Multilayer Network Structure Research Articles

Should the multi-layer transportation network structure be reduced?

ABSTRACT The increasing diversity of transportation modes and the rapid expansion of transportation networks present significant challenges for modeling multi-layer comprehensive transportation networks. It is crucial to determine whether aggregating certain layers is a viable option for balancing complexity reduction and information preservation. This decision defines the layered structures and informs subsequent analyses of these networks. Two-dimensional factors, namely topological structures and transportation attributes, are considered to enhance understanding of the similarities among network layers. The relative entropy and the Gini index are employed as metrics to assess information gain or loss resulting from layer aggregation or segregation, guiding decisions on network reduction. Furthermore, an integrated similarity measure, based on the quantum Jensen-Shannon divergence and the Gower distance, is utilized to identify the optimal aggregation sequences. Two real-world transportation networks serve as case studies. Results demonstrate that these transportation networks are more effectively maintained with layer-separated structures, preserving maximum information.

Feasibility and mechanism of adsorption and bioreduction of hexavalent chromium using Rhodopseudomonas palustris immobilized on multiple materials

Rhodopseudomonas palustris immobilized on multiple materials was used to invistigate Cr(VI) adsorption and bioreduction. The highest Cr(VI) removal (97.5%) was achieved at 276h under the opitimed conditions of 2.5% SA, 8% PVA, and 50% filling degree. The highest adsorption capacity was obtained at 11.75 mg g−1 under 300 mg L−1 Cr(VI). Results from adsorption kinetics and isotherms indicated that Cr(VI) adsorption of immobilized photosynthetic bacteria (IPSB) was consistent with the Freundich model and the pseudo-second-order kinetic model (qe = 14.00 mg g−1). SEM and FTIR analyses verified that the porous multilayer network structure of IPSB provided more adsorption sites and functional groups for the removal of Cr(VI). Furthermore, the maximum Cr(VI) reduction efficiency of IPSB was achieved at 10.80 mg g−1, which correlated with the up-regulation of chrR gene expressions at 100 mg L−1 Cr(VI). This study demonstrated the dual mechanisms of Cr(VI) removal in IPSB-treated Cr wastewater, involving both chemisorption and bioreduction working synergistically.

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

Remediation effect and mechanism of immobilized laccase on actual trichloromethane-contaminated soil samples

Trichloromethane (TCM) in soil causes adverse effects on the soil ecosystem and human health. Therefore, it is urgent to develop an efficient and low-cost soil TCM removal technology. In this study, we prepared sodium alginate-biochar-laccase immobilized pellets (SA-BC-LC) using composite immobilization technology, exploring the fitting impact of artificial neural network model (ANN) and general linear model (GLM) models on laccase loading and the correlation properties of SA-BC-LC. The SA-BC-LC exhibited a porous multi-layer network structure internally. Compared to free laccase, the relative activity of SA-BC-LC remained above 50 % after 5 reuses and still maintained at 48 % after 50 days of storage. Moreover, SA-BC-LC demonstrated significant capacity for adsorption and degradation of TCM in soil with ratios approximately at 31.3 % and 68.7 %, respectively. During the remediation process of TCM-contaminated soil from Taizhou Chemical plant, there was a rapid decrease in TCM concentration within the first hour. Under IM (Immobilization) treatment (added with 5 % SA-BC-LC), the removal rate of TCM reached up to 88.9 % in the first hour. The adsorption of TCM by SA-BC-LC was mainly chemical adsorption, which induced the improvement of the relative abundance of the co-metabolic dechlorinated microbial Pseudomonas. Our research presents a promising material for effectively remediating soils contaminated with chlorinated hydrocarbons (CHs), with excellent removal efficiency.

Interconnectedness between Islamic and conventional banks: a multilayer network view

PurposeThis study aims to examine information (stock return, volatility and extreme risk) spillovers and interconnectedness within dual-banking systems.Design/methodology/approachUsing multilayer information spillover networks, this paper conduct a deep analysis of contagion dynamics among 24 Islamic and 46 conventional banks from 2006 to 2022.FindingsThe findings show the network’s rapid response to financial shocks. Through cross-sector analysis, this paper identify information spillovers between and within Islamic and conventional banking systems. Furthermore, this research illustrates distinct roles played by Islamic and conventional banks within the multilayer network structure, contingent upon the nature of the financial shock.Practical implicationsUnderstanding the differential roles of Islamic and conventional banks in information transmission can aid policymakers and financial institutions in devising more effective risk management strategies, thereby enhancing financial stability within dual-banking systems.Originality/valueThis study contributes to the literature by emphasizing the necessity of examining contagion mechanisms beyond traditional single-layer network structures, shedding light on the shadow dynamics of information transmission in dual-banking systems.

Precision prediction in grinding processes based on displacement signal conversion into recurrent plot

The processing of grinding data and the prediction of accuracy are extremely complex; this paper proposes a novel prediction method to ensure the machining accuracy of computer numerical control (CNC) grinding machines. It consists of two components, namely the filter decomposition recurrence plot (RP) transformation (FDRP) and the deep inverted residual attention network (DIRAN). A pipeline named FDRP has been designed to address the issues in the processing of data and the shortcomings of RP. Firstly, the displacement signals undergo filtering to preserve essential grinding information while effectively removing noise. Secondly, long-time series signals are decomposed and augmented based on the characteristics of the machining process. Lastly, an RP transformation is applied to one-dimensional time series data, resulting in the generation of images that accurately represent the grinding process. Furthermore, this paper proposes a novel machining accuracy prediction model. The DIRAN uses a multi-layer inverse residual network structure combined with attention mechanism to extract the features of two-dimensional RP, and its performance is better than other typical prediction methods. It can be applied to predict the polar angle of the workpiece in industrial processing and reduce the defect rate.

A lightweight transformer based on feature fusion and global–local parallel stacked self-activation unit for bearing fault diagnosis

Due to the complex environment and limited hardware resources in the industrial practice diagnosis tasks, deploying deep learning-based models with large parameters is challenging. A novel lightweight bearing fault diagnosis method, global–local parallel transformer (GLP-Transformer), is proposed for balanced diagnostic performance within resource constraints. In this end-to-end framework, a multi-channel vibration feature fusion embedding block is designed, which extracts multi-position sensor signals to acquire richer original features. Furthermore, a multi-layer network structure with alternately stacked global–local parallel self-activation unit is presented for fault feature mapping processing at low costs. This unit integrates the parameter efficiency of convolutional operation and the global feature extraction expertise of transformer. Experimental verification is performed on publicly available and self-built data platforms. Compared with other methods, GLP-Transformer significantly reduces the requirements for storage and computational resources (Params: 48.28K, FLOPs: 2.74M) while possessing advanced generalization ability and robustness.

A lightweight dynamic dual-damped wavelet-based convolutional neural network for interpretable bearing fault diagnosis

Wavelet-based convolutional neural networks (CNNS) have attracted widespread attention because they can improve the interpretability of intelligent fault diagnosis methods. However, the fault feature representation capability of typical wavelet-based convolution kernel frameworks must be strengthened to improve the diagnostic accuracy of complex faults. In the meantime, the large number of network parameters leads to high computational costs. To address these issues, a lightweight wavelet-based dynamic CNN, which comprises a dual-damping wavelet-based dynamic CNN (DWDC) block and a discrete wavelet transformation (DWT) enhancement (DWTE) block, is put forward. In the DWDC block, a wavelet convolution layer is initially designed, where a dual-damping wavelet is used as the kernel function to improve the match of the convolution kernel with fault impulses. Subsequently, a dynamic convolution layer with multiple parallel small-size convolutional kernels is designed to screen the fault features instead of a multilayer network structure, thereby greatly reducing the number of network parameters. Finally, the DWTE block is constructed by combining the DWT and residual dense block, and it can mine more fault information from the previously extracted features. The experiments on the variable speed bearing dataset, locomotive bearing dataset with constant speed and the Case Western Reserve University dataset prove that the proposed approach outperforms five classical CNN models and six advanced wavelet-based CNN models. In addition, it can effectively solve the issue of data imbalance because of its powerful feature extraction capability.

Surface modified molybdenum disulfide nanosheets for corrosion resistance improvement on polyurethane coatings

Abstract Organic–inorganic hybrid coating has been applied on metallic corrosion protection effectively. Molybdenum disulfide (MoS2) nanosheets with graphene-like two-dimensional lamellar structure were an anticorrosion inorganic additive, rendering the organic coating better corrosion resistant. However, the aggregation and poor solubility are still current issues that should be addressed. Functionalization MoS2 nanosheets with surface modified by polydopamine (PDA) and silane coupling agent (KH560) were prepared in polyurethane (PU) composite coatings to obtain dense and intact multilayer network structure coatings for corrosion protection. KH560-PDA-MoS2/PU coating with crosslinked polymer network structure has a high impedance modulus, large contact angle, and strong hydrophobicity. The coating meets the national technical standards for salt spray testing and nitric acid (HNO3) titration testing, demonstrating excellent corrosion resistance.

A lightweight feature activation guided multi-receptive field attention network for light compensation

Image enhancement techniques are commonly used to improve problems such as lack of brightness, high noise, and low contrast in low-light images. Notably, deep learning-based approaches have recently achieved substantial advancements in this domain. However, learning-based methods often require many parameters and multi-layer network structures to achieve high-quality enhancement effects, which limits their application in real-time image processing. To solve this problem, a lightweight Feature Activation Guided Multi-Receptive Field Attention Network (FAMANet) is designed in this paper. The Wavelet Feature Activation Block (WFAB) introduced in the network utilizes the discrete wavelet transform and residual connection to achieve selective activation of image features, thus reducing the redundant information in the feature map and improving the computational efficiency. In addition, the Multi-Receptive Field Attention (MRFA) introduced in this paper addresses the issue of inadequate pixel information and feature map loss stemming from a single input image by concentrating on the image structure, spanning from intricate details to the overall composition. By better-utilizing image information and distinguishing between global and local features, MRFA can improve the speed and efficiency of real-time image processing. After sufficient experimental validation, FAMANet significantly outperforms state-of-the-art methods in low-light image enhancement and exposure correction tasks.

Research on spacecraft in orbit perception based on artificial neural networks and digital twin technology using grating arrays

In order to improve the safety of spacecraft, the research on artificial neural network and digital twin technology based on, to our best knowledge, a novel fiber Bragg grating (FBG) sensor array is proposed for intelligent sensing monitoring of spacecraft on-orbit collisions. Femtosecond FBG arrays were fabricated on the novel oxide-doped fiber by point-by-point writing technique. The femtosecond FBG is analyzed using the time-dependent perturbation theory of quantum mechanics. The FBG array can achieve high-temperature measurement of 1100 °C and large strain measurement of 15000 µε. The sensing arrays were deployed on the surface of the spacecraft. Constructed the multi-layer perceptron neural network structure and convolutional neural network structure. 1200 samples were trained. Conducted model accuracy testing. The accuracy rate is above 98%, and accuracy verification has been implemented. The digital twin model was designed based on various data such as strain and temperature of the spacecraft structure under impact monitored by FBG sensors. A precise mapping has been formed between the physical entities of spacecraft and digital twins. Empower spacecraft with functions such as self-monitoring, judgment, and response. To ensure the stable and safe operation of spacecraft.

An Adaptive Learning Rate Deep Learning Optimizer Using Long and Short-Term Gradients Based on G–L Fractional-Order Derivative

AbstractDeep learning model is a multi-layered network structure, and the network parameters that evaluate the final performance of the model must be trained by a deep learning optimizer. In comparison to the mainstream optimizers that utilize integer-order derivatives reflecting only local information, fractional-order derivatives optimizers, which can capture global information, are gradually gaining attention. However, relying solely on the long-term estimated gradients computed from fractional-order derivatives while disregarding the influence of recent gradients on the optimization process can sometimes lead to issues such as local optima and slower optimization speeds. In this paper, we design an adaptive learning rate optimizer called AdaGL based on the Grünwald–Letnikov (G–L) fractional-order derivative. It changes the direction and step size of parameter updating dynamically according to the long-term and short-term gradients information, addressing the problem of falling into local minima or saddle points. To be specific, by utilizing the global memory of fractional-order calculus, we replace the gradient of parameter update with G–L fractional-order approximated gradient, making better use of the long-term curvature information in the past. Furthermore, considering that the recent gradient information often impacts the optimization phase significantly, we propose a step size control coefficient to adjust the learning rate in real-time. To compare the performance of the proposed AdaGL with the current advanced optimizers, we conduct several different deep learning tasks, including image classification on CNNs, node classification and graph classification on GNNs, image generation on GANs, and language modeling on LSTM. Extensive experimental results demonstrate that AdaGL achieves stable and fast convergence, excellent accuracy, and good generalization performance.

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

SIS Epidemic Spreading Under Multilayer Population Dispersal in Patchy Environments

We study SIS epidemic spreading models under population dispersal on multi-layer networks. We consider a patchy environment in which each patch comprises individuals belonging to different classes. Individuals disperse to other patches on a multi-layer network in which each layer corresponds to a class. The dispersal on each layer is modeled by a Continuous Time Markov Chain (CTMC). At each time, individuals disperse according to their CTMC and subsequently interact with the local individuals in the patch according to an SIS model. We establish the existence of various equilibria under different parameter regimes and establish their (almost) global asymptotic stability using Lyapunov techniques. We also derive simple conditions that highlight the influence of the multi-layer network on the stability of these equilibria. For this model, we study optimal intervention strategies using a convex optimization framework. Finally, we numerically illustrate the influence of the multi-layer network structure and the effectiveness of the optimal intervention strategies.

Embedding model of multilayer networks structure and its application to identify influential nodes

At present, there are many indexes used to quantify the structure of single layer complex networks, but multilayer networks are affected by the number of layers, which are difficult to represent the structure and complicated to calculate. In this paper, we propose a network embedding method to represent the structure information of multilayer networks, which reduces the computational complexity. After the structure of the multilayer networks is expressed, the key nodes in the network are mined to help control the spread of the epidemic, suppress the spread of rumors, and optimize the structure of the network. However, it is difficult to quantify this problem precisely because of the difference of judgment indexes. On the basis of representing the structure of multilayer networks, we propose a fuzzy Tsallis eXtropy (FTE) method for quantifying influential nodes in multilayer networks by combining fuzzy theory and entropy. In addition, FTE is tested by some practical networks and compared with other methods. The results reveal that the proposed method is effective.

Optimization strategy of cross-border e-commerce supply chain network based on machine learning

Abstract The rapid development of cross-border e-commerce increases the complexity of supply chain management, and machine learning-based supply chain network optimization strategies are essential for improving efficiency and reducing costs. The study first analyzes the cross-border supply chain network topology and determines the multilayer network structure. Subsequently, an optimization model based on particle swarm algorithm (PSO) is proposed, including the mathematical model of the algorithm and improvement measures. The practical effect of the optimization strategy was verified through example analysis. It is found that the supply chain optimized with particle swarm algorithm has significant Improvement in terms of shipping accuracy, surface transportation ratio, and unit transportation cost. For example, the shipping accuracy of product A in 2022 increased to 95.3% compared to 2021, the proportion of surface transportation increased to 96.5%, and the unit transportation cost decreased by RMB 2.175 per kilogram. This study shows that the particle swarm algorithm can effectively optimize the cross-border e-commerce supply chain network, which is significant in achieving efficient supply chain management.

Discrete-Time Quantum Walk on Multilayer Networks.

A Multilayer network is a potent platform that paves the way for the study of the interactions among entities in various networks with multiple types of relationships. This study explores the dynamics of discrete-time quantum walks on a multilayer network. We derive a recurrence formula for the coefficients of the wave function of a quantum walker on an undirected graph with a finite number of nodes. By extending this formula to include extra layers, we develop a simulation model to describe the time evolution of the quantum walker on a multilayer network. The time-averaged probability and the return probability of the quantum walker are studied with Fourier, and Grover walks on multilayer networks. Furthermore, we analyze the impact of decoherence on quantum transport, shedding light on how environmental interactions may impact the behavior of quantum walkers on multilayer network structures.

Bioinspired PM2.5 filter: An AgNWs reinforced PAN/PVP composite membrane with the porous and multilayered network structure

Particulate matter (PM) pollution poses a lethal threat to human health. Fortunately, nature has evolved efficient strategies to construct unique structures that exhibit high PM2.5 purification efficiency, one such example is found in the aloe leaf. Here, inspired by the surface structure of aloe vera leaves, a PAN/PVP nanofiber composite membrane with the porous and multilayered network structure (P-N) was designed and fabricated for efficient air filtration via electrospinning combined with hot-pressing technique. Furthermore, the dynamic purification process of PM2.5 in the P-N was racked and studied through a combination of experimental and computational fluid dynamics methods. The results demonstrated that the flow path of PM2.5 is extended and the tortuosity of flow channel is increased by the P-N, leading to a significant improvement in the purification efficiency. Notably, the P-N/Ag-Nylon composite membrane, fabricated by introducing AgNWs into the P-N, not only capitalizes on the inertia and gravity but also leverages electrostatic interactions to further enhance its adsorption capacity for PM2.5. At a voltage of 9 V, it achieves a pressure drop of 35 Pa with an impressive filtration efficiency of 99.49%. This work is expected to provide new perspectives on the microstructural design of air filtration materials with high purification efficiency.

Enhanced extracellular electron transfer of CoMn2O4@CNT as microbial fuel cell anode

Microbial fuel cells (MFCs) can recover electrical energy from organic wastewater using electrically active microorganisms (EAMs), both for wastewater treatment and electricity generation. However, the relatively low power output density currently prevents MFCs from being used commercially. The main reasons are low microbial load and slow extracellular electron transfer (EET) at the anode interface. Increasing the load of EAMs on the anode and the efficiency of EET are considered to be effective strategies to improve the overall performance of MFCs. Compared to the primitive carbon cloth (CC) anode, the CoMn2O4@CNT complex anode has a multi-layer porous network structure that provides a rich biocompatibility site for bacterial attachment. In addition, CoMn2O4@CNT can release positively charged cobalt ions (Co2+/Co3+) and manganese ions (Mn2+/Mn3+/Mn4+). Bimetallic cooperative regulation can effectively promote the attachment of electronegative microorganisms and the EET rate. In this study, CoMn2O4@CNT anode MFC exhibited excellent electrochemical performance (Rct 15.14 Ω) and electrical production (3269.34 mW m−2 0.62 V), and the output power density was 4.1 times that of CC anode MFC.

Preparation of nano‐silver electromagnetic interference shielding functional coating on PC+ABS plastic via Ar/H2 mixed atmospheric pressure plasma jet

AbstractMetal coatings with micron thickness, high conductivity, and a unique structure that can provide exceptional electromagnetic interference (EMI) shielding performance to insulation substrates are highly desirable, especially if the production method is inexpensive, pollution‐free, and efficient. In this study, the Ar/H2 mixed atmospheric pressure plasma jet (APPJ) was utilized to reduce AgNO3 to produce PC + ABS substrates coated with silver nanoparticles (AgNPs). It was found that the low roughness AgNPs coating has a multilayer network structure, which could exhibit high conductivity and excellent EMI shielding effectiveness (SE). In conclusion, this method is an excellent choice for producing metal EMI shielding coating.