Network Path Research Articles

Research on the development path of higher education model innovation based on quadratic planning algorithm

Abstract According to the internal and external constraints of higher education model innovation and development as well as the innovation power, this paper proposes the higher education model development path based on the quadratic planning algorithm and neural network. Using the establishment of a quadratic planning algorithm and neural network model, the EM-AGA-BP algorithm is selected to construct the quality prediction model of higher education model innovation path of deep neural network based on the establishment of the quality evaluation system. The proposed algorithmic path is analyzed for development prediction, and the algorithm is recommended for 2020-level learners, which are classified into different types based on their learning and the relationship between the achievement of course objectives and the similarity of educational paths is explored separately for different types of learners. In type 4 learners, in addition to the similarity of 20%-40%, the total course goal attainment decreases by 0.0085. For the same type of learners, the more similar the educational path is to the recommended path, the better the total course goal attainment is.

Sandwich structure of negative electrode using N-doped cellulose based carbon aerogel/graphene composite in ultra-battery

This study introduced a new ultra-battery (UB) structure by replacing N-doped monolithic carbon sheets instead of lead grids as a current collector. The carbon structure creates a sandwich construction (C|Pb|C), which serves as an alternative to lead grids and enhances the electrochemical properties of the battery. By removing lead grids, the weight of negative electrodes was reduced, and the specific capacity based on the total weight of the electrode (NAM + lead grids) increased by 1.3 times in deep discharge conditions. Additionally, the cycle life of the UB increased up to 2.35 times in HRPSoC mode. These improvements are due to the 3D and continuous conductive network path and higher charge acceptance of N-doped carbon sheets. These features facilitate the reversibility reaction of lead sulfate to lead by reducing the growth rate and size of created sulfate crystals. FESEM and X-ray analysis verified these phenomena. Linear sweep voltammetry (LSV) has shown that optimizing N-doping processes can control hydrogen evolution reaction (HER) in UBs. Furthermore, the UB's performance was improved and verified through charge/discharge, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) tests.

Goal quest for an intelligent surfer moving in a chaotic flow.

We consider a model of an intelligent surfer moving on the Ulam network generated by a chaotic dynamics in the Chirikov standard map. This directed network is obtained by the Ulam method with a division of the phase space in cells of fixed size forming the nodes of a Markov chain. The goal quest for this surfer is to determine the network path from an initial node A to a final node B with minimal resistance given by the sum of inverse transition probabilities. We develop an algorithm for the intelligent surfer that allows us to perform the quest in a small number of transitions which grows only logarithmically with the network size. The optimal path search is done on a fractal intersection set formed by nodes with small Erdös numbers of the forward and inverted networks. The intelligent surfer exponentially outperforms a naive surfer who tries to minimize its phase space distance to target B. We argue that such an algorithm provides unique hints for motion control in chaotic flows.

Effects of Topological Properties with Local Variable Apertures on Solute Transport through Three-Dimensional Discrete Fracture Networks

In this study, the effects of topological properties with local variable apertures on fluid flow and solute transport through three-dimensional (3D) discrete fracture networks (DFNs) were investigated. A series of 3D DFNs with different fracture density, length, and aperture distribution were generated. The fluid flow and solute transport through the models were simulated by combining the MATLAB code and COMSOL Multiphysics. The effects of network topology and aperture heterogeneity on fluid flow and transport process were analyzed. The results show that the fluid flow and solute transport exhibit a strong channeling effect even in the DFNs with identical aperture, in which the areas of fast and slow migration fit well with the high- and low-flow regions, respectively. More obvious preferential paths of flow and migration are observed in individual fractures for the DFN with heterogeneous aperture than the model with identical aperture. Increasing the fracture length exponent reduces the available flow and transport paths for sparse fracture networks but does not significantly change the flow and transport channels for dense fracture networks. The breakthrough curves (BTCs) shift towards the right and slightly lag behind as the fracture density decreases or the aperture heterogeneity increases. The advection–diffusion equation (ADE) model cannot properly capture the evolution of BTCs for 3D DFNs, especially the long tails of BTCs. Compared to the ADE model, the mobile-immobile model (MIM) model separating the liquid phase into flowing and stagnate regions is proven to better fit the BTCs of 3D DFNs with heterogeneous aperture.

Machine Learning Based Resilience Testing of an Address Randomization Cyber Defense

Moving target defenses (MTDs) are widely used as an active defense strategy for thwarting cyberattacks on cyber-physical systems by increasing diversity of software and network paths. Recently, machine Learning (ML) and deep Learning (DL) models have been demonstrated to defeat some of the cyber defenses by learning attack detection patterns and defense strategies. It raises concerns about the susceptibility of MTD to ML and DL methods. In this paper, we analyze the effectiveness of ML and DL models when it comes to deciphering MTD methods and ultimately evade MTD-based protections in real-time systems. Specifically, we consider a MTD algorithm that periodically randomizes address assignments within the MIL-STD-1553 protocol—a military standard serial data bus. Two ML and DL-based tasks are performed on MIL-STD-1553 protocol to measure the effectiveness of the learning models in deciphering the MTD algorithm: 1) determining whether there is an address assignments change i.e. whether the given system employs a MTD protocol and if it does 2) predicting the future address assignments. The supervised learning models (random forest and k-nearest neighbors) effectively detected the address assignment changes and classified whether the given system is equipped with a specified MTD protocol. On the other hand, the unsupervised learning model (K-means) was significantly less effective. The DL model (long short-term memory) was able to predict the future addresses with varied effectiveness based on MTD algorithm's settings.

Randomly Distributed Crab Burrows Enhance Groundwater Flow and Salt Transport in Creek‐Marsh Systems

AbstractMacropores created by crab burrowing are commonly found in vegetated coastal wetlands. However, their impact on surface water‐groundwater interactions and salt transport is insufficiently understood. In this study, we used numerical models to quantify the impact of crab burrows with random spatial distribution, morphology, and openings on groundwater flow and salt transport in creek‐marsh systems. Results showed that these burrows can lead to a more complex network of preferential flow paths and burrow flushing capabilities than those with a single pipeline. The velocity magnitude varied over seven orders in the burrowed mud layer. Local increases in the hydraulic gradient occurred in the burrows, leading to faster water circulation and salt transport as well as turnover of soil aeration. The salinity front in the burrowed marsh reached two‐m deeper than in the unburrowed marsh. Burrow flushing depth and intensity is significantly enhanced in burrows with multiple openings. This study emphasized the complexity of tortuous burrows regulating groundwater flow and solute transport through modeling realistic crab burrows. This work supports fiddler crabs as ubiquitous “ecoengineers” who can produce more significant impact on local hydrological cycle and ecosystem‐scale geochemical processes than previously indicated.

Inter-Core Crosstalk-Aware Backup Network Design Model against Probabilistic Link Failures in Multi-Core Fiber Optical Path Network

Multi-core fiber (MCF) is one of the promising space-division multiplexing technologies to increase the capacity of optical networks. MCF-based networks have two challenges. One is the inter-core crosstalk (XT) that degrades the quality of optical signals in two neighboring fiber cores. The other is network protection against link failures that cause massive data loss. One way to protect against multiple link failures is to prepare physically separated links as a backup network. Probabilistic protection improves the efficiency of protection by allowing a certain probability of protection failure. Existing studies on backup network design with probabilistic protection do not target MCF-based networks, which raises problems such as protection failure due to the inter-core XT and excessive consumption of optical resources. To address these problems, this paper proposes a XT-aware backup network design model for the MCF optical path networks. The proposed model protects the network against probabilistic multiple link failures. We adopt probabilistic protection that allows a certain probability of protection failure due to the inter-core XT and minimizes the required number of links in the backup network. We present an algorithm to satisfy the probabilistic protection requirement and formulate the model as an integer linear programming problem. We develop a heuristic approach to apply the proposed model to larger networks. Numerical results observe that the proposed model requires fewer links than the dedicated allocation model, which provisions the backup paths in the same manner as the primary paths.

Cluster-Disjoint Multipath Routing Protocol for Real-Time and Reliable Packet Transmission in Wireless Sensor Networks.

Multipath routing protocol, which is one of the typical schemes to improve end-to-end transmission success ratio in Wireless Sensor Networks (WSNs), exploits two or more paths. However, collisions and interference might occur when each path is adjacent. To prevent this problem, they construct node- or link-disjointed paths. Although the paths could prevent the above-mentioned problem, it still has an issue in the area of time delay. To exploit the disjointed paths, pre-path construction is required. In addition, a lot of time is incurred to recover the whole path when the part of the path is damaged. This phenomenon adversely affects the end-to-end transmission success ratio and transmission time. To alleviate this problem, we propose a cluster-disjoint multipath routing protocol. The proposed scheme divides the whole network into grid clusters of a certain size in the network initialization phase. Each sensor could transmit packets through the pre-constructed cluster structure without the path construction phase. Also, even if any node fails, it could be easily replaced by other nodes within the cluster region. The simulation results show that the proposed scheme has an advantage in terms of transmission delay and end-to-end transmission success ratio compared to existing multipath routing protocols.

Research on Safety Risk Transfer in Subway Shield Construction Based on Text Mining and Complex Networks

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction being more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks (SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules, and complex networks to deep mine subway construction safety incident reports and explore the risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risks. Then, association rules are introduced to explore the causal relationships among safety risks. Finally, the key safety risks and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risks in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key safety risk or cutting off the transfer paths. This paper provides new ideas and methods for SCSR identification and influence element mining, and the results of the study help safety managers propose accurate subway construction safety risk control measures.

End-to-end active queue management with Named-Data Networking

The innovative information-based Named-Data Networking (NDN) architecture provides a good opportunity to rethink many of the design decisions that are taken for granted in the Internet today. For example, active queue management (AQM) tasks have been traditionally implemented in the routers to alleviate network congestion before their buffers fill up. However, AQM operations could be performed on an end-to-end basis by taking advantage of NDN features. In this paper, we provide an implementation of an AQM algorithm for the NDN architecture that we use to drive a classical AIMD-based congestion control protocol at the receivers. To accomplish this, we take advantage of the 64-bit Congestion Mark field present in the link layer of NDN packets to encode both rate and delay information about each transmission queue along a network path. In order to make the solution scalable, this information is delivered stochastically, guaranteeing that receivers get accurate and updated information about every pertinent queue. This information is enough to implement the well-known controlled delay (CoDel) AQM algorithm. Simulation results show that our client-located CoDel implementation is able to react to congestion when the bottleneck queuing delay surpasses the 5ms target set by the usual in-network CoDel implementation and, at the same time, get a fair and efficient share of the available transmission capacity.

Predicting spontaneous termination of atrial fibrillation based on dual path network and feature selection

The termination mechanism of Atrial Fibrillation (AF) is still unclear. In order to avoid the potential incomprehensiveness in feature extraction via manual techniques, a Dual Path Network-SVM (DPNet-SVM) was constructed in this study for predicting the spontaneous termination of AF. The dual paths of the network extracted time and time–frequency domain features, respectively. Different sizes of depthwise separable convolution (DS-Conv) were used in both paths, which enhanced feature extraction via exploration of different sensory fields. Then four methods were used for feature selection. Finally, SVM was employed for AF termination prediction. After validating the model performance based on 1-min fragments, we conducted experiments on 10-second fragments. On the AFTDB public dataset, the accuracy, sensitivity and specificity of 95.6%, 93.3% and 91.8% were obtained, respectively. On the clinical data from Shandong Provincial Hospital (SPHD), the three metrics were 94.3%, 92.9% and 95.4%, respectively. The subsequent cross-database experiment proved the model's good generalization capability, and also substantiated its feasibility in practical applications involving wearable ECG data.

The optimization of an EV decommissioned battery recycling network: A third-party approach

In this paper, we solve the urgent problem to construct a recycling network of decommissioned batteries of Electric Vehicles (EVs) and clarify the recycling entities that will be responsible for its reverse logistics (RL) process. We consider the third-party recycling entities to develop a recycling network and conduct a case-study of Xi'an, a key industry of EVs in China to provide a reference for the government and enterprises to develop recycling plans. We scientifically optimize our recycling network, which will have a significant impact on the environmental and economic benefits of electric vehicles (EVs) in Xi'an in the future. Specifically, we consider the costs of transportation, construction, operation, recycling, packaging, and emission, as well as the profits achieved through sales revenue and subsidy offerings. We collect the actual data of potential facility locations in Xi'an, predict the quantity of decommissioned batteries in the future, and develop a fuzzy-based model to solve the optimal results of battery traveling path and distribution in the recycling process network. Our results show that with the rapid growth of the number of decommissioned batteries, third-party revenues will reach about 53.08 billion by 2035. When the facilities split the recycling process load appropriately, the network has increase in revenue while the utilization rate of facilities will decrease. We expect that splitting will be a major trend in the future development of recycling network in Xi'an. Finally, a sensitivity analysis finds that with the environmentally conscious and safe operation of recycling, the negative impact on the third-party enterprises' revenue will be small. Our proposed methodology can serve as a critical framework for other cities and governments to plan their recycling networks and formulate regulations, reflecting on the realistic projection of the scale of decommissioned batteries of EVs and the potential siting and sizing of the recycling facilities.

Mapping and Exploring the Conditions and Purpose of Using Trail Paths in a Medium-Sized Suburban Environment

The current paper presents the results of a questionnaire survey which examines the trail path usage conditions on Peranthi Hill in Arta, a medium-sized city in northwestern Greece. The key element of the survey is the detailed mapping of the suburban trail path network of the city, and it focuses on questions about the types of activities of people who walk or bike on the trail paths and the day, time, duration and distance of their trips, as well as their equipment (bicycles, clothes, shoes, etc.). Further questions focus on maintenance and safety issues (e.g., security, guarding, cleanliness) and infrastructures which are considered necessary in order to improve the trail paths’ environment and their use. Taking into consideration that several routes are being developed on Peranthi Hill, with distinct differences in their layouts, the preferred layout is explored in relation to each activity selected by the users. The results of the questionnaire survey, and the cluster analysis specifically, aim to address these issues and assist stakeholders and policymakers in improving the trail paths in terms of their utility and usability level.

Towards identifying influential nodes in complex networks using semi-local centrality metrics

The influence of the node refers to the ability of the node to disseminate information. The faster and wider the node spreads, the greater its influence. There are many classical topological metrics that can be used to evaluate the influencing ability of nodes. Degree centrality, betweenness centrality, closeness centrality and local centrality are among the most common metrics for identifying influential nodes in complex networks. Degree centrality is very simple but not very effective. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but they are not compatible on large-scale networks due to their high complexity. In order to design a ranking method of influential nodes, in this paper a new semi-local centrality metric is proposed based on the relative change in the average shortest path of the entire network. Meanwhile, our metric provides a quantitative global importance model to measure the overall influence of each node. To evaluate the performance of the proposed centrality metric, we use the Susceptible-Infected-Recovered (SIR) epidemic model. Experimental results on several real-world networks show that the proposed metric has competitive performance in identifying influential nodes with existing equivalent centrality metrics and has high efficiency in dealing with large-scale networks. The effectiveness of the proposed metric has been proven with numerical examples and Kendall's coefficient.

Novel solar forecasting scheme modelled by mixer dual path network and based on sky images

The prediction of global horizontal irradiance has become an effective technique to address the intermittence issue of photovoltaic (PV) power generation. This article proposes a novel deep neural network(DNN), named Mixer Dual Path Network (Mixer-DPN), for promising solar forecasting. It shares common features of cloud images and maintains the flexibility to explore new features through dual-path architecture by combining the Mixer layer and Dual Path Network. Therefore, the proposed model can provide more accurate prediction results compared to the classical DNN-based predictors. Moreover, the proposed model shows a faster convergence speed and smaller model size, which makes it suitable for a practical global horizontal irradiance. The merits of the proposed model are verified by testing it with the data from National Renewable Energy Laboratory comparing it with other DNN-based prediction models. Studies have shown that the new model has achieved excellent results in MSE, MAE and other indicators, and the R2 prediction accuracy rate has increased by 14% compared with the baseline model.

DP-FishNet: Dual-path Pyramid Vision Transformer-based underwater fish detection network

The detection of underwater fish targets is critical for ecological monitoring and marine biodiversity research. However, underwater fish detection is typically constrained by problems including low image quality and variable underwater surroundings. On behalf of further improving the underwater fish detection accuracy in complex underwater environments, this paper proposes a dual-path (DP) Pyramid Vision Transformer (PVT) feature extraction network named DP-FishNet. The backbone network DP-PVT composed from the PVT Network is made up of two feature extraction paths. The first represents the Vision Transformer path, which extracts global features to enhance the distinction between the foreground and background of underwater images. The second is the convolutional neural network path, which enhances the accuracy of detecting small targets by extracting local features. Additionally, to more effectively utilize the feature information extracted by the network, this paper provides a promising solution to employ the content-aware reassembly of features (Carafe) in the feature pyramid network (FPN). The seesaw loss is utilized as a classification loss to address the problem of unbalanced samples caused by the gap in the number of fish populations. According to the experimental findings, the AP and AP50 of the DP-FishNet are 76.0% and 95.2%, respectively. In comparison to currently available advanced two-stage detection algorithms, the quantity of computation and parameters is reduced by approximately 40%. DP-FishNet strengthens the ability to extract global and local features from underwater images and enhances feature reuse. DP-FishNet can be utilized to detect fish targets in actual and complicated underwater habitats.

In-situ construction of CNTs/laterite composites for enhanced microwave absorption

Carbon nanotubes have the significant disadvantage of insufficient magnetic loss as a microwave absorbing material, while transition metal catalysts for the preparation of carbon nanotubes still limit the cost reduction. In this study, a facile in-situ strategy was developed to synthesize CNTs/laterite composites, smartly employing the catalysts performance of laterite to realize the synthesis of CNTs and simultaneously construct three-dimensional (3D) “caterpillar” structure. As expected, the exposed CNTs with high crystallinity in caterpillar-like CNTs/laterite composites are believed to provide a good conductive 3D network and abundant paths for trapping of incident electromagnetic waves, as well as the iron nanoparticles produced by hydrogen reduction during the in-situ catalytic growth of carbon nanotubes on laterite significantly increase the magnetism of the materials and thus increase the magnetic loss. As a result, the RL curves of the CNTs/laterite-paraffin composites show a maximum reflection loss of −77 dB at 3.2 GHz with 4 mm thickness. Generally, this synthesis strategy presented in this study paves a new way for the fabrication of low-cost CNTs-based composite with high performance for microwave absorbing.

Estimation and update of betweenness centrality with progressive algorithm and shortest paths approximation

Betweenness centrality is one of the key measures of the node importance in a network. However, it is computationally intractable to calculate the exact betweenness centrality of nodes in large-scale networks. To solve this problem, we present an efficient CBCA (Centroids based Betweenness Centrality Approximation) algorithm based on progressive sampling and shortest paths approximation. Our algorithm firstly approximates the shortest paths by generating the network centroids according to the adjacency information entropy of the nodes; then constructs an efficient error estimator using the Monte Carlo Empirical Rademacher averages to determine the sample size which can achieve a balance with accuracy; finally, we present a novel centroid updating strategy based on network density and clustering coefficient, which can effectively reduce the computation burden of updating shortest paths in dynamic networks. The experimental results show that our CBCA algorithm can efficiently output high-quality approximations of the betweenness centrality of a node in large-scale complex networks.

Modeling of Solar Eclipse effects on the sub-ionospheric VLF/LF signals observed by multiple stations over Japan

We present the numerical modeling of field amplitude perturbations for two VLF/LF transmitters (JJI: 22.2 kHz, JJY: 40.0 kHz) signals recorded at six places (UEC-VLF network) in Japan during the Annular Solar Eclipse of 2012 (ASE-2012). We determine the characteristics of the D-region ionospheric perturbation according to the antumbra and penumbral shadow of the annular solar eclipse. A space–time dependent 2-Dimensional Elliptical Gaussian distribution Model (2DEGM) is introduced to get the effect of ASE-2012 on the Wait’s D-region parameters, such as effective VLF/LF reflection height (h′) and sharpness factor (β). The Long Wave Propagation Capability (LWPC) code is used to simulate the VLF signal amplitude profile with the help of modelled Wait’s parameters. A method of comparison of observed and LWPC simulated VLF signal amplitude across all the considered signal propagation paths is introduced to estimate final 2DEGM related parameters and hence, to model D-region electron density perturbations during ASE-2012. Using 2DEGM + LWPC, (i) we consistently simulate the observed VLF signal amplitude profiles for almost all the propagation paths of UEC-VLF network and (ii) evaluate the percentage change in D-region electron density due to ASE-2012.

Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet

In today’s digital era, the demand for uninterrupted and efficient data streaming is paramount across various sectors, from entertainment to industrial automation. While the traditional single-path solutions often fell short in ensuring rapid and consistent data transfers, Multipath TCP (MPTCP) emerges as a promising alternative, enabling simultaneous data transfer across multiple network paths. The efficacy of MPTCP, however, hinges on the choice of appropriate congestion control (CC) algorithms. Addressing the present knowledge gap, this research provides a thorough evaluation of key MPTCP CC algorithms in the context of streaming applications in open Internet environments. Our findings reveal that BALIA stands out as the most suitable choice for MPTCP streaming, adeptly balancing waiting time, throughput, and Head-of-Line blocking reduction. Conversely, the wVegas algorithm, with its delay-centric approach, proves less adequate for multipath streaming. This study underscores the imperative to fine-tune MPTCP for streaming applications, at the same time offering insights for future development areas and innovations.