Double-layer Network Research Articles

Risk Identification of Critical Infrastructures Considering Dual Interdependency: A Complex Network Approach

Critical Infrastructures (CIs) are exposed to various risks, which hinder their successful operation and induce great losses. Due to the interdependency between risks and that between CIs, the identification of the most prominent risks becomes complex and challenging. However, existing studies rarely considered the dual interdependency of risks and CIs. This study proposes a double-layer network for multiple interdependent risks and CIs. The Design Structure Matrix (DSM) and Restart Random Walk (RRW) algorithm are combined to determine the impact of risks on CIs by incorporating the strength of dual interdependency. The LeaderRank algorithm is then used to rank these risk factors and an illustrative example is given to validate the model. The proposed model and algorithms can systematically quantify complex interdependencies embedded in the operation of CIs susceptible to multiple risks, and provide decision-makers with evidence to prioritize risks.

Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning

The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.

Timetable synchronization optimization in a subway–bus network

The subway and bus together contribute to the structure of public transport network in a metropolis. However, integrated service of public transport is not satisfied due to the lower timetable synchronization between these two modes at transfer stations. In this paper, a double-layer public transport network composed of subway/bus lines and stations is first created. Then, two stages are included in the timetable optimization process. In the first stage, a user equilibrium assignment model and a capacity restriction iterative algorithm are employed to obtain the passenger distribution in the subway–bus public transport transfer network. Based on this distribution, with the consideration of the synchronization and section service level, a mixed-integer linear programming (MILP) model is applied to optimize the timetables of the subway and bus networks in the second stage. Computational results using real Automatic Fare Collection System (AFC) data from the public transport system in Beijing are reported, and these results show that the suggested model is efficient in improving passenger service levels.

DLBN: Group Storage Mechanism Based on Double-Layer Blockchain Network

Blockchain, which stores data in an appending form, cannot achieve the purpose of expanding the storage capacity by increasing the number of nodes. As the system runs, nodes will face problems of insufficient storage space. In the existing peer-to-peer (P2P) blockchain network model, all network nodes participate in data storage, and the generated blocks need to be verified among the network-wide nodes. This approach suffers from low system transaction throughput and data storage redundancy. In order to solve the above existing problems, this article proposes a block data storage model based on the double-layer blockchain network (DLBN), which improves the internal data composition structure of the blockchain. The DLBN contains two types of blockchain nodes, which form the storage and consensus layers of the system, respectively. The consensus layer is responsible for tasks, such as transaction sequencing, validation, and block packing, thus increasing the system transaction throughput. The nodes in the storage layer are divided into multiple storage units (SUs), and all nodes in the SU jointly maintain a copy of the complete blockchain, thereby reducing the storage pressure on the nodes. Based on the DLBN model, we design a reputation-based consensus mechanism, block storage allocation algorithm, and transaction query optimization algorithm, respectively. Through experimental verification and analysis, the storage model based on the DLBN can effectively improve the system transaction throughput and reduce the node storage capacity while ensuring system security.

State of Health Estimation Based on the Long Short-Term Memory Network Using Incremental Capacity and Transfer Learning.

Battery state of health (SOH) estimating is essential for the safety and preservation of electric vehicles. The degradation mechanism of batteries under different aging conditions has attracted considerable attention in SOH prediction. In this article, the discharge voltage curve early in the cycle is considered to be strongly characteristic during cell aging. Therefore, the battery aging state can be quantitatively characterized by an incremental capacity analysis (ICA) of the voltage distribution. Due to the interference of vibration noise of the test platform, the discrete wavelet transform (DWT) methods are accustomed to soften the premier incremental capacity curves in different hierarchical decompositions. By analyzing the battery aging mechanism, the peak of the curve and its corresponding voltage are used in the characterization of capacity decay by grey relation analysis (GRA) and to optimize the input of the deep learning model, and finally, the double-layer long short-term memory network (LSTM) model is used to train the data. The results demonstrate that the proposed model can predict the SOH of a single battery cycle using only small batch data and the relative error is less than 2%. Further, by freezing the LSTM layer for transfer learning, it can be used for battery health estimation in different loading modes. The results of training and verification show that this method has high accuracy and reliability in SOH estimation.

Highly sensitive fabric strain sensor with double-layer conductive networks for joint rehabilitation therapy

The application of wearable electronic devices in telemedicine services to provide personalized treatment plans is becoming a trend in the global healthcare system. However, the low sensitivity and limited capability to discriminate strain levels of flexible sensors have consistently hindered joint recovery evaluation and motion management. Here, a simple dyeing and spray method was introduced to fabricate a highly sensitive double conductive layer sensor consisting of graphene and Ag layer. The sensor simultaneously achieved ultra-high sensitivity (gauge factor of 3.6 × 105), fast response (80 ms) and stable cycling durability (>5000 times) related to differences in conductivity of the graphene fabric and the Ag layer. Moreover, the sensitivity of the sensor varied significantly over the different strain ranges. Based on these sensing properties, the sensor detected not only subtle muscle movements (such as facial expressions, throat movements and abdominal breathing) but also large joint movements at different angles, showing enormous potential in the evaluation of joint rehabilitation treatments.

Predicting photovoltaic power generation using double-layer bidirectional long short-term memory-convolutional network

Predicting photovoltaic power generation using double-layer bidirectional long short-term memory-convolutional network

How do gravity waves triggered by a typhoon propagate from the troposphere to the upper atmosphere?

Abstract. Gravity waves (GWs) strongly affect atmospheric dynamics and photochemistry and the coupling between the troposphere, stratosphere, mesosphere, and thermosphere. In addition, GWs generated by strong disturbances in the troposphere (e.g. thunderstorms and typhoons) can affect the atmosphere of Earth from the troposphere to the thermosphere. However, the fundamental process of GW propagation from the troposphere to the thermosphere is poorly understood because it is challenging to constrain this process using observations. Moreover, GWs tend to dissipate rapidly in the thermosphere because the molecular diffusion increases exponentially with height. In this study, a double-layer airglow network was used to capture concentric GWs (CGWs) over China that were excited by Typhoon Chaba (2016). We used ERA5 reanalysis data and Multi-functional Transport Satellite-1R observations to quantitatively describe the propagation processes of typhoon-generated CGWs from the troposphere, through the stratosphere and mesosphere, to the thermosphere. We found that the CGWs in the mesopause region were generated directly by the typhoon in the troposphere. However, the backward-ray-tracing analysis suggested that CGWs in the thermosphere originated from the secondary waves generated by the dissipation of the CGW and/or nonlinear processes in the mesopause region.

Experience-based systematic construction hazard warning

PurposeHazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated for individual characters and is not prioritized for the entire system. This study proposes a hazard warning scheme that prioritizes hazard characters from the inspection process based on the inspectors' experience.Design/methodology/approachFirst, hazard descriptions were decomposed into their characters, forming a double-layer network. Second, warning schemes based on cascading effects were proposed. Third, character-based warning schemes were simulated for various experiences.FindingsThe results show that when a specific hazard is detected, the degree centrality is the most effective parameter for prioritization, and hazard characters should be prioritized based on betweenness centrality for experienced inspectors, whereas degree centrality is preferred for novice inspectors.Originality/valueThe warning scheme theoretically supplements the information-processing theory in construction hazard warnings and provides a practical warning scheme with priority for the development of automated hazard navigation systems.

Large-Area Saline-Alkali-Tolerant Rice Growth Environment Monitoring System Based on LoRa+UAV

A wireless sensor network (WSN) monitoring system for saline-alkali-tolerant rice based on long-range + unmanned aerial vehicle (LoRa + UAV) has been proposed in this paper to meet the requirements of difficult deployment, low scalability, and poor network reliability of traditional farmland environmental monitoring systems. The system automatically forms a double-layer star topology network through the terminal node and central node (intranet), central node, and UAV mobile node (extranet). Data transmission is carried out through LoRa technology, routing planning is carried out for UAV mobile receiving nodes at different flight altitudes, and an early warning mechanism for UAV energy is added. It effectively prevents the UAV from destroying the communication capability of the external network due to energy exhaustion during operation. The system transmits information remotely to the server of the management center through a 5G network, which analyzes and processes the data and publishes it on the Web. The system has been tested in the field, and the reliable communication range of the node is 1∼4 km under the saline-alkali-tolerant rice environment, and the service life of the terminal node can reach more than 3 months. During continuous operation, the maximum packet loss rate of the internal and external integrated networks is 1.45%. The system can stably and real-time monitor saline-alkali-tolerant rice environmental information, and the error rate is less than 10%, which can meet the performance requirements of the saline-alkali-tolerant rice environmental monitoring systems, such as low power consumption, wide coverage, stability, and reliability, and has a good engineering significance.

Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network

In the post-epidemic period, the international economic structure has been readjusted, with risks contagious across financial and economic systems. This paper primarily uses the high-frequency TENET network and the Granger-causality network to describe the interconnectedness between the tail risk of stock volatility and investor sentiment, then the two-layer network is constructed by the generalized variance decomposition method to examine the inter-layer connectedness. Based on the two-layer network, the heterogeneity frequency response of network connectedness and dynamic network structure are further analyzed from the perspective of frequency domain. The study found that the tail risk of high-frequency stock volatility displays industry heterogeneity and time-varying property, and investor sentiment contagion network provides information transmission medium for stock risk. The double-layer network study found that stock volatility in consumer goods industry exhibits higher risk spillover to investor sentiment. The diversified financial industry, real estate industry and energy industry in the two-layer network are systemically important industries. In addition, the study of the frequency domain dynamic network found that the connectedness volatility in the short-term risk network of stock volatility was significantly higher than that of the investor sentiment network, and the short-term risk spillover effect of the network played a leading role in the total risk spillover. The research conclusions provide reference for preventing systemic risks from the perspective of systemically important industries and cyclical fluctuations.

Ultra-Wideband Full-Metal Planar Array Antenna With a Combination of Ridge Gap Waveguide and E-Plane Groove Gap Waveguide

An ultrawideband (UWB) full-metal planar array antenna based on a combination of ridge gap waveguide (RGW) and E-plane groove gap waveguide (E-GGW) is presented for millimeter-wave (mmWave) applications. First, the radiation element of a double-step double-ridged slot structure is proposed and designed. By removing the vertical walls of the cavity in the H-plane, the impedance matching characteristics presents a UWB bandwidth of 53%. Second, a UWB double-layer feeding network is achieved by combining RGWs and double-side-pin E-GGWs for the radiation elements. Finally, an 8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 8 full-metal planar array antenna with the presented elements and network is designed, prototyped, and measured. The measured results agree well with the simulated results. The proposed antenna provides an impedance matching bandwidth ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert &lt; -10$ </tex-math></inline-formula> dB) of 46.8% from 18.8 to 30.3 GHz. The maximum gain is 27.7 dBi at 27 GHz and a stable gain of 26 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm ~1.7$ </tex-math></inline-formula> dBi is obtained over the operation band.

A novel neural network and grey correlation analysis method for computation of the heat transfer limit of a loop heat pipe (LHP)

A loop heat pipe (LHP) has the advantages of larger heat transfer capacity and anti-gravity operational performance. The current prediction models for LHP heat transfer capacity have the difficulties in popularization of data volume and determination of accurate parametrical data, leading to the uncertain and varying outcomes that are inconsistent and away from reality. To address these challenges, this paper developed a first-of-its-kind big-data-driven LHP heat transfer limit prediction model by employing the neural network and grey correlation analysis method, which have advantages of high precision and large data volume. A double-layer feedforward neural network with sigmoid hidden neuron and linear output neuron was constructed to predict the heat transfer limit of the LHP. The grey scale analysis is applied to select the variables with correlation coefficient greater than 0.5, thus giving the clear identification of the both input parameters (e.g. refrigerant temperature, filling liquid quantity, height difference between evaporator and condenser, and number of heat pipe array) and output ones (heat transfer limit). The previously validated LHP heat transfer limit calculation model is used to calculate the heat transfer limit corresponding to the selected parameters, thus formulating 1,010,038 sets of data points. Of those calculated datasets, 707,026 (70% of data) are treated as a training set, 151,506 (15% of data) as a verification set, and 151,506 groups of data (15% of data) as the test sets for training. After several optimization and debugging, the number of hidden layer neurons is determined to be 100. The correlation coefficient (R), mean square error (MSE) and mean relative error (MRE) are 0.9997, 52.7 and 0.32% respectively, all of which are within reasonable accuracy range. The results show that the model has good prediction accuracy and consistence and is an effective tool to characterize and optimize the LHP in various application synergies.

Double-Layered Conductive Network Design of Flexible Strain Sensors for High Sensitivity and Wide Working Range.

For flexible strain sensors, the optimization between sensitivity and working range is a significant challenge due to the fact that high sensitivity and high working range are usually difficult to obtain at the same time. Herein, a breathable flexible strain sensor with a double-layered conductive network structure was designed and developed, which consists of a thermoplastic polyurethane (TPU)/carbon nanotube (CNT) layer (as a substrate layer) and a Ag nanowire (AgNW) layer. The TPU/CNT layer is made of electrospinning TPU with CNTs deposited onto the surface of TPU fibers, and the flexible TPU/CNT mat guarantees the integrity of the conductive path under a large strain. The AgNW layer was prepared by depositing different amounts of AgNWs on the surface of the TPU/CNT layer, and the high-conductivity AgNWs offer a low initial resistance. Benefitting from the synergistic two-layer structure, the as-obtained flexible strain sensor exhibits a very high sensitivity (up to 1477.7) and a very wide working range (up to 150%). Besides, the fabricated sensor exhibits fast response (88 ms), excellent dynamical stability (7000 cycles), and excellent breathability. The working mechanism of the strain sensor was further investigated using various techniques (microscopy, equivalent circuit, and thermal effects of current). Furthermore, the as-fabricated flexible strain sensors accurately detect the omnidirectional human motions, including subtle and large human motions. This work provides an efficient approach to achieve the optimization between high sensitivity and large working range of strain sensors, which may have great potential applications in health monitoring, body motion detection, and human-machine interactions.

Promoting the Diffusion of New Energy Vehicles under Dual Credit Policy: Asymmetric Competition and Cooperation in Complex Network

This study aims to investigate the impact of dual credit policy on the diffusion of new energy vehicles (NEVs) from the perspective of complex interactions among heterogeneous manufacturers. Thus, the market competition and credit cooperation relationships, constituting the complex interrelated system in NEV diffusion, are considered in this paper. To this end, we established a double-layer complex network to depict the asymmetric competition and cooperation structure, and developed an evolution game model on network, revealing the diffusion rule and trend of NEVs among manufacturers. Simulation results show that the existence of credit cooperation relationship can effectively improve the diffusion of NEVs, especially when implementing cooperative strategy that prioritizes helping manufacturers with low sale profits. Such a cooperative strategy is effective for maintaining high diffusion of NEVs even under low NEV credit’s trading price. Meanwhile, the monopoly competitive structure characterized by scale free is harmful to NEV diffusion. However, credit cooperation can eliminate the by-effect of monopoly structure greatly by adopting the strategy of distributing by sale profits. In addition, manufacturers are advised to learn from their competitors during the evolutionary process, which should be the focus of manufacturers to maximize the NEV diffusion under small world competitive structure.

The impact of social resource allocation on epidemic transmission in complex networks

This paper focuses on the social biological communication process on double-layer complex networks. We propose a virus resource asymmetric coupling propagation model to simulate the propagation process of virus affected by recovered resources, and use a generalized discrete Markov chain method to describe the propagation dynamics. This paper mainly considers the effects of initial seed fraction, network edge coincidence degree, degree distribution heterogeneity and public resource allocation ratio on virus transmission mechanism. We find that by increasing the edge coincidence rate between nodes in the two-tier network, the propagation range of the virus changes from discontinuous growth to continuous growth with the growth of propagation probability. When the heterogeneity of network degree distribution is weak and the initial seed fraction is small, with the increase of the proportion of public resources allocation, the critical probability of virus transmission gradually increases, and the critical probability of virus outbreak first decreases and then increases. At this time, there is a critical value for the public resource allocation proportion. When the public resource allocation proportion exceeds this value, the hybrid resource allocation strategy can be better than the neighbor resource allocation strategy. In other cases, increasing the proportion of public resources allocation will reduce the critical probability of virus transmission and increase the critical probability of virus global diffusion.

Risk propagation and evolution analysis of multi-level handlings at automated terminals based on double-layer dynamic network model

The propagation and evolution of risks in the system adversely affects the safety of automated terminal handlings. Therefore, it is necessary to explore the formation and development process of handling risks, predict the path and pattern of risk propagation and summarize the evolution features to improve the efficiency of multi-level handlings at automated terminals (ATMH). This paper proposes a double-layer dynamics risk propagation and evolution model. First, a complex network of risk relationships is established, and the Gillespie algorithm is employed to simulate the risk propagation dynamics based on the metric of state transition value and the dynamic mean-field theory. Next, an evolution hypernetwork is constructed and the average smooth hyperdegree distribution is derived using Poisson process and continuity methods. The simulation results show that the risk propagation link group obtained by the empirical testing is credible. The theoretical predictions of risk evolution agree well with the numerical simulations. The greater the probability of risk propagation, the more active the evolution. The model provides a certain theoretical reference for the risk management of automated terminals.

Quantum Information Protection Scheme Based on Reinforcement Learning for Periodic Surface Codes

Quantum information transfer is an information processing technology with high speed and high entanglement with the help of quantum mechanics principles. To solve the problem of quantum information getting easily lost during transmission, we choose topological quantum error correction codes as the best candidate codes to improve the fidelity of quantum information. The stability of topological error correction codes brings great convenience to error correction. The quantum error correction codes represented by surface codes have produced very good effects in the error correction mechanism. In order to solve the problem of strong spatial correlation and optimal decoding of surface codes, we introduced a reinforcement learning decoder that can effectively characterize the spatial correlation of error correction codes. At the same time, we use a double-layer convolutional neural network model in the confrontation network to find a better error correction chain, and the generation network can approach the best correction model, to ensure that the discriminant network corrects more nontrivial errors. To improve the efficiency of error correction, we introduced a double-Q algorithm and ResNet network to increase the error correction success rate and training speed of the surface code. Compared with the previous MWPM 0.005 decoder threshold, the success rate has slightly improved, which can reach up to 0.0068 decoder threshold. By using the residual neural network architecture, we saved one-third of the training time and increased the training accuracy to about 96.6%. Using a better training model, we have successfully increased the decoder threshold from 0.0068 to 0.0085, and the depolarized noise model being used does not require a priori basic noise, so that the error correction efficiency of the entire model has slightly improved. Finally, the fidelity of the quantum information has successfully improved from 0.2423 to 0.7423 by using the error correction protection schemes.

Key Repairing Node Identification in Double-Layer Logistic Networks

As important infrastructure, logistic networks need to be designed not only for robustness but also for transportation efficiency. In order to improve transportation efficiency, different types of logistic networks integrate to form a double-layer coupled network. When some nodes fail in this double-layer coupled network, especially in the case of limited repair resources, how to evaluate the node that needs to give priority to repair is of great significance. In this study, an evaluation method of key repairing node is proposed to find the key node which should be repaired first to restore the network performance. By comparing with traditional evaluation methods of key nodes, the effectiveness of the proposed method is verified.

Effect of a lone electron pair and tetrel interactions on the structure of Pb(II) CPs constructed from pyrimidine carboxylates and auxiliary inorganic ions

Three new Pb(II) coordination polymers (CPs) of pyrimidine-2-carboxylate ligand and different anionic co-ligands (perchlorate, chloride and nitrate) were synthesized and characterized by physicochemical (CHN, FT-IR, TG) and structural (SC-XRD) methods. Considering only strong primary coordination bonds the Pb(II) centres in all three complexes are hemidirectionally coordinated by organic ligands due to stereochemical activity of a lone electron pair. The Pb nodes and ligand linkers formed single- or double-layered 2D networks with honeycomb-like topology. The primary coordination sphere is sterically suitable for establishing the interactions at longer distances, where Pb centre act as Lewis acid. The MEP calculations confirmed the presence of a σ-hole in the proximity of Pb centres. Thus, all the Pb centres participate in Pb···O or Pb···Cl tetrel interactions with auxiliary inorganic ions, which together with secondary interactions of ligands and water molecules extended the structures into 3D supramolecular assemblies. The QTAIM calculations allowed for estimation of the energy of primary and secondary/tetrel interactions and to propose the theoretical criteria based on the |VBCP|/GBCP, HBCP/ρBCP, and DI parameters for distinguishing among interactions.