identification of enterprises can both promote the diffusion of AI technology.

Antibiotics combination exposure increases the resistance to Aeromonas hydrophila infection by improving the microbial network of zebrafish

Normal intestinal microbiota are critical for host health, whereas the use of antibiotics will alter the composition of intestinal microbiota. Understanding the influence of antibiotics on intestinal microbiota of host will further advance our knowledge of host-microbe-antibiotic interactions. Here zebrafish was used as a model to investigate the effect of antibiotics on host susceptibility to pathogen infection. Metronidazole treatment (Group J) and antibiotics combination treatment (metronidazole, ceftazidime, and levofloxacin (Group L)) significantly altered the microbiota composition at 0 and 5 days post-exposure (dpe). At 15 dpe, the intestinal microbial composition of each group tended to be consistent. Meanwhile, the survival rate of group L after Aeromonas hydrophila challenge was significantly higher than that of the control group at 15 dpe. The network analysis indicated antibiotics exposure increased the nodes, edges, and proportion of positive correlations in the network. What's more, after antibiotics combination exposure, the number of nodes with multiple connections and the average degree of network also increased. These findings indicated that antibiotics could increase the resistance to pathogen infection by improving the microbial network, which provides a new perspective on the prevention and treatment of fish diseases.

The global production pattern of the semiconductor industry: an empirical research based on trade network

Semiconductors are an important electronic component and play a central role in many industrial fields, whose production system is spread all over the world, involving trade in many raw materials, mechanical equipment, and finished products. Comprehending a broad global production picture of the semiconductor industry can be an arduous task. To tackle this complexity, the social network analysis method was used to capture the flows of products between economies in the semiconductor industrial chain. This study analyzed the evolution of the trade pattern of four key commodities in the semiconductor industrial chain, with a focus on identifying the status of major economies in the global semiconductor trade system. Additionally, the influencing factors of the formation of the trade networks were explored by using the Exponential Random Graph Model. The results showed that: (1) From 2001 to 2019, the import and export trade pattern of four key commodities in the semiconductor industrial chain has shown the characteristics of “rising in the east and decreasing in the west”, but the change range shows strong commodity heterogeneity, the change of the spatial pattern of integrated circuits trade is the most significant, and the integrated circuits trade proportion of Asian economies has increased hugely with more than 80%. (2) The trade connectivity of various commodities in the semiconductor industrial chain has increased, upstream support products with high technical requirements such as wafers and equipment have the lowest average degree and weak trade liquidity, and the average degree of trade network of packaging materials and integrated circuits is relatively high, with strong trade liquidity and closer and more prosperous trade connections. (3) From 2001 to 2019, the “core-periphery” system of global semiconductor industry trade has changed significantly, with the core circle undergoing obvious iterative reorganization. for example, China and Singapore have achieved status improvement to the core circle in the base materials trade network, while Malaysia has been squeezed into the semi-peripheral circle. (4) The formation of trade networks of various commodities in the semiconductor industrial chain was driven by three aspects: network self-organization, exogenous endowment attribute, and exogenous network embeddedness. Among them, the influence of the technological innovation level of economies on different commodity trade networks showed heterogeneous.

Non-flooding conditions caused by water table drawdown alter microbial network complexity and decrease multifunctionality in alpine wetland soils

Several soil functions of alpine wetland depend on microbial communities, including carbon storage and nutrient cycling, and soil microbes are highly sensitive to hydrological conditions. Wetland degradation is often accompanied by a decline in water table. With the water table drawdown, the effects of microbial network complexity on various soil functions remain insufficiently understood. In this research, we quantified soil multifunctionality of flooded and non-flooded sites in the Lalu Wetland on the Tibetan Plateau. We employed high-throughput sequencing to investigate the microbial community responses to water table depth changes, as well as the relationships between microbial network properties and soil multifunctionality. Our findings revealed a substantial reduction in soil multifunctionality at both surface and subsurface soil layers (0–20 cm and 20–40 cm) in non-flooded sites compared to flooded sites. The α-diversity of bacteria in the surface soil of non-flooded sites was significantly lower than that in flooded sites. Microbial network properties (including the number of nodes, number of edges, average degree, density, and modularity of co-occurrence networks) exhibited significant correlations with soil multifunctionality. This study underscores the adverse impact of non-flooded conditions resulting from water table drawdown on soil multifunctionality in alpine wetland soils, driven by alterations in microbial community structure. Additionally, we identified soil pH and moisture content as pivotal abiotic factors influencing soil multifunctionality, with microbial network complexity emerging as a valuable predictor of multifunctionality.

Probability Distribution of China Aviation Network Average Degree of Edge Vertices and Its Evolutionary Trace Based on Complex Network

Probability Distribution of China Aviation Network Average Degree of Edge Vertices and Its Evolutionary Trace Based on Complex Network

The Win-Win Effects of an Invasive Plant Biochar on a Soil-Crop System: Controlling a Bacterial Soilborne Disease and Stabilizing the Soil Microbial Community Network.

Biochar is increasingly being recognized as an effective soil amendment to enhance plant health and improve soil quality, but the complex relationships among biochar, plant resistance, and the soil microbial community are not clear. In this study, biochar derived from an invasive plant (Solidago canadensis L.) was used to investigate its impacts on bacterial wilt control, soil quality, and microbial regulation. The results reveal that the invasive plant biochar application significantly reduced the abundance of Ralstonia solanacearum in the soil (16.8-32.9%) and wilt disease index (14.0-49.2%) and promoted tomato growth. The biochar treatment increased the soil organic carbon, nutrient availability, soil chitinase, and sucrase activities under pathogen inoculation. The biochar did not influence the soil bacterial community diversity, but significantly increased the relative abundance of beneficial organisms, such as Bacillus and Sphingomonas. Biochar application increased the number of nodes, edges, and the average degree of soil microbial symbiotic network, thereby enhancing the stability and complexity of the bacterial community. These findings suggest that the invasive plant biochar produces win-win effects on plant-soil systems by suppressing soilborne wilt disease, enhancing the stability of the soil microbial community network, and promoting resource utilization, indicating its good potential in sustainable soil management.

Accident spread and risk propagation mechanism in complex industrial system network

Due to the increasing complexity of complex industrial systems, it's indispensable to analyze the process of accident spread and risk propagation on complex systems. An epidemiological model was used to study the phenomena of accident spread and risk propagation in complex industrial systems. It explains the mechanism and pattern of accident spread and risk propagation in complex industrial network, establishes a dynamic model for risk propagation in complex industrial network. The research findings are as follows: (1) In complex industrial systems networks, risk decreases when risk propagation velocity is below a threshold, but it increases and approaches a nonzero equilibrium point when risk propagation exceeds the threshold. (2) The risk propagation threshold is positively linked to information integrity, system robustness, and resilience, while it is negatively associated with the risk preference of management personnel. The risk propagation range decreases as the risk propagation threshold increases. (3) A higher average network degree leads to a higher risk propagation threshold and a smaller risk propagation range. Conversely, network heterogeneity has the opposite effect on the risk propagation threshold and range compared to the average degree. The research conclusions have important theoretical and practical implications for risk management in complex industrial systems.

Structure and assembly mechanism of soil bacterial community under different soil salt intensities in arid and semiarid regions

Soil salinization has become the most expansive form of soil degradation in arid and semiarid regions, and the management of soil salinization is imperative for achieving sustainable development. Soil microorganisms are supposed to play an integral role in controlling soil salinization, and the effects of high-salt environments on microbial community have been widely investigated, but there is currently limited comprehensive study on taxon co-occurrence patterns and assembly processes under different salt intensities. Here, based on high-throughput sequencing technologies, we analysed bacterial community structure and assembly mechanism under salt intensity in arid and semiarid regions. The results demonstrated that bacterial diversity was negatively correlated with soil salinity, and community structure also varied with changes in salt intensity. Solonchaks (soils with high soluble salt accumulation) had the lowest average degree of bacterial co-occurrence network, and there was a lower level of connectivity and correlation among bacteria in solonchaks compared to other salt-affected soils. The highest competitive connections among soil bacteria were detected in light-intensity saline soils, whereas overall collaborative connections increased with soil salinity. For co-occurrence network stability, the rare taxa (with each taxon’s relative abundance < 0.1%) were more essential than the abundant taxa (> 1%). As soil salinity increased, stochastic processes gradually dominated the community assembly, and the dispersal limitation contributed from 45.18% to 58.73%. These findings offered valuable information about how soil salt intensity affected soil bacterial community and would be useful in controlling soil salinization.

Assessing Supply Chain Resilience During the Pandemic Using Network Analysis

Disruptions induced by the COVID-19 pandemic have wreaked havoc in supply chain networks. To gain an understanding of the dynamics that had been at play, we construct a real supply chain network (scale-free) based on a seed firm (Apple), its customers, and its first- and second-tier suppliers, yielding a network of a total of 883 firms. We then use visualization to derive insight into various network characteristics and develop an agent-based model to capture the disruption of the network over a period of 400 days from the onset of the pandemic. The disruptions experienced by firms depend on the stringency of measures taken to curb the pandemic in their respective countries and the severity of disruptions experienced by suppliers in a specific region. We specifically find that spatial complexity, degree centrality, betweenness centrality, and closeness centrality have changed significantly throughout our observation period. We thus subsequently theorize on the influence of some of these characteristics on supply chain resilience (SCRes), and through our empirical tests, we find that, at the network level, Average degree and spatial complexity significantly influence SCRes. At the firm-level, we find that powerful firms within the network influence SCRes based on their betweenness centrality and closeness Centrality. Implications for managerial practice and academic research are discussed.

Government Supervision in Curbing the Spread of COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) has brought severe challenges to global economic development and government management. According to a report on the United Nations News website (news. un. org), as of May 3, 2023, the cumulative number of confirmed cases of COVID-19 worldwide has reached 765 million, and more than 6 million people have lost their lives as a result. How to evaluate the effect of government management has become a key issue. In this research, the SEIR model is used to estimate the network average degree of Chinese cities under network limitation. The results show that government management has reduced the average degree of the COVID-19 network and the prevention of first-tier cities will be more difficult. This study scientifically evaluates the government management of the epidemic and provides ideas for emergency management of infectious diseases that affect the public.

Research on Public Air Route Network Planning of Urban Low-Altitude Logistics Unmanned Aerial Vehicles

As urban populations continue to grow and road traffic congestion worsens, traditional ground logistics has become less efficient. This has led to longer logistics times and increased costs. Therefore, unmanned aerial vehicle (UAV) logistics has become increasingly popular. However, free-planned routes cannot meet the safety and efficiency requirements of urban airspace mobility. To address this issue, a public air route network for low-altitude logistics UAVs needs to be established in urban areas. This paper proposes a public route network planning method based on the obstacle-based Voronoi diagram and A* algorithm, as follows: Firstly, construct a city airspace grid model in which the characteristics of the airspace are mapped onto the grid map. Introduce an obstacle clustering algorithm based on DBSCAN to generate representative obstacle points as the Voronoi seed nodes. Utilize the Voronoi diagram to establish the initial route network. Then, conduct an improved path planning by employing the A* algorithm for obstacle avoidance in route edges that pass through obstacles. To ensure the safe operation of drones, set constraints on the route safety interval. This process will generate a low-altitude public air route network for urban areas. After considering the flight costs of logistics UAVs at different altitudes, the height for the route network layout is determined. Finally, the route network evaluation indicators are established. The simulation results demonstrate that compared with the city road network planning method and the central radial network planning method, the total route length is shortened by 7.1% and 9%, respectively, the airspace coverage is increased by 9.8% and 35%, respectively, the average network degree is reduced by 52.6% and 212%, respectively, and the average flight time is reduced by 19.4s and 3.7s, respectively. In addition, by solving the network model using the Dijkstra algorithm, when the energy cost and risk cost weights are 0.6 and 0.4, respectively, and the safety interval is taken as 15 m, the total path cost value of the planned trajectory is minimized.

Different effects of taproot and fibrous root crops on pore structure and microbial network in reclaimed soil

Understanding the effects of plant roots on the pore structure and microbial community of soil is crucial to recovery and improve soil productivity in mining areas. This study aims to assess the impact of taproot (TR) and fibrous root (FR) crops on the physicochemical properties, pore structure, and microbial communities and networks in reclaimed mine soil. Results showed that reclamation positively influenced pore structure and microbial diversity. Tillage with TR and FR crops significantly increased porosity, total pore volume, and area of mining soil (p < 0.05). Compared with TR, FR produced more macropores, mesopores, and micropores. In addition, the module group, average degree, density, and connectivity of microbial network in FR cultivated soil were higher than those in TR cultivated soil. The microbial network map showed that FR had more keystone taxa than TR, and mainly consisted of Acidobacteria and Proteobacteria. In the FR microbial network, Rhizobiales, Betaproteobacteria, and Acidobacteria_Gp11 play critical roles as module hubs and Noviherbaspirillum and Zavarzinella as connectors. Furthermore, most of the key microbes were significantly correlated (p < 0.05) with the total pore area and probably tended to live in pores >75 μm and 0.1–5 μm in size. Therefore, FR crops were more effective than TR crops in improving pore structure and enhancing the development of microbial network in reclaimed soil.

Critical transition of thermal rectification on complex networks

Thermal rectification is a mechanism that controls the direction of heat conduction, allowing it to flow freely in one direction and hindering it in the opposite direction. In this study, we propose a heat conduction model on a complex network where the node masses are non-uniformly distributed according to mi∼kiα. Our findings show that the existence of a critical point, α=1, determines the working mode of thermal rectification. For α&amp;gt;1, the working mode of thermal rectification is positive, whereas for α&amp;lt;1, the working mode is negative. Additionally, we discovered that this critical transition is a general phenomenon and does not vary with changes in network size, average degree, or degree distribution. By conducting theoretical analyses based on phonon spectra, we also identified the physical mechanism of the critical transition. These results provide a new approach to implement and enrich thermal diodes, opening up new possibilities for more efficient thermal management.

Pattern Formation in a Predator–Prey Model with Allee Effect and Hyperbolic Mortality on Multiplex Networks

With the rapid development of network science, Turing patterns on complex networks have attracted extensive attention from researchers. In this paper, we focus on spatial patterns in multiplex ER (Erdös-Rényi) random networks, taking the predator–prey model with Allee effect and hyperbolic mortality as an example. In theory, the threshold condition for generating Turing patterns is given using the Turing instability theory of multiplex networks. Numerically, we design relevant experiments to explore the impact of network topology on Turing patterns. The factors considered include model parameters, diffusion rate, average degree of the network, and differences in the average degree of different layers. The results indicate that the importance of diffusion rate and network average degree for Turing patterns is affirmed on the single-layer network. For multiplex networks, the differentiation of average degrees in different layers controls the generation of Turing patterns, which are not affected by the diffusion rates of the two populations. More interestingly, we observe the switching of Turing patterns and spatiotemporal patterns. We believe that these findings contribute to a better understanding of self-organization on complex networks.

Improve tea quality by enhancing the stability of bacterial community interaction and nitrogen-fixing function in pecan-tea interplanted plantation

Using the 16 S rDNA high-throughput sequencing, the effects of interplanted pecan (Carya illinoinensis) in a typical tea (Camellia sinensis) plantation on soil bacterial species composition and marker species were comparatively analyzed to determine whether nested pecan increases soil microbial diversity in the tea tree rhizosphere. We explored the effects of the underlying mechanisms of this complex ecosystem on tea quality by determining soil physicochemical properties using two types of planting modes: intercropping with pecan versus tea tree monoculture. Our results showed that Rhizobium (Allorhizobium, Neorhizobium, Pararhizobium, and Rhizobium), and Enterobacter, had pollution-degrading effects and were enriched in the soil bacterial communities of the pecan interplanted model. There was a significant enrichment of prebiotic functional bacteria, such as Pedosphaeraceae and Coriobateriaceae_UCG-002, which stimulate plant growth and improve disease resistance, while the functions of Chloroflexi and Firmicutes, which are dominant denitrifying bacteria in the soil, were inhibited. The average degree of soil microbial co-occurrence network increased from 13.40 to 18.17, and the number of nodes in the network increased from 117 to 166, indicating that the network increased in complexity and decreased in mutual exclusivity, and the complex interactions among bacterial populations tended to be stable. The intercropping mode reduced the phenol/ammonia ratio in tea from 2.37 to 1.38, improving the tea quality. Therefore, the soil microbial community composition under this complex model promotes material cycling in microecosystems and effectively reduces the transmission risk of soil-borne diseases. The soil microbial community structure of the tea rhizosphere could be re-balanced and shifted toward a more favorable tea quality formation by the introduction of pecan into tea plantations.

Pattern formation and bifurcation analysis of delay induced fractional-order epidemic spreading on networks

The spontaneous emergence of ordered structures, known as Turing patterns, in complex networks is a phenomenon that holds potential applications across diverse scientific fields, including biology, chemistry, and physics. Here, we present a novel delayed fractional-order susceptible–infected–recovered–susceptible (SIRS) reaction–diffusion model functioning on a network, which is typically used to simulate disease transmission but can also model rumor propagation in social contexts. Our theoretical analysis establishes the Turing instability resulting from delay, and we support our conclusions through numerical experiments. We identify the unique impacts of delay, average network degree, and diffusion rate on pattern formation. The primary outcomes of our study are: (i) Delays cause system instability, mainly evidenced by periodic temporal fluctuations; (ii) The average network degree produces periodic oscillatory states in uneven spatial distributions; (iii) The combined influence of diffusion rate and delay results in irregular oscillations in both time and space. However, we also find that fractional-order can suppress the formation of spatiotemporal patterns. These findings are crucial for comprehending the impact of network structure on the dynamics of fractional-order systems.