To control the tri-modal microstructure and performance,a prediction model of tri-modal microstructure in the isothermal local loading forming of titanium alloy was developed.The staged isothermal local loading experi...To control the tri-modal microstructure and performance,a prediction model of tri-modal microstructure in the isothermal local loading forming of titanium alloy was developed.The staged isothermal local loading experiment on TA15alloy indicates that there exist four important microstructure evolution phenomena in the development of tri-modal microstructure,i.e.,the generation of lamellarα,content variation of equiaxedα,spatial orientation change of lamellarαand globularization of lamellarα.Considering the laws of these microstructure phenomena,the microstructure model was established to correlate the parameters of tri-modal microstructure and processing conditions.Then,the developed microstructure model was integrated with finite element(FE)model to predict the tri-modal microstructure in the isothermal local loading forming.Its reliability and accuracy were verified by the microstructure observation at different locations of sample.Good agreements between the predicted and experimental results suggest that the developed microstructure model and its combination with FE model are effective in the prediction of tri-modal microstructure in the isothermal local loading forming of TA15alloy.展开更多
Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,inc...Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,including random noise interference,less distinguishability between multi-class faults,and the new fault emerging.To address these issues,this study formulates fault diagnosis in uncertain industrial processes as a multilevel refined fault diagnosis problem.A hierarchical stochastic network approach is proposed to refine fault diagnosis of multiclass faults.This method considers the augmentation of fault categories as naturally following a hierarchical structure.At each hierarchical stage,stochastic network methods are designed according to the sources of uncertainty.For fault feature extraction,a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the messagepassing process,ensuring the extraction of high-quality fault features and providing the provision of differentiated information.Subsequently,multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally.This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability.Finally,the feasibility and effectiveness of the proposed method are validated using two industrial processes.The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data,achieving a satisfactory fault diagnosis performance.展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
Novel insights into complex biological processes very often critically depend on the establishment of new potent read-out tools and improved protocols.A lot has been learned over the past four decades on physiological...Novel insights into complex biological processes very often critically depend on the establishment of new potent read-out tools and improved protocols.A lot has been learned over the past four decades on physiological functions and,importantly,disease-related roles of the prion protein(PrP),a relatively broadly expressed membrane-anchored glycoprotein with high levels in several cell types of the nervous and immune system and with well-established key roles in different progressive and fatal neurodegenerative protein misfolding diseases(proteopathies).展开更多
In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate tim...In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.展开更多
Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.H...Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient(PT)modeling method to address these difficulties,providing a fresh way forward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.展开更多
Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutua...Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.展开更多
The dynamic characteristic of complex network failure and recovery is one of the main research topics in complex networks. Real world systems such as traffic jams and Internet recovery could be described by the comple...The dynamic characteristic of complex network failure and recovery is one of the main research topics in complex networks. Real world systems such as traffic jams and Internet recovery could be described by the complex network theory. We propose a model to study the recovery process in complex networks. Two different recovery mechanisms are considered in three kinds of networks： external recovery and internal recovery. By simulating the process of the nodes recovery in networks, it is found that the system exhibits the feature of first-order phase transition only when the external recovery is considered. Internal recovery cannot induce such a kind of transitions. As external recovery and internal recovery coexist on networks, the systems will retain the most efficient part of external recovery and internal recovery. Meanwhile, a hysteresis could be observed when increasing or decreasing the failure probability. Finally, a largest degree node protection strategy is proposed for improving the robustness of networks.展开更多
Based on the capture force field potential model and the adiabatic invariant proposed by Bates, adopting improved average dipole orientation (IADO) theory, the force constants between transition metal ions and benzene...Based on the capture force field potential model and the adiabatic invariant proposed by Bates, adopting improved average dipole orientation (IADO) theory, the force constants between transition metal ions and benzene (bz) and also a series of inner-sphere reoganization energy (REin) were calculated. The reasons for the differences between theoretical predictions and experimental results were discussed.展开更多
In this article, some properties of complex Wiener-It? multiple integrals and complex Ornstein-Uhlenbeck operators and semigroups are obtained. Those include Stroock’s formula, Hu-Meyer formula, Clark-Ocone formula, ...In this article, some properties of complex Wiener-It? multiple integrals and complex Ornstein-Uhlenbeck operators and semigroups are obtained. Those include Stroock’s formula, Hu-Meyer formula, Clark-Ocone formula, and the hypercontractivity of complex Ornstein-Uhlenbeck semigroups. As an application, several expansions of the fourth moments of complex Wiener-It? multiple integrals are given.展开更多
The express delivery induslry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidime...The express delivery induslry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidimensional space, is difficult to utilize and process. This paper proposes an automatic data acquisition fi-amework to resolve such difficulty, which synthetically utilize intelligent inemet of things （IoT）, semantic web and complext event processing （CEP） technology. We also implement a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages. The simulation results show that the system has good performance and feasible enough to deal with the complex business which need data support fTom multidimensional space.展开更多
The copolymerization process of triphenylmethyl methacrylate (TrMA) and methylmethacrylate (MMA) using chiral anionic complex initiator (-) SP-FlLi (Scheme 1) has beenstudied in toluene and THF, respectively. The copo...The copolymerization process of triphenylmethyl methacrylate (TrMA) and methylmethacrylate (MMA) using chiral anionic complex initiator (-) SP-FlLi (Scheme 1) has beenstudied in toluene and THF, respectively. The copolymer obtained in toluene possessed muchhigher specific rotation than that in THF. These copolymers have shown a tendency to a random and a like alternating structure, respectively.展开更多
WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enh...WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.展开更多
Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industria...Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industrial process,this paper introduces the concept of intelligent ODM in industrial process,shapes its future directions,and highlights key technical challenges.By the tight conjoining of and coordination between industrial artificial intelligence(AI)with industrial control and optimization technologies,as well as the Industrial Internet with industrial computer management and control systems,an intelligent operational optimization decision-making methodology is proposed for complex industrial process.The intelligent ODM methodology and its successful application demonstrate that the tight conjoining of and coordination between next-generation information technologies with industrial control and optimization technologies will promote the development of industrial intelligent ODM.Finally,main research directions and ideas are outlined for realizing intelligent ODM in industrial process.展开更多
Seeds of two-line sterile rice cultivars Zhu 1S and Lu 18S were carried into space by ＂Shijian 8＂ breeding recoverable satellite, then planted in ground. Mutagenic effects from space induction were compared with tho...Seeds of two-line sterile rice cultivars Zhu 1S and Lu 18S were carried into space by ＂Shijian 8＂ breeding recoverable satellite, then planted in ground. Mutagenic effects from space induction were compared with those from y-irradiation and complex processing of space induction and y-irradiation. The results showed that agronomic effect was stimulated in Mo progenies of the two-line sterile rice varieties treated by space flight, and their radiosensitivities to the irradiation of space flight performed non-sensitive. The order of mutation frequency was determined to be SP ＋ γ 〉 γ 〉 SP in M2 generation. And a series of mutated elites（individuals） were screened. Physiological indices of mutants screened like the activity of protective enzymes were measured to explore the physiological and biochemical basis of biological effect in space environment to two-line sterile rice. The results of this study show that space mutation breeding is an effective novel mean for breeding.展开更多
One research crucial to wider adoption of Radio Frequency Identification （RFID） technology is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, ...One research crucial to wider adoption of Radio Frequency Identification （RFID） technology is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, RFID readings are usually of high volume and velocity, and have the attributes representing their reading objects, occurrence times and spots. Based on these characteristics and the Non-deterministic Finite Automata （NFA） implementation framework, this paper studies the performance issues of RFID complex event processing and proposes corresponding optimization techniques. Our techniques include： （1） taking advantage of negation events or exclusiveness between events to prune intermediate results, thus reduces memory consumption; （2） with different selectivities of complex events, purposefully reordering the join operations between events to improve overall efficiency, achieve higher stream throughput; （3） utilizing the slot-based or B＋-tree-based approach to optimizing the processing performance with the time window constraint. We present the analytical results of these techniques and validate their effectiveness through experiments.展开更多
Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pes...Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to harvesting.Pesticide residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly consumers.The residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in food.There is an obligation to provide a warning about this amount of pesticide use in farming.Previous technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential data.The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers.Overall,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.展开更多
Windblown sand flux and dune field evolving toward the oasis have been a common ecological and environmental threat confronted by many countries.Meanwhile,it is also a kind of complex dynamical process involving multi...Windblown sand flux and dune field evolving toward the oasis have been a common ecological and environmental threat confronted by many countries.Meanwhile,it is also a kind of complex dynamical process involving multiple temporal and spatial scales which is still out of accurate description through current field observations.Available models and reliable quantitative simulations are of significant value to predict the spreading rate of desertification and provide an optimal design for sand prevention.This paper presents a'triple-jump'method to realize quantitative simulations to the formation and evolution of an aeolian dune field from an arbitrary initial configuration.Simulated results achieve a satisfactory agreement with observations qualitatively and quantitatively,which also reveal the characteristics and dynamical behaviors of dunes and dune field.Such a paradigm is of a good level of generality,which provides an exploratory probe into the subject of multi-scale physics.展开更多
In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive ...In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive at the central server out of order, due to the differences of network latencies. Most state-of-the-art techniques process out-of-order events by buffering the events until the total event order within a specified range can be guaranteed. Their main problems are leading to increasing response time and reducing system throughput. This paper aims to build a high performance out-of- order event processing mechanism, which can match events as soon as they arrive instead of buffering them till all arrive. A suffix-automaton-based event matching algorithm is proposed to speed up query processing, and a confidence-based accuracy evaluation is proposed to control the query result quality. The performance of our approach is evaluated through detailed accuracy and response time analysis. As experimental results show, our approach can obviously speed up the query matching time and produce reasonable query results.展开更多
基金Projects(51605388,51575449)supported by the National Natural Science Foundation of ChinaProject(B08040)supported by the "111" Project,China+1 种基金Project(131-QP-2015)supported by the Research Fund of the State Key Laboratory of Solidification Processing(NWPU),ChinaProject supported by the Open Research Fund of State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology,China
文摘To control the tri-modal microstructure and performance,a prediction model of tri-modal microstructure in the isothermal local loading forming of titanium alloy was developed.The staged isothermal local loading experiment on TA15alloy indicates that there exist four important microstructure evolution phenomena in the development of tri-modal microstructure,i.e.,the generation of lamellarα,content variation of equiaxedα,spatial orientation change of lamellarαand globularization of lamellarα.Considering the laws of these microstructure phenomena,the microstructure model was established to correlate the parameters of tri-modal microstructure and processing conditions.Then,the developed microstructure model was integrated with finite element(FE)model to predict the tri-modal microstructure in the isothermal local loading forming.Its reliability and accuracy were verified by the microstructure observation at different locations of sample.Good agreements between the predicted and experimental results suggest that the developed microstructure model and its combination with FE model are effective in the prediction of tri-modal microstructure in the isothermal local loading forming of TA15alloy.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)the Science and Technology Innovation Program of Hunan Province(2022RC1089)+1 种基金the Central South University Innovation Driven Research Programme(2023CXQD040)the Fundamental Research Funds for the Central Universities of Central South University(2025ZZTS0213).
文摘Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,including random noise interference,less distinguishability between multi-class faults,and the new fault emerging.To address these issues,this study formulates fault diagnosis in uncertain industrial processes as a multilevel refined fault diagnosis problem.A hierarchical stochastic network approach is proposed to refine fault diagnosis of multiclass faults.This method considers the augmentation of fault categories as naturally following a hierarchical structure.At each hierarchical stage,stochastic network methods are designed according to the sources of uncertainty.For fault feature extraction,a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the messagepassing process,ensuring the extraction of high-quality fault features and providing the provision of differentiated information.Subsequently,multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally.This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability.Finally,the feasibility and effectiveness of the proposed method are validated using two industrial processes.The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data,achieving a satisfactory fault diagnosis performance.
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
基金supported by the CJD Foundation,USA,the Alzheimer Forschung Initiative(AFI)e.V.,Germany,and Werner-Otto-Stiftung,Germany(all to HCA),ChinaScholarship Council(grant#202108080249 to FS)Deutsche Forschungsgemeinschaft(DFG)CRC877“Proteolysis as a regulatory event in pathophysiology”(project A12 to MG),Slovene Research and InnovationAgency(grant number P4-0176 to VCS).
文摘Novel insights into complex biological processes very often critically depend on the establishment of new potent read-out tools and improved protocols.A lot has been learned over the past four decades on physiological functions and,importantly,disease-related roles of the prion protein(PrP),a relatively broadly expressed membrane-anchored glycoprotein with high levels in several cell types of the nervous and immune system and with well-established key roles in different progressive and fatal neurodegenerative protein misfolding diseases(proteopathies).
基金Project(61025015) supported by the National Natural Science Funds for Distinguished Young Scholars of ChinaProject(21106036) supported by the National Natural Science Foundation of China+2 种基金Project(200805331103) supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(NCET-08-0576) supported by Program for New Century Excellent Talents in Universities of ChinaProject(11B038) supported by Scientific Research Fund for the Excellent Youth Scholars of Hunan Provincial Education Department,China
文摘In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.
基金supported by the National Natural Science Foundation of China(21978203,21676183).
文摘Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient(PT)modeling method to address these difficulties,providing a fresh way forward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.
文摘Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.
基金Supported by the National Natural Science foundation of China under Grant No 11474221
文摘The dynamic characteristic of complex network failure and recovery is one of the main research topics in complex networks. Real world systems such as traffic jams and Internet recovery could be described by the complex network theory. We propose a model to study the recovery process in complex networks. Two different recovery mechanisms are considered in three kinds of networks： external recovery and internal recovery. By simulating the process of the nodes recovery in networks, it is found that the system exhibits the feature of first-order phase transition only when the external recovery is considered. Internal recovery cannot induce such a kind of transitions. As external recovery and internal recovery coexist on networks, the systems will retain the most efficient part of external recovery and internal recovery. Meanwhile, a hysteresis could be observed when increasing or decreasing the failure probability. Finally, a largest degree node protection strategy is proposed for improving the robustness of networks.
文摘Based on the capture force field potential model and the adiabatic invariant proposed by Bates, adopting improved average dipole orientation (IADO) theory, the force constants between transition metal ions and benzene (bz) and also a series of inner-sphere reoganization energy (REin) were calculated. The reasons for the differences between theoretical predictions and experimental results were discussed.
基金Supported by NSFC(11871079)NSFC (11731009)Center for Statistical Science,PKU
文摘In this article, some properties of complex Wiener-It? multiple integrals and complex Ornstein-Uhlenbeck operators and semigroups are obtained. Those include Stroock’s formula, Hu-Meyer formula, Clark-Ocone formula, and the hypercontractivity of complex Ornstein-Uhlenbeck semigroups. As an application, several expansions of the fourth moments of complex Wiener-It? multiple integrals are given.
文摘The express delivery induslry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidimensional space, is difficult to utilize and process. This paper proposes an automatic data acquisition fi-amework to resolve such difficulty, which synthetically utilize intelligent inemet of things （IoT）, semantic web and complext event processing （CEP） technology. We also implement a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages. The simulation results show that the system has good performance and feasible enough to deal with the complex business which need data support fTom multidimensional space.
文摘The copolymerization process of triphenylmethyl methacrylate (TrMA) and methylmethacrylate (MMA) using chiral anionic complex initiator (-) SP-FlLi (Scheme 1) has beenstudied in toluene and THF, respectively. The copolymer obtained in toluene possessed muchhigher specific rotation than that in THF. These copolymers have shown a tendency to a random and a like alternating structure, respectively.
基金supported by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0093/2023/RIA2,0145/2023/RIA3).
文摘WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.
基金supported by the Research Program of the Liaoning Liaohe Laboratory(LLL23ZZ-05-012)China Academy of Engineering Institute of Land Cooperation Consulting Project(2023-DFZD-60-02)+3 种基金the Key Research and Development Program of Liaoning Province(2023JH26/10200011)the National Natural Science Foundation of China(61991404)the National Key Research and Development Program of China(2024YFB3309700)the Science and Technology Major Project 2024 of Liaoning Province(2024JH1/11700048).
文摘Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industrial process,this paper introduces the concept of intelligent ODM in industrial process,shapes its future directions,and highlights key technical challenges.By the tight conjoining of and coordination between industrial artificial intelligence(AI)with industrial control and optimization technologies,as well as the Industrial Internet with industrial computer management and control systems,an intelligent operational optimization decision-making methodology is proposed for complex industrial process.The intelligent ODM methodology and its successful application demonstrate that the tight conjoining of and coordination between next-generation information technologies with industrial control and optimization technologies will promote the development of industrial intelligent ODM.Finally,main research directions and ideas are outlined for realizing intelligent ODM in industrial process.
基金Supported by The Ministry of National Agricultural Special PublicSector Research "Nuclear Technology Application in Agriculture"(No.200803034)Science and Technology Innovation Projects ofHunan Academy of Agricultural Sciences(2009hnnkycx13)the National Science&Technology Pillar Program in the Eleventh Five-year Plan period(2008BAD7B02)~~
文摘Seeds of two-line sterile rice cultivars Zhu 1S and Lu 18S were carried into space by ＂Shijian 8＂ breeding recoverable satellite, then planted in ground. Mutagenic effects from space induction were compared with those from y-irradiation and complex processing of space induction and y-irradiation. The results showed that agronomic effect was stimulated in Mo progenies of the two-line sterile rice varieties treated by space flight, and their radiosensitivities to the irradiation of space flight performed non-sensitive. The order of mutation frequency was determined to be SP ＋ γ 〉 γ 〉 SP in M2 generation. And a series of mutated elites（individuals） were screened. Physiological indices of mutants screened like the activity of protective enzymes were measured to explore the physiological and biochemical basis of biological effect in space environment to two-line sterile rice. The results of this study show that space mutation breeding is an effective novel mean for breeding.
基金supported by the National Natural Science Foundation of China under Grant No.60720106001
文摘One research crucial to wider adoption of Radio Frequency Identification （RFID） technology is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, RFID readings are usually of high volume and velocity, and have the attributes representing their reading objects, occurrence times and spots. Based on these characteristics and the Non-deterministic Finite Automata （NFA） implementation framework, this paper studies the performance issues of RFID complex event processing and proposes corresponding optimization techniques. Our techniques include： （1） taking advantage of negation events or exclusiveness between events to prune intermediate results, thus reduces memory consumption; （2） with different selectivities of complex events, purposefully reordering the join operations between events to improve overall efficiency, achieve higher stream throughput; （3） utilizing the slot-based or B＋-tree-based approach to optimizing the processing performance with the time window constraint. We present the analytical results of these techniques and validate their effectiveness through experiments.
文摘Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to harvesting.Pesticide residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly consumers.The residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in food.There is an obligation to provide a warning about this amount of pesticide use in farming.Previous technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential data.The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers.Overall,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.
基金supported by the National Natural Science Foundation of China(10872082,11002064)the Science Foundation of Ministry of Education of China(308022)
文摘Windblown sand flux and dune field evolving toward the oasis have been a common ecological and environmental threat confronted by many countries.Meanwhile,it is also a kind of complex dynamical process involving multiple temporal and spatial scales which is still out of accurate description through current field observations.Available models and reliable quantitative simulations are of significant value to predict the spreading rate of desertification and provide an optimal design for sand prevention.This paper presents a'triple-jump'method to realize quantitative simulations to the formation and evolution of an aeolian dune field from an arbitrary initial configuration.Simulated results achieve a satisfactory agreement with observations qualitatively and quantitatively,which also reveal the characteristics and dynamical behaviors of dunes and dune field.Such a paradigm is of a good level of generality,which provides an exploratory probe into the subject of multi-scale physics.
基金supported by the National Natural Science Foundation of China under Grant Nos.61003058,60933001the Fundamental Research Funds for the Central Universities under Grant No.N090104001
文摘In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive at the central server out of order, due to the differences of network latencies. Most state-of-the-art techniques process out-of-order events by buffering the events until the total event order within a specified range can be guaranteed. Their main problems are leading to increasing response time and reducing system throughput. This paper aims to build a high performance out-of- order event processing mechanism, which can match events as soon as they arrive instead of buffering them till all arrive. A suffix-automaton-based event matching algorithm is proposed to speed up query processing, and a confidence-based accuracy evaluation is proposed to control the query result quality. The performance of our approach is evaluated through detailed accuracy and response time analysis. As experimental results show, our approach can obviously speed up the query matching time and produce reasonable query results.
