Dynamic Network Model Research Articles

Tác động lan truyền giữa tăng trưởng tín dụng và giá tài sản tài chính: Bằng chứng từ mô hình TVP-VAR

The article assesses the spillover effects among credit growth, stock, and bond returns through a dynamic network model with time-varying parameters, TVP-VAR, from 2010 to 2023Q2. The findings reveal a dynamic interconnection among credit growth, stock price volatility, and bond prices over time. Notably, credit growth emerges as the primary shock spillover to the system. However, credit growth spillover effects are mainly toward stock returns dynamics, while the connection between credit growth and bond returns is much less clear. Conversely, stock return exhibits spillover effects on bond return, indicating changes in risk appetite among market participants during various financial market cycles. Based on the empirical results, the authors propose some policy implications

Congestion tracking control of multi-bottleneck TCP networks with input-saturation and dead-zone

<abstract><p>This paper discusses the congestion control challenges in a network employing multi-bottleneck Transmission Control Protocol/Active Queue Management (TCP/AQM). The study specifically focuses on networks characterized by input nonlinearity and unknown disturbances. We regard the network as a whole, and consider the influence between multiple nodes and unknown disturbance, a dynamic model of multi-bottleneck network is established. And the impact of dead zone and saturation on the system is taken into account for the first time in the model, the builded TCP/AQM model is more practicable. Based on the characteristics of fuzzy logic systems (FLS), combined with backstepping technology and Lyapunov function, an adaptive congestion control algorithm is designed to make full use of the link resources of each node and improve the network utilization. Ultimately, the proposed algorithm's efficacy and superiority are substantiated through simulation.</p></abstract>

Quantitative study of the effect of high-performance zinc alloy coating technology on the life of mechanical components

Abstract Corrosion of mechanical components has been of wide interest in recent decades. Corrosion is caused by physicochemical action between a metal and its environment, which can result in changes in the metal’s properties and functional damage in mechanical components. The main corrosion manifestations of zinc alloy coatings in marine environments are discussed in this paper, which first explores the application of high-performance aluminum alloy coatings in industry. Subsequently, the ZAS35 alloy used for this paper was experimentally prepared, and orthogonal tests were utilized to determine the optimum matching values of process parameters for zinc alloy coatings to generate materials. The hardness and wear resistance of ZAS35 were evaluated against other zinc alloys. An iterative learning control algorithm was employed to determine the thickness of the zinc alloy coating. The optimal control of the steady-state process of coating thickness can be achieved by using the NARX dynamic neural network model as a predictive identification model for zinc alloy coating thickness. Finally, data were collected using an optical microscope to quantitatively analyze the effect of zinc alloy plating on mechanical life. When the thickness of zinc alloy is 1.7 μm, the probability of life is [947.56,978.36]×103h interval t=0.999, which is improved by 409~684.11×103h compared with the plating thickness of 1.10μm . After adding aluminum elements to zinc plating, the corrosion potential of the plating decreases from −800mV to −1000mV, and the zinc-aluminum alloy prevents electrochemical corrosion of the plated layer with cathodic corrosion inhibition.

A Novel Attention-Based Dynamic Multi-Graph Spatial-Temporal Graph Neural Network Model for Traffic Prediction

A Novel Attention-Based Dynamic Multi-Graph Spatial-Temporal Graph Neural Network Model for Traffic Prediction

Event-Triggered $H_{\infty}$ Tracking Control for Dynamic Artificial Neural Network Models With Time-Varying Delays

Event-Triggered $H_{\infty}$ Tracking Control for Dynamic Artificial Neural Network Models With Time-Varying Delays

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

Active Security Defense Model for New Power Systems

This article proposes a C-PDR active dynamic security model based on the WPDRRC network dynamic security model, combined with the new power system security characteristics, starting from pre judgment, in-process detection, and post response. This model constructs a comprehensive defense in depth through boundary defense, network security protection, host intrusion prevention, application and data intrusion prevention, and can fundamentally address the network threats faced by the power system.

Fibre crosslinking drives the emergence of order in a three-dimensional dynamical network model.

The extracellular-matrix (ECM) is a complex interconnected three-dimensional network that provides structural support for the cells and tissues and defines organ architecture as key for their healthy functioning. However, the intimate mechanisms by which ECM acquire their three-dimensional architecture are still largely unknown. In this paper, we study this question by means of a simple three-dimensional individual based model of interacting fibres able to spontaneously crosslink or unlink to each other and align at the crosslinks. We show that such systems are able to spontaneously generate different types of architectures. We provide a thorough analysis of the emerging structures by an exhaustive parametric analysis and the use of appropriate visualization tools and quantifiers in three dimensions. The most striking result is that the emergence of ordered structures can be fully explained by a single emerging variable: the number of links per fibre in the network. If validated on real tissues, this simple variable could become an important putative target to control and predict the structuring of biological tissues, to suggest possible new therapeutic strategies to restore tissue functions after disruption, and to help in the development of collagen-based scaffolds for tissue engineering. Moreover, the model reveals that the emergence of architecture is a spatially homogeneous process following a unique evolutionary path, and highlights the essential role of dynamical crosslinking in tissue structuring.

Robustness paradox of cascading dynamics in interdependent networks

Cascading failure process in interdependent networks has always been an important field of network cascading analysis. Unlike the previous studies, we take people's demand for minimizing travel costs into consideration in this article and propose a network dynamics model based on the cost constraint. On this basis, we pay attention to the characteristics of different layers in the interdependent network, and taking the real-world traffic network for example, we define different load propagation modes for different layers. Then, we carry out the simulation experiment on cascade failure in the artificial network. By changing the structure of the network and the parameters in the model, such as the capability value of the network side and the connectivity of the network, we are able to focus on the effects of traditional protection strategies during the simulation and obtain some interesting conclusions. It is generally believed that increasing the quantity of connections in the network or improving the quality of edges will enhance the network robustness effectively. However, our experimental results show that these methods may actually reduce network robustness in some cases. On the one hand, we find that the resurrection of some special edges in the network is the main reason for the capacity paradox, as these edges will destroy the stable structure of the original network. On the other hand, neither improving the internal connectivity of a single-layer network nor enhancing the coupling strength between interdependent networks will effectively improve network robustness. This is because as the number of edges increases, some critical edges may appear in the network, attracting a large amount of the network load and leading the network robustness to decrease. These conclusions remind us that blindly investing resources in network construction cannot achieve the best protection effect. Only by scientifically designing the network structures and allocating network resources reasonably can the network robustness be effectively improved.

Performance Analysis of a Self-Organized Network Dynamics Model for Public Opinion Information

Performance Analysis of a Self-Organized Network Dynamics Model for Public Opinion Information

Assessing the progress of the mining industry towards green growth in China: A three-stage dynamic network slacks-based measure approach

Promoting green growth in the mining sector requires efficient use of mineral resources, improving economic operations' quality and performance and strengthening ecological and environmental governance. Local economies face different and conflicting plans, along with potential implementation constraints, while implementing green growth initiatives. Efficiency in mining green growth is a crucial aspect to monitor the progress of China's local mining industry in the future. This paper presents a framework for three-stage network structure evaluation of mining industry's green growth efficacy. A dynamic network model, using Slacks-Based Measure (SBM) approach, analyzes the green growth of the mining industry in 21 Chinese provinces between 2005 and 2019. The Malmquist index is used to analyze the dynamic patterns and endogeneity of mining green growth efficiency. The results show that the efficiency of most provinces exhibited a decrease before rebounding during the observation period. Overall efficiency is strongly correlated with improved eco-performance, which is a result of green mining. There are substantial discrepancies in the pace and extent of green growth across different provinces, with a few boasting growth rates exceeding 25% annually. Eastern provinces, particularly Hubei, Fujian, and Shandong, demonstrate impressive trends and substantial potential. However, the unsatisfactory situation in the Northeast and Xinjiang regions persists. Their lack of effectiveness can be attributed to various sub-processes.

Local dynamic neural network for quantitative analysis of mixed gases

The Gas sensor array is commonly used in combination with quantitative analysis method for detecting mixed gases. Artificial neural network (ANN) is usually employed to achieve a quantitative analysis of the mixed gases. However, the current ANN models typically require a large number of test samples to obtain low relative errors. In this work, we fabricated a gas sensor array composed of four gas sensors (i.e., In2O3: NO2, Pd-ZnO: NH3, Au-SnO2: CH4, Pd-LaFeO3: CO2) and proposed a local dynamic neural network (LDNN) model for quantitative analysis of four mixed gases. By constructing and extracting features through a pre-trained autoencoder network, only a small sample size (25 local points) is input into the LDNN for training to realize the concentration prediction of four gases. The results show that the MAEs (mean absolute error) of the predicted concentrations of NO2, NH3, CH4, and CO2 are 0.01 ppm, 0.04 ppm, 0.13 ppm, and 42.67 ppm, while the MREs (mean relative error) are 0.19%, 0.85%, 1.17%, and 1.06%, respectively. Moreover, the MREs of the predicted concentrations for four gases are less than 2% within the 95% confidence interval. This work provides an effective quantitative analysis method with small sample size, simple structure and high-precision for the detection of four mixed gases.

Non-linear dynamic state-space network modeling for decoding neurodegeneration.

Non-linear dynamic state-space network modeling for decoding neurodegeneration.

Extraction of brain function pattern with visual-capture-task fMRI using dynamic time-window method in ADHD children

Attention deficit/Hyperactivity disorder (ADHD) has a great impact on children's development. This paper uses a novel adaptive brain state extraction algorithm to construct a dynamic time-window brain network, which captures the brain function pattern characteristics of ADHD children with higher temporal resolution. The test data were acquired by functional magnetic resonance imaging (fMRI) obtained from 23 children with ADHD during the visual-capture-task [age: (8.27 ± 2.77)]. A spatial standard deviation method is used after the initial data processing, to extract the brain activity pattern state; An improved clustering algorithm is constructed to verify the changes made to the dynamic time-window brain network model. There can be seen clear differences between each state within 0.05 s after the test. The results show that our improved new framework can effectively obtain the characteristics of dynamic brain functional connection strength changes during the task. In addition, the new algorithm is able to capture the dynamic changes of the brain network, with an 80 % improvement compared to traditional methods for the average modularity value Q. This work demonstrates a novel approach to find out the pattern changes between dynamic brain function connections, which can be of great significance for the adjuvant treatment of children with ADHD.

A dynamic attractor network model of memory formation, reinforcement and forgetting.

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic understanding of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting.

An integrated model to investigate optimized CO2 utilization and risk mitigation in offshore hydrocarbon reservoirs

This study addresses the dual challenge of balancing increasing energy demands with climate change mitigation through optimized carbon dioxide (CO2) utilization (CO2U) for enhanced oil recovery (EOR). This work presents a composite dynamic risk model for CO2-EOR operations, synergizing deterministic and data-driven methodologies for post-CO2 injection initiation. The primary focus is to gain a further understanding of the impact of CO2 injection on reservoir functionality, stability, and potential leaks in offshore deployment. The integrated novel model involves a spatial and temporal deterministic model with a data-driven strategy, incorporating an artificial neural network (ANN) fine-tuned via particle swarm optimization (PSO) for co-optimization of oil production and CO2 storage. Furthermore, an innovative early warning index system (EWIS) is proposed by integrating a dynamic Bayesian network (DBN) model for probabilistic loss evaluation and pressure maintenance impact assessment. The proposed DBN strategy generates dynamic risk profiles, facilitating a cost-efficient and computationally simpler risk monitoring system. The findings from the study show a recovery of 17.14 thousand barrels (Mbbls) from the total original oil-in-place (OOIP) through this CO2-EOR approach in its debut year, coupled with over 74% CO2 retention. The proposed model offers a robust tool for concurrent optimization of oil production and CO2 storage, whilst ensuring meticulous dynamic risk analysis throughout CO2U processes.

Long-term Hydrometeorological Time-series Analysis over the Central Highland of West Papua

This article presents an innovative data-driven approach for examining long-term temporal rainfall patterns in the central highlands of West Papua, Indonesia. We utilized wavelet transforms to identify signs of a negative temporal correlation between the El Niño-Southern Oscillation (ENSO) and the 12-month Standardized Precipitation Index (SPI-12). Based on this cause-and-effect relationship, we employed dynamic causality modeling using the Nonlinear Autoregressive with Exogenous input (NARX) model to predict SPI-12. The Multivariate ENSO Index (MEI) was used as an attribute variable in this predictive framework. Consequently, this dynamic neural network model effectively captured common patterns within the SPI-12 time series. The implications of this study are significant for advancing data-driven precipitation models in regions characterized by intricate topography within the Indonesian Maritime Continent (IMC).

Construction of a dynamic network for retinal vessel segmentation based on computer vision

This paper is focused on the field of computer vision in order to investigate the presentation properties of retinal blood vessels. Combining the structure of convolutional neural networks, activation functions, and common metrics in semantic segmentation, a dynamic network model for retinal vessel segmentation based on computer vision is constructed. The purpose of this paper is to discuss the results of retinal vascular segmentation based on computer vision. The image connection and alignment pattern selection process is also established to match retinal vessel images by computer vision. The performance of the dynamic network constructed here and the results of retinal vessel segmentation were then analyzed in three publicly available datasets, DRIVE (digital retinal images for vessel extraction), CHASE_DB1, and STARE (structured snalysis of the retinal. The ROC (retinopathy online challenge) curves on all three datasets exceeded 0.9, showing high performance, and the area under the PR curve exceeded 0.88. The accuracy of the results for retinal vessel segmentation was around 96%. Based on the semantic segmentation direction in the field of computer vision in this study, the dynamic network for retinal vessel segmentation can be well constructed.

Multidimensional evolutionary analysis of China’s BIM technology policy based on quantitative mapping

ABSTRACT In the wave of the digital era, Building Information Modelling (BIM) has emerged as the primary tool for intelligent construction and digital transformation in the Architecture, Engineering, and Construction (AEC) industry. China's focus on policy guidance for the dissemination and application of BIM technology has promoted high-quality development and technological innovation in the AEC industry. However, few studies have described the multi-dimensional evolution picture of BIM technology policy in China, and lack of effective grasp and prediction of policy evolution law and future direction. An improved policy quantitative analysis framework is built using natural language processing (NLP) based on deep learning in this paper. The dynamic characteristics of 242 Chinese BIM technology policy documents from 1998 to 2022 in the PKULAW database are quantitatively analyzed by the framework, and the four-stage integrated evolution of Chinese BIM technology policy is quantitatively mapped in a visual form through network analysis and dynamic topic model (DTM). Furthermore, this research conducts a comprehensive discussion from three dimensions, providing valuable insights for policy makers and industry participants in China. The findings contribute to the deep integration of policy optimization and the construction industry, while also aiming to share China's experience in BIM technology governance with other countries and regions. Ultimately, this study seeks to support the global AEC industry in its digital transformation and development by offering valuable knowledge and perspectives.

Spatial-temporal traffic data imputation based on dynamic multi-level generative adversarial networks for urban governance

Complete and accurate traffic data lays an essential foundation for urban governance and intelligent transportation system. Data missing is an inevitable issue in road network analysis with the expansion of urban roads and increasing application of data monitors. Data imputation is regarded as a cost-effective approach for solving data missing problem. However, the spatial-temporal features of massive traffic data have not been comprehensively considered in the previous related studies, and non-uniform distribution of missing data often causes poor results. Therefore, we propose a dynamic multi-level generative adversarial networks (MLGAN) model for the imputation of large-scale spatial-temporal traffic data. The model first captures the spatial-temporal features of traffic data through composite features extractor based on the bidirectional gated recurrent unit (BGRU) and the graph convolutional network (GCN) structure. Then, different sampler structures with varied missing rate are adopted to analyze the feature data and present the reconstruction results. Enhanced by the dynamic stratification strategy, the model is less sensitive to the variation of missing rates. Finally, data imputation experiments are conducted to validate the proposed approach using the data collected from the PeMS dataset. The results show that the proposed model has superior data imputation performance on large-scale dataset compared with the traditional models.