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
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
Bananas are highly perishable after harvest,and processing them into dried products is a crucial approach to reducing losses and adding their economic values.To address the inefficiency and prolonged duration of tradi...Bananas are highly perishable after harvest,and processing them into dried products is a crucial approach to reducing losses and adding their economic values.To address the inefficiency and prolonged duration of traditional hot air drying(HAD)and the quality inconsistency associated with single infrared drying(IRD),this study proposed a novel hot air-infrared combined drying(HAD-IRD)strategy.The effects of HAD,IRD,and HAD-IRD on the drying kinetics,color,rehydration capacity,moisture diffusion mechanism,and sensory quality of banana slices were systematically investigated.The parameters of the combined drying process were optimized using an L_(9)(3^(3))orthogonal experimental design.Results indicated that both IRD and HAD-IRD significantly reduced drying time compared to single HAD.While single IRD achieved a rapid drying rate,the lack of effective convective airflow led to potential case-hardening and unstable product quality.In contrast,the HAD-IRD strategy demonstrated a synergistic effect.The optimal parameters were determined as follows:hot air temperature of 70℃,infrared temperature of 60℃,and radiation distance of 16 cm.Under these optimized conditions,HAD-IRD reduced the total drying time by over 70%while simultaneously yielding products with superior color,higher sensory scores,and improved rehydration ratio.This study confirms that HAD-IRD is an efficient and high-quality drying method for banana slices,providing a reliable theoretical foundation and technical solution for the drying of thermosensitive fruits.展开更多
This paper suggests that a single class rather than methods should be used as the slice scope to compute class cohesion. First, for a given attribute, the statements in all methods that last define the attribute are c...This paper suggests that a single class rather than methods should be used as the slice scope to compute class cohesion. First, for a given attribute, the statements in all methods that last define the attribute are computed. Then, the forward and backward data slices for this attribute are generated by using the class as the slice scope and are combined to compute the corresponding class data slice. Finally, the class cohesion is computed based on all class data slices for the attributes. Compared to traditional cohesion metrics that use methods as the slice scope, the proposed metrics that use a single class as slice scope take into account the possible interactions between the methods. The experimental results show that class cohesion can be more accurately measured when using the class as the slice scope.展开更多
Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluatio...Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluation is introduced to make decision in slicing schemes for a processing part. The application in determining the slicing scheme for a computer mouse during prototyping shows that the method increases the rationality during decision- making and improves quality and efficiency for the prototyping part.展开更多
To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and intern...To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and internal node state, we find the deficiency of only using port information. Then, we define the gate level number computing method and the concept of slice, and propose using slice analysis to distill switching density as coefficients in a special circuit stage and participate in Bayesian inference with port information. Experiments show that this method can reduce the power-per-cycle estimation error by 21.9% and the root mean square error by 25.0% compared with the original model, and maintain a 700 ＋ speedup compared with the existing gate-level power analysis technique.展开更多
A two-phase monadic approach is presented for monadically slicing programs with procedures. In the monadic slice algorithm for interprocedural programs, phase 1 initializes the slice table of formal parameters in a pr...A two-phase monadic approach is presented for monadically slicing programs with procedures. In the monadic slice algorithm for interprocedural programs, phase 1 initializes the slice table of formal parameters in a procedure with the given labels, and then captures the callees＇ influence on callers when analyzing call statements. Phase 2 captures the callees＇ dependence on callers by replacing all given labels appearing in the corresponding sets of formal parameters. By the introduction of given labels, this slice method can obtain similar summary information in system-dependence-graph（SDG）-based algorithms for addressing the calling-context problem. With the use of the slice monad transformer, this monadic slicing approach achieves a high level of modularity and flexibility. It shows that the monadic interprocedural algorithm has less complexity and it is not less precise than SDG algorithms.展开更多
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.
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funded by the National Natural Science Foundation of China,grant number 52306124(received by Dan Huang),URL:https://mp.weixin.qq.com/s/HHNYjgYKAynqYR7ySxYwzQ(accessed on 01 January 2025)the Changsha Municipal Natural Science Foundation,grant number kq2402259(received by Shuai Huang),URL:http://kjj.changsha.gov.cn/zfxxgk/tzgg_27202/202501/t20250122_11726939.html(accessed on 01 January 2025)the Regional Joint Funds of the Natural Science Foundation of Hunan Province,grant num-ber 2025JJ70463(received by Shuai Huang),URL:https://kjt.hunan.gov.cn/kjt/xxgk/tzgg/tzgg_1/202502/t20250212_33585991.html(accessed on 01 January 2025).
文摘Bananas are highly perishable after harvest,and processing them into dried products is a crucial approach to reducing losses and adding their economic values.To address the inefficiency and prolonged duration of traditional hot air drying(HAD)and the quality inconsistency associated with single infrared drying(IRD),this study proposed a novel hot air-infrared combined drying(HAD-IRD)strategy.The effects of HAD,IRD,and HAD-IRD on the drying kinetics,color,rehydration capacity,moisture diffusion mechanism,and sensory quality of banana slices were systematically investigated.The parameters of the combined drying process were optimized using an L_(9)(3^(3))orthogonal experimental design.Results indicated that both IRD and HAD-IRD significantly reduced drying time compared to single HAD.While single IRD achieved a rapid drying rate,the lack of effective convective airflow led to potential case-hardening and unstable product quality.In contrast,the HAD-IRD strategy demonstrated a synergistic effect.The optimal parameters were determined as follows:hot air temperature of 70℃,infrared temperature of 60℃,and radiation distance of 16 cm.Under these optimized conditions,HAD-IRD reduced the total drying time by over 70%while simultaneously yielding products with superior color,higher sensory scores,and improved rehydration ratio.This study confirms that HAD-IRD is an efficient and high-quality drying method for banana slices,providing a reliable theoretical foundation and technical solution for the drying of thermosensitive fruits.
基金The National Natural Science Foundation of China(No.60425206,60633010)the High Technology Research and Development Program of Jiangsu Province(No.BG2005032)
文摘This paper suggests that a single class rather than methods should be used as the slice scope to compute class cohesion. First, for a given attribute, the statements in all methods that last define the attribute are computed. Then, the forward and backward data slices for this attribute are generated by using the class as the slice scope and are combined to compute the corresponding class data slice. Finally, the class cohesion is computed based on all class data slices for the attributes. Compared to traditional cohesion metrics that use methods as the slice scope, the proposed metrics that use a single class as slice scope take into account the possible interactions between the methods. The experimental results show that class cohesion can be more accurately measured when using the class as the slice scope.
基金Supported by the Science and Technology Support Key Project of Jiangsu Province (DE2008365)~~
文摘Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluation is introduced to make decision in slicing schemes for a processing part. The application in determining the slicing scheme for a computer mouse during prototyping shows that the method increases the rationality during decision- making and improves quality and efficiency for the prototyping part.
文摘To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and internal node state, we find the deficiency of only using port information. Then, we define the gate level number computing method and the concept of slice, and propose using slice analysis to distill switching density as coefficients in a special circuit stage and participate in Bayesian inference with port information. Experiments show that this method can reduce the power-per-cycle estimation error by 21.9% and the root mean square error by 25.0% compared with the original model, and maintain a 700 ＋ speedup compared with the existing gate-level power analysis technique.
基金The National Outstanding Young Scientist Foundation by NSFC(No.60703086,60503020)
文摘A two-phase monadic approach is presented for monadically slicing programs with procedures. In the monadic slice algorithm for interprocedural programs, phase 1 initializes the slice table of formal parameters in a procedure with the given labels, and then captures the callees＇ influence on callers when analyzing call statements. Phase 2 captures the callees＇ dependence on callers by replacing all given labels appearing in the corresponding sets of formal parameters. By the introduction of given labels, this slice method can obtain similar summary information in system-dependence-graph（SDG）-based algorithms for addressing the calling-context problem. With the use of the slice monad transformer, this monadic slicing approach achieves a high level of modularity and flexibility. It shows that the monadic interprocedural algorithm has less complexity and it is not less precise than SDG algorithms.
