Evolution Of Networks Research Articles

Drifting towards multidimensional collaborative innovation: the co-evolution of collaboration networks and policy in China’s nuclear power industry

Purpose Nuclear power is a stable and reliable energy source that can improve energy structure while reducing carbon emissions, which is of great significance for environmental protection and combating climate change. As a unique industry, it is facing rare development opportunities in China and has broad market prospects. However, the characteristics of technical difficulty, loose organizational structure and uneven regional distribution limit the expansion of the nuclear power industry. This paper aims to a better understanding of the accumulation process for innovation capability from the perspective of network evolution and provides policy guidance for the market development of the nuclear power industry (NPI). Design/methodology/approach Methodologically, social network analysis is used to explore the co-evolution of multidimensional collaboration networks. First, the development and policy evolution of the NPI is introduced to divide the evolution periods. Then, the authors identify and analyze the core organizations, technologies and regions that promote nuclear power patent collaboration. Furthermore, three levels of collaboration networks based on organizations, technologies and regions are constructed to analyze the coevolution of patent networks in China’s NPI. Findings The results show that nuclear power enterprises always play the foremost role in the organizational collaboration network (OCN), and the dominance of foreign enterprises is replaced by Chinese state-owned enterprises in the third period. The technology hotspot has shifted from nuclear power plant construction to the control system. The regional collaboration network was initially formed in the coastal areas and gradually moved inland, with Guangdong and Beijing becoming the two cores of the network. The scale of three collaboration networks is still expanding but the speed has slowed down. Originality/value In response to the pain points of the NPI, this research focuses on multidimensional collaborative innovation, investigates the dynamic evolution process of collaborative innovation networks in China’s NPI and links policy evolution with network evolution creatively. The ultimate result not only helps nuclear power enterprises integrate innovative resources in complex environments but also promotes industrial upgrading and market development.

Reconstructing and tracing the evolution of the road networks in the Haidai region of China during the Bronze and Early Iron Ages

Reconstructing ancient transportation networks is critical to studying past human mobility patterns. China’s Haidai region was a thriving political and economic hub during the Bronze and Early Iron Ages. We used GIS spatial analysis techniques to build a “Settlement Interaction Model” based on archaeological data from the Haidai region during the Bronze and Early Iron Age (Shang Dynasty, Western Zhou Dynasty, Spring & Autumn Period, and Warring States period). The eight-level road network maps with traffic attributes were distinguished based on topography and settlement size. The total lengths of the road networks were estimated to be 19,112 km in the Shang Dynasty, 35,269 km in the Western Zhou Dynasty, 51,555 km in the Spring & Autumn Period, and 77,456 km in the Warring States Period, with the average road flows of 6.6, 31.7, 42.8, and 75.5, respectively. The Z score and one-sample t-test (p < 0.01) confirmed the reliability of the reconstructed road networks. The Shang Dynasty saw the sporadic appearance of simple road routes. More complex routes emerged during the Western Zhou Dynasty and Spring & Autumn Period. The road networks were finally built during the Warring States Period. The development of road networks was closely related to population growth and urbanization. Exploring methods for reconstructing road networks may help us uncover ancient road networks and better understand ancient cultural exchanges.

How does node centrality in a financial network affect asset price prediction?

In complex financial networks, systemically important nodes usually play crucial roles. Asset price forecasting is important for describing the evolution of a financial network. Naturally, the question arises as to whether node centrality affects the effectiveness of price forecasting. To explore this, we examine networks composed of major global assets and investigate how node centrality affects price forecasting using a hybrid random forest algorithm. Our findings reveal two counterintuitive phenomena: (i) factors with low centrality usually have better prediction ability, and (ii) nodes with low centrality can be predicted more accurately in direction. These unexpected observations can be explained from the perspective of information theory. Moreover, our research suggests a criterion for factor selection: when predicting an asset price in a complex system, factors with low centrality should be selected rather than only factors with high centrality.

Synergistic regional emission reductions in China: Network evolution, spatial and temporal characteristics, and driving factor

Synergistic regional emission reduction (SRER) is an important part of the strategy to achieve carbon neutrality. In this paper, a system efficiency value-added model is proposed to measure the strength of SRER network relationships for 40,186 sets of city pairs and the SRER levels of 284 cities in China from 2011 to 2021. Subsequently, the evolution characteristics of the SRER network, the spatial and temporal characteristics of urban SRER levels, and the driving factors of the SRER network evolution are further analyzed. The results are as follows: SRER network is shifting from a fan-shaped network pattern of “three-region clustering and sporadic coverage” to a shuttle-shaped nested network pattern of “east-west linkage and multi-region support”. Time series analysis found that urban SRER level in China showed fluctuating growth characteristics during the study period. Spatial analysis reveals that the spatial autocorrelation of urban SRER level is decreasing, and the spatial distribution range is expanding towards the southwest. Emission reduction comparative advantages (CAER), green innovation division of labor (GIDL) and urban economic linkages (LCEL) are driver factors of SRER network evolution. This study proposes an evaluation method for the urban SRER level from network relationships perspective. Subsequently, network evolution characterization, spatial and temporal characterization, and driving factor analysis were conducted. The results of the study provide important theoretical guidance and inspiration for grasping the current status and enhancement paths of China's urban SRER level.

Diffusion of "Dual Carbon" Policies among Chinese Cities: A Network Evolution Analysis

Numerous regions across China have introduced "dual carbon" policies, yet the response of cities to these policies remains unclear. In this study, we used emission data from 271 Chinese cities for the years 2010, 2015, and 2020 to construct a complex network of urban carbon emissions with a modified gravity model. To further investigate the drivers of the "dual carbon" policy diffusion, we conducted a simulation of network evolution under the two policy diffusion mechanisms of coercion and learning. Our findings indicate that the "dual carbon" policy exhibits a network diffusion effect, with cost-effectiveness serve as a critical factor influencing its spread. And the China's low carbon price lacks incentives for reduction, carbon pricing needs to be increased and differentiated emission reduction incentives, penalties for over-emission, and carbon quotas based on the type of decoupling of the city need to be introduced to facilitate the diffusion of policies. Additionally, through counterfactual scenario analysis and an exploration of network structural characteristics, we highlighted the vital role of cities with high betweenness centrality within urban agglomerations in facilitating efficient policy diffusion. This study also offers a process for selecting pilot cities, providing a valuable reference for future policy initiatives.

The evolution of intergranular networks during grain growth and its effect on percolation behavior

Triple junctions (TJs) are line defects in three-dimensional (3D) polycrystalline materials where three grains meet. Transport along the TJs depends on the connectivity between them. Here, we investigate the connectivity of more than 6000 TJs in a 3D microstructure of pure iron (Fe) through the lens of bond percolation. Our efforts are made possible by synchrotron-based x-ray diffraction tomography, which allows us to resolve the TJ network both temporally and spatially, during grain growth. In the framework of standard percolation theory, we determine a percolation threshold of the TJs, above which there exists a continuous pathway of TJs that travels infinitely far. Our experimental results indicate a surprisingly different percolation threshold (by 16%) compared to models and theories. The reason for the discrepancy is that the real TJ network has a topological disorder that is not present in the idealized microstructures considered in prevailing models and theories. Leveraging the wealth of time-resolved data we trace the origin of this disorder by following the topological transitions that accompany grain growth. Overall, the insights obtained in this study can help guide the design of polycrystalline materials via defect engineering.

Genomes of multicellular algal sisters to land plants illuminate signaling network evolution.

Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

Approximate inference for longitudinal mechanistic HIV contact network

Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.

Controlling splat boundary network evolution towards the development of strong and ductile cold sprayed refractory metals: The role of powder characteristics

Feedstock powder characteristics such as composition (specific alloying elements and concentrations and impurity levels), microstructure, thickness/composition of surface oxide layers, and particle size distribution play a crucial role in determining the overall mechanical properties of cold sprayed deposits. Herein, we report on two deposits consolidated via cold spray processing from differently-sourced batches of nominally identical elemental refractory powders under identical spraying conditions, which exhibit bending strength and ductility values that differ by more than a factor of two – and with the weakest sample displaying negligible effective ductility. Through chemical, microstructural and micromechanical characterization of both the feedstock powders and cold sprayed deposits, we consider the possible influences of feedstock characteristics on the mechanical performance of cold spray consolidated deposits. It is shown that while differences in interstitial oxygen and hydrogen content may result in differences in the intrinsic yield characteristics of the feedstock material, both feedstocks maintain the ductile behavior required to induce good metallurgical bonding upon impact of optimally sized powder particles. We conclude that the deposits formed from the two feedstock powders are indistinguishable and exhibit high ductility when characterized locally within the relatively undeformed bulk of a single particle or splat. However, the two sprayed deposits show low ductility or brittle behavior when loaded in tension across intersplat boundary domains comprising material that has undergone extensive deformation. In addition, one feedstock incorporates a broader particle size distribution, with a long tail of larger-than-optimal particles. These larger particles are accelerated below the critical impact velocity, resulting in observed imperfect metallurgical bonding at splat interfaces and higher porosity at such intersplat boundaries. This result, combined with the overall differences in the morphology of the intersplat boundary network between the two deposits, explains the difference in macroscopic mechanical behavior. We conclude that accurate control of the powder feedstock particle size distribution is essential for optimizing the mechanical integrity of cold sprayed refractory elements and alloys.

Origin of urban form large siberian city irkutsk

The problem: the disclosure of the laws and features of the development of a large Siberian city will help to understand both its structure and the process of formation of a city-numerous form. The generation of the city involves the natural basis, the population in its pro-strange form, which is expressed in the growth of populated cells of the city. People satisfy the needs for housing, in places of employment, in social services, in a high-quality environment. The historical process of becoming an urban planning form is complex and contradictory. Currently, there is no formula that reveals the dependence of the urban planning form on the totality of development arguments: state policy, the interests of developers, the interests and needs of the population, contradictions that lie in the nature of the city’s investment. Purpose: to identify the prerequisites for explaining the urban planning form of Irkutsk by natural, historical, economic, socio-cultural and religious factors. Methods: identification of city-forming factors, historical process of seeds or primary cells of the planning structure, ways to increment this cell tissue along the axes of the planning framework, analysis of the evolution of the street network, forms of planning packaging of different areas of the city with different genetic codes. Results: the main regularities of origin of a town-planning form of the city of Irkutsk are revealed.

Advancements in computer network technologies: A review

&lt;p&gt;This article presents a comprehensive review of recent advancements in computer network technologies, emphasizing their impact on various sectors. The rapid evolution of computer networks has transformed communication, work, and business. From 5G networks and the Internet of Things (IoT) to network security and quantum networking, this review explores the current state of computer networks and their prospects.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

Evolution of Energy Efficient Wireless Sensor Network: A Survey on Energy and Clustering Issues

In different fields of human endeavor, wireless sensor networks (WSNs) are being developed to collect, aggregate, fuse, and send data  collected by a wide range of sensors in real-time. When developing a wireless network's data transmission routing system, energy  consumption is among the most essential components. Designing an energy-efficient (WSN) routing framework is challenging. Several  energyefficient routing algorithms exist for effectively transferring data packets between cluster members (CM) and cluster heads (CH).  Some suggest the change of state of the nodes that made up the network from active to sleep in the event where the nodes are not  sensing of sending data as a method of energy optimisation while others reduce the distance between the nodes because the distance  between nodes is directly proportional to the amount of energy use during data transmission. Whatever the method may be, achieving  reduced energy consumption in a WSN environment is an enormous task due to the need for both long network life and network  efficiency. This paper reviews some research and energy saving techniques that had been carried out on WSN over a period of time. 

LAFL Factors in Seed Development and Phase Transitions.

Development is a chain reaction in which one event leads to another until the completion of a life cycle. Phase transitions are milestone events in the cycle of life. LEAFY COTYLEDON1 (LEC1), ABA INSENSITIVE3 (ABI3), FUSCA3 (FUS3), and LEC2 proteins, collectively known as LAFL, are master transcription factors (TFs) regulating seed and other developmental processes. Since the initial characterization of the LAFL genes (44, 71, 98-100), more than three decades of active research has generated tremendous amounts of knowledge about these TFs, whose roles in seed development and germination have been comprehensively reviewed (63, 43, 65, 81). Recent advances in cell biology and genetic and genomic tools have allowed the characterization of the LAFL regulatory networks in previously challenging tissues at a higher throughput and resolution in reference species and crops. In this review, we provide a holistic perspective by integrating advances at the epigenetic, transcriptional, posttranscriptional, and protein levels to exemplify the spatiotemporal regulation of the LAFL networks in Arabidopsis seed development and phase transitions, and we briefly discuss the evolution of these TF networks.

Hybrid deep convolution network and differential evolution algorithm for solving non-permutation flow shop scheduling problem

With explosive growth of industrial big data, workshop scheduling faces problems such as high complexity, multi-dimensionality and low stability. Recent years, the wide application of deep learning provides new idea for scheduling problem. In this paper, a hybrid deep convolution network and differential evolution algorithm is proposed to solve the non-permutation flow shop scheduling problem with the goal of minimizing total completion time. Mining relationship between job attributes and process priority by deep convolutional network is core idea of this method. In this paper, differential evolution algorithm is used to obtain the data set for deep learning, and neighborhood search algorithm is used to optimize scheduling solution. Additionally, a method combining k-means algorithm and data statistics is proposed, which provides a reasonable way for priority division. The experimental results show that this method can greatly improve scheduling efficiency.

Service-aware real-time slicing for virtualized beyond 5G networks

Edge Intelligence is expected to play a vital role in the evolution of 5G networks, empowering them with the capability to make real-time decisions regarding various allocations related to their management and service provisioning to end-users. This shift facilitates the transition from a network-aware approach, where applications are developed to manage network quality fluctuations, to a service-aware network that self-adjusts based on the hosted applications. In this paper, we design and implement a service-aware network managed from the network edge. We utilize and assess various Machine Learning models to classify cellular network traffic flows in the backhaul, aiming to predict their future impact on network load. Leveraging these predictions, the network can proactively and autonomously reallocate slices in the Radio Access Network via programmable APIs, ensuring the demands of the traffic-generating applications are met. The approach integrates innovative MLOps methodologies for distributed and online training, enabling continuous model refinement and adaptation to evolving network dynamics. Our framework was tested in a real-world environment with realistic traffic scenarios, and the results were evaluated in real-time, down to a granularity of 10ms. Our findings indicate that the network can swiftly adjust to traffic, providing users with slices tailored to their application needs. Notably, our experiments show that under the studied settings, the users experienced up to 4 times lower latency (jitter) and nearly 4 times higher throughput when interacting with various applications, compared to the standard non-AI/ML unit. Furthermore, our dynamic scheme significantly optimizes resource allocation, ensuring energy efficiency by avoiding over- and under-provisioning of resources.

Investigation of surrounding rock stability during proximal coal seams mining process and feasibility of ground control technology

The goaf formation of upper seam stope in proximity coal seams mining would influence the stability of the surrounding rock mass due to mining stress redistribution, which will seriously threaten the subsequent mining process and even ground environmental protection. Therefore, developing safe and efficient mining techniques, robust support strategies, and comprehensive safety evaluation systems is crucial for ensuring the overall safety of the mining process. Drawing upon on-site data, this study conducted a detailed feasibility analysis of the mechanized caving method. We developed a simulation model using similar materials, tailored for scenarios with overlying goaf, to demonstrate how lower seam mining was affected by the caving zone of the upper seam, leading to the progressive evolution of fracture networks. Furthermore, we employed numerical simulation models to juxtapose the simulated deformation areas with the actual potential deformation zones in the mining area. The mine seismic prediction (MSP) technique was utilized for the precise identification of unfavorable geological formations ahead of the working face, thereby preempting potential fractures or irregularities due to the presence of overlying goaf. In addition, a comprehensive support strategy was designed and implemented to bolster the stability of the roadways, thereby reinforcing the safety measures within the mining process. This study presents an integrated approach to investigate mining activities in the lower layers during stratified extraction, particularly how the overlying goaf of the upper seam affects the mining of the lower seam, leading to the progressive evolution of fracture networks. The findings and methodologies outlined in this work hold substantial practical significance, offering a pathway to enhance the reliability and safety of the mining process.

Thermal stability of Ni-based superalloys fabricated through additive manufacturing: A review

Additive manufacturing (AM) is a cutting-edge technique that builds parts by adding layers from a digital design and depositing materials selectively where required. AM is vital in industries like aerospace, healthcare, and automotive because of benefits like design flexibility, cost-efficiency, and customization over conventional methods. Ni-based superalloys AM processes involve phenomena like solidification, phase change, different microstructure characteristics, and melting pool boundaries, affecting both microstructure and mechanical properties. Ni-based superalloys are regarded as potential candidates for high-temperature applications, thanks to their capability to benefit from various strengthening mechanisms such as precipitation hardening and solid solution strengthening. This comprehensive review explores the thermal stability and high temperature softening resistance of additively manufactured (AMed) Ni-based superalloys, from microstructural and mechanical points of view. This review explores the thermal stability and high temperature softening resistance of additively manufactured Ni-based superalloys. It explores the interplay of alloying elements at high temperature, additive manufacturing parameters, and initial microstructure, focusing on grain boundary/sub-boundary networks evolution. The influence of solute drag effect, second phases Zenner pinning effect, and post-processing treatments on microstructural changes such as second phase characteristics, recrystallization (static and dynamic), grain growth phenomena, and textural variations is also analyzed. High temperature mechanical properties of AM-Ni-based superalloys are evaluated, including yield and tensile strength, elongation, work-hardening behavior, creep, and fatigue properties. Hydrogen embrittlement is a significant reason for failure, so this issue is briefly assessed in AM-Ni-based superalloys. Results show that understanding relationships between processing, microstructure, post-processing treatment, and properties is crucial for thermal stability control.

Unveiling the evolution of urban rail transit network: considering ridership attributes

ABSTRACT Previous research on urban rail transit (URT) evolution mainly focused on network topology, neglecting ridership attributes. This study extracts ridership and network topology indicators from Chinese URT data. Employing a self-organizing mapping neural network model, it divides China’s URT development into four stages. The initial stage and the development stage form the framework of URT network. The network diameter reaches the maximum in the networked operation stage. In the mature stage, URT network densification occurs alongside a significant increase in resident ridership. It is also found that each network indicator has a significant nonlinear relationship with ridership attributes. These findings are of guiding significance for urban planners to accurately understanding URT’s future development and rational network planning and construction.

Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.

Analytic prediction for acceptable pricing in industry interaction with complex network evolution based on knowledge graph fusion

This paper proposes a trend prediction analysis method based on the evolution of knowledge graph fusion for the analysis of price fluctuation trends with limited information and lacking historical data. Based on an actual business case, the article first establishes a knowledge graph and corresponding data model based on the price analysis and application scenarios in business decision-making and based on the collected data. Afterward, the evolution mechanism model and the corresponding theoretical basis are described. After that, the feasibility of the proposed method is proved theoretically and via simulation verification. This method does not require tags and historical data as necessary support, thereby reducing the data requirements of machine learning predictions, and can be used as a supplementary solution to complete trend analysis with less requirement on data curation. The method's stability and feasibility have been substantiated through experiments, showcasing distinct effects on the trend emanating from different interrelated knowledge graphs. Recognizing the potentially conflicting constraints between diverse correlations, we undertake a comprehensive evaluation of each map's influence. This evaluation is achieved through knowledge graph fusion processing, enabling a predictive assessment under the collective impact of various factors.