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.

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.

An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.

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.

SDN Orchestration for Dynamic End-to-End Control of Data Center Multi-Domain Optical Networking

New and emerging use cases, such as the interconnection of geographically distributed data centers(DCs), are drawing attention to the requirement for dynamic end-to-end service provisioning, spanning multiple and heterogeneous optical network domains. This heterogeneity is, not only due to the diverse data transmission and switching technologies, but also due to the different options of control plane techniques. In light of this, the problem of heterogeneous control plane interworking needs to be solved, and in particular, the solution must address the specific issues of multi-domain networks, such as limited domain topology visibility, given the scalability and confidentiality constraints. In this article, some of the recent activities regarding the Software-Defined Networking(SDN) orchestration are reviewed to address such a multi-domain control plane interworking problem. Specifically, three different models, including the single SDN controller model, multiple SDN controllers in mesh, and multiple SDN controllers in a hierarchical setting, are presented for the DC interconnection network with multiple SDN/Open Flow domains or multiple Open Flow/Generalized Multi-Protocol Label Switching( GMPLS) heterogeneous domains. I n addition, two concrete implementations of the orchestration architectures are detailed, showing the overall feasibility and procedures of SDN orchestration for the end-to-endservice provisioning in multi-domain data center optical networks.

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.

Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.

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.

Application of virtual reality technology improves the functionality of brain networks in individuals experiencing pain

Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.

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.

Unlocking the future:Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.

Development of geo-electrical meter based on networking

Further development of earthquake equipments is closely associated with that of computer technology. Because Embedded PC104 module has the equivalent functions of PC,it has been widely used in recent years,and can provide a new and flexible hardware design environment,but its applications in observation instruments of earth-quake precursor are rare. The present paper introduces in detail the realization of a networked geo-electrical meter by applying the low price,high reliability embedded PC104 industrial computer.

基于USB Networking实现嵌入式linux系统GDB远程调试

简要介绍了在以 Intel Xscale系列的微处理器 PXA2 5 5为平台的嵌入式 linux下 ,利用 pxa2 5 5片上的 USBclient控制器实现 USB Networking功能 ,并建立目标板环境的 SSH server和 GDB remote server,实现

A Survey: Typical Security Issues of Software-Defined Networking

Software-Defined Networking (SDN) has been a hot topic for future network development, which implements the different layers of control plane and data plane respectively. Despite providing high openness and programmability, the “three-layer two-interface” architecture of SDN changes the traditional network and increases the network attack nodes, which results in new security issues. In this paper, we firstly introduced the background, architecture and working process of SDN. Secondly, we summarized and analyzed the typical security issues from north to south: application layer, northbound interface, control layer, southbound interface and data layer. Another contribution is to review and analyze the existing solutions and latest research progress of each layer, mainly including: authorized authentication module, application isolation, DoS/DDoS defense, multi-controller deployment and flow rule consistency detection. Finally, a conclusion about the future works of SDN security and an idealized global security architecture is proposed.

Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base,how to use triple information to generate natural questions is a key technology in question answering systems.The traditional way of generating questions require a lot of manual intervention and produce lots of noise.To solve these problems,we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions.The semi-automated model can generate question templates and real questions combining the knowledge base and center graph.The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network.Meanwhile,the attention mechanism is utilized in the decoding layer,which makes the triples and generated questions more relevant.Finally,the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.

A Novel Load Balancing Strategy of Software-Defined Cloud/Fog Networking in the Internet of Vehicles

The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.

Blockchain-Based Secure Distributed Control for Software Defined Optical Networking

Software defined optical networking(SDON)is a critical technology for the next generation network with the advantages of programmable control and etc.As one of the key issues of SDON,the security of control plane has also received extensive attention,especially in certain network scenarios with high security requirement.Due to the existence of vulnerabilities and heavy overhead,the existing firewalls and distributed control technologies cannot solve the control plane security problem well.In this paper,we propose a distributed control architecture for SDON using the blockchain technique(BlockCtrl).The proposed BlockCtrl model introduces the advantages of blockchain into SDON to achieve a high-efficiency fault tolerant control.We have evaluated the performance of our proposed architecture and compared it to the existing models with respect to various metrics including processing rate,recovery latency and etc.The numerical results show that the BlockCtrl is capable of attacks detection and fault tolerant control in SDON with high performance on resource utilization and service correlation.

A Survey on Information-Centric Networking: Rationales, Designs and Debates

The basic function of the Internet is to delivery data(what) to serve the needs of all applications. IP names the attachment points(where) to facilitate ubiquitous interconnectivity as the current way to deliver data. The fundamental mismatch between data delivery and naming attachment points leads to a lot of challenges, e.g., mapping from data name to IP address, handling dynamics of underlying topology, scaling up the data distribution, and securing communication, etc. Informationcentric networking(ICN) is proposed to shift the focus of communication paradigm from where to what, by making the named data the first-class citizen in the network, The basic consensus of ICN is to name the data independent from its container(space dimension) and session(time dimension), which breaks the limitation of point-to-point IP semantic. It scales up data distribution by utilizing available resources, and facilitates communication to fit diverse connectivity and heterogeneous networks. However, there are only a few consensuses on the detailed design of ICN, and quite a few different ICN architectures are proposed. This paper reveals the rationales of ICN from the perspective of the Internet evolution, surveys different design choices, and discusses on two debatable topics in ICN, i.e.,self-certifying versus hierarchical names, and edge versus pervasive caching. We hope this survey helps clarify some mis-understandings on ICN and achieve more consensuses.

A Survey of Applications Research on Content-Centric Networking

As a named data-based clean-slate future Internet architecture,Content-Centric Networking(CCN)uses entirely different protocols and communication patterns from the host-to-host IP network.In CCN,communication is wholly driven by the data consumer.Consumers must send Interest packets with the content name and not by the host’s network address.Its nature of in-network caching,Interest packets aggregation and hop-byhop communication poses unique challenges to provision of Internet applications,where traditional IP network no long works well.This paper presents a comprehensive survey of state-of-the-art application research activities related to CCN architecture.Our main aims in this survey are(a)to identify the advantages and drawbacks of CCN architectures for application provisioning;(b)to discuss the challenges and opportunities regarding service provisioning in CCN architectures;and(c)to further encourage deeper thinking about design principles for future Internet architectures from the perspective of upper-layer applications.