With the continuous advancement of the tiered diagnosis and treatment system,the medical consortium model has gained increasing attention as an important approach to promoting the vertical integration of healthcare re...With the continuous advancement of the tiered diagnosis and treatment system,the medical consortium model has gained increasing attention as an important approach to promoting the vertical integration of healthcare resources.Within this context,laboratory data,as a key component of healthcare information systems,urgently requires efficient sharing and intelligent analysis.This paper designs and constructs an intelligent early warning system for laboratory data based on a cloud platform tailored to the medical consortium model.Through standardized data formats and unified access interfaces,the system enables the integration and cleaning of laboratory data across multiple healthcare institutions.By combining medical rule sets with machine learning models,the system achieves graded alerts and rapid responses to abnormal key indicators and potential outbreaks of infectious diseases.Practical deployment results demonstrate that the system significantly improves the utilization efficiency of laboratory data,strengthens public health event monitoring,and optimizes inter-institutional collaboration.The paper also discusses challenges encountered during system implementation,such as inconsistent data standards,security and compliance concerns,and model interpretability,and proposes corresponding optimization strategies.These findings provide a reference for the broader application of intelligent medical early warning systems.展开更多
The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new trend.The existing R...The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new trend.The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation,which ignores remote sensing data characteristics such as large image size,large-scale change,multiple data channels,and geographic knowledge embedding,thus impairing computational efficiency and accuracy.We construct a LuoJiaAI platform composed of a standard large-scale sample database(LuoJiaSET)and a dedicated deep learning framework(LuoJiaNET)to achieve state-of-the-art performance on five typical remote sensing interpretation tasks,including scene classification,object detection,land-use classification,change detection,and multi-view 3D reconstruction.The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application.In addition,LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium(OGC)standards for better developing and sharing Earth Artificial Intelligence(AI)algorithms and products on benchmark datasets.LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism,showing great potential in high-precision remote sensing mapping applications.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive...The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.展开更多
With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their...With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their nationalism into four types and explores its transformation alongside globalization cognition. The result shows that moderate nationalism is the mainstream. This has raised their attention to globalization, with greater focus on relations between China and developing countries, and nations along the Belt and Road Initiative. Their personal experiences and cultural exposure foster a more inclusive global vision, shaping the evolution of nationalism and global dialogue.展开更多
Portable ratiometric fluorescent platforms have emerged as promising tools for multifarious detection,yet remain unexplored for point-of-care monitoring doxorubicin(DOX),one of clinically antineoplastic drugs.To this ...Portable ratiometric fluorescent platforms have emerged as promising tools for multifarious detection,yet remain unexplored for point-of-care monitoring doxorubicin(DOX),one of clinically antineoplastic drugs.To this end,we herein develop a portable self-calibrating platform namely carbon dots(C-dots)-embedded hydrogel sensors with a smartphone-assisted high-throughput imaging device,for DOX sensing.The prepared green-emitting(λ_(em)=508 nm)and negatively-charged C-dots(−11.40±1.21 mV),which have sufficient spectral overlap with the absorption band of DOX(∼500 nm),can strongly bind with positively-charged DOX molecules by electrostatic attraction effects.As a result,DOX molecules are selectively and rapid(20 s)determined with a detection limit of 10.26 nmol/L via Förster resonance energy transfer processes,demonstrating a remarkably chromatic shift from green to red.Further integrated with a 3D-printed smartphone-assisted device,the platform enabled high-throughput quantification,achieving recoveries of 96.40%-101.85%in human urine/serum(RSDs<2.94%,n=3).Notably,the dual linear detection ranges of the platform align with the reported clinical DOX concentrations in urine and plasma(0-4 h post-administration),validating their capability for direct quantification of DOX in clinical samples without special pre-treatment processes.By virtue of attractive analytical performances and robust feasibility,this platform bridges laboratory precision and point-of-care testing needs,offering promising potential for personalized chemotherapy and multiplexed analyte screening.展开更多
The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubi...The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubiquitous connectivity.However,existing research reveals significant gaps in dynamic resource allocation,joint optimization,and equitable service provisioning under varying channel conditions,limiting practical deployment of these technologies.This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning(FAIRDQL)framework for joint resource management and phase configuration in HAPS-RIS systems.Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control,priority-based user scheduling,and dynamic learning mechanisms.The FAIR-DQL approach utilizes advanced reinforcement learning with experience replay and fairness-aware reward functions to balance competing objectives while adapting to dynamic environments.Key findings demonstrate substantial improvements:9.15 dB SINR gain,12.5 bps/Hz capacity,78%power efficiency,and 0.82 fairness index.The framework achieves rapid 40-episode convergence with consistent delay performance.These contributions establish new benchmarks for fairness-aware resource allocation in aerial communications,enabling practical HAPS-RIS deployments in rural connectivity,emergency communications,and urban networks.展开更多
Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions....Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions.To address this issue,this paper employs a combined approach of theoretical analysis and case study,introducing the SICAS(Sense-Interest-Connection-Action-Share)user consumption behavior analysis model and selecting“CITIC Academy”as the case study subject.It systematically examines and summarizes the platform’s operational practices and specific strategies,aiming to offer strategic insights and practical references for the operational improvement and sustainable,high-quality development of trade publishing knowledge service platforms.展开更多
We are sorry for the mistakes of Affiliation,"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,Donghua University,Shanghai 201620,China"should be replaced by&quo...We are sorry for the mistakes of Affiliation,"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,Donghua University,Shanghai 201620,China"should be replaced by"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,College of Materials Science and Engineering,Donghua University,Shanghai 201620,China".We apologized for the inconvenience caused by this error.展开更多
The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved...The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.展开更多
This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including th...This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including the design of experiments,surrogate models,model validation and selection,prediction,optimization,and sensitivity analysis.Moreover,it also includes an exclusive ensemble surrogate modeling technique,the extended hybrid adaptive function,which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate.To improve ease of use,DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging,dropping,and connecting algorithm blocks into a workflow instead of writing massive code.In addition,DADOS allows users to visualize the results to gain more insights into the design problems,allows multi-person collaborating on a project at the same time,and supports multi-disciplinary optimization.This paper also details the architecture and the user interface of DADOS.Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization.Since DADOS is a cloud-based system,anyone can access DADOS at www.dados.com.cn using their web browser without the need for installation or powerful hardware.展开更多
With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issu...With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.展开更多
Cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible net-work management of heterogeneous cloud access(H-CRAN),vast coverage of satellite net...Cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible net-work management of heterogeneous cloud access(H-CRAN),vast coverage of satellite networks,and good communication quality of terrestrial networks.Thanks to the complementary coverage characteristics,any-time and anywhere high-speed communications can be achieved to meet the various needs of users.The scarcity of spectrum resources is a common prob-lem in both satellite and terrestrial networks.In or-der to improve resource utilization,the spectrum is shared not only within each component but also be-tween satellite beams and terrestrial cells,which intro-duces inter-component interferences.To this end,this paper first proposes an analytical framework which considers the inter-component interferences induced by spectrum sharing(SS).An intelligent SS scheme based on radio map(RM)consisting of LSTM-based beam prediction(BP),transfer learning-based spec-trum prediction(SP)and joint non-preemptive prior-ity and preemptive priority(J-NPAP)-based propor-tional fair spectrum allocation is than proposed.The simulation result shows that the spectrum utilization rate of CB-STSSN is improved and user blocking rate and waiting probability are decreased by the proposed scheme.展开更多
An e-tag used on the freeway is a kind of passive sensors composed of sensors and radio- frequency identification （RFID） tags. The principle of the electronic toll collection system is that the sensor emits radio wa...An e-tag used on the freeway is a kind of passive sensors composed of sensors and radio- frequency identification （RFID） tags. The principle of the electronic toll collection system is that the sensor emits radio waves touching the e-tag within a certain range, the e-tag will respond to the radio waves by induction, and the sensor will read and write information of the vehicles. Although the RFID technology is popularly used in campus management systems, there is no e-tag technology application used in a campus parking system. In this paper, we use the e-tag technology on a campus parking management system based on the cloud-based construction. By this, it helps to achieve automated and standardized management of the campus parking system, enhance management efficiency, reduce the residence time of the vehicles at the entrances and exits, and improve the efficiency of vehicles parked at the same time.展开更多
We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and c...We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.展开更多
Mixed redundancy strategies are generally used in cloud-based systems,with different node switch mechanisms from traditional fault-tolerant strategies.Existing studies often concentrate on optimizing a single strategy...Mixed redundancy strategies are generally used in cloud-based systems,with different node switch mechanisms from traditional fault-tolerant strategies.Existing studies often concentrate on optimizing a single strategy in cloud computing environment and ignore the impact of mixed redundancy strategies.Therefore,a model is proposed to evaluate and optimize the reliability and performance of cloud-based degraded systems subject to a mixed active and cold standby redundancy strategy.In this strategy,node switching is triggered by a continual monitoring and detection mechanism when active nodes fail.To evaluate the transient availability and the expected job completion rate of systems with such kind of strategy,a continuous-time Markov chain model is built on the state transition process and a numerical method is used to solve the model.To choose the optimal redundancy for the mixed strategy under system constraints,a greedy search algorithm is proposed after sensitivity analysis.Illustrative examples were presented to explain the process of calculating the transient probability of each system state and in turn,the availability and performance of the whole system.It was shown that the near-optimal redundancy solution could be obtained using the optimizationmethod.The comparison with optimization of the traditional mixed redundancy strategy proved that the system behavior was different using different kinds of mixed strategies and less redundancy was assigned for the new type of mixed strategy under the same system constraint.展开更多
With ever-increasing applications of IoT, and due to the heterogeneous and bursty nature of these applications, scalability has become an important research issue in building cloud-based IoT/M2M systems. This research...With ever-increasing applications of IoT, and due to the heterogeneous and bursty nature of these applications, scalability has become an important research issue in building cloud-based IoT/M2M systems. This research proposes a dynamic SDN-based network slicing mechanism to tackle the scalability problems caused by such heterogeneity and fluctuation of IoT application requirements. The proposed method can automatically create a network slice on-the-fly for each new type of IoT application and adjust the QoS characteristics of the slice dynamically according to the changing requirements </span><span style="font-family:Verdana;">of an IoT application. Validated with extensive experiments, the proposed me</span><span style="font-family:Verdana;">chanism demonstrates better platform scalability when compared to a static slicing system.展开更多
Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate predictio...Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.展开更多
While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated th...While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated the impact of a blended teaching model incorporating AI tools on the Superstar Learning Platform for Chinese university EFL students.Using a mixed-methods approach,60 first-year students were randomized into an experimental group(using the AI-enhanced model)and a control group(traditional instruction)for 16 weeks.Data included test scores,learning behaviors(duration,task completion),satisfaction surveys,and interviews.Results showed the experimental group significantly outperformed the control group on post-tests and achieved larger learning gains.These students also demonstrated greater engagement through longer study times and higher task completion rates,and reported significantly higher satisfaction.Interviews confirmed these findings,with students attributing benefits to the model’s personalized guidance,structured content presentation(knowledge graphs),immediate responses,flexibility,and varied interaction methods.However,limitations were noted,including areas where the platform’s AI could be improved(e.g.,for assessing speaking/translation)and ongoing challenges with student self-discipline.The study concludes that this AI-enhanced blended model significantly improved student performance,engagement,and satisfaction in this EFL context.The findings offer practical insights for educators and platform developers,suggesting AI integration holds significant potential while highlighting areas for refinement.展开更多
This paper presents an overview of the recent developments in hybrid wind-wave energy.With the focus on floating concepts,the possible configurations introduced in the literature are categorized and depicted,and the m...This paper presents an overview of the recent developments in hybrid wind-wave energy.With the focus on floating concepts,the possible configurations introduced in the literature are categorized and depicted,and the main conclusions obtained from the references are summarized.Moreover,offshore wind and wave resources are discussed in terms of complementarity and supplementarity,offering a new perspective to developing hybrid wind-wave energy systems that look for synergies not limited to maximizing power output.Then,the feasibility of the concepts under development is discussed in detail,with focus on technical feasibility,dynamic feasibility and limitations of the methods employed.The hybrid configurations that surpassed the experimental validation phase are highlighted,and the experimental results are summarized.By compiling more than 40 floating wind turbine concepts,new relations are drawn between power,wind turbine dimensions,platforms’draft and displacement,which are further related to the payload allowance of the units to accommodate wave devices and onboard power take-off systems.Bearing in mind that it is a challenge to model the exact dynamics of hybrid floating wind-wave platforms,this paper elucidates the current research gaps,limitations and future trends in the field.Lastly,based on the overview and topics discussed,several major conclusions are drawn concerning hybrid synergies,dynamics and hydrodynamics of hybrid platforms,feasibility of concepts,among other regards.展开更多
文摘With the continuous advancement of the tiered diagnosis and treatment system,the medical consortium model has gained increasing attention as an important approach to promoting the vertical integration of healthcare resources.Within this context,laboratory data,as a key component of healthcare information systems,urgently requires efficient sharing and intelligent analysis.This paper designs and constructs an intelligent early warning system for laboratory data based on a cloud platform tailored to the medical consortium model.Through standardized data formats and unified access interfaces,the system enables the integration and cleaning of laboratory data across multiple healthcare institutions.By combining medical rule sets with machine learning models,the system achieves graded alerts and rapid responses to abnormal key indicators and potential outbreaks of infectious diseases.Practical deployment results demonstrate that the system significantly improves the utilization efficiency of laboratory data,strengthens public health event monitoring,and optimizes inter-institutional collaboration.The paper also discusses challenges encountered during system implementation,such as inconsistent data standards,security and compliance concerns,and model interpretability,and proposes corresponding optimization strategies.These findings provide a reference for the broader application of intelligent medical early warning systems.
基金supported by the Chinese National Natural Science Foundation Projects[grant number 41901265]Major Program of the National Natural Science Foundation of China[grant number 92038301]supported in part by the Special Fund of Hubei Luojia Laboratory[grant number 220100028].
文摘The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new trend.The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation,which ignores remote sensing data characteristics such as large image size,large-scale change,multiple data channels,and geographic knowledge embedding,thus impairing computational efficiency and accuracy.We construct a LuoJiaAI platform composed of a standard large-scale sample database(LuoJiaSET)and a dedicated deep learning framework(LuoJiaNET)to achieve state-of-the-art performance on five typical remote sensing interpretation tasks,including scene classification,object detection,land-use classification,change detection,and multi-view 3D reconstruction.The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application.In addition,LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium(OGC)standards for better developing and sharing Earth Artificial Intelligence(AI)algorithms and products on benchmark datasets.LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism,showing great potential in high-precision remote sensing mapping applications.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
文摘The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.
文摘With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their nationalism into four types and explores its transformation alongside globalization cognition. The result shows that moderate nationalism is the mainstream. This has raised their attention to globalization, with greater focus on relations between China and developing countries, and nations along the Belt and Road Initiative. Their personal experiences and cultural exposure foster a more inclusive global vision, shaping the evolution of nationalism and global dialogue.
基金supported by the National NaturalScience Foundation of China(No.22274001)the Key Project of Natural Science Research of the Education Department of Anhui Province(No.2022AH051032)the Excellent Research and Innovation Team of Universities in Anhui Province(No.2024AH010016).
文摘Portable ratiometric fluorescent platforms have emerged as promising tools for multifarious detection,yet remain unexplored for point-of-care monitoring doxorubicin(DOX),one of clinically antineoplastic drugs.To this end,we herein develop a portable self-calibrating platform namely carbon dots(C-dots)-embedded hydrogel sensors with a smartphone-assisted high-throughput imaging device,for DOX sensing.The prepared green-emitting(λ_(em)=508 nm)and negatively-charged C-dots(−11.40±1.21 mV),which have sufficient spectral overlap with the absorption band of DOX(∼500 nm),can strongly bind with positively-charged DOX molecules by electrostatic attraction effects.As a result,DOX molecules are selectively and rapid(20 s)determined with a detection limit of 10.26 nmol/L via Förster resonance energy transfer processes,demonstrating a remarkably chromatic shift from green to red.Further integrated with a 3D-printed smartphone-assisted device,the platform enabled high-throughput quantification,achieving recoveries of 96.40%-101.85%in human urine/serum(RSDs<2.94%,n=3).Notably,the dual linear detection ranges of the platform align with the reported clinical DOX concentrations in urine and plasma(0-4 h post-administration),validating their capability for direct quantification of DOX in clinical samples without special pre-treatment processes.By virtue of attractive analytical performances and robust feasibility,this platform bridges laboratory precision and point-of-care testing needs,offering promising potential for personalized chemotherapy and multiplexed analyte screening.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project,number PNURSP2025R757Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubiquitous connectivity.However,existing research reveals significant gaps in dynamic resource allocation,joint optimization,and equitable service provisioning under varying channel conditions,limiting practical deployment of these technologies.This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning(FAIRDQL)framework for joint resource management and phase configuration in HAPS-RIS systems.Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control,priority-based user scheduling,and dynamic learning mechanisms.The FAIR-DQL approach utilizes advanced reinforcement learning with experience replay and fairness-aware reward functions to balance competing objectives while adapting to dynamic environments.Key findings demonstrate substantial improvements:9.15 dB SINR gain,12.5 bps/Hz capacity,78%power efficiency,and 0.82 fairness index.The framework achieves rapid 40-episode convergence with consistent delay performance.These contributions establish new benchmarks for fairness-aware resource allocation in aerial communications,enabling practical HAPS-RIS deployments in rural connectivity,emergency communications,and urban networks.
文摘Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions.To address this issue,this paper employs a combined approach of theoretical analysis and case study,introducing the SICAS(Sense-Interest-Connection-Action-Share)user consumption behavior analysis model and selecting“CITIC Academy”as the case study subject.It systematically examines and summarizes the platform’s operational practices and specific strategies,aiming to offer strategic insights and practical references for the operational improvement and sustainable,high-quality development of trade publishing knowledge service platforms.
文摘We are sorry for the mistakes of Affiliation,"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,Donghua University,Shanghai 201620,China"should be replaced by"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,College of Materials Science and Engineering,Donghua University,Shanghai 201620,China".We apologized for the inconvenience caused by this error.
文摘The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.
基金Supported by National Key Research and Development Program of China (Grant No.2018YFB1700704)National Natural Science Foundation of China (Grant No.52075068)。
文摘This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including the design of experiments,surrogate models,model validation and selection,prediction,optimization,and sensitivity analysis.Moreover,it also includes an exclusive ensemble surrogate modeling technique,the extended hybrid adaptive function,which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate.To improve ease of use,DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging,dropping,and connecting algorithm blocks into a workflow instead of writing massive code.In addition,DADOS allows users to visualize the results to gain more insights into the design problems,allows multi-person collaborating on a project at the same time,and supports multi-disciplinary optimization.This paper also details the architecture and the user interface of DADOS.Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization.Since DADOS is a cloud-based system,anyone can access DADOS at www.dados.com.cn using their web browser without the need for installation or powerful hardware.
基金financially supported by the National Natural Science Foundation of China(No.61303216,No.61272457,No.U1401251,and No.61373172)the National High Technology Research and Development Program of China(863 Program)(No.2012AA013102)National 111 Program of China B16037 and B08038
文摘With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.
基金the National Nat-ural Science Foundation of China under Grants 61771163the Natural Science Foundation for Out-standing Young Scholars of Heilongjiang Province un-der Grant YQ2020F001the Science and Technol-ogy on Communication Networks Laboratory under Grants SXX19641X072 and SXX18641X028.(Cor-respondence author:Min Jia)。
文摘Cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible net-work management of heterogeneous cloud access(H-CRAN),vast coverage of satellite networks,and good communication quality of terrestrial networks.Thanks to the complementary coverage characteristics,any-time and anywhere high-speed communications can be achieved to meet the various needs of users.The scarcity of spectrum resources is a common prob-lem in both satellite and terrestrial networks.In or-der to improve resource utilization,the spectrum is shared not only within each component but also be-tween satellite beams and terrestrial cells,which intro-duces inter-component interferences.To this end,this paper first proposes an analytical framework which considers the inter-component interferences induced by spectrum sharing(SS).An intelligent SS scheme based on radio map(RM)consisting of LSTM-based beam prediction(BP),transfer learning-based spec-trum prediction(SP)and joint non-preemptive prior-ity and preemptive priority(J-NPAP)-based propor-tional fair spectrum allocation is than proposed.The simulation result shows that the spectrum utilization rate of CB-STSSN is improved and user blocking rate and waiting probability are decreased by the proposed scheme.
文摘An e-tag used on the freeway is a kind of passive sensors composed of sensors and radio- frequency identification （RFID） tags. The principle of the electronic toll collection system is that the sensor emits radio waves touching the e-tag within a certain range, the e-tag will respond to the radio waves by induction, and the sensor will read and write information of the vehicles. Although the RFID technology is popularly used in campus management systems, there is no e-tag technology application used in a campus parking system. In this paper, we use the e-tag technology on a campus parking management system based on the cloud-based construction. By this, it helps to achieve automated and standardized management of the campus parking system, enhance management efficiency, reduce the residence time of the vehicles at the entrances and exits, and improve the efficiency of vehicles parked at the same time.
基金supported by the National Natural Science Foundation of China(Grant No.92365206)the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)+1 种基金supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.
基金supported by the National Natural Science Foundation of China(Grant No.61309005)the Basic and Frontier Research Program of Chongqing(Grant No.cstc2014jcyj A40015)
文摘Mixed redundancy strategies are generally used in cloud-based systems,with different node switch mechanisms from traditional fault-tolerant strategies.Existing studies often concentrate on optimizing a single strategy in cloud computing environment and ignore the impact of mixed redundancy strategies.Therefore,a model is proposed to evaluate and optimize the reliability and performance of cloud-based degraded systems subject to a mixed active and cold standby redundancy strategy.In this strategy,node switching is triggered by a continual monitoring and detection mechanism when active nodes fail.To evaluate the transient availability and the expected job completion rate of systems with such kind of strategy,a continuous-time Markov chain model is built on the state transition process and a numerical method is used to solve the model.To choose the optimal redundancy for the mixed strategy under system constraints,a greedy search algorithm is proposed after sensitivity analysis.Illustrative examples were presented to explain the process of calculating the transient probability of each system state and in turn,the availability and performance of the whole system.It was shown that the near-optimal redundancy solution could be obtained using the optimizationmethod.The comparison with optimization of the traditional mixed redundancy strategy proved that the system behavior was different using different kinds of mixed strategies and less redundancy was assigned for the new type of mixed strategy under the same system constraint.
文摘With ever-increasing applications of IoT, and due to the heterogeneous and bursty nature of these applications, scalability has become an important research issue in building cloud-based IoT/M2M systems. This research proposes a dynamic SDN-based network slicing mechanism to tackle the scalability problems caused by such heterogeneity and fluctuation of IoT application requirements. The proposed method can automatically create a network slice on-the-fly for each new type of IoT application and adjust the QoS characteristics of the slice dynamically according to the changing requirements </span><span style="font-family:Verdana;">of an IoT application. Validated with extensive experiments, the proposed me</span><span style="font-family:Verdana;">chanism demonstrates better platform scalability when compared to a static slicing system.
基金The research work was financially supported by the National Natural Science Foundation of China(Grant Nos.51979238 and 52301338)the Sichuan Science and Technology Program(Grant Nos.2023NSFSC1953 and 2023ZYD0140).
文摘Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.
基金supported by the 2024“Special Research Project on the Application of Artificial Intelligence in Empowering Teaching and Education”of Zhejiang Province Association of Higher Education(KT2024165).
文摘While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated the impact of a blended teaching model incorporating AI tools on the Superstar Learning Platform for Chinese university EFL students.Using a mixed-methods approach,60 first-year students were randomized into an experimental group(using the AI-enhanced model)and a control group(traditional instruction)for 16 weeks.Data included test scores,learning behaviors(duration,task completion),satisfaction surveys,and interviews.Results showed the experimental group significantly outperformed the control group on post-tests and achieved larger learning gains.These students also demonstrated greater engagement through longer study times and higher task completion rates,and reported significantly higher satisfaction.Interviews confirmed these findings,with students attributing benefits to the model’s personalized guidance,structured content presentation(knowledge graphs),immediate responses,flexibility,and varied interaction methods.However,limitations were noted,including areas where the platform’s AI could be improved(e.g.,for assessing speaking/translation)and ongoing challenges with student self-discipline.The study concludes that this AI-enhanced blended model significantly improved student performance,engagement,and satisfaction in this EFL context.The findings offer practical insights for educators and platform developers,suggesting AI integration holds significant potential while highlighting areas for refinement.
基金supported by the Portuguese Foundation for Science and Technology(Fundação para a Ciência e Tecnologia-FCT)it contributes to the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineering(Grant No.UIDB/UIDP/00134/2020)funded the first author for his PhD Scholarship(Grant No.SFRH/BD/145602/2019).
文摘This paper presents an overview of the recent developments in hybrid wind-wave energy.With the focus on floating concepts,the possible configurations introduced in the literature are categorized and depicted,and the main conclusions obtained from the references are summarized.Moreover,offshore wind and wave resources are discussed in terms of complementarity and supplementarity,offering a new perspective to developing hybrid wind-wave energy systems that look for synergies not limited to maximizing power output.Then,the feasibility of the concepts under development is discussed in detail,with focus on technical feasibility,dynamic feasibility and limitations of the methods employed.The hybrid configurations that surpassed the experimental validation phase are highlighted,and the experimental results are summarized.By compiling more than 40 floating wind turbine concepts,new relations are drawn between power,wind turbine dimensions,platforms’draft and displacement,which are further related to the payload allowance of the units to accommodate wave devices and onboard power take-off systems.Bearing in mind that it is a challenge to model the exact dynamics of hybrid floating wind-wave platforms,this paper elucidates the current research gaps,limitations and future trends in the field.Lastly,based on the overview and topics discussed,several major conclusions are drawn concerning hybrid synergies,dynamics and hydrodynamics of hybrid platforms,feasibility of concepts,among other regards.
