In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
A decellularized extracellular matrix(dECM)constitutes a pivotal biomaterial created by decellularizing the natural extracellular matrix(ECM).This material serves as a supportive medium for intricate cellular interact...A decellularized extracellular matrix(dECM)constitutes a pivotal biomaterial created by decellularizing the natural extracellular matrix(ECM).This material serves as a supportive medium for intricate cellular interactions,fostering cell growth,differentiation,and organization.However,challenges persist in decellularization,necessitating a balance between preserving the ECM structural integrity and achieving effective cellular removal.An approach to enhancing decellularization involves pre-eliminating unnecessary tissues and effectively reducing final DNA levels to lower than 50 ng/mg ECM on preprocessed tissues.Although this strategic step augments decellularization efficiency,the current manual execution method depends on the operator’s skill.To address this limitation,this study proposed an automated raw tissue slicing system that does not require tissue preparation for slicing.Through carefully controlled tissue applanation pressure and oscillatory incisions with optimized parameters,the system achieved a precision within±10µm in obtaining submillimeter-scale tissue slices of the porcine cornea while avoiding significant microscopic complications in the tissue structure,as observed by tissue histology.These findings suggested the system’s capability to streamline and automate preliminary tissue slicing operations.The efficacy of this approach for decellularization was validated by processing porcine corneas using the proposed system and subsequently decellularizing the processed tissues.DNA level analysis revealed that sliced,subdivided tissues created by this system could expedite DNA reduction even at the initial steps of decellularization,enhancing the overall decellularization procedure.展开更多
Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-toler...Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.展开更多
Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resou...Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.展开更多
This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method u...This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.展开更多
DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity,capable of crippling critical infrastructures and disrupting services globally.As networks continue to expand and threats become m...DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity,capable of crippling critical infrastructures and disrupting services globally.As networks continue to expand and threats become more sophisticated,there is an urgent need for Intrusion Detection Systems(IDS)capable of handling these challenges effectively.Traditional IDS models frequently have difficulties in detecting new or changing attack patterns since they heavily depend on existing characteristics.This paper presents a novel approach for detecting unknown Distributed Denial of Service(DDoS)attacks by integrating Sliced Iterative Normalizing Flows(SINF)into IDS.SINF utilizes the Sliced Wasserstein distance to repeatedly modify probability distributions,enabling better management of high-dimensional data when there are only a few samples available.The unique architecture of SINF ensures efficient density estimation and robust sample generation,enabling IDS to adapt dynamically to emerging threats without relying heavily on predefined signatures or extensive retraining.By incorporating Open-Set Recognition(OSR)techniques,this method improves the system’s ability to detect both known and unknown attacks while maintaining high detection performance.The experimental evaluation on CICIDS2017 and CICDDoS2019 datasets demonstrates that the proposed system achieves an accuracy of 99.85%for known attacks and an F1 score of 99.99%after incremental learning for unknown attacks.The results clearly demonstrate the system’s strong generalization capability across unseen attacks while maintaining the computational efficiency required for real-world deployment.展开更多
5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent lat...5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and dynamic.Slicing enables service providers to develop various network slice architectures.As users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different expectations.This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks.Rules of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning strategies.Therefore,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice handover.The algorithm’s performance is assessed by means of connection-level metrics considering the Quality of Service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being dropped.Hence,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional capacity.Moreover,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action spaces.The results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.展开更多
Slicing and post-treatment of SiC crystals have been a significant challenge in the integrated circuit and microelectronics industry.To compete with wire-sawing and mechanical grinding technology,a promis-ing approach...Slicing and post-treatment of SiC crystals have been a significant challenge in the integrated circuit and microelectronics industry.To compete with wire-sawing and mechanical grinding technology,a promis-ing approach combining laser slicing and laser polishing technologies has been innovatively applied to increase utilization and decrease damage defects for single crystal 4H-SiC.Significant material utiliza-tion has been achieved in the hybrid laser processes,where material loss is reduced by 75%compared to that of conventional machining technologies.Without any special process control or additional treat-ment,an internally modified layer formed by laser slicing can easily separate the 4H-SiC crystals using an external force of about∼3.6 MPa.The modified layer has been characterized using a micro-Raman method to determine residual stress.The sliced surface exhibits a combination of smooth and coarse appearances around the fluvial morphology,with an average surface roughness of over S_(a) 0.89μm.An amorphous phase surrounds the SiC substrate,with two dimensions of lattice spacing,d=0.261 nm and d=0.265 nm,confirmed by high-resolution transmission electron microscopy(HRTEM).The creation of laser-induced periodic surface nanostructures in the laser-polished surface results in a flatter surface with an average roughness of less than S_(a) 0.22μm.Due to the extreme cooling rates and multiple thermal cy-cles,dissociation of Si-C bonding,and phase separation are identified on the laser-polished surface,which is much better than that of the machining surface.We anticipate that this approach will be applicable to other high-value crystals and will have promising viability in the aerospace and semiconductor industries.展开更多
Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain.Its highly heterogeneous network structu...Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain.Its highly heterogeneous network structure and diversified application requirements call for the applying of network slicing technology.Guaranteeing robust network slicing is essential for Industrial Internet,but it faces the challenge of complex slice topologies caused by the intricate interaction relationships among Network Functions(NFs)composing the slice.Existing works have not concerned the strengthening problem of industrial network slicing regarding its complex network properties.Towards this end,we aim to study this issue by intelligently selecting a subset of most valuable NFs with the minimum cost to satisfy the strengthening requirements.State-of-the-art AlphaGo series of algorithms and the advanced graph neural network technology are combined to build the solution.Simulation results demonstrate the superior performance of our scheme compared to the benchmark schemes.展开更多
In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Se...In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.展开更多
Multi-layer slopes are widely found in clay residue receiving fields.A generalized horizontal slice method(GHSM)for assessing the stability of multi-layer slopes that considers the energy dissipation between adjacent ...Multi-layer slopes are widely found in clay residue receiving fields.A generalized horizontal slice method(GHSM)for assessing the stability of multi-layer slopes that considers the energy dissipation between adjacent horizontal slices is presented.In view of the upper-bound limit analysis theory,the energy equation is derived and the ultimate failure mode is generated by comparing the sliding surface passing through the slope toe(mode A)with that below(mode B).In addition,the influence of the number of slices on the stability coefficients in the GHSM is studied and the stable value is obtained.Compared to the original method(Chen’s method),the GHSM can acquire more precise results,which takes into account the energy dissipation in the inner sliding soil mass.Moreover,the GHSM,limit equilibrium method(LEM)and numerical simulation method(NSM)are applied to analyze the stability of a multi-layer slope with different slope angles and the results of the safety factor and failure mode are very close in each case.The ultimate failure modes are shown to be mode B when the slope angle is not more than 28°.It illustrates that the determination of the ultimate sliding surface requires comparison of multiple failure modes,not only mode A.展开更多
Objective: To investigate the effect of ginger slice acupoint application combined with moxibustion on chemotherapy-induced vomiting in postoperative breast cancer patients. Methods: Sixty postoperative breast cancer ...Objective: To investigate the effect of ginger slice acupoint application combined with moxibustion on chemotherapy-induced vomiting in postoperative breast cancer patients. Methods: Sixty postoperative breast cancer patients undergoing chemotherapy were randomly divided into an observation group and a control group, with 30 patients in each group. The control group received antiemetic treatment with dolasetron, while the observation group received ginger slice acupoint application combined with moxibustion in addition to antiemetic treatment to address chemotherapy-induced vomiting. The vomiting response on days 1-3 was compared between the two groups, along with R-INVR retching scores and patient satisfaction with the intervention methods. Results: On days 2 and 3 of chemotherapy, the observation group showed significantly less vomiting than the control group, with differences reaching a highly significant level (P < 0.001). On day 3, the R-INVR score in the observation group was significantly lower than that of the control group, with a highly significant difference (P < 0.001). The satisfaction score in the observation group was 8.38 ± 0.81, higher than the control group’s 7.65 ± 0.71, with a statistically significant difference (P < 0.05). Conclusion: Ginger slice acupoint application combined with moxibustion effectively alleviates chemotherapy-induced vomiting in postoperative breast cancer patients, improves quality of life, and is worth promoting clinically.展开更多
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
基金supported by the Alchemist Project 1415180884(No.20012378,Development of Meta Soft Organ Module Manufacturing Technology without Immunity Rejection and Module Assembly Robot System)funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)the Technology Development Program(No.S3318933)funded by the Ministry of SMEs and Startups(MSS,Republic of Korea).
文摘A decellularized extracellular matrix(dECM)constitutes a pivotal biomaterial created by decellularizing the natural extracellular matrix(ECM).This material serves as a supportive medium for intricate cellular interactions,fostering cell growth,differentiation,and organization.However,challenges persist in decellularization,necessitating a balance between preserving the ECM structural integrity and achieving effective cellular removal.An approach to enhancing decellularization involves pre-eliminating unnecessary tissues and effectively reducing final DNA levels to lower than 50 ng/mg ECM on preprocessed tissues.Although this strategic step augments decellularization efficiency,the current manual execution method depends on the operator’s skill.To address this limitation,this study proposed an automated raw tissue slicing system that does not require tissue preparation for slicing.Through carefully controlled tissue applanation pressure and oscillatory incisions with optimized parameters,the system achieved a precision within±10µm in obtaining submillimeter-scale tissue slices of the porcine cornea while avoiding significant microscopic complications in the tissue structure,as observed by tissue histology.These findings suggested the system’s capability to streamline and automate preliminary tissue slicing operations.The efficacy of this approach for decellularization was validated by processing porcine corneas using the proposed system and subsequently decellularizing the processed tissues.DNA level analysis revealed that sliced,subdivided tissues created by this system could expedite DNA reduction even at the initial steps of decellularization,enhancing the overall decellularization procedure.
基金supported by the Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.
文摘Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.
基金supported by an Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(RS-2024-00438156,Development of Security Resilience Technology Based on Network Slicing Services in a 5G Specialized Network).
文摘This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.
基金supported by the National Science and Technology Council,Taiwan with grant numbers NSTC 112-2221-E-992-045,112-2221-E-992-057-MY3,and 112-2622-8-992-009-TD1.
文摘DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity,capable of crippling critical infrastructures and disrupting services globally.As networks continue to expand and threats become more sophisticated,there is an urgent need for Intrusion Detection Systems(IDS)capable of handling these challenges effectively.Traditional IDS models frequently have difficulties in detecting new or changing attack patterns since they heavily depend on existing characteristics.This paper presents a novel approach for detecting unknown Distributed Denial of Service(DDoS)attacks by integrating Sliced Iterative Normalizing Flows(SINF)into IDS.SINF utilizes the Sliced Wasserstein distance to repeatedly modify probability distributions,enabling better management of high-dimensional data when there are only a few samples available.The unique architecture of SINF ensures efficient density estimation and robust sample generation,enabling IDS to adapt dynamically to emerging threats without relying heavily on predefined signatures or extensive retraining.By incorporating Open-Set Recognition(OSR)techniques,this method improves the system’s ability to detect both known and unknown attacks while maintaining high detection performance.The experimental evaluation on CICIDS2017 and CICDDoS2019 datasets demonstrates that the proposed system achieves an accuracy of 99.85%for known attacks and an F1 score of 99.99%after incremental learning for unknown attacks.The results clearly demonstrate the system’s strong generalization capability across unseen attacks while maintaining the computational efficiency required for real-world deployment.
基金This work was supported partially by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991514504)by theMSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2023-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and dynamic.Slicing enables service providers to develop various network slice architectures.As users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different expectations.This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks.Rules of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning strategies.Therefore,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice handover.The algorithm’s performance is assessed by means of connection-level metrics considering the Quality of Service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being dropped.Hence,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional capacity.Moreover,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action spaces.The results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.
基金supported by National Natural Science Foundation of China(No.62304249)a project funded by China Postdoctoral Science Foundation(No.2023M733704).
文摘Slicing and post-treatment of SiC crystals have been a significant challenge in the integrated circuit and microelectronics industry.To compete with wire-sawing and mechanical grinding technology,a promis-ing approach combining laser slicing and laser polishing technologies has been innovatively applied to increase utilization and decrease damage defects for single crystal 4H-SiC.Significant material utiliza-tion has been achieved in the hybrid laser processes,where material loss is reduced by 75%compared to that of conventional machining technologies.Without any special process control or additional treat-ment,an internally modified layer formed by laser slicing can easily separate the 4H-SiC crystals using an external force of about∼3.6 MPa.The modified layer has been characterized using a micro-Raman method to determine residual stress.The sliced surface exhibits a combination of smooth and coarse appearances around the fluvial morphology,with an average surface roughness of over S_(a) 0.89μm.An amorphous phase surrounds the SiC substrate,with two dimensions of lattice spacing,d=0.261 nm and d=0.265 nm,confirmed by high-resolution transmission electron microscopy(HRTEM).The creation of laser-induced periodic surface nanostructures in the laser-polished surface results in a flatter surface with an average roughness of less than S_(a) 0.22μm.Due to the extreme cooling rates and multiple thermal cy-cles,dissociation of Si-C bonding,and phase separation are identified on the laser-polished surface,which is much better than that of the machining surface.We anticipate that this approach will be applicable to other high-value crystals and will have promising viability in the aerospace and semiconductor industries.
基金supported by National Key R&D Program of China(2022YFB3104200)in part by National Natural Science Foundation of China(62202386)+2 种基金in part by Basic Research Programs of Taicang(TC2021JC31)in part by Fundamental Research Funds for the Central Universities(D5000210817)in part by Xi’an Unmanned System Security and Intelligent Communications ISTC Center,and in part by Special Funds for Central Universities Construction of World-Class Universities(Disciplines)and Special Development Guidance(0639022GH0202237 and 0639022SH0201237).
文摘Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain.Its highly heterogeneous network structure and diversified application requirements call for the applying of network slicing technology.Guaranteeing robust network slicing is essential for Industrial Internet,but it faces the challenge of complex slice topologies caused by the intricate interaction relationships among Network Functions(NFs)composing the slice.Existing works have not concerned the strengthening problem of industrial network slicing regarding its complex network properties.Towards this end,we aim to study this issue by intelligently selecting a subset of most valuable NFs with the minimum cost to satisfy the strengthening requirements.State-of-the-art AlphaGo series of algorithms and the advanced graph neural network technology are combined to build the solution.Simulation results demonstrate the superior performance of our scheme compared to the benchmark schemes.
基金supported by the National Natural Science Foundation of China(Grant No.61971057).
文摘In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.
基金support provided by the National Key R&D Program of China(No.2017YFC1501304)the National Natural Science Foundation of China(Nos.42090054,41922055 and 41931295)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(No.CUGGC09).
文摘Multi-layer slopes are widely found in clay residue receiving fields.A generalized horizontal slice method(GHSM)for assessing the stability of multi-layer slopes that considers the energy dissipation between adjacent horizontal slices is presented.In view of the upper-bound limit analysis theory,the energy equation is derived and the ultimate failure mode is generated by comparing the sliding surface passing through the slope toe(mode A)with that below(mode B).In addition,the influence of the number of slices on the stability coefficients in the GHSM is studied and the stable value is obtained.Compared to the original method(Chen’s method),the GHSM can acquire more precise results,which takes into account the energy dissipation in the inner sliding soil mass.Moreover,the GHSM,limit equilibrium method(LEM)and numerical simulation method(NSM)are applied to analyze the stability of a multi-layer slope with different slope angles and the results of the safety factor and failure mode are very close in each case.The ultimate failure modes are shown to be mode B when the slope angle is not more than 28°.It illustrates that the determination of the ultimate sliding surface requires comparison of multiple failure modes,not only mode A.
文摘Objective: To investigate the effect of ginger slice acupoint application combined with moxibustion on chemotherapy-induced vomiting in postoperative breast cancer patients. Methods: Sixty postoperative breast cancer patients undergoing chemotherapy were randomly divided into an observation group and a control group, with 30 patients in each group. The control group received antiemetic treatment with dolasetron, while the observation group received ginger slice acupoint application combined with moxibustion in addition to antiemetic treatment to address chemotherapy-induced vomiting. The vomiting response on days 1-3 was compared between the two groups, along with R-INVR retching scores and patient satisfaction with the intervention methods. Results: On days 2 and 3 of chemotherapy, the observation group showed significantly less vomiting than the control group, with differences reaching a highly significant level (P < 0.001). On day 3, the R-INVR score in the observation group was significantly lower than that of the control group, with a highly significant difference (P < 0.001). The satisfaction score in the observation group was 8.38 ± 0.81, higher than the control group’s 7.65 ± 0.71, with a statistically significant difference (P < 0.05). Conclusion: Ginger slice acupoint application combined with moxibustion effectively alleviates chemotherapy-induced vomiting in postoperative breast cancer patients, improves quality of life, and is worth promoting clinically.
