AIM: To provide appropriate treatment, it is crucial to share the clinical status of pancreas head cancer among multidisciplinary treatment members.METHODS: A retrospective analysis of the medical records of 113 patie...AIM: To provide appropriate treatment, it is crucial to share the clinical status of pancreas head cancer among multidisciplinary treatment members.METHODS: A retrospective analysis of the medical records of 113 patients who underwent surgery for pancreas head cancer from January 2008 to December 2012 was performed. We developed preoperative defining system of pancreatic head cancer by describing “resectability - tumor location - vascular relationship - adjacent organ involvement - preoperative CA19-9 (initial bilirubin level) - vascular anomaly”. The oncologic correlations with this reporting system were evaluated.RESULTS: Among 113 patients, there were 75 patients (66.4%) with resectable, 34 patients (30.1%) with borderline resectable, and 4 patients (3.5%) with locally advanced pancreatic cancer. Mean disease-free survival was 24.8 mo (95%CI: 19.6-30.1) with a 5-year disease-free survival rate of 13.5%. Pretreatment tumor size ≥ 2.4 cm [Exp(B) = 3.608, 95%CI: 1.512-8.609, P = 0.044] and radiologic vascular invasion [Exp(B) = 5.553, 95%CI: 2.269-14.589, P = 0.002] were independent predictive factors for neoadjuvant treatment. Borderline resectability [Exp(B) = 0.222, P = 0.008], pancreatic head cancer involving the pancreatic neck [Exp(B) = 9.461, P = 0.001] and arterial invasion [Exp(B) = 6.208, P = 0.010], and adjusted CA19-9 ≥ 50 [Exp(B) = 1.972 P = 0.019] were identified as prognostic clinical factors to predict tumor recurrence.CONCLUSION: The suggested preoperative defining system can help with designing treatment plans and also predict oncologic outcomes.展开更多
Sinclair is regarded as the father of semantic prosody study,however,the term "semantic prosody" was proposed by another famous linguist——Louw in 1993.From then on,several linguists proposed the definition...Sinclair is regarded as the father of semantic prosody study,however,the term "semantic prosody" was proposed by another famous linguist——Louw in 1993.From then on,several linguists proposed the definitions for this term and thus the features of semantic prosody become progressively clear.展开更多
Owing to the dramatic mobile IP growth,the emerging Internet of Things,and cloud-based applications,wireless networking is witnessing a paradigm shift.By fully exploiting spatial degrees of freedom,massive multiple-in...Owing to the dramatic mobile IP growth,the emerging Internet of Things,and cloud-based applications,wireless networking is witnessing a paradigm shift.By fully exploiting spatial degrees of freedom,massive multiple-input-multiple-output(MIMO) systems promise significant gains in data rates and link reliability.Although the research community has recognized the theoretical benefits of these systems,building the hardware of such complex systems is a challenge in practice.This paper presents a time division duplex(TDD)-based 128-antenna massive MIMO prototype system from theory to reality.First,an analytical signal model is provided to facilitate the setup of a feasible massive MIMO prototype system.Second,a link-level simulation consistent with practical TDDbased massive MIMO systems is conducted to guide and validate the massive MIMO system design.We design and implement the TDDbased 128-antenna massive MIMO prototype system with the guidelines obtained from the link-level simulation.Uplink real-time video transmission and downlink data transmission under the configuration of multiple single-antenna users are achieved.Comparisons withstate-of-the-art prototypes demonstrate the advantages of the proposed system in terms of antenna number,bandwidth,latency,and throughput.The proposed system is also equipped with scalability,which makes the system applicable to a wide range of massive scenarios.展开更多
Software-Defined Networking(SDN)enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions.Recently Machine Learning(ML)...Software-Defined Networking(SDN)enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions.Recently Machine Learning(ML)techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems(IDSs)considering logically centralized control and global view of the network provided by SDN.Many IDSs have developed using advances in machine learning and deep learning.This study presents a comprehensive review of recent work ofML-based IDS in context to SDN.It presents a comprehensive study of the existing review papers in the field.It is followed by introducing intrusion detection,ML techniques and their types.Specifically,we present a systematic study of recent works,discuss ongoing research challenges for effective implementation of ML-based intrusion detection in SDN,and promising future works in this field.展开更多
Meeting Summary Autism is a neurodevelopmental disorder defined by the of a set of characteristic behavioral features. One of the most common neurodevelopmental disorders, autism is recognized as heterogeneous in etio...Meeting Summary Autism is a neurodevelopmental disorder defined by the of a set of characteristic behavioral features. One of the most common neurodevelopmental disorders, autism is recognized as heterogeneous in etiology, phenotype, behavioral trajectory and response to treatment. While the etiology and specific pathogenetic mechanisms underlying autism are unknown, those mechanisms which underlie a small subset of etiologically-defined neurodevelopmental disorders （e.g., Fragile X Syndrome, tuberous sclerosis）, that are associated with autism and autistic behaviors, have been well described.展开更多
This pilot study focuses on a real measurements and enhancements of a software defined radio-based system for vehicle-to everything visible light communication(SDR-V2X-VLC).The presented system is based on a novel ada...This pilot study focuses on a real measurements and enhancements of a software defined radio-based system for vehicle-to everything visible light communication(SDR-V2X-VLC).The presented system is based on a novel adaptive optimization of the feed-forward software defined equalization(FFSDE)methods of the least mean squares(LMS),normalized LMS(NLMS)and QR decomposition-based recursive least squares(QR-RLS)algorithms.Individual parameters of adaptive equalizations are adjusted in real-time to reach the best possible results.Experiments were carried out on a conventional LED Octavia III taillight drafted directly from production line and universal software radio peripherals(USRP)from National Instruments.The transmitting/receiving elements used multistate quadrature amplitude modulation(M-QAM)implemented in LabVIEW programming environment.Experimental results were verified based on bit error ratio(BER),error vector magnitude(EVM)and modulation error ratio(MER).Experimental results of the pilot study unambiguously confirmed the effectiveness of the proposed solution(longer effective communication range,higher immunity to interference,deployment of higher state QAM modulation formats,higher transmission speeds etc.),as the adaptive equalization significantly improved BER,MER and EVM parameters.The best results were achieved using the QR-RLS algorithm.The results measured on deployed QR-RLS algorithm had significantly better Eb/N0(improved by approx.20 dB)and BER values(difference by up to two orders of magnitude).展开更多
Existing at present ways of estimetion oftoxic xenobiotic action on hemogenesis inthe experement are determinined by averageseparate meanigs dependant or dose and re-gime of injections of studied substancesof indices ...Existing at present ways of estimetion oftoxic xenobiotic action on hemogenesis inthe experement are determinined by averageseparate meanigs dependant or dose and re-gime of injections of studied substancesof indices of peripheral blood on展开更多
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 ...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.展开更多
The advent of the latest technologies like the Internet of things(IoT)transforms the world from a manual to an automated way of lifestyle.Meanwhile,IoT sector open numerous security challenges.In traditional networks,...The advent of the latest technologies like the Internet of things(IoT)transforms the world from a manual to an automated way of lifestyle.Meanwhile,IoT sector open numerous security challenges.In traditional networks,intrusion detection and prevention systems(IDPS)have been the key player in the market to ensure security.The challenges to the conventional IDPS are implementation cost,computing power,processing delay,and scalability.Further,online machine learning model training has been an issue.All these challenges still question the IoT network security.There has been a lot of research for IoT based detection systems to secure the IoT devices such as centralized and distributed architecture-based detection systems.The centralized system has issues like a single point of failure and load balancing while distributed system design has scalability and heterogeneity hassles.In this study,we design and develop an agent-based hybrid prevention system based on software-defined networking(SDN)technology.The system uses lite weight agents with the ability to scaleup for bigger networks and is feasible for heterogeneous IoT devices.The baseline profile for the IoT devices has been developed by analyzing network flows from all the IoT devices.This profile helps in extracting IoT device features.These features help in the development of our dataset that we use for anomaly detection.For anomaly detection,support vector machine has been used to detect internet control message protocol(ICMP)flood and transmission control protocol synchronize(TCP SYN)flood attacks.The proposed system based on machine learning model is fully capable of online and offline training.Other than detection accuracy,the system can fully mitigate the attacks using the software-defined technology SDN technology.The major goal of the research is to analyze the accuracy of the hybrid agent-based intrusion detection systems as compared to conventional centralized only solutions,especially under the flood attack conditions generated by the distributed denial of service(DDoS)attacks.The system shows 97%to 99%accuracy in simulated results with no false-positive alarm.Also,the system shows notable improvement in terms of resource utilization and performance under attack scenarios. The R-IDPS is scalable, and thesystem is suitable for heterogeneous IoT devices and networks.展开更多
This paper puts forward a method to design the user-defined component based on the user-defined modeling environment CBuilder of RTDS simulator. And also develops the user-defined component model with algorithm descri...This paper puts forward a method to design the user-defined component based on the user-defined modeling environment CBuilder of RTDS simulator. And also develops the user-defined component model with algorithm described by C language, visual graphics appearance, and the component function. And it generates the dynamic link library which has the same execution efficiency as that of the included model of RTDS. This paper takes the IEEE type EXST1 static excitation system as an example to build the user-defined component. The closed-loop tests on the user-defined component and the included one of RTDS are performed to examine the accuracy of the proposed method. By comparison, the test results show that the external characteristics of the user-defined component and the included model of RTDS are basically the same in the initialization process, the step process of the terminal voltage reference value and the case of the large disturbance.展开更多
Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network...Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network.On the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and robberies.These issues have a significant detrimental impact on organizations,enterprises,and even economies.Accuracy,high performance,and real-time systems are necessary to achieve this goal.Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last decade.In this paper,a novel HFS-LGBM IDS is proposed for SDN.First,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset.In thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature subset.The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of attacks.The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.展开更多
With ever-increasing applications of IoT, and due to the heterogeneous and bursty nature of these applications, scalability has become an important research issue in building cloud-based IoT/M2M systems. This research...With ever-increasing applications of IoT, and due to the heterogeneous and bursty nature of these applications, scalability has become an important research issue in building cloud-based IoT/M2M systems. This research proposes a dynamic SDN-based network slicing mechanism to tackle the scalability problems caused by such heterogeneity and fluctuation of IoT application requirements. The proposed method can automatically create a network slice on-the-fly for each new type of IoT application and adjust the QoS characteristics of the slice dynamically according to the changing requirements </span><span style="font-family:Verdana;">of an IoT application. Validated with extensive experiments, the proposed me</span><span style="font-family:Verdana;">chanism demonstrates better platform scalability when compared to a static slicing system.展开更多
A distinct set of homoeologous cellulose synthase catalytic subunit(CesA) genes are coordinately up-regulated with the onset of secondary wall formation in cotton fiber as shown by quantitative-RT-
The Software Defined Network (SDN) is a concept based on a decoupling between the control plan and the data plan of a network. Thus, the network becomes programmable and can be coupled to the business applications of ...The Software Defined Network (SDN) is a concept based on a decoupling between the control plan and the data plan of a network. Thus, the network becomes programmable and can be coupled to the business applications of the users. The study that is discussed in this article looks at load planning and balancing in distributed controllers. To do this, a model and theoretical methods of performance evaluation related to appropriate software tools, to predict and control the quality of service offered to users is exposed. This paper exposed also a distributed architecture of controllers and then a module based on an adaptive load balancing algorithm that is fault tolerant and fluctuates controller loads. The experiments show a significant gain in efficiency of our solution.展开更多
Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive s...Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow entries.As SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes crucial.In particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade requirements.This paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource consumption.The DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory usage.DFR employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and scalability.DFR employs flow entry aggregation techniques to reduce switch memory usage.Instead of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC address.This reduces the switches’Ternary Content-Addressable Memory(TCAM)consumption.Additionally,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network operations.The performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN controller.For different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed various failure recovery models:restoration-based,protection by flow entries,protection by group entries and protection by Vlan-tagging model in terms of recovery time,switch memory consumption and controller overhead which represented the number of flow entry updates to recover from the failure.Experimental results demonstrate that DFR achieves recovery times under 20 milliseconds,satisfying carrier-grade requirements for rapid failure recovery.Additionally,DFR reduces switch memory usage by up to 95%compared to traditional protection methods and minimizes controller load by eliminating the need for controller intervention during failure recovery.Theresults underscore the efficiency and scalability of the DFR model,making it a practical solution for enhancing network resilience in SDN environments.展开更多
Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhance...Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.展开更多
Continuity is the foremost defining characteristic of Chinese civilization.It ensures that the evolution of this civilization respects the fine traditions and stays on the right course throughout its creative course.M...Continuity is the foremost defining characteristic of Chinese civilization.It ensures that the evolution of this civilization respects the fine traditions and stays on the right course throughout its creative course.Meanwhile,creativity imparts ceaseless vitality to this civilization,serving as the intrinsic propulsive force that fuels its evolution.The rise of Song Studies marks one of the symbolic events in the transformation in the Chinese ideological and cultural realm.It encapsulates the dialectical unity between continuity and creativity that characterizes Chinese civilization.Song Studies adhere to the traditional classic interpretations,center the core values around“benevolence,”and pursue the way of sages,which reflect the continuity of Chinese civilization.They also embody creativity.This is manifested in the emergence of a new academic spirit that emphasizes the seeking of truth and reason,a new academic ethos that involves questioning classics in the process of their dissemination and interpreting them with one’s own insight,as well as the fusion of philosophical speculation with social practice.Conducting an integrated analysis of the defining characteristics of Chinese civilization and the rise of Song Studies helps us gain a thorough understanding of the dialectical unity between the continuity and creativity of Chinese civilization through the interactions between history and reality.It also enables us to effectively address the relationship between inheritance and transformation,as well as between maintaining core values and pursuing creativity throughout its ongoing development.展开更多
Following the work of Li-Shi-Qing, we propose the definition of the relative volume function for an AH manifold. It is not a constant function in general and we study the regularity of this function. We use this funct...Following the work of Li-Shi-Qing, we propose the definition of the relative volume function for an AH manifold. It is not a constant function in general and we study the regularity of this function. We use this function to provide an accurate characterization of the height of the geodesic defining function for the AH manifold with a given boundary metric. Furthermore, it is shown that such functions are uniformly bounded from below at infinity and the bound only depends on the dimension. In the end, we apply this function to study the capacity of balls in AH manifolds and demonstrate that the “relative p—capacity function” coincides with the relative volume function under appropriate curvature conditions.展开更多
The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 n...The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 new models on display-85 percent of them new energy vehicles-this year’s show spotlighted how the global auto industry is pivoting rapidly towards an era of software-defined and AI-powered mobility.展开更多
文摘AIM: To provide appropriate treatment, it is crucial to share the clinical status of pancreas head cancer among multidisciplinary treatment members.METHODS: A retrospective analysis of the medical records of 113 patients who underwent surgery for pancreas head cancer from January 2008 to December 2012 was performed. We developed preoperative defining system of pancreatic head cancer by describing “resectability - tumor location - vascular relationship - adjacent organ involvement - preoperative CA19-9 (initial bilirubin level) - vascular anomaly”. The oncologic correlations with this reporting system were evaluated.RESULTS: Among 113 patients, there were 75 patients (66.4%) with resectable, 34 patients (30.1%) with borderline resectable, and 4 patients (3.5%) with locally advanced pancreatic cancer. Mean disease-free survival was 24.8 mo (95%CI: 19.6-30.1) with a 5-year disease-free survival rate of 13.5%. Pretreatment tumor size ≥ 2.4 cm [Exp(B) = 3.608, 95%CI: 1.512-8.609, P = 0.044] and radiologic vascular invasion [Exp(B) = 5.553, 95%CI: 2.269-14.589, P = 0.002] were independent predictive factors for neoadjuvant treatment. Borderline resectability [Exp(B) = 0.222, P = 0.008], pancreatic head cancer involving the pancreatic neck [Exp(B) = 9.461, P = 0.001] and arterial invasion [Exp(B) = 6.208, P = 0.010], and adjusted CA19-9 ≥ 50 [Exp(B) = 1.972 P = 0.019] were identified as prognostic clinical factors to predict tumor recurrence.CONCLUSION: The suggested preoperative defining system can help with designing treatment plans and also predict oncologic outcomes.
文摘Sinclair is regarded as the father of semantic prosody study,however,the term "semantic prosody" was proposed by another famous linguist——Louw in 1993.From then on,several linguists proposed the definitions for this term and thus the features of semantic prosody become progressively clear.
基金supported in part by the National Science Foundation(NSFC) for Distinguished Young Scholars of China with Grant 61625106the National Natural Science Foundation of China under Grant 61531011the Hong Kong,Macao and Taiwan Science and Technology Cooperation Program of China(2016YFE0123100)
文摘Owing to the dramatic mobile IP growth,the emerging Internet of Things,and cloud-based applications,wireless networking is witnessing a paradigm shift.By fully exploiting spatial degrees of freedom,massive multiple-input-multiple-output(MIMO) systems promise significant gains in data rates and link reliability.Although the research community has recognized the theoretical benefits of these systems,building the hardware of such complex systems is a challenge in practice.This paper presents a time division duplex(TDD)-based 128-antenna massive MIMO prototype system from theory to reality.First,an analytical signal model is provided to facilitate the setup of a feasible massive MIMO prototype system.Second,a link-level simulation consistent with practical TDDbased massive MIMO systems is conducted to guide and validate the massive MIMO system design.We design and implement the TDDbased 128-antenna massive MIMO prototype system with the guidelines obtained from the link-level simulation.Uplink real-time video transmission and downlink data transmission under the configuration of multiple single-antenna users are achieved.Comparisons withstate-of-the-art prototypes demonstrate the advantages of the proposed system in terms of antenna number,bandwidth,latency,and throughput.The proposed system is also equipped with scalability,which makes the system applicable to a wide range of massive scenarios.
基金supported by King Khalid University,Saudi Arabia underGrant No.RGP.2/61/43.
文摘Software-Defined Networking(SDN)enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions.Recently Machine Learning(ML)techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems(IDSs)considering logically centralized control and global view of the network provided by SDN.Many IDSs have developed using advances in machine learning and deep learning.This study presents a comprehensive review of recent work ofML-based IDS in context to SDN.It presents a comprehensive study of the existing review papers in the field.It is followed by introducing intrusion detection,ML techniques and their types.Specifically,we present a systematic study of recent works,discuss ongoing research challenges for effective implementation of ML-based intrusion detection in SDN,and promising future works in this field.
文摘Meeting Summary Autism is a neurodevelopmental disorder defined by the of a set of characteristic behavioral features. One of the most common neurodevelopmental disorders, autism is recognized as heterogeneous in etiology, phenotype, behavioral trajectory and response to treatment. While the etiology and specific pathogenetic mechanisms underlying autism are unknown, those mechanisms which underlie a small subset of etiologically-defined neurodevelopmental disorders （e.g., Fragile X Syndrome, tuberous sclerosis）, that are associated with autism and autistic behaviors, have been well described.
基金This research was funded by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project,Project Number CZ.02.1.01/0.0/0.0/16_019/0000867 and by 543 the Ministry of Education of the Czech Republic,Project No.SP2021/32.
文摘This pilot study focuses on a real measurements and enhancements of a software defined radio-based system for vehicle-to everything visible light communication(SDR-V2X-VLC).The presented system is based on a novel adaptive optimization of the feed-forward software defined equalization(FFSDE)methods of the least mean squares(LMS),normalized LMS(NLMS)and QR decomposition-based recursive least squares(QR-RLS)algorithms.Individual parameters of adaptive equalizations are adjusted in real-time to reach the best possible results.Experiments were carried out on a conventional LED Octavia III taillight drafted directly from production line and universal software radio peripherals(USRP)from National Instruments.The transmitting/receiving elements used multistate quadrature amplitude modulation(M-QAM)implemented in LabVIEW programming environment.Experimental results were verified based on bit error ratio(BER),error vector magnitude(EVM)and modulation error ratio(MER).Experimental results of the pilot study unambiguously confirmed the effectiveness of the proposed solution(longer effective communication range,higher immunity to interference,deployment of higher state QAM modulation formats,higher transmission speeds etc.),as the adaptive equalization significantly improved BER,MER and EVM parameters.The best results were achieved using the QR-RLS algorithm.The results measured on deployed QR-RLS algorithm had significantly better Eb/N0(improved by approx.20 dB)and BER values(difference by up to two orders of magnitude).
文摘Existing at present ways of estimetion oftoxic xenobiotic action on hemogenesis inthe experement are determinined by averageseparate meanigs dependant or dose and re-gime of injections of studied substancesof indices of peripheral blood on
基金extend their appreciation to Researcher Supporting Project Number(RSPD2023R582)King Saud University,Riyadh,Saudi Arabia.
文摘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.
基金The authors would like to express their gratitude to the sponsor,as this research was funded by the University of Malaya in Malaysia(GrantNo.GPF017D-2019).
文摘The advent of the latest technologies like the Internet of things(IoT)transforms the world from a manual to an automated way of lifestyle.Meanwhile,IoT sector open numerous security challenges.In traditional networks,intrusion detection and prevention systems(IDPS)have been the key player in the market to ensure security.The challenges to the conventional IDPS are implementation cost,computing power,processing delay,and scalability.Further,online machine learning model training has been an issue.All these challenges still question the IoT network security.There has been a lot of research for IoT based detection systems to secure the IoT devices such as centralized and distributed architecture-based detection systems.The centralized system has issues like a single point of failure and load balancing while distributed system design has scalability and heterogeneity hassles.In this study,we design and develop an agent-based hybrid prevention system based on software-defined networking(SDN)technology.The system uses lite weight agents with the ability to scaleup for bigger networks and is feasible for heterogeneous IoT devices.The baseline profile for the IoT devices has been developed by analyzing network flows from all the IoT devices.This profile helps in extracting IoT device features.These features help in the development of our dataset that we use for anomaly detection.For anomaly detection,support vector machine has been used to detect internet control message protocol(ICMP)flood and transmission control protocol synchronize(TCP SYN)flood attacks.The proposed system based on machine learning model is fully capable of online and offline training.Other than detection accuracy,the system can fully mitigate the attacks using the software-defined technology SDN technology.The major goal of the research is to analyze the accuracy of the hybrid agent-based intrusion detection systems as compared to conventional centralized only solutions,especially under the flood attack conditions generated by the distributed denial of service(DDoS)attacks.The system shows 97%to 99%accuracy in simulated results with no false-positive alarm.Also,the system shows notable improvement in terms of resource utilization and performance under attack scenarios. The R-IDPS is scalable, and thesystem is suitable for heterogeneous IoT devices and networks.
文摘This paper puts forward a method to design the user-defined component based on the user-defined modeling environment CBuilder of RTDS simulator. And also develops the user-defined component model with algorithm described by C language, visual graphics appearance, and the component function. And it generates the dynamic link library which has the same execution efficiency as that of the included model of RTDS. This paper takes the IEEE type EXST1 static excitation system as an example to build the user-defined component. The closed-loop tests on the user-defined component and the included one of RTDS are performed to examine the accuracy of the proposed method. By comparison, the test results show that the external characteristics of the user-defined component and the included model of RTDS are basically the same in the initialization process, the step process of the terminal voltage reference value and the case of the large disturbance.
文摘Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network.On the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and robberies.These issues have a significant detrimental impact on organizations,enterprises,and even economies.Accuracy,high performance,and real-time systems are necessary to achieve this goal.Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last decade.In this paper,a novel HFS-LGBM IDS is proposed for SDN.First,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset.In thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature subset.The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of attacks.The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.
文摘With ever-increasing applications of IoT, and due to the heterogeneous and bursty nature of these applications, scalability has become an important research issue in building cloud-based IoT/M2M systems. This research proposes a dynamic SDN-based network slicing mechanism to tackle the scalability problems caused by such heterogeneity and fluctuation of IoT application requirements. The proposed method can automatically create a network slice on-the-fly for each new type of IoT application and adjust the QoS characteristics of the slice dynamically according to the changing requirements </span><span style="font-family:Verdana;">of an IoT application. Validated with extensive experiments, the proposed me</span><span style="font-family:Verdana;">chanism demonstrates better platform scalability when compared to a static slicing system.
文摘A distinct set of homoeologous cellulose synthase catalytic subunit(CesA) genes are coordinately up-regulated with the onset of secondary wall formation in cotton fiber as shown by quantitative-RT-
文摘The Software Defined Network (SDN) is a concept based on a decoupling between the control plan and the data plan of a network. Thus, the network becomes programmable and can be coupled to the business applications of the users. The study that is discussed in this article looks at load planning and balancing in distributed controllers. To do this, a model and theoretical methods of performance evaluation related to appropriate software tools, to predict and control the quality of service offered to users is exposed. This paper exposed also a distributed architecture of controllers and then a module based on an adaptive load balancing algorithm that is fault tolerant and fluctuates controller loads. The experiments show a significant gain in efficiency of our solution.
文摘Link failure is a critical issue in large networks and must be effectively addressed.In software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive approaches.Reactive schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow entries.As SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes crucial.In particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade requirements.This paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource consumption.The DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory usage.DFR employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and scalability.DFR employs flow entry aggregation techniques to reduce switch memory usage.Instead of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC address.This reduces the switches’Ternary Content-Addressable Memory(TCAM)consumption.Additionally,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network operations.The performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN controller.For different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed various failure recovery models:restoration-based,protection by flow entries,protection by group entries and protection by Vlan-tagging model in terms of recovery time,switch memory consumption and controller overhead which represented the number of flow entry updates to recover from the failure.Experimental results demonstrate that DFR achieves recovery times under 20 milliseconds,satisfying carrier-grade requirements for rapid failure recovery.Additionally,DFR reduces switch memory usage by up to 95%compared to traditional protection methods and minimizes controller load by eliminating the need for controller intervention during failure recovery.Theresults underscore the efficiency and scalability of the DFR model,making it a practical solution for enhancing network resilience in SDN environments.
文摘Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.
基金a phased achievement of the“Research on Chinese Confucian Orthodoxy”(Project No.:17ZDA010),a major project of the National Social Science Fund of China.
文摘Continuity is the foremost defining characteristic of Chinese civilization.It ensures that the evolution of this civilization respects the fine traditions and stays on the right course throughout its creative course.Meanwhile,creativity imparts ceaseless vitality to this civilization,serving as the intrinsic propulsive force that fuels its evolution.The rise of Song Studies marks one of the symbolic events in the transformation in the Chinese ideological and cultural realm.It encapsulates the dialectical unity between continuity and creativity that characterizes Chinese civilization.Song Studies adhere to the traditional classic interpretations,center the core values around“benevolence,”and pursue the way of sages,which reflect the continuity of Chinese civilization.They also embody creativity.This is manifested in the emergence of a new academic spirit that emphasizes the seeking of truth and reason,a new academic ethos that involves questioning classics in the process of their dissemination and interpreting them with one’s own insight,as well as the fusion of philosophical speculation with social practice.Conducting an integrated analysis of the defining characteristics of Chinese civilization and the rise of Song Studies helps us gain a thorough understanding of the dialectical unity between the continuity and creativity of Chinese civilization through the interactions between history and reality.It also enables us to effectively address the relationship between inheritance and transformation,as well as between maintaining core values and pursuing creativity throughout its ongoing development.
文摘Following the work of Li-Shi-Qing, we propose the definition of the relative volume function for an AH manifold. It is not a constant function in general and we study the regularity of this function. We use this function to provide an accurate characterization of the height of the geodesic defining function for the AH manifold with a given boundary metric. Furthermore, it is shown that such functions are uniformly bounded from below at infinity and the bound only depends on the dimension. In the end, we apply this function to study the capacity of balls in AH manifolds and demonstrate that the “relative p—capacity function” coincides with the relative volume function under appropriate curvature conditions.
文摘The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 new models on display-85 percent of them new energy vehicles-this year’s show spotlighted how the global auto industry is pivoting rapidly towards an era of software-defined and AI-powered mobility.
