A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,...A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.展开更多
Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to i...Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.展开更多
The emergence of Accountable Care Organizations(ACOs)in the landscape of the U.S.healthcare system marks a paradigm shift in healthcare operations.The potential impact of ACOs has been a topic of intense debate.Tradit...The emergence of Accountable Care Organizations(ACOs)in the landscape of the U.S.healthcare system marks a paradigm shift in healthcare operations.The potential impact of ACOs has been a topic of intense debate.Traditional analytical approaches do not lend themselves to examining the complex phenomenon of the emergence and growth of ACOs in the healthcare network.We adopt a complex adaptive system lens to examine the growth of ACOs among physician groups and explore factors that influence this growth.We also discuss the impact of ACOs on the profit of physician groups.An agent-based model was built to simulate physician groups'ACO entrance and exit based on a set of simple rules and their complex interactions with other agents.Based on the simulation results,we derive patterns of ACO expansion and contraction,following four stages of wait-and-see,rollercoaster,fast growth,and stabilizing.Findings suggest that the growth of ACOs is sensitive to the initial state of ACO membership.When the initial size of ACO membership increases,it helps to eliminate the rollercoaster stage.In addition,the growth of the ACO varies depending on the cost–quality tradeoff.When both cost and quality objectives can be met simultaneously,the growth of ACO membership follows wait-and-see and fast growth stages followed by a different stage that we term sticky state.The impact of ACOs on physician groups’cumulative profit varies by the service quality level of the physician group.Physician groups affiliated with insurance companies charging the lowest or the highest level of health insurance premiums are worse off with the ACO option.However,the ACO benefits physician groups affiliated with an insurance company charging a moderate level of premiums.展开更多
In order to realize the required scalable and adaptive system management, an interactive intelligent agency framework, iSMAcy （intelligent System Management Agency） , is proposed as an integrated solution to realize...In order to realize the required scalable and adaptive system management, an interactive intelligent agency framework, iSMAcy （intelligent System Management Agency） , is proposed as an integrated solution to realize distributed autonomoas system management. Firstly, it is a multiagent platform that supports inter-agent communication and cooperation. Secondly, the functional agents are based on intentional agent architecture that achieves balance between goal-directed behavior and situated reactive action. An example of applying the iSMAcy system to a network management environment has been described to illustrate and validate the scalable and adaptive management capability of the intelligent agency framework.展开更多
According to the complex adaptive systems theory, tourist destinations may be regarded as complex adaptive systems formed by multiple adaptive agent interactions and composed of an agent system, tourist attraction sub...According to the complex adaptive systems theory, tourist destinations may be regarded as complex adaptive systems formed by multiple adaptive agent interactions and composed of an agent system, tourist attraction subsystem, tourist service facility subsystem, and external environment system. This paper explores the spatial evolutionary progress of the Southern Anhui tourist area. The period 1979 to 1990 comprised the formation stage of spatial agglomerates, during which tourist attractions centering on Huangshan Scenic Area and Jiuhuashan Scenic Area were gradually exploited and formed scale agglomeration;tourism spatial structure began to show the characteristics of agglomeration development, and Gini indexes of the number of tourists and tourism revenue increased significantly from 0.26 to 0.29, and from 0.33 to 0.35, respectively. From 1991 to 2008, the system experienced a growth stage in which Huangshan Scenic Area and Jiuhuashan Scenic Area were further developed with improved tourist service facilities. Rapid development of Xidi-Hongcun Scenic Area and establishment of Fantawild Tourist Area promoted the formation of more spatial agglomerates with larger scales;Gini indexes of the number of tourists and tourism revenue presented fluctuating changes, reaching low points of 0.15 and 0.25 in 2000 and 0.12 and 0.22 in 2007, respectively. From 2009 to the present day, the system has remained in a blowout-development stage, during which non-linear interactions among agents are strengthened;various emerging development factors generate cultural tourism, vacation tourism, rural tourism and other new tourism products jointly with traditional development factors. New tourism products form a large number of new spatial agglomerates that are interconnected, accelerating the spatial flow of tourists and tourism revenue and reducing the differences in tourism development levels within the region;Gini indexes of the number of tourists and tourism revenue declined steadily from 0.17 and 0.23 in 2009 to 0.12 and 0.15 in 2016.展开更多
Normal and abnormal hematopoiesis is working as a complex adaptive system. From this perspective, the development and the behavior of hematopoietic cell lineages appear as a balance between normal and abnormal hematop...Normal and abnormal hematopoiesis is working as a complex adaptive system. From this perspective, the development and the behavior of hematopoietic cell lineages appear as a balance between normal and abnormal hematopoiesis in the setting of a functioning or malfunctioning microenvironment under the control of the immune system and the influence of hereditary and environmental events.展开更多
Language not only functions as a communication tool,it has fundamental functions.People’s social interaction and their past experience can affect people’s choice of language,as language is a complex,adaptive system....Language not only functions as a communication tool,it has fundamental functions.People’s social interaction and their past experience can affect people’s choice of language,as language is a complex,adaptive system.The paper tries to comment on"A comment on Language Is a Complex Adaptive System:Position Paper"from several aspects to conclude that Language Is a Complex Adaptive System:Position Paper is a comprehensive,creative and influential academic paper which is characteristic of high originality,well-compact organization,detailed literature review.展开更多
q-axis rotor flux can be chosen to form a model reference adaptive system(MRAS)updating rotor time constant online in induction motor drives.This paper presents a stability analysis of such a system with Popov’s hype...q-axis rotor flux can be chosen to form a model reference adaptive system(MRAS)updating rotor time constant online in induction motor drives.This paper presents a stability analysis of such a system with Popov’s hyperstability concept and small-signal linearization technique.At first,the stability of q-axis rotor flux based MRAS is proven with Popov’s Hyperstability theory.Then,to find out the guidelines for optimally designing the coefficients in the PI controller,acting as the adaption mechanism in the MRAS,small-signal model of the estimation system is developed.The obtained linearization model not only allows the stability to be verified further through Routh criterion,but also reveals the distribution of the characteristic roots,which leads to the clue to optimal PI gains.The theoretical analysis and the resultant design guidelines of the adaptation PI gains are verified through simulation and experiments.展开更多
Language is a special social phenomenon and is always on the changing process with the development of society. During the evolving process of language, new language varieties will continuously emerge due to the change...Language is a special social phenomenon and is always on the changing process with the development of society. During the evolving process of language, new language varieties will continuously emerge due to the changes of some social and cultural factors. Cyber language is universally accepted as one type of the social language varieties. Basically, cyber language can be treated as a complex adaptive system which is influenced by the interaction between users’ cognition, social culture and the surrounding environments. Thus it is safe to say that cyber language is always undergoing a dynamic evolving process. With the usage-based language model as the theoretical foundation, this paper proposes a Complex Adaptive System (CAS) approach to analyze the expression of Appreciation to explore the complex, dynamic and nonlinear development of cyber language from the angle of meaning construction, grammaticalization and functional adaption respectively. It is found that the expression of Appreciation is experiencing adaptively a semantic connotations development and a process of grammatical functions expansion as well. This paper suggests that the emergence and development of cyber language is a novel and trendy social language phenomenon. Network language can achieve its process and evolution under the huge impact of social changes and social promotions. When faced with the changing surroundings, cyber language itself enjoys a timely adaption and responsive development to keep up with the new environments, which reflects the basic principle of language development, namely, language changes with the development of society.展开更多
This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergen...This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergence in social systems.By integrating mathematical models,agent-based modeling,network dynamic analysis,and hybrid modeling approaches,the study applies CAS theory to case studies in economic markets,political decision-making,and social interactions.The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena,such as market fluctuations,opinion polarization,and sudden outbreaks of social movements.This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture,but also offers valuable decision-support tools for public policy formulation,social governance,and risk management.Emphasizing the importance of interdisciplinary approaches,this work outlines future research directions in high-performance computing,artificial intelligence,and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances a...Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.展开更多
This article presents an adaptive optimal control method for a semi-active suspension system.The model of the suspension system is built,in which the components of uncertain parameters and exogenous disturbance are de...This article presents an adaptive optimal control method for a semi-active suspension system.The model of the suspension system is built,in which the components of uncertain parameters and exogenous disturbance are described.The adaptive optimal control law consists of the sum of the optimal control component and the adaptive control component.First,the optimal control law is designed for the model of the suspension system after ignoring the components of uncertain parameters and exogenous disturbance caused by the road surface.The optimal control law expresses the desired dynamic characteristics of the suspension system.Next,the adaptive component is designed with the purpose of compensating for the effects caused by uncertain parameters and exogenous disturbance caused by the road surface;the adaptive component has adaptive parameter rules to estimate uncertain parameters and exogenous disturbance.When exogenous disturbances are eliminated,the system responds with an optimal controller designed.By separating theoretically the dynamic of a semi-active suspension system,this solution allows the design of two separate controllers easily and has reduced the computational burden and the use of too many tools,thus allowing for more convenient hardware implementation.The simulation results also show the effectiveness of damping oscillations of the proposed solution in this article.展开更多
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat...Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.展开更多
The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are ...The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance.In order to improve the intelligence and adaptability of production scheduler,a novel distributed scheduling architecture is proposed,which has the ability to autonomously allocate tasks and handle disturbances.All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines.Firstly,the multi-agent technology is applied to build a self-organizing manufacturing system,enabling each machine to be equipped with the ability of active information interaction and joint-action execution.Secondly,various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents,thereby flexibly achieving global perception of environmental state.To ensure the adaptability and superiority of production decisions in dynamic environment,deep reinforcement learning is applied to build a smart production scheduler:Based on the perceived environment state,the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration.The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed.Compared to heuristic dispatching rules,the proposed method achieves an average performance improvement of 34.0%in three scenarios in terms of order tardiness.The proposed system can provide a new reference for the design of smart manufacturing systems.展开更多
Aeropropulsion System Test Facility (ASTF) is required to accurately control the pressure and temperature of the airflow to test the performance of the aero-engine. However, the control accuracy of ASTF is significant...Aeropropulsion System Test Facility (ASTF) is required to accurately control the pressure and temperature of the airflow to test the performance of the aero-engine. However, the control accuracy of ASTF is significantly affected by the flow disturbance caused by aero-engine acceleration and deceleration. This would reduce the credibility of ASTF’s test results for the aero-engine. Therefore, first, this paper proposes a feedforward compensation-based L1 adaptive control method for ASTF to address this problem. The baseline controller is first designed based on ideal uncoupled closed-loop dynamics to achieve dynamic decoupling. Then, L1 adaptive control is adopted to deal with various uncertainties and ensure good control performance. To further enhance the anti-disturbance performance, a feedforward strategy based on disturbance prediction is designed in the L1 adaptive control framework to compensate for the unmatched flow disturbance, which cannot be measured directly. In addition, this strategy takes into account the effects of actuator dynamics. With this method, the feedforward term can be determined from the nominal model parameters despite uncertainties. Finally, to demonstrate the effectiveness of the proposed method, various comparative experiments are performed on a hardware-in-the-loop system of ASTF. The experimental results show that the proposed method possesses excellent tracking performance, anti-disturbance performance and robustness.展开更多
This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an ...This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an adaptive exponential reaching law with a continuous barrier function,the proposed approach eliminates chattering and ensures robust performance under model uncertainties.The methodology combines adaptive SMC with dynamic switching to estimate and compensates for unknown uncertainties,providing smooth and stable control.Finally,the performance and effectiveness of the proposed approach are compared with those of a previous study.展开更多
In this paper,a pair of dynamic high-gain observer and output feedback controller is proposed for nonlinear systems with multiple unknown time delays.By constructing Lyapunov-Krasovskii functionals,it shows that globa...In this paper,a pair of dynamic high-gain observer and output feedback controller is proposed for nonlinear systems with multiple unknown time delays.By constructing Lyapunov-Krasovskii functionals,it shows that global state asymptotic regulation can be ensured by introducing a single dynamic gain;furthermore,global asymptotic stabilization can be achieved by choosing a sufficiently large static scaling gain when the upper bounds of all system parameters are known.Especially,the output coefficient is allowed to be non-differentiable with unknown upper bound.This paper proposes a generalized Lyapunov matrix inequality based dynamic-gain scaling method,which significantly simplifies the design computational complexity by comparing with the classic backstepping method.展开更多
For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies ...For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.展开更多
In recent years,the world has seen an exponential increase in energy demand,prompting scientists to look for innovative ways to exploit the power sun’s power.Solar energy technologies use the sun’s energy and light ...In recent years,the world has seen an exponential increase in energy demand,prompting scientists to look for innovative ways to exploit the power sun’s power.Solar energy technologies use the sun’s energy and light to provide heating,lighting,hot water,electricity and even cooling for homes,businesses,and industries.Therefore,ground-level solar radiation data is important for these applications.Thus,our work aims to use a mathematical modeling tool to predict solar irradiation.For this purpose,we are interested in the application of the Adaptive Neuro Fuzzy Inference System.Through this type of artificial neural system,10 models were developed,based on meteorological data such as the Day number(Nj),Ambient temperature(T),Relative Humidity(Hr),Wind speed(WS),Wind direction(WD),Declination(δ),Irradiation outside the atmosphere(Goh),Maximum temperature(Tmax),Minimum temperature(Tmin).These models have been tested by different static indicators to choose the most suitable one for the estimation of the daily global solar radiation.This study led us to choose the M8 model,which takes Nj,T,Hr,δ,Ws,Wd,G0,and S0 as input variables because it presents the best performance either in the learning phase(R^(2)=0.981,RMSE=0.107 kW/m^(2),MAE=0.089 kW/m2)or in the validation phase(R^(2)=0.979,RMSE=0.117 kW/m^(2),MAE=0.101 kW/m^(2)).展开更多
基金The National Science Foundation funded this research under the Dy-namics of Coupled Natural and Human Systems program(Grants No.DEB-1212183 and BCS-1826839)support from San Diego State University and Auburn University.
文摘A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.
基金supported by the Shanghai Philosophy and Social Science Foundation(2022ECK004)Shanghai Soft Science Research Project(23692123400)。
文摘Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.
文摘The emergence of Accountable Care Organizations(ACOs)in the landscape of the U.S.healthcare system marks a paradigm shift in healthcare operations.The potential impact of ACOs has been a topic of intense debate.Traditional analytical approaches do not lend themselves to examining the complex phenomenon of the emergence and growth of ACOs in the healthcare network.We adopt a complex adaptive system lens to examine the growth of ACOs among physician groups and explore factors that influence this growth.We also discuss the impact of ACOs on the profit of physician groups.An agent-based model was built to simulate physician groups'ACO entrance and exit based on a set of simple rules and their complex interactions with other agents.Based on the simulation results,we derive patterns of ACO expansion and contraction,following four stages of wait-and-see,rollercoaster,fast growth,and stabilizing.Findings suggest that the growth of ACOs is sensitive to the initial state of ACO membership.When the initial size of ACO membership increases,it helps to eliminate the rollercoaster stage.In addition,the growth of the ACO varies depending on the cost–quality tradeoff.When both cost and quality objectives can be met simultaneously,the growth of ACO membership follows wait-and-see and fast growth stages followed by a different stage that we term sticky state.The impact of ACOs on physician groups’cumulative profit varies by the service quality level of the physician group.Physician groups affiliated with insurance companies charging the lowest or the highest level of health insurance premiums are worse off with the ACO option.However,the ACO benefits physician groups affiliated with an insurance company charging a moderate level of premiums.
文摘In order to realize the required scalable and adaptive system management, an interactive intelligent agency framework, iSMAcy （intelligent System Management Agency） , is proposed as an integrated solution to realize distributed autonomoas system management. Firstly, it is a multiagent platform that supports inter-agent communication and cooperation. Secondly, the functional agents are based on intentional agent architecture that achieves balance between goal-directed behavior and situated reactive action. An example of applying the iSMAcy system to a network management environment has been described to illustrate and validate the scalable and adaptive management capability of the intelligent agency framework.
基金National Natural Science Foundation of China,No.51278239
文摘According to the complex adaptive systems theory, tourist destinations may be regarded as complex adaptive systems formed by multiple adaptive agent interactions and composed of an agent system, tourist attraction subsystem, tourist service facility subsystem, and external environment system. This paper explores the spatial evolutionary progress of the Southern Anhui tourist area. The period 1979 to 1990 comprised the formation stage of spatial agglomerates, during which tourist attractions centering on Huangshan Scenic Area and Jiuhuashan Scenic Area were gradually exploited and formed scale agglomeration;tourism spatial structure began to show the characteristics of agglomeration development, and Gini indexes of the number of tourists and tourism revenue increased significantly from 0.26 to 0.29, and from 0.33 to 0.35, respectively. From 1991 to 2008, the system experienced a growth stage in which Huangshan Scenic Area and Jiuhuashan Scenic Area were further developed with improved tourist service facilities. Rapid development of Xidi-Hongcun Scenic Area and establishment of Fantawild Tourist Area promoted the formation of more spatial agglomerates with larger scales;Gini indexes of the number of tourists and tourism revenue presented fluctuating changes, reaching low points of 0.15 and 0.25 in 2000 and 0.12 and 0.22 in 2007, respectively. From 2009 to the present day, the system has remained in a blowout-development stage, during which non-linear interactions among agents are strengthened;various emerging development factors generate cultural tourism, vacation tourism, rural tourism and other new tourism products jointly with traditional development factors. New tourism products form a large number of new spatial agglomerates that are interconnected, accelerating the spatial flow of tourists and tourism revenue and reducing the differences in tourism development levels within the region;Gini indexes of the number of tourists and tourism revenue declined steadily from 0.17 and 0.23 in 2009 to 0.12 and 0.15 in 2016.
文摘Normal and abnormal hematopoiesis is working as a complex adaptive system. From this perspective, the development and the behavior of hematopoietic cell lineages appear as a balance between normal and abnormal hematopoiesis in the setting of a functioning or malfunctioning microenvironment under the control of the immune system and the influence of hereditary and environmental events.
文摘Language not only functions as a communication tool,it has fundamental functions.People’s social interaction and their past experience can affect people’s choice of language,as language is a complex,adaptive system.The paper tries to comment on"A comment on Language Is a Complex Adaptive System:Position Paper"from several aspects to conclude that Language Is a Complex Adaptive System:Position Paper is a comprehensive,creative and influential academic paper which is characteristic of high originality,well-compact organization,detailed literature review.
文摘q-axis rotor flux can be chosen to form a model reference adaptive system(MRAS)updating rotor time constant online in induction motor drives.This paper presents a stability analysis of such a system with Popov’s hyperstability concept and small-signal linearization technique.At first,the stability of q-axis rotor flux based MRAS is proven with Popov’s Hyperstability theory.Then,to find out the guidelines for optimally designing the coefficients in the PI controller,acting as the adaption mechanism in the MRAS,small-signal model of the estimation system is developed.The obtained linearization model not only allows the stability to be verified further through Routh criterion,but also reveals the distribution of the characteristic roots,which leads to the clue to optimal PI gains.The theoretical analysis and the resultant design guidelines of the adaptation PI gains are verified through simulation and experiments.
文摘Language is a special social phenomenon and is always on the changing process with the development of society. During the evolving process of language, new language varieties will continuously emerge due to the changes of some social and cultural factors. Cyber language is universally accepted as one type of the social language varieties. Basically, cyber language can be treated as a complex adaptive system which is influenced by the interaction between users’ cognition, social culture and the surrounding environments. Thus it is safe to say that cyber language is always undergoing a dynamic evolving process. With the usage-based language model as the theoretical foundation, this paper proposes a Complex Adaptive System (CAS) approach to analyze the expression of Appreciation to explore the complex, dynamic and nonlinear development of cyber language from the angle of meaning construction, grammaticalization and functional adaption respectively. It is found that the expression of Appreciation is experiencing adaptively a semantic connotations development and a process of grammatical functions expansion as well. This paper suggests that the emergence and development of cyber language is a novel and trendy social language phenomenon. Network language can achieve its process and evolution under the huge impact of social changes and social promotions. When faced with the changing surroundings, cyber language itself enjoys a timely adaption and responsive development to keep up with the new environments, which reflects the basic principle of language development, namely, language changes with the development of society.
文摘This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergence in social systems.By integrating mathematical models,agent-based modeling,network dynamic analysis,and hybrid modeling approaches,the study applies CAS theory to case studies in economic markets,political decision-making,and social interactions.The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena,such as market fluctuations,opinion polarization,and sudden outbreaks of social movements.This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture,but also offers valuable decision-support tools for public policy formulation,social governance,and risk management.Emphasizing the importance of interdisciplinary approaches,this work outlines future research directions in high-performance computing,artificial intelligence,and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金the National Key Research and Development Program of China(2021YFA0717900)National Natural Science Foundation of China(62471251,62405144,62288102,22275098,and 62174089)+1 种基金Basic Research Program of Jiangsu(BK20240033,BK20243057)Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB402).
文摘Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.
基金supported in part by the Thai Nguyen University of Technology,Vietnam.
文摘This article presents an adaptive optimal control method for a semi-active suspension system.The model of the suspension system is built,in which the components of uncertain parameters and exogenous disturbance are described.The adaptive optimal control law consists of the sum of the optimal control component and the adaptive control component.First,the optimal control law is designed for the model of the suspension system after ignoring the components of uncertain parameters and exogenous disturbance caused by the road surface.The optimal control law expresses the desired dynamic characteristics of the suspension system.Next,the adaptive component is designed with the purpose of compensating for the effects caused by uncertain parameters and exogenous disturbance caused by the road surface;the adaptive component has adaptive parameter rules to estimate uncertain parameters and exogenous disturbance.When exogenous disturbances are eliminated,the system responds with an optimal controller designed.By separating theoretically the dynamic of a semi-active suspension system,this solution allows the design of two separate controllers easily and has reduced the computational burden and the use of too many tools,thus allowing for more convenient hardware implementation.The simulation results also show the effectiveness of damping oscillations of the proposed solution in this article.
文摘Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.
基金supported by the Scientific Research Foundation of Nanjing Institute of Technology(No.YKJ202425)the National Natural Science Foundation of China(No.72301130).
文摘The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance.In order to improve the intelligence and adaptability of production scheduler,a novel distributed scheduling architecture is proposed,which has the ability to autonomously allocate tasks and handle disturbances.All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines.Firstly,the multi-agent technology is applied to build a self-organizing manufacturing system,enabling each machine to be equipped with the ability of active information interaction and joint-action execution.Secondly,various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents,thereby flexibly achieving global perception of environmental state.To ensure the adaptability and superiority of production decisions in dynamic environment,deep reinforcement learning is applied to build a smart production scheduler:Based on the perceived environment state,the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration.The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed.Compared to heuristic dispatching rules,the proposed method achieves an average performance improvement of 34.0%in three scenarios in terms of order tardiness.The proposed system can provide a new reference for the design of smart manufacturing systems.
基金supported by the“Shuimu Tsinghua Scholar”Project,China(No.2024SM223)the National Science and Technology Major Project,China(No.Y2022-V-0002-0028).
文摘Aeropropulsion System Test Facility (ASTF) is required to accurately control the pressure and temperature of the airflow to test the performance of the aero-engine. However, the control accuracy of ASTF is significantly affected by the flow disturbance caused by aero-engine acceleration and deceleration. This would reduce the credibility of ASTF’s test results for the aero-engine. Therefore, first, this paper proposes a feedforward compensation-based L1 adaptive control method for ASTF to address this problem. The baseline controller is first designed based on ideal uncoupled closed-loop dynamics to achieve dynamic decoupling. Then, L1 adaptive control is adopted to deal with various uncertainties and ensure good control performance. To further enhance the anti-disturbance performance, a feedforward strategy based on disturbance prediction is designed in the L1 adaptive control framework to compensate for the unmatched flow disturbance, which cannot be measured directly. In addition, this strategy takes into account the effects of actuator dynamics. With this method, the feedforward term can be determined from the nominal model parameters despite uncertainties. Finally, to demonstrate the effectiveness of the proposed method, various comparative experiments are performed on a hardware-in-the-loop system of ASTF. The experimental results show that the proposed method possesses excellent tracking performance, anti-disturbance performance and robustness.
文摘This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an adaptive exponential reaching law with a continuous barrier function,the proposed approach eliminates chattering and ensures robust performance under model uncertainties.The methodology combines adaptive SMC with dynamic switching to estimate and compensates for unknown uncertainties,providing smooth and stable control.Finally,the performance and effectiveness of the proposed approach are compared with those of a previous study.
基金supported by the Zhejiang Provincial Natural Science Foundation(LY24F030011,LY23F030005)the National Natural Science Foundation of China(62373131).
文摘In this paper,a pair of dynamic high-gain observer and output feedback controller is proposed for nonlinear systems with multiple unknown time delays.By constructing Lyapunov-Krasovskii functionals,it shows that global state asymptotic regulation can be ensured by introducing a single dynamic gain;furthermore,global asymptotic stabilization can be achieved by choosing a sufficiently large static scaling gain when the upper bounds of all system parameters are known.Especially,the output coefficient is allowed to be non-differentiable with unknown upper bound.This paper proposes a generalized Lyapunov matrix inequality based dynamic-gain scaling method,which significantly simplifies the design computational complexity by comparing with the classic backstepping method.
基金The National Key R&D Program of China(2021ZD0201300)the National Natural Science Foundation of China(624B2058,U1913602 and 61936004)+1 种基金the Innovation Group Project of the National Natural Science Foundation of China(61821003)the 111 Project on Computational Intelligence and Intelligent Control(B18024).
文摘For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.
文摘In recent years,the world has seen an exponential increase in energy demand,prompting scientists to look for innovative ways to exploit the power sun’s power.Solar energy technologies use the sun’s energy and light to provide heating,lighting,hot water,electricity and even cooling for homes,businesses,and industries.Therefore,ground-level solar radiation data is important for these applications.Thus,our work aims to use a mathematical modeling tool to predict solar irradiation.For this purpose,we are interested in the application of the Adaptive Neuro Fuzzy Inference System.Through this type of artificial neural system,10 models were developed,based on meteorological data such as the Day number(Nj),Ambient temperature(T),Relative Humidity(Hr),Wind speed(WS),Wind direction(WD),Declination(δ),Irradiation outside the atmosphere(Goh),Maximum temperature(Tmax),Minimum temperature(Tmin).These models have been tested by different static indicators to choose the most suitable one for the estimation of the daily global solar radiation.This study led us to choose the M8 model,which takes Nj,T,Hr,δ,Ws,Wd,G0,and S0 as input variables because it presents the best performance either in the learning phase(R^(2)=0.981,RMSE=0.107 kW/m^(2),MAE=0.089 kW/m2)or in the validation phase(R^(2)=0.979,RMSE=0.117 kW/m^(2),MAE=0.101 kW/m^(2)).
