Multiplexing Networks Research Articles

Orbital angular momentum mode multiplexing communication in multimode fibers

Vortex beams with orbital angular momentum (OAM) have infinite mode orthogonality, which can greatly boost communication capacity through mode-division multiplexing. However, current exploration of OAM beam transmission primarily focuses on free-space or specialty fibers limited by the OAM spiral phase wavefront characteristics. Due to the mode field phase sensitivity, these methods are constrained by turbulence interference and fabrication error tolerance, restricting transmission distance of OAM beam at practical application. Herein, we propose an OAM transmission scheme using commercial multimode fiber (MMF) exploiting the eigenmodes superposition theory. Leveraging linear superposition of HE and EH vector modes with ± (π/2) phase shift over MMF, OAM excitation and transmission can be supported with low interference. Simultaneously, due to the decreased demand for fabrication precision in the gradient core structure, the tolerance for machining errors is enhanced. As a proof-of-concept, we have demonstrated a multidimensional OAM multiplexing communication with 640-channel (4 OAM modes, 80 wavelengths and 2 polarization states) over a 5 km MMF, successfully transmitting QPSK-OFDM signals at a total rate of 15 Tbit/s. The system demonstrated OAM mode purity exceeding 65% and bit error rate (BER) below the forward error correction (FEC) threshold (3.8 × 10−3). Multiplexed OAM signals can be effectively transmitted in MMF over short to medium distances with acceptable mode crosstalk, while remaining compatible with wavelength and polarization dimensions, enabling the construction of multi-dimensional multiplexing local area networks.

The coupled dynamics of information-behavior-epidemic propagation considering the heterogeneity of adoption thresholds and network structures in multiplex networks

During epidemics, individual responses to prevention and control information vary significantly, affecting the execution of preventive measures. This paper introduces a novel model to analyze the co-evolution of the information-behavior-epidemic dynamic, accounting for the heterogeneity in individual behavioral adoption thresholds and network structures. The model consists of three layers: the upper layer, utilizing scale-free social networks to represent the diffusion of information; the middle and lower layers, employing community networks, to illustrate individuals’ decision-making and epidemic transmission processes, respectively. We applied the Heaviside step function to depict the effects of information and local environmental factors on individuals’ decisions. Through the microscopic Markov chain approach (MMCA), we determined the epidemic threshold. Our methodology also included extensive Monte Carlo (MC) simulations to validate our theoretical predictions. Our results reveal several key insights: (i) reducing community mobility and the threshold for adopting preventive behaviors can significantly restrain the scale of the epidemic and delay outbreaks; (ii) the level of individual activity in the physical contact network has a significant influence on epidemic transmission; (iii) integrating individuals’ behavior adoption thresholds induces a two-stage phase change, deviating from the continuous phase transition typically observed in epidemic transmission.

Investigating urban mobility through multi-source public transportation data: A multiplex network perspective

The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.

LC-MANet: Location-constrained joint optic disc and cup segmentation via multiplex aggregation network

Many early ophthalmology-related diseases can be detected through fundus images. The cup-to-disc ratio (CDR) is an important criterion for the early screening and diagnosis of glaucoma. However, accurately calculating it is a challenging task primarily due to blurred boundaries and the physiological structure of the fundus. To alleviate these issues, this paper designs a location-constrained joint optic disc (OD) and optic cup (OC) segmentation method using the multiplex aggregation network (LC-MANet). This method incorporates independent segmentation and joint segmentation within a coarse-to-fine framework. In the independent segmentation stage, this paper utilizes the initial OD and OC as prior location auto-context information to constrain the joint segmentation of OD and OC. The proposed multiplex aggregation network combines features from the corresponding layer and across layers to capture global and local information. This paper employs a multi-channel fusion strategy in image preprocessing to suppress vascular interference in OD independent segmentation. To emphasize the superiority of our method, it is important to highlight the significant number of test images used in the evaluation. Our experiments are conducted on three public datasets, namely RIM-ONE (60 images), Drishti-GS (51 images), and REFUGE (400 images). Experimental results demonstrate that our method outperforms most others in terms of Dice Coefficient and absolute error. The accuracy of glaucoma screening achieved by our proposed method for the Drishti-GS, REFUGE, and RIM-ONE-r3 datasets is 0.9216, 0.9600, and 0.9000, respectively. The proposed framework successfully designs a multiplex aggregation network for a coarse-to-fine joint segmentation model and achieves state-of-the-art performance in OD and OC segmentation. Furthermore, it can be applied to other medical image segmentation tasks.

Coupled Simultaneous Evolution of Policy, Enterprise Innovation Awareness, and Technology Diffusion in Multiplex Networks

This study comprehensively examines the coupling effect of government policies, enterprise behavior, and existing technology on the diffusion of innovative technology. Utilizing multiplex network theory, a multiplex network model is constructed to couple policy incentives, enterprise innovation consciousness, and technology diffusion. Both global- and local-order parameters are introduced to characterize the interaction mechanisms between new and old technologies. By employing the microscopic Markov chain approach (MMCA), the threshold for technology diffusion is derived, theoretically revealing the mechanisms behind the diffusion of innovative technology. Considering the heterogeneity of enterprises, a numerical simulation is conducted on a scale-free network. The results indicate that, as the intensity of policy incentives increases, the threshold for technology diffusion decreases, promoting the spread of innovative technology. Additionally, the coupling relationship between existing technology and innovative technology influences the diffusion scale of the latter. The innovation behavior of enterprises further facilitates the adoption and dissemination of innovative technology.

Finite/fixed-time cluster synchronization for directed and multiplex coupled dynamic networks

This article deals with finite/fixed-time cluster synchronization (FnTCS/FdTCS) issue for directed and multiplex coupled dynamic networks (MCDNs). Finite/fixed-time synchronization of multiplex networks has become a popular research topic, which indicates that, state information is utilized for synchronization, and the settling time depends on original condition for a range of finite time, while the time is bounded with arbitrary original value for fixed-time one. Through constructing appropriate feedback control protocol, FnTCS and FdTCS matters for MCDNs are settled under re-arranging variables' order technique. The present model contains the case that outer matrices (OMs) can be directed, with competitive elements, and not even connected, while previous and related researches assumed that OMs were undirected, cooperative and connected, so existing results can be improved for more general and broader regulations. This strategy above is presented to solve directed CDNs with multiweights from angle of inner and outer matrices, and we prove that if the weighted group of added OMs for each dimension is strongly connected, then cluster synchronization rules are established in finite/fixed time. Moreover, we also earn finite/fixed-time synchronization criteria when the cluster is single. In addition, the issue of FnTCS/FdTCS is developed for directed and multiplex coupled reaction-diffusion networks (MCRDNs) as an application. The validity of these gained results is verified by simulated examples.

A novel multi‐ary PolSK modulation for downstream signal in wavelength reusing wavelength division multiplexing passive optical network

A novel multi‐ary PolSK modulation for downstream signal in wavelength reusing wavelength division multiplexing passive optical network

Impact of community structure on the spread of epidemics on time-varying multiplex networks.

Community structure plays a crucial role in realistic networks and different communities can be created by groups of interest and activity events, and exploring the impact of community properties on collective dynamics is an active topic in the field of network science. Here, we propose a new coupled model with different time scales for online social networks and offline epidemic spreading networks, in which community structure is added into online social networks to investigate its role in the interacting dynamics between information diffusion and epidemic spreading. We obtain the analytical equations of epidemic threshold by MMC (Microscopic Markov Chain) method and conduct a large quantities of numerical simulations using Monte Carlo simulations in order to verify the accuracy of the MMC method, and more valuable insights are also obtained. The results indicate that an increase in the probability of the mobility of an individual can delay the spread of epidemic-related information in the network, as well as delaying the time of the peak of the infection density in the network. However, an increase in the contact ability of mobile individuals produces a facilitating effect on the spread of epidemics. Finally, it is also found that the stronger the acceptance of an individual to information coming from a different community, the lower the infection density in the network, which suggests that it has an inhibitory effect on the disease spreading.

Sagnac interferometer current transformer based on pulse multiplexing network structure

The monitoring of current plays an important role in accurately grasping the working status of the power system. In this paper, a Sagnac interferometer (SI) current transformer (CT) based on pulse multiplexing network structure is proposed. The network multiplexing structure can multiplex a pulsed light into multiple sensing signals, eliminating interference from light source power jitter and sudden changes in optical path loss. SI consists of a uniaxial working coupler, a 45° Faraday rotator, two quarter wave plates (QWPs), and spun fiber. The uniaxial working coupler used can achieve slow axis operation and fast axis cutoff, and convert light into linear polarization light. After passing QWPs, the forward and backward light changes from linear polarization to circular polarization, which can be used for current sensing. Experiments have shown that it has good linearity and repeatability. The proposed sensor has the advantages of low cost and simple structure, which has broad prospects in applications with not very high precision requirements.

Design and Initial Tests of a Fast Neutron Radiography Detector Prototype with Silicon Photomultiplier Readouts

Among non-destructive testing (NDT) techniques, fast neutron radiography with a higher penetration capability has achieved rapid advancements. However, the application of the radiography detector in many fast neutron imaging systems is limited by unfavorable detection efficiency and imaging spatial resolution. In this paper, a fast neutron radiography detector was designed, which was composed of a pixelated EJ200 scintillator array, a 16 × 16 silicon photomultiplier (SiPM) array, and capacitive multiplexing network readout electronics. The main parameters of the detector were optimized using Monte Carlo simulations. In addition, the prototype of the detector was fabricated and tested under a 14 MeV D-T neutron source. The preliminary test results demonstrated that the spatial resolution of the prototype reached 1.2 mm. Moreover, the conflict between spatial resolution and detection efficiency could be mitigated by using a pixelated scintillator structure. Overall, SiPMs enabled the extensive application of the imaging system because of their excellent photon detection performance, relatively low price, and joint possibility for large areas.

Finding multifaceted communities in multiplex networks

Identifying communities in multilayer networks is crucial for understanding the structural dynamics of complex systems. Traditional community detection algorithms often overlook the presence of overlapping edges within communities, despite the potential significance of such relationships. In this work, we introduce a novel modularity measure designed to uncover communities where nodes share specific multiple facets of connectivity. Our approach leverages a null network, an empirical layer of the multiplex network, not a random network, that can be one of the network layers or a complement graph of that, depending on the objective. By analyzing real-world social networks, we validate the effectiveness of our method in identifying meaningful communities with overlapping edges. The proposed approach offers valuable insights into the structural dynamics of multiplex systems, shedding light on nodes that share similar multifaceted connections.

Consensus Subspace Graph Regularization based on prior information for multiplex network clustering

Multiplex network clustering can identify the common cluster structure shared by all layers, which is of great significance for downstream research such as personalized recommendations of social relationships and the mining of social media communication behaviors. Traditional multiplex network clustering methods rely only on the topological structure and are suitable for networks with clear structures. However, real-world networks often suffer from sparse connectivity and noisy edges. In addition, the current methods based on prior information only use the obtained consensus prior information as a preprocessing method for multiplex networks, and do not fully utilize and integrate the prior information in the clustering process, resulting in a low accuracy. To solve the problem of insufficient utilization of prior information, we propose a Consensus Subspace Graph Regularization (CSGR) approach for multiplex network clustering, which integrates topological information with the consensus prior information of a network. We first construct the consensus prior matrix of a network by a non-overlapping greedy search method, which represents a subset of the network that consists of nodes with high edge density. Then we construct a graph regularization term, which encodes the consensus prior information, to optimize the generation of a joint low-dimensional representation within the consensus subspace for the entire multiplex network. Finally, we employ the consensus prior matrix to denoise each network layer, thereby yielding a precise low-dimensional representation of each layer for consensus subspace fusion. In addition, comprehensive experiments have demonstrated that CSGR improves the clustering accuracy of real-world networks by an average of 1.56%.

Flight delay propagation in the multiplex network system of airline networks

The flight delay propagation and recovery processes in airline networks differ due to differences in airline network structures, capacity distributions, and base airport locations. When each airline network is regarded as a network layer, flight delay propagation and interaction in a multiplex network system (MNS) composed of multiple airlines will become more complex and variable. In this paper, flight delays from different airlines are considered propagation sources, and an MNS-based flight delay propagation model is established under the framework of the susceptible-infected-susceptible model. By comparing the independent propagation process of delays in a single-layer airline network and the interactive propagation process of delays in an MNS, we investigate the impact of network structure on flight delays to propose corresponding delay control strategies for different airlines. The results show that unlike in single-layer networks, in an MNS, the behavior of airlines at an airport facilitates or inhibits delay propagation in other layers by influencing the network characteristics and propagation rate. The same airport may even play different roles in different layers, depending on the function it has. Therefore, not all flights are infected or have aggravated delays when accessing the MNS; instead, they are impacted by combined factors such as on-time performance, network structure, and flight interaction.

Dimension reduction approach for understanding resource-flow resilience to climate change

Networked dynamics are essential for assessing the resilience of lifeline infrastructures. The dimension-reduction approach was designed as an efficient way to map the high-dimensional dynamics to a low-dimensional representation capturing system-level behavior while taking into consideration network structure. However, its application to socio-technical systems has not been considered yet. Here, we extend the dimension-reduction approach to resource-flow dynamics in multiplex networks. We apply it to the San Francisco fuel transportation network, considering the flow between refineries, terminals and gas stations. We capture the aggregated dynamics between the facilities of each type and identify macroscopic conditions for the system to supply a given demand of fuel. By considering multiple sea level rise scenarios between 2020 and 2100, we address the impact of coastal flooding due to climate change on the maximum suppliable demand. Finally, we analyze the system’s transient response to production failures, investigating the temporary interruption in production and the duration it takes for complete demand satisfaction to become unachievable after the interruption.

Impact of message fatigue in information-disease coupled dynamics on temporal simplicial networks

During information diffusion, individuals typically experience fatigue and mental exhaustion due to repeated exposure to similar information, which is a state called message fatigue. To address message fatigue in information diffusion and group interactions, we developed a novel coupled information-disease spreading model within the framework of multiplex temporal networks. Here, the information network simulates the process of disease-related information diffusion using a temporal simplicial network. Furthermore, we utilize a threshold model to characterize the effects of message fatigue. We describe the dynamic evolutionary equations of the model by the microscopic Markov chain (MMC) approach to determine the epidemic threshold. We also validated the proposed model using a number of Monte Carlo (MC) simulations. The experimental results of the MC simulations and MMC theoretical results are in good agreement. Our research has shown that message fatigue can lead to a two-stage change in the information-disease coupled spreading. Raising the threshold for message fatigue and preventing a large number of individuals from experiencing it can help inhibit the disease spreading. In short, excessive publicizing of related-disease information can backfire. The results can facilitate us to accurately capture the spreading characteristics of real-world diseases.

Wayward associations: When and why people think of similar-sounding words

Words with similar meanings sometimes sound similar, which carries both risks, such as confusion, and rewards, such as ease of comprehension. It has been argued that languages evolve to balance these competing pressures, so words more often overlap in both form and meaning when they are less likely to be confused. By measuring the phonological similarity of responses to English cues in a word association megastudy, we provide evidence of a tendency to activate similar-sounding words in response to words that reside in sparse semantic neighbourhoods and in response to words for abstract concepts. Crucially, we provide evidence that the availability of similar-sounding associates helps people retrieve and represent the meanings of words from sparse neighbourhoods and words for abstract concepts, as measured by reaction time in semantic decisions and by accuracy in recognition memory. We propose that phonological connections compensate for weak semantic connections when representing word meanings, which we discuss in terms of multiplex networks, models of word-meaning access, and theories of language evolution.

Method to link medicines to diseases using multiplex networks

The reuse of well-established medicines using computational modeling has gained a lot of attention due to its tremendous benefits. Based on this perspective, a new method for linking known medicines to diseases is proposed. The creation of a new treatment or medicine can be financially and temporally costly and the reuse of medicines is one possibility to accelerate this process efficiently. The main purpose of the reuse of medicines is to reduce some stages of the development of new medicines, motivating the proposition of several methods nowadays. In this work, a new method is developed aiming to connect known medicines to diseases based on available networks of protein interactions and available lists of medicines that affect protein action. The concepts of multiplex networks are used to connect subgraphs of vertices that represent medicines and proteins. The core of the procedure is determined by a weighting strategy constructed to define precisely the more relevant connections. The method was compared to other network link methods in the literature and a case study was presented and evaluated by the proposed method.

Unpacking the effects of R&D strategy proximity on inter-firm knowledge flow: evidence from the global 3D printing industry

ABSTRACT The determinants of inter-organisational knowledge flow have shifted from bilateral and static factors to network and dynamic factors. Therefore, this paper aims to explore the impact mechanism of firms’ dynamic behaviours on knowledge flow in a multiplex inter-firm strategy proximity network. The paper proposes a conceptual model to examine how the different research and development (R&D) dynamic strategies impact the inter-firm knowledge flow from a network-to-network lens, considering the moderating effect of the firm size heterogeneity. To test the hypotheses, we conducted an empirical study on 150 listed firms in the global 3D printing industry from 2005 to 2016 using multi-source heterogeneous data and Quadratic Assignment Procedure (QAP) regression model. Results show that the proximities of the R&D internationalisation strategy and R&D domain strategy are positively associated with the inter-firm knowledge flow. Firm size heterogeneity plays a moderating role, and these effects vary at different stages of industrialisation.

Free-standing microscale photonic lantern spatial mode (De-)multiplexer fabricated using 3D nanoprinting

Photonic lantern (PL) spatial multiplexers show great promise for a range of applications, such as future high-capacity mode division multiplexing (MDM) optical communication networks and free-space optical communication. They enable efficient conversion between multiple single-mode (SM) sources and a multimode (MM) waveguide of the same dimension. PL multiplexers operate by facilitating adiabatic transitions between the SM arrayed space and the single MM space. However, current fabrication methods are forcing the size of these devices to multi-millimeters, making integration with micro-scale photonic systems quite challenging. The advent of 3D micro and nano printing techniques enables the fabrication of freestanding photonic structures with a high refractive index contrast (photopolymer-air). In this work we present the design, fabrication, and characterization of a 6-mode mixing, 375 µm long PL that enables the conversion between six single-mode inputs and a single six-mode waveguide. The PL was designed using a genetic algorithm based inverse design approach and fabricated directly on a 7-core fiber using a commercial two-photon polymerization-based 3D printer and a photopolymer. Although the waveguides exhibit high index contrast, low insertion loss (−2.6 dB), polarization dependent (−0.2 dB) and mode dependent loss (−4.4 dB) were measured.

Time-division multiplexing wireless light communication network

Owing to emission-detection spectral overlap, a quantum well (QW) diode can detect shorter-wavelength photons emitted from another diode sharing the same QW active region. Therefore, a wireless light communication system can be established by using identical QW diodes that function separately as transmitters and receivers. Here, we investigate the irreversibility between light emission and detection of the QW diode and present a time-division multiplexing (TDM) visible light communication (VLC) network using five identical blue QW diodes that are defined by software as transmitters or receivers to achieve real-time TDM-integrated interconnection via the same optical paths. These results indicate the great potential of realizing an advanced TDM VLC network for diverse applications.