Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

Multi-UAV Collaborative Mission Planning Method for Self-Organized Sensor Data Acquisition

In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.

Equilibrium reconstruction method for self-organized plasmas on reversed field pinches with polarimeter-interferometer

In the reversed field pinch(RFP),plasmas exhibit various self-organized states.Among these,the three-dimensional(3D)helical state known as the“quasi-single-helical”(QSH)state enhances RFP confinement.However,accurately describing the equilibrium is challenging due to the presence of 3D structures,magnetic islands,and chaotic regions.It is difficult to obtain a balance between the available diagnostic and the real equilibrium structure.To address this issue,we introduce KTX3DFit,a new 3D equilibrium reconstruction code specifically designed for the Keda Torus eXperiment(KTX)RFP.KTX3DFit utilizes the stepped-pressure equilibrium code(SPEC)to compute 3D equilibria and uses polarimetric interferometer signals from experiments.KTX3DFit is able to reconstruct equilibria in various states,including axisymmetric,doubleaxis helical(DAx),and single-helical-axis(SHAx)states.Notably,this study marks the first integration of the SPEC code with internal magnetic field data for equilibrium reconstruction and could be used for other 3D configurations.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Dynamic Networking Method of Vehicles in VANET

Vehicular Ad-hoc Networks(VANETs)make it easy to transfer information between vehicles,and this feature is utilized to enable collaborative decision-making between vehicles to enhance the safety,economy,and entertainment of vehicle operation.The high mobility of vehicles leads to a time-varying topology between vehicles,which makes inter-vehicle information transfer challenging in terms of delay control and ensuring the stability of collaborative decision-making among vehicles.The clustering algorithm is a method aimed at improving the efficiency of VANET communication.Currently,most of the research based on this method focuses on maintaining the stability of vehicle clustering,and few methods focus on the information interaction and collaborative decisionmaking of vehicles in the region.In this context,this paper proposes a networking method for intra-regional vehicle information interaction,through an efficient information transmission mechanism,vehicles can quickly obtain the required information and make more accurate decisions.Firstly,this networking method utilizes DBSCAN and the proposed vehicle scoring model to form clusters,ensuring the stability and adaptability of clusters;secondly,in the process of interacting with the information,the cosine similarity is utilized to check the similarity of the information to eliminate the highly similar information,effectively reducing redundant information;and lastly,in the case of a consensus reached by the cluster,the frequency of broadcasting of information between vehicles is reduced as a way to minimize the waste of communication resources.The proposed method is simulated based on Python and Sumo platforms,and several metrics such as cluster clustering situation,information volume,and state change rate are analyzed.The results show that the method maintains better cluster stability with a 60%and 92%reduction in information overhead compared to the FVC and HCAR algorithms,respectively.

The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers

Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article.

Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

Examining the Quality Metrics of a Communication Network with Distributed Software-Defined Networking Architecture

Software-Defined Networking(SDN),with segregated data and control planes,provides faster data routing,stability,and enhanced quality metrics,such as throughput(Th),maximum available bandwidth(Bd(max)),data transfer(DTransfer),and reduction in end-to-end delay(D(E-E)).This paper explores the critical work of deploying SDN in large-scale Data Center Networks(DCNs)to enhance its Quality of Service(QoS)parameters,using logically distributed control configurations.There is a noticeable increase in Delay(E-E)when adopting SDN with a unified(single)control structure in big DCNs to handle Hypertext Transfer Protocol(HTTP)requests causing a reduction in network quality parameters(Bd(max),Th,DTransfer,D(E-E),etc.).This article examines the network performance in terms of quality matrices(bandwidth,throughput,data transfer,etc.),by establishing a large-scale SDN-based virtual network in the Mininet environment.The SDN network is simulated in three stages:(1)An SDN network with unitary controller-POX to manage the data traffic flow of the network without the server load management algorithm.(2)An SDN network with only one controller to manage the data traffic flow of the network with a server load management algorithm.(3)Deployment of SDN in proposed control arrangement(logically distributed controlled framework)with multiple controllers managing data traffic flow under the proposed Intelligent Sensing Server Load Management(ISSLM)algorithm.As a result of this approach,the network quality parameters in large-scale networks are enhanced.

Networking Observation and Applications of Chinese Ocean Satellites

This paper presents the networking observation capabilities of Chinese ocean satellites and their diverse applications in ocean disaster prevention,ecological monitoring,and resource development.Since the inaugural launch in 2002,China has achieved substantial advancements in ocean satellite technology,forming an observation system composed of the HY-1,HY-2,and HY-3 series satellites.These satellites are integral to global ocean environmental monitoring due to their high resolution,extensive coverage,and frequent observations.Looking forward,China aims to further enhance and expand its ocean satellite capabilities through ongoing projects to support global environmental protection and sustainable development.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).

A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting characteristics.Nevertheless,existing NDN faces three significant challenges,including security,privacy,and routing.In particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious content.For instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road accidents.In such a situation,trust in the content-providing vehicles brings a new challenge.On the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another challenge.Moreover,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in VNDN.In this connection,our contribution is threefold.Unlike existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate nodes.Based on ML evaluation results,vehicles accept or discard served content.Secondly,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ledger.Finally,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)approach.We implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive Bayes.The qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

Self-Organized Optimization of Transport on Complex Networks

We propose a self-organized optimization mechanism to improve the transport capacity of complex gradient networks. We find that, regardless of network topology, the congestion pressure can be strongly reduced by the self-organized optimization mechanism. Furthermore, the random scale-free topology is more efficient to reduce congestion compared with the random Poisson topology under the optimization mechanism. The reason is that the optimization mechanism introduces the correlations between the gradient field and the local topology of the substrate network. Due to the correlations, the cutoff degree of the gradient network is strongly reduced and the number of the nodes exerting their maximal transport capacity consumedly increases. Our work presents evidence supporting the idea that scale-free networks can efficiently improve their transport capacity by self- organized mechanism under gradient-driven transport mode.

Recent Trends of In-Vehicle Time Sensitive Networking Technologies, Applications and Challenges

With the vigorous development of automobile industry,in-vehicle network is also constantly upgraded to meet data transmission requirements of emerging applications.The main transmission requirements are low latency and certainty especially for autonomous driving.Time sensitive networking(TSN)based on Ethernet gives a possible solution to these requirements.Previous surveys usually investigated TSN from a general perspective,which referred to TSN of various application fields.In this paper,we focus on the application of TSN to the in-vehicle networks.For in-vehicle networks,we discuss all related TSN standards specified by IEEE 802.1 work group up to now.We further overview and analyze recent literature on various aspects of TSN for automotive applications,including synchronization,resource reservation,scheduling,certainty,software and hardware.Application scenarios of TSN for in-vehicle networks are analyzed one by one.Since TSN of in-vehicle network is still at a very initial stage,this paper also gives insights on open issues,future research directions and possible solutions.

Self-Organized Public-Key Management for Mobile Ad Hoc Networks Based on a Bidirectional Trust Model

In traditional networks , the authentication is performed by certificate authoritys（CA）,which can＇t be built in distributed mobile Ad Hoc Networks however. In this pa per, we propose a fully self-organized public key management based on bidirectional trust model without any centralized authority that allows users to generate their public-private key pairs, to issue certificates, and the trust relation spreads rationally according to the truly human relations. In contrast with the traditional self-organized public-key management, the average certificates paths get more short, the authentication passing rate gets more high and the most important is that the bidirectional trust based model satisfys the trust re quirement of hosts better.

UltraStar:A Lightweight Simulator of Ultra-Dense LEO Satellite Constellation Networking for 6G

The mega-constellation network has gained significant attention recently due to its great potential in providing ubiquitous and high-capacity connectivity in sixth-generation(6G)wireless communication systems.However,the high dynamics of network topology and large scale of mega-constellation pose new challenges to the constellation simulation and performance evaluation.In this paper,we introduce UltraStar,a lightweight network simulator,which aims to facilitate the complicated simulation for the emerging mega-constellation of unprecedented scale.Particularly,a systematic and extensible architecture is proposed,where the joint requirement for network simulation,quantitative evaluation,data statistics and visualization is fully considered.For characterizing the network,we make lightweight abstractions of physical entities and models,which contain basic representatives of networking nodes,structures and protocol stacks.Then,to consider the high dynamics of Walker constellations,we give a two-stage topology maintenance method for constellation initialization and orbit prediction.Further,based on the discrete event simulation(DES)theory,a new set of discrete events is specifically designed for basic network processes,so as to maintain network state changes over time.Finally,taking the first-generation Starlink of 11927 low earth orbit(LEO)satellites as an example,we use UltraStar to fully evaluate its network performance for different deployment stages,such as characteristics of constellation topology,performance of end-to-end service and effects of network-wide traffic interaction.The simulation results not only demonstrate its superior performance,but also verify the effectiveness of UltraStar.

Always-optimally-coordinated candidate selection algorithm for peer-to-peer files sharing system in mobile self-organized networks

In order to improve the performance of peer-to-peer files sharing system under mobile distributed en- vironments, a novel always-optimally-coordinated （AOC） criterion and corresponding candidate selection algorithm are proposed in this paper. Compared with the traditional min-hops criterion, the new approach introduces a fuzzy knowledge combination theory to investigate several important factors that influence files transfer success rate and efficiency. Whereas the min-hops based protocols only ask the nearest candidate peer for desired files, the selection algorithm based on AOC comprehensively considers users＇ preferences and network requirements with flexible balancing rules. Furthermore, its advantage also expresses in the independence of specified resource discovering protocols, allowing for scalability. The simulation results show that when using the AOC based peer selection algorithm, system performance is much better than the rain-hops scheme, with files successful transfer rate improved more than 50% and transfer time re- duced at least 20%.

Long-lasting,reinforced electrical networking in a high-loading Li_(2)S cathode for high-performance lithium–sulfur batteries

Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein,a high-loading Li_(2)S-based cathode with micrometric Li_(2)S particles composed of two-dimensional graphene(Gr)and one-dimensional carbon nanotubes(CNTs)in a compact geometry is developed,and the role of CNTs in stable cycling of high-capacity Li–S batteries is emphasized.In a dimensionally combined carbon matrix,CNTs embedded within the Gr sheets create robust and sustainable electron diffusion pathways while suppressing the passivation of the active carbon surface.As a unique point,during the first charging process,the proposed cathode is fully activated through the direct conversion of Li_(2)S into S_(8) without inducing lithium polysulfide formation.The direct conversion of Li_(2)S into S_(8) in the composite cathode is ubiquitously investigated using the combined study of in situ Raman spectroscopy,in situ optical microscopy,and cryogenic transmission electron microscopy.The composite cathode demonstrates unprecedented electrochemical properties even with a high Li_(2)S loading of 10 mg cm^(–2);in particular,the practical and safe Li–S full cell coupled with a graphite anode shows ultra-long-term cycling stability over 800 cycles.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.