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Mapping quantitative trait loci underlying body weight changes that act at different times during high-fat diet challenge in collaborative cross mice 认领 引用
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作者 Hanifa J.Abu-Toamih Atamni Iqbal M.Lone +5 位作者 Ilona Binenbaum Kareem Midlej Eleftherios Pilalis Richard Mott Aristotelis Chatziioannou Fuad A.Iraqi 《Animal Models and Experimental Medicine》 CAS CSCD 2026年第3期621-629,共9页
Over one billion people worldwide suffer from obesity,and the number is continually rising.This epidemic is partly caused by the modern lifestyle.Animal models,espe-cially mouse models,are crucial to identifying the g... Over one billion people worldwide suffer from obesity,and the number is continually rising.This epidemic is partly caused by the modern lifestyle.Animal models,espe-cially mouse models,are crucial to identifying the genetic components of complex disorders and exploring the potential applications of these genetic findings.The body weight of the animals used in research is often measured regularly to monitor their health.Only endpoint measurements,such as ultimate body weight,are frequently examined in quantitative trait locus(QTL)studies;time series data,including weekly or biweekly body weight,are usually disregarded.QTL mapping using biweekly body weight measurements may be particularly intriguing in examining body weight gain in obesity research and identifying more genes associated with obesity and related metabolic disorders.This study is focused on identifying quantitative trait loci(QTLs)underlying body weight changes by analyzing biweekly weight measurements in col-laborative cross(CC)mice maintained on a high-fat diet for 12 weeks.QTL analysis,uti-lizing 525 mice from 55 CC lines(308 males and 217 females),revealed genome-wide significant QTLs on different chromosomes for body weight changes over 12 weeks.This study unveiled 62 body weight QTLs,among which 28 novel QTLs associated with defined traits were observed and found not reported previously.In addition,34 more QTLs were fine-mapped,as the genomic interval positions of these had been previously identified.These findings highlight genomic regions that influence body weight in CC mice,underscoring the value of time series data in identifying novel ge-netic factors. 展开更多
关键词 candidate genes collaborative cross mice high-fat diet obesity QTL mapping
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Collaborative Area Coverage Method for UAV Swarm Under Complex Boundary Conditions:A Region Partitioning Approach 认领 引用
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作者 Jiabin Yu Haocun Wang +4 位作者 Bingyi Wang Yang Lu Xin Zhang Qian Sun Zhiyao Zhao 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第1期524-548,共25页
Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps oft... Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages. 展开更多
关键词 Complex boundaries UAV swarm Collaborative area coverage Map preprocessing Region partitioning
An Adaptive Hybrid Edge-Cloud Collaborative Offloading Method for Large-Scale Computational Tasks of Intelligent Machine Tool:Low-Latency,Energy-Efficient,and Secure 认领 引用
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作者 Zhiwen Lin Kaien Wei +4 位作者 Yiqiao Wang Chuanhai Chen Jinyan Guo Qiang Cheng Zhifeng Liu 《Engineering》 SCIE EI CSCD 2026年第1期201-218,共18页
Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generat... Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generation of vast multi-source sensor data streams and numerous computational tasks with interdependent data relationships.The stringent real-time constraints and intricate dependency structures present considerable challenges to traditional single-mode computational frameworks.Furthermore,there is a growing demand for computational offloading solutions in intelligent machine tools that extend beyond merely optimizing latency.These solutions must also address energy management for sustainable manufacturing and ensure security to protect sensitive industrial data.This paper introduces an adaptive hybrid edge-cloud collaborative offloading mechanism that combines single-edge-cloud collaboration with multi-edge-cloud collaboration.This mechanism is capable of dynamically switching between collaborative modes based on the status of computational nodes,task characteristics,dependency complexity,and resource availability,ultimately facilitating low-latency,energy-efficient,and secure task processing.A novel hybrid hyper-heuristic algorithm has been developed to address largescale task allocation challenges in heterogeneous edge-cloud environments,enabling the flexible allocation of computational resources and performance optimization.Extensive experiments indicate that the proposed approach achieves average enhancements of 27.36%in task processing time and 7.89%in energy efficiency when compared to state-of-the-art techniques,all while maintaining superior security performance.Validation through case studies on a digital twin gantry five-axis machining center illustrates that the mechanism effectively coordinates task execution across multi-source concurrent data processing,complex dependency task collaboration,high-computational machine learning workloads,and continuous batch task deployment scenarios,achieving a 37.03%reduction in latency and a 25.93%optimization in energy use relative to previous generation collaboration methods.These results provide both theoretical and technical backing for sustainable and secure computational offloading in intelligent machine tools,thereby contributing to the evolution of next-generation smart manufacturing systems. 展开更多
关键词 Intelligent machine tools Edge-cloud collaboration Task offloading Resilient resources Sustainable computing
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A Formation Inversion Algorithm Based on Collaborative Fuzzy Gradient Neural Dynamics for Natural Gamma Logging While Drilling 认领 引用
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作者 Juntao Liu Ruoxiao Liu +2 位作者 Wenxin Li Zhenming Su Long Jin 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期498-513,共16页
A formation inversion algorithm with real-time performance and accuracy is crucial for natural gamma logging while drilling(LWD).However,traditional inversion algorithms are often limited by high computational resourc... A formation inversion algorithm with real-time performance and accuracy is crucial for natural gamma logging while drilling(LWD).However,traditional inversion algorithms are often limited by high computational resource consumption and insufficient accuracy.To address these issues,an improved forward method for natural gamma LWD is proposed.The inverse problem is subsequently modelled using the proposed forward method through which the search methodology and region of formation information are determined.On this basis,a collaborative fuzzy gradient neural dynamics(CFGND)algorithm is proposed,which combines the advantages of the collaborative mechanism in swarm intelligence algorithms and fuzzy gradient neural dynamics(FGND)to improve its accuracy and real-time performance.Specifically,the collaborative mechanism is applied to conduct a global search using all possible formation information.Concurrently,the FGND algorithm initiates a local search from each particle and dynamically and intelligently adjusts the learning rate of the neural dynamics through a fuzzy logic system during the process to achieve rapid and stable local convergence.The CFGND algorithm subsequently updates its globally optimal solution using the optimal solution obtained from the FGND algorithm.This iterative process continues until the termination condition is met.Theoretical analysis proves the existence of an optimal solution for the inverse problem and the convergence of the CFGND algorithm.The results of simulations and experiments demonstrate that the proposed formation inversion algorithm features high accuracy and sufficient real-time performance. 展开更多
关键词 collaborative mechanism fuzzy logic system logging while drilling(LWD) neural dynamics
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Collaborative scheduling problem pertaining to launch and recovery operations for carrier aircraft 认领 引用
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作者 GUO Fang HAN Wei +3 位作者 LIU Yujie SU Xichao LIU Jie LI Changjiu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期287-306,共20页
The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a coll... The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a collaborative scheduling problem inherent to the operational processes of carrier aircraft,where launch and recovery tasks are conducted concurrently on the flight deck.The objective is to minimize the cumulative weighted waiting time in the air for recovering aircraft and the cumulative weighted delay time for launching aircraft.To tackle this challenge,a multiple population self-adaptive differential evolution(MPSADE)algorithm is proposed.This method features a self-adaptive parameter updating mechanism that is contingent upon population diversity,an asynchronous updating scheme,an individual migration operator,and a global crossover mechanism.Additionally,comprehensive experiments are conducted to validate the effectiveness of the proposed model and algorithm.Ultimately,a comparative analysis with existing operation modes confirms the enhanced efficiency of the collaborative operation mode. 展开更多
关键词 carrier aircraft collaborative scheduling problem launch recovery multiple population differential evolution
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A Deception Defense Timing Selection Method Based on Time-Delayed FlipIt Game in Cloud-Edge Collaborative Networks 认领 引用
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作者 Jinchuan Pei Yuxiang Hu +2 位作者 Hongtao Yu Zihao Wang Menglong Li 《Computers, Materials & Continua》 SCIE EI 2026年第7期1523-1542,共20页
In the cloud-edge collaborative network,advanced persistent threats(APTs)pose a serious security risk to critical network assets.Although network deception defense can mislead attackers’cognition,its effectiveness de... In the cloud-edge collaborative network,advanced persistent threats(APTs)pose a serious security risk to critical network assets.Although network deception defense can mislead attackers’cognition,its effectiveness depends on dynamically selecting appropriate rotation timings of the deception defense.However,the deployment of deception resources and state updates is not completed instantaneously,and existing methods ignore the state transition delay and the dynamic interaction between the attackers and defenders during the real attack and defense process.To address this,we propose a deception defense timing selection method based on the time-delayed FlipIt game.Firstly,a network state evolution model integrating state transition delay is constructed,and the dynamic transfer process between node states is characterized by a set of delay differential equations.Secondly,a cloud-edge collaborative defense architecture is designed.On this basis,a time-delayed FlipIt game model(TD-FlipIt)is established,and the gate control mechanism is introduced to formalize the defense cooling period as a constraint for the rotation action of deception resources.Subsequently,we use the multi-agent deep deterministic policy gradient(MADDPG)algorithm to solve the rotation strategy for deception defense timing.Experimental results show that the proposed method can effectively optimize the selection of defense timing,ensuring defense effectiveness while reducing resource consumption,and providing effective support for defense in the cloud-edge collaborative environment. 展开更多
关键词 Cloud-edge collaborative network deception defense timing FlipIt game multi-agent reinforcement learning
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Efficient foundation model service provision over airborne networks:Collaborative tuning and inference 认领 引用
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作者 Zihan CHEN Longyu ZHOU +2 位作者 Shengyu ZHANG Chenyuan FENG Dusit NIYATO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期98-110,共13页
Deploying foundation models across distributed airborne networks offers a promising solution for delivering flexible,high-coverage,and on-demand generative AI services.However,the deployment and tuning of foundation m... Deploying foundation models across distributed airborne networks offers a promising solution for delivering flexible,high-coverage,and on-demand generative AI services.However,the deployment and tuning of foundation models present critical challenges on airborne platforms such as Unmanned Aerial Vehicles(UAVs),due to the intensive computational requirements,substantial memory footprint,and high communication overhead,particularly given these platforms'limited power and memory capacity as well as the limited communication connections.In view of these,a collaborative fine-tuning and inference framework for deploying foundation models over UAV networks is proposed,which employs a split model deployment strategy to distribute computational loads across multiple UAVs.The framework also incorporates a multi-stage fine-tuning approach utilizing a large vision model-based knowledge distillation and personalized local tuning to further enhance performance while maintaining system stability despite UAV mobility.The proposed framework could achieve foundation model fine-tuning in a memory-and computationefficient manner.To further improve the communication and computation efficiency,two variants of the framework are proposed via leveraging over-the-air computations and parameter-efficient fine-tuning techniques in communication and local computation.Extensive experimental evaluation demonstrates the superior and stable performance of the proposed framework compared to baselines in terms of generalization,communication efficiency,memory efficiency,and scalability. 展开更多
关键词 Airborne networks Federated learning Collaborative learning Large model fine-tuning UAV communications Foundation models
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Collaborative ISAC Mechanism for Base Station and Users with Reference Signals 认领 引用
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作者 Sun Chunlei Wang Zhigang +5 位作者 Ren Yuzheng Liu Xuqing Qu Aixi Sun Chen Li Haojin Zhang Haijun 《China Communications》 SCIE EI CSCD 2026年第3期182-198,共17页
Integrated sensing and communication(IS AC)is emerging as a key technology for future cellular networks.This paper focuses on the collaborative ISAC mechanism between base stations(BSs)and users using reference signal... Integrated sensing and communication(IS AC)is emerging as a key technology for future cellular networks.This paper focuses on the collaborative ISAC mechanism between base stations(BSs)and users using reference signals(RSs).The main challenges we address are the joint optimization of downlink communication and sensing resources,the selection of users as sensing anchors,as well as the fusion of estimation data between the BS and users.We formulate the collaborative ISAC problem as a multiobjective programming framework,which can balance system performance in sensing and individual benefits in communication.Particularly,to ensure fairness,we propose minimizing the largest sensing age across all users.On this basis,we put forward an efficient solution algorithm that enables a low-complexity computation of the Pareto front when it exists.Simulation results demonstrate that the proposed collaborative ISAC mechanism is capable of efficiently enhancing the system’s sensing capacity while ensuring fairness in user scheduling for sensing. 展开更多
关键词 collaborative ISAC integrated sensing and communication reference signals resource scheduling sensing anchor selection
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A collaborative optimization design method of platform location and well trajectory for a complex-structure well factory 认领 引用
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作者 WANG Ge GAO Deli HUANG Wenjun 《Petroleum Exploration and Development》 SCIE 2026年第1期261-271,共11页
Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drill... Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drilling extension limits,underground target distribution and trajectory collision risks,a model of platform location-wellbore trajectory collaborative optimization for a complex-structure well factory is developed.A hybrid heuristic algorithm is proposed by combining an improved sparrow search algorithm(ISSA)for optimizing platform parameters in the outer layer and a directed artificial bee colony algorithm(DABC)for optimizing trajectory parameters in the inner layer.The alternating iteration of ISSA-DABC facilitates the resolution of the collaborative optimization problem.The ISSA-DABC provides an effective solution to the platform-trajectory collaborative optimization problem for complex-structure well factories and overcomes the tendency of the traditional platform-trajectory stepwise optimization workflow to become trapped in local optima and yield inconsistent designs.The ISSA-DABC has a strong global search capability,fast convergence and good robustness,and can simultaneously satisfy multiple engineering constraints on drilling footage,trajectory complexity and collision risk,and enables automated,workflow-wide generation of constraint-compliant,near-globally optimal platform-trajectory configurations.Field applications further demonstrate that ISSA-DABC significantly reduces the objective function value and collision risk,yielding more rational platform layouts and well factory design parameters. 展开更多
关键词 complex-structure well factory ISSA DABC platform-trajectory collaborative optimization well factory parameter optimization
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Deformation characteristics of arch shoulder and collaborative support technology of concrete-filled steel tubes in Hudi Coal Mine 认领 引用
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作者 ZUO Jian-ping ZHU Fan +1 位作者 LIU De-jun WANG Jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第4期1838-1858,共21页
Aiming at the problem of large deformation of arch shoulder in deep high stress roadway of Hudi Coal Mine,through field sampling,experimental test and numerical simulation,the deformation mechanism of arch shoulder un... Aiming at the problem of large deformation of arch shoulder in deep high stress roadway of Hudi Coal Mine,through field sampling,experimental test and numerical simulation,the deformation mechanism of arch shoulder under the coupling action of high stress,soft and hard rock strata of roof,weakening of surrounding rock and disturbance of space staggered roadway was revealed.According to the research results,high-stress increases the range of the plastic zone,and the soft and hard rock strata change the expansion form of the plastic zone.With the decrease of the vertical distance of the space staggered roadway,the insufficient bearing capacity of the supporting material and other factors lead to the increase of the deformation of the shoulder angle and the side,forming the deformation characteristics of the arch shoulder.Based on this,the active and passive collaborative control technology is proposed,and the targeted support concept of"unloading control+strong support+collaborative"is adopted.The optimization scheme controls the deformation of roadway within 8%of the section size,significantly reduces the range of the plastic zone,and effectively solves the problem of difficult support of arch shoulder deformation. 展开更多
关键词 roadway arch shoulder deformation multi-factor coupling action plastic zone expansion active and passive collaborative differential support
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Efficient and Secure Data Storage in 5G Industrial Internet Collaborative Systems 认领 引用
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作者 Wang Jigang Liu Dong +1 位作者 Wan Changsheng Lu Ping 《ZTE Communications》 2026年第1期45-55,共11页
Security and access control for data storage in 5G industrial Internet collaborative systems are facing significant challenges.The characteristics of 5 G networks,such as low latency and high speed,facilitate data tra... Security and access control for data storage in 5G industrial Internet collaborative systems are facing significant challenges.The characteristics of 5 G networks,such as low latency and high speed,facilitate data transmission in the industrial Internet but also increase vulnerability to attacks like theft and tampering.Moreover,in 5G industrial Internet collaborative system environments,data flows across multiple entities and links,which necessitates a flexible access control model to meet specific data access requirements.Traditional role-based and attribute-based access control mechanisms are difficult to apply in such dynamic application scenarios.To address these challenges,we propose a novel data storage solution for 5G industrial Internet collaborative systems.Similar to existing approaches,it provides integrity and confidentiality protection for transmitted data.In terms of security,only authenticated data owners and users can obtain file decryption keys,preventing malicious attackers from data forgery.Regarding access control,decryption is permitted only to authorized data users,safeguarding against unauthorized file access.Furthermore,by introducing an attribute-based encryption mechanism,only data users with specific attributes can decrypt files.In terms of efficiency,our approach utilizes bilinear and modular exponentiation operations solely during the authentication process.For handling substantial data loads,lightweight cryptographic algorithms are employed.Consequently,our solution achieves higher efficiency compared with other known methods.Experimental results demonstrate the feasibility of our approach in real-world applications. 展开更多
关键词 5G industrial Internet collaborative systems data storage identity-based authentication access control
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Multi-agent reinforcement learning with layered autonomy and collaboration for enhanced collaborative confrontation 认领 引用
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作者 Xiaoyu XING Haoxiang XIA 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期370-388,共19页
Addressing optimal confrontation methods in multi-agent attack-defense scenarios is a complex challenge.Multi-Agent Reinforcement Learning(MARL)provides an effective framework for tackling sequential decision-making p... Addressing optimal confrontation methods in multi-agent attack-defense scenarios is a complex challenge.Multi-Agent Reinforcement Learning(MARL)provides an effective framework for tackling sequential decision-making problems,significantly enhancing swarm intelligence in maneuvering.However,applying MARL to unmanned swarms presents two primary challenges.First,defensive agents must balance autonomy with collaboration under limited perception while coordinating against adversaries.Second,current algorithms aim to maximize global or individual rewards,making them sensitive to fluctuations in enemy strategies and environmental changes,especially when rewards are sparse.To tackle these issues,we propose an algorithm of MultiAgent Reinforcement Learning with Layered Autonomy and Collaboration(MARL-LAC)for collaborative confrontations.This algorithm integrates dual twin Critics to mitigate the high variance associated with policy gradients.Furthermore,MARL-LAC employs layered autonomy and collaboration to address multi-objective problems,specifically learning a global reward function for the swarm alongside local reward functions for individual defensive agents.Experimental results demonstrate that MARL-LAC enhances decision-making and collaborative behaviors among agents,outperforming the existing algorithms and emphasizing the importance of layered autonomy and collaboration in multi-agent systems.The observed adversarial behaviors demonstrate that agents using MARL-LAC effectively maintain cohesive formations that conceal their intentions by confusing the offensive agent while successfully encircling the target. 展开更多
关键词 Attack-defense confrontation Collaborative confrontation Autonomous agents Multi-agent systems Reinforcement learning Maneuvering decisionmaking
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Psychophysiological rehabilitation for postpartum depression:A physiotherapy-led,nurse-collaborative model from calabar metropolis 认领 引用
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作者 Emmanuel Okon ENANG Ofonime Lawson ESANGOBONG +2 位作者 Patience ANSCAR Ezra Onyedikachi MADU Elijah Chidiebere UTOBO 《Journal of Integrative Nursing》 2026年第1期21-29,共9页
Background:Postpartum depression(PPD)profoundly disrupts maternal well-being,yet conventional care in many low-resource settings remains narrowly focused on psychosocial counseling.Integrating physiotherapy,delivered ... Background:Postpartum depression(PPD)profoundly disrupts maternal well-being,yet conventional care in many low-resource settings remains narrowly focused on psychosocial counseling.Integrating physiotherapy,delivered in collaboration with nursing support,offers a novel psychophysiological approach to recovery by enhancing autonomic regulation,self-efficacy,and functional capacity.Objective:This study examined the effectiveness of a physiotherapy-led,nurse-collaborative psychophysiological intervention in reducing the severity of PPD among postpartum women,with secondary effects on perceived stress,autonomic function,and quality of life,in Calabar Metropolis,Nigeria.Materials and Methods:A single-blind randomized controlled trial recruited 148 women(6-12 weeks postpartum)randomized to intervention(n=74)or control(n=74).The intervention group received physiotherapy-led,nurse-collaborative psychophysiological intervention,which integrated aerobic exercise,progressive muscle relaxation,mindfulness breathing,and heart rate variability(HRV)biofeedback over 12 weeks under nurse supervision.Controls received structured maternal education,emotional support,and wellness sessions reflecting enhanced standard care.Outcomes(depressive symptoms,perceived stress,autonomic function,and quality of life)were assessed using the Edinburgh Postnatal Depression Scale,Perceived Stress Scale,HRV analysis,and the Maternal Postpartum Quality of Life Questionnaire,respectively,at baseline,6,and 12 weeks.Results:By week 12,the intervention group demonstrated a 37% reduction in depressive scores,32% reduction in stress,14%increase in HRV,and 9-point gain in quality-of-life indices(all P<0.001).Collaborative nursing support enhanced adherence(91%)and emotional engagement.Conclusion:A physiotherapy-led,nurse-collaborative model significantly optimized psychophysiological recovery and maternal quality of life.This integrative,nonpharmacological approach represents a transformative pathway for holistic postpartum mental health care. 展开更多
关键词 Holistic care nursing collaboration physiotherapy postpartum depression psychophysiology
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Energy-Efficient Computation Offloading and Resource Allocation in Cloud-Edge Collaborative Computing Systems 认领 引用
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作者 Cheng Zhaoping Jiang Tao +3 位作者 Ke Chenxi Zhang Guoqiang Peng Miaoran Feng Mingjie 《China Communications》 SCIE EI CSCD 2026年第3期316-329,共14页
With the increasing adoption of cloud–edge collaborative computing in delay-sensitive applications,sleep control of edge nodes has become a key approach to reducing operational energy consumption.However,existing sch... With the increasing adoption of cloud–edge collaborative computing in delay-sensitive applications,sleep control of edge nodes has become a key approach to reducing operational energy consumption.However,existing schemes have not balanced energy efficiency and performance under edge node sleep control and still lack a joint optimization mechanism for computation offloading and resource allocation.This paper tackles the problem of optimizing energy-efficient computation offloading and resource allocation(CORA)in cloud-edge collaborative computing systems,where edge servers can dynamically enter sleep mode to reduce power consumption.We model the problem as a mixed-integer nonlinear programming formulation,with the objective of minimizing a weighted sum of overall task latency and the cumulative energy consumption of all IoT devices and edge servers.To handle the hybrid nature of discrete and continuous decision variables and the complex system dynamics,we reformulate the problem for each device as a Markov Decision Process and develop a deep deterministic policy gradient with multi-agent algorithm tailored for such hybrid action spaces.Simulation results show that the proposed CORA strategy achieves superior performance compared to three benchmark schemes with reduced latency and energy consumption. 展开更多
关键词 cloud-edge collaborative computing computation offloading deep reinforcement learning energy-efficient design
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Cloud-Edge-End Collaborative SC3 System in Smart Manufacturing:A Survey 认领 引用
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作者 Xuehan Li Tao Jing +3 位作者 Yang Wang Bo Gao Jing Ai Minghao Zhu 《Computers, Materials & Continua》 SCIE EI 2026年第5期77-110,共34页
With the deep integration of cloud computing,edge computing and the Internet of Things(IoT)technologies,smart manufacturing systems are undergoing profound changes.Over the past ten years,an extensive body of research... With the deep integration of cloud computing,edge computing and the Internet of Things(IoT)technologies,smart manufacturing systems are undergoing profound changes.Over the past ten years,an extensive body of research on cloud-edge-end systems has been generated.However,challenges such as heterogeneous data fusion,real-time processing and system optimization still exist,and there is a lack of systematic review studies.In this paper,we review a cloud-edge-end collaborative sensing-communication-computing-control(SC3)system.This system integrates four layers of sensing,communication,computing and control to address the complex challenges of real-time decision making,resource scheduling and system optimization.The paper combs through the key implementation methods of intelligent sensing,data preprocessing,task offloading and resource allocation in this system,and analyzes their advantages and disadvantages.Onthis basis,feasible methods for overall systemoptimization are further explored.Finally,the paper summarizes the main challenges facing the deep integration of cloud-edgeend and proposes prospective research directions,providing a structured knowledge base and development framework for subsequent research.The paper aims to stimulate further exploration of multilevel collaborative mechanisms for smart manufacturing systems to enhance the real-time decision-making and overall performance of the smart manufacturing system. 展开更多
关键词 Smart manufacturing sensing-communication-computing-control(SC3)system cloud-edge-end collaborative
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Privacy-Preserving Transformer Inference with Optimized Homomorphic Encryption and Secure Collaborative Computing 认领 引用
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作者 Tao Bai Yang Tang +2 位作者 Kuan Shao Zhenyong Zhang Yuanteng Liu 《Computers, Materials & Continua》 SCIE EI 2026年第7期1242-1265,共24页
In recent years,the rapid development of artificial intelligence has greatly promoted the application of Machine Learning as a Service(MLaaS).Users can upload their requirements through front-end applications,and the ... In recent years,the rapid development of artificial intelligence has greatly promoted the application of Machine Learning as a Service(MLaaS).Users can upload their requirements through front-end applications,and the server provides model inference services after receiving the user input.However,MLaaS may lead to serious privacy breaches.Large language model services are typical representatives of MLaaS,and the Transformer is a typical structure in large language models.Therefore,this paper proposes a privacy-protected Transformer inference scheme based on the CKKS fully homomorphic encryption scheme to optimize computational and communication efficiency.Firstly,this paper implements efficient matrix multiplication based on ring multiplication and optimizes the matrix partition parameters to adapt to different types(including ciphertext-plaintext and ciphertext-ciphertext)and different matrix dimensions.Secondly,this paper optimizes and designs secure Softmax,LayerNorm,and Gelu protocols based on parameter fuzzing and collaborative computing to perform efficient,secure atomic computations over ciphertexts.Finally,experiments on text classification were conducted on the IMDB and AGNEWS datasets.The results show that,under our experimental settings(including an AMD Ryzen 75700G CPU with 32 GB RAM and 8-thread parallel computing using the Lattigo library),the scheme proposed in this paper completes the inference process within 3 s,with communication costs below 1 GB,and the computing accuracy is comparable to that of plaintext computing. 展开更多
关键词 Machine learning as a service privacy preservation Transformer collaborative computing
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The Architecture of Regional Collaborative Innovation:Multi-Mechanism Analysis of Network Evolution in the Greater Bay Area 认领 引用
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作者 Xinyu Guo Sumin Feng +1 位作者 Rende Li Liangrong Song 《Journal of Data and Information Science》 CSCD 2026年第2期66-84,共19页
Purpose:Understanding formation mechanisms of regional collaborative innovation networks is crucial for effective innovation policy design,yet existing research lacks comprehensive empirical examination of how multipl... Purpose:Understanding formation mechanisms of regional collaborative innovation networks is crucial for effective innovation policy design,yet existing research lacks comprehensive empirical examination of how multiple mechanisms operate simultaneously across network evolution phases.Design/methodology/approach:This study conducts the first longitudinal multi-mechanism analysis by examining 57,846 collaborative patent applications among 3,582 organizations in the Guangdong-Hong Kong-Macao Greater Bay Area(2009-2023).Using phase-segmented exponential random graph models across three developmental stages,we assess five key mechanisms:preferential attachment,triadic closure,brokerage potential,organizational homophily,and experience accumulation.Findings:Our findings reveal a paradoxical“elite-driven community closure”trajectory.Despite network expansion,declining density indicates heightened partner selectivity.Preferential attachment effects persist throughout all phases,sustaining elite hub dominance,while experience accumulation effects diminish over time.Strengthening triadic closure alongside persistently negative brokerage effects creates closure-dominated topologies limiting cross-boundary knowledge flows.Most critically,persistent organizational homophily constrains industry-university-research integration despite policy incentives,demonstrating that structural inertia resists policy intervention more than previously assumed.Research limitations:The analysis focuses on collaboration quantity rather than quality outcomes and applies specifically to patent-based collaboration networks.Practical implications:Three targeted policy adjustments are recommended:(1)reducing cross-type collaboration transaction costs through institutional innovation,(2)counteracting preferential attachment concentration via peripheral engagement strategies,and(3)creating reputation-sharing infrastructure to extend trust beyond homophily clusters.Originality/value:Our mechanism-based approach advances network evolution theory by revealing how microlevel collaboration decisions aggregate into macro-level structural patterns. 展开更多
关键词 regional collaborative innovation greater bay area ERGM social network analysis
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Time‑Delay Collaborative Control of Multiple Aerial Manipulators Using Nonsingular Terminal Sliding Mode 认领 引用
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作者 WU Zhiyu WANG Yaoyao +2 位作者 WANG Hanzhuo CHEN Jiawang SUN Lizhuang 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2026年第2期225-237,共13页
To enhance the overall performance of multiple aerial manipulators under complex lumped disturbances,a nonsingular terminal sliding mode(NTSM)controller based on time-delay estimation(TDE)and deviation coupling contro... To enhance the overall performance of multiple aerial manipulators under complex lumped disturbances,a nonsingular terminal sliding mode(NTSM)controller based on time-delay estimation(TDE)and deviation coupling control(DCC)is proposed.The stability of the controller is proven using the Lyapunov stability theory.Comparative experiments are conducted using a system of multiple aerial manipulators.The results demonstrate that,compared with a PID controller based on TDE,the proposed controller reduces the integral of absolute error(IAE),integral of time-weighted absolute error(ITAE),and integral of squared error(ISE)by at least 45.8%,44.1%,and 66.5%respectively,thereby achieving superior overall control performance. 展开更多
关键词 multiple aerial manipulators time-delay estimation(TDE) terminal sliding mode collaborative control
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HCF-MFGB:Hybrid Collaborative Filtering Based on Matrix Factorization and Gradient Boosting 认领 引用
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作者 Salahudin Robo Triyanna Widiyaningtyas Wahyu Sakti Gunawan Irianto 《Computers, Materials & Continua》 SCIE EI 2026年第2期1630-1648,共19页
Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used i... Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach. 展开更多
关键词 Recommendation systems hybrid collaborative filtering SVD++ XGBoost K-Means clustering user demographics meta item
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Collaborative Scheduling Strategy for Computation and Power among Multiple Data Centers Based on New Energy State Recognition 认领 引用
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作者 Qian Yang Shenglei Du +3 位作者 Boyang Chen Yalu Sun Ding Li Zhiheng Zhang 《Energy Engineering》 EI 2026年第8期346-373,共28页
In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon ... In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon neutrality”,data centers have gradually introduced new energy power such as wind and photovoltaic power.However,the randomness and volatility of their output pose challenges to efficient absorption.Based on the spatiotemporal complementary characteristics of new energy output in multiple data centers and the spatiotemporal migration capability of computing tasks,this paper proposes a new energy-aware adaptive collaborative scheduling strategy for computation and power.The strategy first constructs a regionally differentiated load model to accurately depict the characteristic differences among the Jiangsu-Zhejiang-Shanghai mixed computing power hub,the Gansu highefficiency computing power base,and the coastal green computing power nodes.Then,a dual-mode scheduling algorithm based on Lyapunov optimization is designed,integrating a prediction-reaction mechanism to achieve dynamic balance between system stability and new energy absorption rate.Furthermore,a V-parameter adaptive adjustment mechanism and a hierarchical fault-tolerant guarantee system are proposed to cope with new energy fluctuations and improve system robustness.Simulation results show that the proposed strategy achieves an average new energy absorption rate of 62.3%and 52.8%in normal weather and severe weather scenarios,respectively.The carbon emission per unit computing power is reduced by 20.9%,and the computing power-electricity efficiency is improved by 9.1%,which is significantly better than the static scheduling strategy.This verifies its effectiveness and practicability in improving new energy utilization,ensuring service quality,and reducing carbon emissions. 展开更多
关键词 Collaborative scheduling for computation and power data centers Lyapunov optimization V-parameter adjustment mechanism wind-solar volatility
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