Double-layer Network Research Articles

Releasing the power of innovation: How can a double-layer innovation network promote enterprise innovation from a patent perspective?

Releasing the power of innovation: How can a double-layer innovation network promote enterprise innovation from a patent perspective?

Modeling takeover time for non-driving related tasks using hybrid deep neural networks in conditionally automated vehicles

To enhance the safety of the transition from autonomous to manual driving control, one of the key factors is quantifying the safe takeover transition, namely the takeover time. Previous studies have focused on factors influencing takeover time and predicting takeover time using traditional methods, but there is a lack of research utilizing deep learning networks and driver physiological factors for takeover time prediction. Toward this end, we propose a hybrid deep learning network model dedicated to predicting drivers’ takeover time with a dataset obtained from a bench experiment involving 46 drivers. Firstly, to address the potential information loss commonly associated with traditional manual feature extraction techniques, a convolutional neural network (CNN) is developed to automatically extract physiological spatial representations from drivers. Additionally, a double-layer bidirectional long short-term memory network (DBiLSTM) is incorporated to model the physiological time representation dimension, enabling a more refined representation of physiological signals. Then, to tackle the complexities introduced by individual driver variations, a self-attention mechanism is enabling the model to automatically prioritize critical features, thereby enhancing the accuracy and reliability of its predictions. The results demonstrate that the proposed approach surpasses traditional machine learning network models in predictive accuracy for takeover time prediction, thereby exhibiting novelty in the field of vehicle takeover control research. This provides a more reliable technical foundation for anticipating the durations of vehicle takeovers.

Risk sharing framework and systemic tolerance in Indian banks: Double layer network approach

Interconnectedness spreads systemic risk and is critical in enhancing banks’ systemic tolerance through interbank liquidity and lines of credit. Literature on systemic risk has not considered the importance of interconnectedness in providing liquidity to improve banks’ systemic tolerance. As a bank’s resistivity towards systemic disruption depends on its tolerance, the current article develops a model to measure the systemic tolerance of individual banks in a two-layer interbank network using ΔCoVaR. It estimates systemic tolerance distance through a risk-sharing framework and analyzes the significance of macroeconomic and bank-specific factors in explaining systemic tolerance. The results support that systemic tolerance values are higher during the down-cycle than the up-cycle, signaling the importance of interconnectedness in protecting against systemic crises. The empirics further substantiate that risk-sharing distance is lower, and structure is complex with clusters during economic down-cycle. This highlights that banks couple with each other during stressful environments and empirically validate the importance of interbank and lines of credit in enhancing systemic tolerance and, therefore, possess the regulator to develop a robust interbank market through regulatory guidelines.

Study of Weight Quantization Associations over a Weight Range for Application in Memristor Devices

The development of hardware-based cognitive computing systems critically hinges upon the integration of memristor devices capable of versatile weight expression across a spectrum of resistance levels while preserving consistent electrical properties. This investigation aims to explore the practical implementation of a digit recognition system utilizing memristor devices with minimized weighting levels. Through the process of weight quantization for digits represented by 25 or 49 input signals, the study endeavors to ascertain the feasibility of digit recognition via neural network computation. The integration of memristor devices into the system architecture is poised to streamline the representation of the resistors required for weight expression, thereby facilitating the realization of neural-network-based cognitive systems. To minimize the information corruption in the system caused by weight quantization, we introduce the concept of “weight range” in this work. The weight range is the range between the maximum and minimum values of the weights in the neural network. We found that this has a direct impact on weight quantization, which reduces the number of digits represented by a weight below a certain level. This was found to help maintain the information integrity of the entire system despite the reduction in weight levels. Moreover, to validate the efficacy of the proposed methodology, quantized weights are systematically applied to an array of double-layer neural networks. This validation process involves the construction of cross-point array circuits with dimensions of 25 × 10 and 10 × 10, followed by a meticulous examination of the resultant changes in the recognition rate of randomly generated numbers through device simulations. Such endeavors contribute to advancing the understanding and practical implementation of hardware-based cognitive computing systems leveraging memristor devices and weight quantization techniques.

Modeling and Analysis of Public Transport Network in Hohhot Based on Complex Network

In the urban public transport network, the transfer of buses and subways provides convenience for residents to travel efficiently. But in actual operation, it is found that accidents, natural disasters, and other damage are inevitable. These sudden events may lead to route suspensions and service delays, ultimately resulting in network paralysis. In this paper, complex network theory is used to construct a weighted double-layer network model. Carrying capacity is considered the edge weight. The model analyzes the impact of these sudden events on network performance. It also conducts in-depth research on network structure and node importance. A collective influence (CI) algorithm is proposed as a centrality index to evaluate node importance. Based on the dynamic nature of the attacks, the network state is divided into initial network and current network. Taking Hohhot as an example, the results show that the network based on a CI algorithm node attack has the worst invulnerability. The network invulnerability based on an edge weight attack is better than that of edge betweenness. Compared with the current network, the invulnerability of the initial network is stronger. This indicates that ongoing changes and adaptations in the network may accelerate the decline in overall performance. At the same time, targeted interventions on key nodes and edges can enhance the network’s invulnerability. Planners can continuously monitor network performance to provide a basis for dynamic management and real-time adjustments. Additionally, effective information about critical routes to the public helps ensure the sustainable operation of the public transportation network.

Dissemination dynamics based on delayed behavior on double-layer networks

Adoption behavior spreads socially due to a variety of variables, and the classic dynamic model is insufficient to fully understand the impact of psychological elements on humans. To illustrate the intricacy of personal decision-making and information dissemination, this paper presents the psychological mechanism of latency. This research analyzes the dynamic process on double-layer networks using an edge division theory based method. The simulation results show the phenomena of phase transition. Global outbreak is possible when latency behavior is weak; global outbreak did not happen when latency behavior becomes strong. There is good agreement between the theoretical analysis and experimental simulation results. It is extremely important for the study of psychology, sociology, and dynamics.

Resilience optimization of bus-metro double-layer network against extreme weather events

The resilience of bus and metro systems to extreme weather events is a critical concern in urban planning, given their growing complexity and interconnectivity. Traditional studies often simplify or overlook the interdependency between different transportation modes, focusing on recovery strategies for a single mode to enhance system resilience. This study proposes an integrated resilience assessment framework for bus and metro systems, conceptualized as a Bus-Metro Double-Layer Network (B-M DLN). The framework considers both network structure and system function to accurately evaluate the B-M DLN resilience. A resilience optimization model for B-M DLN based on Genetic Algorithm (GA) is established to suggest the optimal recovery sequence of damaged stations, emphasizing the importance of station repair time, node strength, and node degree in recovery prioritization. Through a case analysis of Xi’an City, China, the B-M DLN shows significantly enhanced resilience when applying the optimal recovery strategy, especially in large-scale failure scenarios.

A double-layer optimization strategy for distribution networks considering 5G base station clusters

The reliability of the power supply for 5G base stations (BSs) is increasing. A large amount of BS backup energy storage (BES) remains underutilized. This study establishes a double-layer optimization distribution network (DN) considering BS clusters. An energy consumption characteristics and scheduling ability model of the BSs was established to address the differences in the characteristics of different traffic flows. A double-tier planning model for BS-joining grid market ancillary services is proposed. The upper-layer model addresses optimal tidal flow problems in DNs to minimize integrated operating costs, while the lower-layer model focuses on BES economic optimization. The double-layer model changes into a single-layer linear model using the Karush–Kuhn–Tucker (KKT) condition and the Big M method. Simulation validation using the IEEE-33 node DN proves that this approach can reduce DN operating costs, regulate voltage fluctuations, and guarantee economical and safe DN operation.

Fast, Zero-Reference Low-Light Image Enhancement with Camera Response Model.

Low-light images are prevalent in intelligent monitoring and many other applications, with low brightness hindering further processing. Although low-light image enhancement can reduce the influence of such problems, current methods often involve a complex network structure or many iterations, which are not conducive to their efficiency. This paper proposes a Zero-Reference Camera Response Network using a camera response model to achieve efficient enhancement for arbitrary low-light images. A double-layer parameter-generating network with a streamlined structure is established to extract the exposure ratio K from the radiation map, which is obtained by inverting the input through a camera response function. Then, K is used as the parameter of a brightness transformation function for one transformation on the low-light image to realize enhancement. In addition, a contrast-preserving brightness loss and an edge-preserving smoothness loss are designed without the requirement for references from the dataset. Both can further retain some key information in the inputs to improve precision. The enhancement is simplified and can reach more than twice the speed of similar methods. Extensive experiments on several LLIE datasets and the DARK FACE face detection dataset fully demonstrate our method's advantages, both subjectively and objectively.

Synthesis, crystal structures and fluorescence recognition properties of two novel chiral coordination compounds based on alanine derivate

Two novel chiral coordination compounds {[Cd(D-pmala)(2,2′-bipy)(H2O)]⋅1·25H2O}n (1), {[Pb4(D-pmala)4(5,5′-DM-2,2′-bipy)4(H2O)2]⋅7H2O}n (2) (D-pmala = N-(4-carboxyl) benzoyl-d-alanine, 2,2′-bipy = 2,2′-Bipyridine, 5,5′-DM-2,2′-bipy = 5,5′-Dimethyl-2,2′- Bipyridine) have been successfully synthesized under hydrothermal conditions. Their structures were identified using single-crystal X-ray crystallography and characterized by infrared spectroscopy, elemental analysis, thermogravimetric analysis, powder X-ray diffraction and solid-state circular dichroism spectra. Crystallographic analysis indicate that compound 1 features a "Z"-shaped one-dimensional chain structure, which further extends into three-dimensional supramolecular architectures through hydrogen-bonding interactions. Compound 2 displays a two-dimensional layer structure, a two-dimensional double-layer network structures is further formed through the hydrogen-bonding interactions. Moreover, The fluorescence recognition properties of compound 1 indicate that it is highly selective in detecting Cu2+ ions and tyrosine (Tyr) enantiomers in aqueous solutions, which indicates that compound 1 can be expected to be a bifunctional fluorescent recognition sensor to detect metal ions and amino acid enantiomers.

Deep learning approach for unified recognition of driver speed and lateral intentions using naturalistic driving data

Driver intention recognition is a critical component of advanced driver assistance systems, with significant implications for improving vehicle safety, intelligence, and fuel economy. However, previous research on driver intention recognition has not fully considered the influence of the driving environment on speed intentions and has not exploited the temporal dependency inherent in the lateral intentions to prevent erroneous changes in recognition. Furthermore, the coupling of speed and lateral intentions was overlooked; they were generally considered separately. To address these limitations, a unified recognition approach for speed and lateral intentions based on deep learning is presented in this study. First, extensive naturalistic driving data are collected, and information related to road slope and driving trajectories is extracted. A comprehensive classification of driver intentions is then performed. Toeplitz inverse covariance-based clustering and trajectory clustering methods are applied separately to label speed and lateral intentions, so that the influence of driving environments and the coupling of speed and lateral intentions are integrated into intention recognition. Finally, a deep-learning-based unified recognition model for driver intention is developed. This model uses a hierarchical recognition approach for speed intentions and includes a double-layer networks architecture with long short-term memory for the recognition of lateral intention. The validation results show that the created driver intention recognition model can accurately and stably recognize both speed and lateral intentions in complex driving environments.

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques.

Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

PurposeThis study aims to use geographical information on social media for public opinion risk identification during a crisis.Design/methodology/approachThis study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.FindingsIn the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.Originality/valuePublic opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

A bacterial cellulose/polyvinyl alcohol/nitro graphene oxide double layer network hydrogel efficiency antibacterial and promotes wound healing

In this study, graphene oxide (GO) was chemically modified utilizing concentrated nitric acid to produce a nitrated graphene oxide derivative (NGO) with enhanced oxidation level, improved dispersibility, and increased antibacterial activity. A double-layer composite hydrogel material (BC/PVA/NGO) with a core-shell structure was fabricated by utilizing bacterial cellulose (BC) and polyvinyl alcohol (PVA) binary composite hydrogel scaffold as the inner network template, and hydrophilic polymer (PVA) loaded with antibacterial material (NGO) as the outer network. The fabrication process involved physical crosslinking based on repeated freezing and thawing. The resulting BC/PVA/NGO hydrogel exhibited a porous structure, favorable mechanical properties, antibacterial efficacy, and biocompatibility. Subsequently, the performance of BC/PVA/NGO hydrogel in promoting wound healing was evaluated using a mouse skin injury model. The findings demonstrated that the BC/PVA/NGO hydrogel treatment group facilitated improved wound healing in the mouse skin injury model compared to the control group and the BC/PVA group. This enhanced wound healing capability was attributed primarily to the excellent antibacterial and tissue repair properties of the BC/PVA/NGO hydrogel.

An Effective Method to Prepare Curcumin-Loaded Soy Protein Isolate Nanoparticles Co-Stabilized by Carrageenan and Fucoidan.

In this study, a novel and simple strategy is proposed based on 3D network formed by easily blending polysaccharide carrageenan (Car) and fucoidan (Fuc) without a crosslinker. The Fuc/Car dual coating effectively assists the self-assembly of soy protein-isolated (SPI)/curcumin (Cur, C) composite microcapsules (SPI/C) and achieves an excellent curcumin encapsulation efficiency (EE) up to 95.28% with a 4.16% loading capacity (LC) under optimal conditions. The resulting nanocomposites achieved a satisfying redispersibility in aqueous solution and enhanced the water solubility with a lower size dispersity index (PDI) of 0.12 and a larger zeta potential of -29.67 mV. The Fuc/Car double-layer network not only dramatically improved its thermal stability and photostability, but also provided controlled release and enhanced antioxidant activity in in vitro conditions. The underlying mechanism of the self-assembly of the curcumin-loaded nanoparticles was also addressed. The results proved the feasibility of the encapsulation of unstable hydrophobic bioactive substances (curcumin) with the dual anionic polysaccharide Fuc/Car co-stabilized SPI nanoparticles. This study paves the way for an alternative way of developing novel curcumin delivery systems and will have broad prospects in the pharmaceutical industries.

Distributed algorithms of solving linear matrix equations via double-layered networks

This paper focuses on designing distributed algorithms for solving the linear matrix equation in the form of AXB=F via a double-layered multi-agent network. Firstly, we equivalently transform the least squares (LS) problem of AXB=F into two subproblems. We introduce a double-layered multi-agent network, in which each agent only has access to partial information of matrices A, B and F, to solve these two subproblems in a distributed structure. Subsequently, we develop continuous-time and discrete-time distributed algorithms to obtain an LS solution of AXB=F. In particular, the sufficient and necessary conditions about the step-sizes are established, under which the discrete-time algorithm can linearly converge to an LS solution. Compared with the previous results in which each agent needs to communicate multiple matrix variables with the neighbors, the proposed algorithms effectively reduce communication burden for each agent since only one matrix variable is communicated in the upper-layer network and the communication variables in the lower-layered network are vectors with lower dimensions than matrices. Finally, an application example is provided to demonstrate the effectiveness of the proposed algorithms.

DLAN:Modeling user long- and short-term preferences based on double-layer attention network for next point-of-interest recommendation

The next Point-of-Interest (POI) recommendation, in recent years, has attracted an extensive amount of attention from the academic community. RNN-based methods cannot establish effective long-term dependencies among the input sequences when capturing the user’s motion patterns, resulting in inadequate exploitation of user preferences. Besides, the majority of prior studies often neglect high-order neighborhood information in users’ check-in trajectory and their social relationships, yielding suboptimal recommendation efficacy. To address these issues, this paper proposes a novel Double-Layer Attention Network model, named DLAN. Firstly, DLAN incorporates a multi-head attention module that can combine first-order and high-order neighborhood information in user check-in trajectories, thereby effectively and parallelly capturing both long- and short-term preferences of users and overcoming the problem that RNN-based methods cannot establish long-term dependencies between sequences. Secondly, this paper designs a user similarity weighting layer to measure the influence of other users on the target users leverage the social relationships among them. Finally, comprehensive experiments are conducted on user check-in data from two cities, New York (NYC) and Tokyo (TKY), and the results demonstrate that DLAN achieves a performance in Accuracy and Mean Reverse Rank enhancement by 8.07% -36.67% compared to the state-of-the-art method. Moreover, to investigate the effect of dimensionality and the number of heads of the multi-head attention mechanism on the performance of the DLAN model, we have done sufficient sensitivity experiments.

CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

Identifying and preventing malicious network behavior is a challenge for establishing a secure network communication environment or system. Malicious activities in a network system can seriously threaten users’ privacy and potentially jeopardize the entire network infrastructure and functions. Furthermore, cyber-attacks have grown in complexity and number due to the ever-evolving digital landscape of computer and network devices in recent years. Analyzing network traffic using network intrusion detection systems (NIDSs) has become an integral security measure in modern networks to identify malicious and suspicious activities. However, most intrusion detection datasets contain imbalance classes, making it difficult for most existing classifiers to achieve good performance. In this paper, we propose a double-layer feature extraction and feature fusion technique (CNN-GRU-FF), which uses a modified focal loss function instead of the traditional cross-entropy to handle the class imbalance problem in the IDS datasets. We use the NSL-KDD and UNSW-NB15 datasets to evaluate the effectiveness of the proposed model. From the research findings, it is evident our CNN-GRU-FF method obtains a detection rate of 98.22% and 99.68% using the UNSW-NB15 and NSL-KDD datasets, respectively while maintaining low false alarm rates on both datasets. We compared the proposed model’s performance with seven baseline algorithms and other published methods in literature. It is evident from the performance results that our proposed method outperforms the state-of-the-art network intrusion detection methods.

Cavitation diagnosis method of centrifugal pump based on characteristic frequency and kurtosis

Centrifugal pumps are important equipment in industrial production. At present, vibration signals are often used to diagnose cavitation in centrifugal pumps, but the vibration signals are easy to be disturbed and the fault characteristics are unstable to be detected. In this paper, a single stage centrifugal pump is taken as the study object, and the vibration signals of various parts of the centrifugal pump cavitation state are collected under different flow conditions. The short-time Fourier transform and one-third octave analysis are performed on the filtered signals, and the characteristic frequency of cavitation and the energy near the characteristic frequency with the development of cavitation are obtained. Based on vibration signals, the vibration root mean square (rms) and kurtosis values of different cavitation states are obtained. Flow state, kurtosis, and rms are used as input variables in the double-layer backpropagation neural network model to identify and classify the cavitation states of centrifugal pumps. The results show that the trained neural network model can accurately identify and classify the cavitation state of the centrifugal pump under the conditions of low flow rate, rated flow rate, and large flow rate, and the accuracy is more than 99.5%. This study provides a new technique for diagnosing cavitation in centrifugal pumps.

Assessing the collaborative quality value in nuclear power intelligent construction: A value chain analysis method based on double-layer GERT network

Due to the large number of participants and the multi-dimensional heterogeneity of economic activities, the value creation links are hidden and the value relationship is difficult to identify. Only paying attention to the effect and cost input of a single enterprise’s quality business cannot maximize the value of collaborative quality control, and cannot meet the requirements of nuclear power intelligent construction. Therefore, we propose to use collaborative quality value to characterize the value creation process and coordinate the interest relationship and value distribution among participants. On this basis, a collaborative quality value chain analysis method based on the double-layer GERT network is studied to quantitatively calculate the collaborative quality value. Specifically, the collaborative quality assurance mode is constructed and the interaction behavior among participants is analyzed. The value transfer and enhancement across participants is represented by the double-layer GERT network model, and the global and local values of the value chain are quantitatively measured based on the equivalent transfer function. Then, the key quality assurance business and activities that restrict the whole chain value-added are explored, and the collaborative quality value-added strategies are proposed. Finally, the effectiveness of this method is verified by a case study involving the fabrication of reactor pressure vessel (RPV) support inside a nuclear power project. This study provides a new method for realizing the value evaluation of collaborative quality control and maximizing the collaborative quality value of the whole value chain.