Understanding the reinforcement effect of the newly developed prestressed reinforcement components(PRCs)(a system composed of prestressed steel bars(PSBs),protective sleeves,lateral pressure plates(LPPs),and anchoring...Understanding the reinforcement effect of the newly developed prestressed reinforcement components(PRCs)(a system composed of prestressed steel bars(PSBs),protective sleeves,lateral pressure plates(LPPs),and anchoring elements)is technically significant for the rational design of prestressed subgrade.A three-dimensional finite element model was established and verified based on a novel static model test and utilized to systematically analyze the influence of prestress levels and reinforcement modes on the reinforcement effect of the subgrade.The results show that the PRCs provide additional confining pressure to the subgrade through the diffusion effect of the prestress,which can therefore effectively improve the service performance of the subgrade.Compared to the unreinforced conventional subgrades,the settlements of prestressreinforced subgrades are reduced.The settlement attenuation rate(Rs)near the LPPs is larger than that at the subgrade center,and increasing the prestress positively contributes to the stability of the subgrade structure.In the multi-row reinforcement mode,the reinforcement effect of PRCs can extend from the reinforced area to the unreinforced area.In addition,as the horizontal distance from the LPPs increases,the additional confining pressure converted by the PSBs and LPPs gradually diminishes when spreading to the core load bearing area of the subgrade,resulting in a decrease in the Rs.Under the singlerow reinforcement mode,PRCs can be strategically arranged according to the local areas where subgrade defects readily occurred or observed,to obtain the desired reinforcement effect.Moreover,excessive prestress should not be applied near the subgrade shoulder line to avoid the shear failure of the subgrade shoulder.PRCs can be flexibly used for preventing and treating various subgrade defects of newly constructed or existing railway lines,achieving targeted and classified prevention,and effectively improving the bearing performance and deformation resistance of the subgrade.The research results are instructive for further elucidating the prestress reinforcement effect of PRCs on railway subgrades.展开更多
Lunar core samples are the key materials for accurately assessing and developing lunar resources.However,the difficulty of maintaining borehole stability in the lunar coring process limits the depth of lunar coring.He...Lunar core samples are the key materials for accurately assessing and developing lunar resources.However,the difficulty of maintaining borehole stability in the lunar coring process limits the depth of lunar coring.Here,a strategy of using a reinforcement fluid that undergoes a phase transition spontaneously in a vacuum environment to reinforce the borehole is proposed.Based on this strategy,a reinforcement liquid suitable for a wide temperature range and a high vacuum environment was developed.A feasibility study on reinforcing the borehole with the reinforcement liquid was carried out,and it is found that the cohesion of the simulated lunar soil can be increased from 2 to 800 kPa after using the reinforcement liquid.Further,a series of coring experiments are conducted using a selfdeveloped high vacuum(vacuum degree of 5 Pa)and low-temperature(between-30 and 50℃)simulation platform.It is confirmed that the high-boiling-point reinforcement liquid pre-placed in the drill pipe can be released spontaneously during the drilling process and finally complete the reinforcement of the borehole.The reinforcement effect of the borehole is better when the solute concentration is between0.15 and 0.25 g/mL.展开更多
AIM:To investigate the refractive and the histological changes in guinea pig eyes after posterior scleral reinforcement with scleral allografts.METHODS:Four-week-old guinea pigs were implanted with scleral allografts,...AIM:To investigate the refractive and the histological changes in guinea pig eyes after posterior scleral reinforcement with scleral allografts.METHODS:Four-week-old guinea pigs were implanted with scleral allografts,and the changes of refraction,corneal curvature and axis length were monitored for 51d.The effects of methylprednisolone(MPS)on refraction parameters were also evaluated.And the microstructure and ultra-microstructure of eyes were observed on the 9d and 51d after operation.Repeated-measures analysis of variance and one-way analysis of variance were used.RESULTS:The refraction outcome of the implanted eye decreased after operation,and the refraction change of the 3 mm scleral allografts group was significantly different with control group(P=0.005)and the sham surgical group(P=0.004).After the application of MPS solution,the reduction of refraction outcome was statistically suppressed(P=0.008).The inflammatory encapsulation appeared 9d after surgery.On 51d after operation,the loose implanted materials were absorbed,while the adherent implanted materials with MPS group were still tightly attached to the recipient’s eyeball.CONCLUSION:After implantation of scleral allografts,the refraction of guinea pig eyes fluctuated from a decrease to an increase.The outcome of the scleral allografts is affected by implantation methods and the inflammatory response.Stability of the material can be improved by MPS.展开更多
This paper explores how reinforcement learning(RL)can improve intelligent education systems.RL helps make learning personal,flexible,and efficient by choosing actions based on student needs and rewards like better sco...This paper explores how reinforcement learning(RL)can improve intelligent education systems.RL helps make learning personal,flexible,and efficient by choosing actions based on student needs and rewards like better scores or engagement.We study its use in custom learning paths,smart testing,and teacher support,showing how it beats old methods that don’t adapt.The paper also suggests future ideas—like better RL tools,teamwork learning,and mixing RL with big language models—while noting fairness challenges.Using pretend data with 1000 students,we test RL’s power to plan learning step by step.Results show RL can lift learning by 2025%in areas like tutoring and class focus.This work gives a clear plan for using RL to make education smarter and fairer,pointing to a bright future for adaptive learning.展开更多
As an evaluation index,the natural frequency has the advantages of easy acquisition and quantitative evaluation.In this paper,the natural frequency is used to evaluate the performance of external cable reinforced brid...As an evaluation index,the natural frequency has the advantages of easy acquisition and quantitative evaluation.In this paper,the natural frequency is used to evaluate the performance of external cable reinforced bridges.Numerical examples show that compared with the natural frequencies of first-order modes,the natural frequencies of higher-order modes are more sensitive and can reflect the damage situation and external cable reinforcement effect of T-beam bridges.For damaged bridges,as the damage to the T-beam increases,the natural frequency value of the bridge gradually decreases.When the degree of local damage to the beam reaches 60%,the amplitude of natural frequency change exceeds 10%for the first time.The natural frequencies of the firstorder vibration mode and higher-order vibration mode can be selected as indexes for different degrees of the damaged T-beam bridges.For damaged bridges reinforced with external cables,the traditional natural frequency of the first-order vibration mode cannot be used as the index,which is insensitive to changes in prestress of the external cable.Some natural frequencies of higher-order vibration modes can be selected as indexes,which can reflect the reinforcement effect of externally prestressed damaged T-beam bridges,and its numerical value increases with the increase of external prestressed cable force.展开更多
With the development of modern society,people put forward higher requirements for building safety,which makes the construction project face new challenges.Reinforced concrete frame structure as a common engineering ty...With the development of modern society,people put forward higher requirements for building safety,which makes the construction project face new challenges.Reinforced concrete frame structure as a common engineering type,although the construction technology has been relatively mature,but its earthquake collapse ability still needs to be strengthened.This paper analyzes the specific factors that affect the seismic collapse ability of reinforced concrete frame structure,summarizes the previous research results,and puts forward innovative application of fiber-reinforced polymer(FRP)composite materials,play the role of smart materials,improve the isolation and energy dissipation devices,etc.,to promote the continuous optimization of reinforced concrete frame structure design,and show better seismic performance.展开更多
Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies dri...Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading strategies.However,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy performance.In order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy consumption.Firstly,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov property.Secondly,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission delays.Thirdly,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real ones.Numerical results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.展开更多
Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered dri...Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered driven separately by social reinforcement or higher-order interactions.However,due to the limitations of empirical data and theoretical analysis,how the higher-order network structure affects the explosive information spreading under the role of social reinforcement has not been fully explored.In this work,we propose an information-spreading model by considering the social reinforcement in real and synthetic higher-order networks,describable as hypergraphs.Depending on the average group size(hyperedge cardinality)and node membership(hyperdegree),we observe two different spreading behaviors:(i)The spreading progress is not sensitive to social reinforcement,resulting in the information localized in a small part of nodes;(ii)a strong social reinforcement will promote the large-scale spread of information and induce an explosive transition.Moreover,a large average group size and membership would be beneficial to the appearance of the explosive transition.Further,we display that the heterogeneity of the node membership and group size distributions benefit the information spreading.Finally,we extend the group-based approximate master equations to verify the simulation results.Our findings may help us to comprehend the rapidly information-spreading phenomenon in modern society.展开更多
Squat reinforced concrete(RC)shear walls are essential structural elements in low-rise buildings,valued for their high strength and stiffness.However,research on their seismic behavior remains limited,as most studies ...Squat reinforced concrete(RC)shear walls are essential structural elements in low-rise buildings,valued for their high strength and stiffness.However,research on their seismic behavior remains limited,as most studies focus on tall,slender walls,which exhibit distinct failure mechanisms and deformation characteristics.This study addresses this gap by conducting an extensive review of existing research on the seismic performance of squat RC shear walls.Experimental studies,analytical models,and numerical simulations are examined to provide insights into key factors affecting wall behavior during seismic events,including material properties,wall geometry,reinforcement detailing,and loading conditions.The review aims to support safer design practices by identifying current knowledge gaps and offering guidance on areas needing further investigation.The findings are expected to aid researchers and practitioners in refining seismic design codes,ultimately contributing to the development of more resilient squat RC shear walls for earthquake-prone regions.This research underscores the importance of improving structural resilience to enhance the safety and durability of buildings.展开更多
To mitigate the challenges in managing the damage level of reinforced concrete(RC)pier columns subjected to cyclic reverse loading,this study conducted a series of cyclic reverse tests on RC pier columns.By analyzing ...To mitigate the challenges in managing the damage level of reinforced concrete(RC)pier columns subjected to cyclic reverse loading,this study conducted a series of cyclic reverse tests on RC pier columns.By analyzing the outcomes of destructive testing on various specimens and fine-tuning the results with the aid of the IMK(Ibarra Medina Krawinkler)recovery model,the energy dissipation capacity coefficient of the pier columns were able to be determined.Furthermore,utilizing the calibrated damage model parameters,the damage index for each specimen were calculated.Based on the obtained damage levels,three distinct pre-damage conditions were designed for the pier columns:minor damage,moderate damage,and severe damage.The study then predicted the variations in hysteresis curves and damage indices under cyclic loading conditions.The experimental findings reveal that the displacement at the top of the pier columns can serve as a reliable indicator for controlling the damage level of pier columns post-loading.Moreover,the calibrated damage index model exhibits proficiency in accurately predicting the damage level of RC pier columns under cyclic loading.展开更多
In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users t...In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the UAV.To solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based approaches.Specifically,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL approach.The simulation results show that both approaches can solve the proposed optimization problem and obtain good results.Moreover,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.展开更多
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.展开更多
Granite residual soil (GRS) is a type of weathering soil that can decompose upon contact with water, potentially causing geological hazards. In this study, cement, an alkaline solution, and glass fiber were used to re...Granite residual soil (GRS) is a type of weathering soil that can decompose upon contact with water, potentially causing geological hazards. In this study, cement, an alkaline solution, and glass fiber were used to reinforce GRS. The effects of cement content and SiO_(2)/Na2O ratio of the alkaline solution on the static and dynamic strengths of GRS were discussed. Microscopically, the reinforcement mechanism and coupling effect were examined using X-ray diffraction (XRD), micro-computed tomography (micro-CT), and scanning electron microscopy (SEM). The results indicated that the addition of 2% cement and an alkaline solution with an SiO_(2)/Na2O ratio of 0.5 led to the densest matrix, lowest porosity, and highest static compressive strength, which was 4994 kPa with a dynamic impact resistance of 75.4 kN after adding glass fiber. The compressive strength and dynamic impact resistance were a result of the coupling effect of cement hydration, a pozzolanic reaction of clay minerals in the GRS, and the alkali activation of clay minerals. Excessive cement addition or an excessively high SiO_(2)/Na2O ratio in the alkaline solution can have negative effects, such as the destruction of C-(A)-S-H gels by the alkaline solution and hindering the production of N-A-S-H gels. This can result in damage to the matrix of reinforced GRS, leading to a decrease in both static and dynamic strengths. This study suggests that further research is required to gain a more precise understanding of the effects of this mixture in terms of reducing our carbon footprint and optimizing its properties. The findings indicate that cement and alkaline solution are appropriate for GRS and that the reinforced GRS can be used for high-strength foundation and embankment construction. The study provides an analysis of strategies for mitigating and managing GRS slope failures, as well as enhancing roadbed performance.展开更多
Despite its immense potential,the application of digital twin technology in real industrial scenarios still faces numerous challenges.This study focuses on industrial assembly lines in sectors such as microelectronics...Despite its immense potential,the application of digital twin technology in real industrial scenarios still faces numerous challenges.This study focuses on industrial assembly lines in sectors such as microelectronics,pharmaceuticals,and food packaging,where precision and speed are paramount,applying digital twin technology to the robotic assembly process.The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments.Based on this system,a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed,significantly enhancing learning efficiency through an improved intermediate reward mechanism.Experiments on communication and interaction between the digital twin system and the physical robot validate the effectiveness of this method.The system not only enhances the integration of digital twin technology,deep reinforcement learning and robotics,offering an efficient solution for path planning and target grasping inDelta robots,but also underscores the transformative potential of digital twin technology in intelligent manufacturing,with extensive applicability across diverse industrial domains.展开更多
In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing num...In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources.Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs.In this paper,we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy.To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle(V2V)user pairs,we propose a data-driven Multi-Agent Deep Reinforcement Learning(MADRL)resource allocation scheme for the HVN.Simulation results demonstrate that compared to existing algorithms,the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission,while providing close-to-limit performance.展开更多
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards grea...This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.展开更多
Newly built tunnels often encounter a series of defects within the first few years of operation.If not promptly addressed and reinforced,these defects threaten the tunnel's durability and stability and bring sever...Newly built tunnels often encounter a series of defects within the first few years of operation.If not promptly addressed and reinforced,these defects threaten the tunnel's durability and stability and bring severe challenges to its safe operation.This study aims to explore reinforcement techniques for addressing defects in newly built tunnels.The research begins with an analysis of common defects found in newly built tunnels,followed by a case study of the Jinfeng Tunnel in Chongqing,examining the post-construction defects.The actual reinforcement strategies and methods employed for the tunnel are then discussed.Finally,based on the research findings,this study provides insights and references for tunnel operation and construction units in China,aiming to enhance the overall quality of tunnel engineering in the country,align with sustainable development goals,and promote further improvements at a macro level.展开更多
Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs...Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.展开更多
To meticulously dissect the cracking issue in the transverse diaphragm concrete,situated at the anchor point of a colossal large-span,single cable plane cable-stayed bridge,this research paper adopts an innovative lay...To meticulously dissect the cracking issue in the transverse diaphragm concrete,situated at the anchor point of a colossal large-span,single cable plane cable-stayed bridge,this research paper adopts an innovative layered modeling analysis methodology for numerical simulations.The approach is structured into three distinct layers,each tailored to address specific aspects of the cracking phenomenon.The foundational first layer model operates under the assumption of linear elasticity,adhering to the Saint Venant principle.It narrows its focus to the crucial zone between the Bp20 transverse diaphragm and the central axis of pier 4’s support,encompassing the critically cracked diaphragm beneath the N1 cable anchor.This layer provides a preliminary estimate of potential cracking zones within the concrete,serving as a baseline for further analysis.The second layer model builds upon this foundation by incorporating material plasticity into its considerations.It pinpoints its investigation to the immediate vicinity of the cracked transverse diaphragm associated with the N1 cable,aiming to capture the intricate material behavior under stress.This layer’s predictions of crack locations and patterns exhibit a remarkable alignment with actual detection results,confirming its precision and reliability.The third and most intricate layer delves deep into the heart of the matter,examining the cracked transverse diaphragm precisely where the cable force attains its maximum intensity.By leveraging advanced extended finite element technology,this layer offers an unprecedented level of detail in tracing the progression of concrete cracks.Its findings reveal a close correlation between predicted and observed crack widths,validating the model’s proficiency in simulating real-world cracking dynamics.Crucially,the boundary conditions for each layer are meticulously aligned with those of the overarching model,ensuring consistency and integrity throughout the analysis.These results not only enrich our understanding of the cracking mechanisms but also underscore the efficacy of reinforcing cracked concrete sections with external steel plates.In conclusion,this study represents a significant contribution to the field of bridge engineering,offering both theoretical insights and practical solutions for addressing similar challenges.展开更多
Recently,Network Functions Virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G era.NFV decomposes the network resource paradigm,demonstrating the efficient utilization...Recently,Network Functions Virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G era.NFV decomposes the network resource paradigm,demonstrating the efficient utilization of Network Functions(NFs)to enable configurable service priorities and resource demands.Telecommunications Service Providers(TSPs)face challenges in network utilization,as the vast amounts of data generated by the Internet of Things(IoT)overwhelm existing infrastructures.IoT applications,which generate massive volumes of diverse data and require real-time communication,contribute to bottlenecks and congestion.In this context,Multiaccess Edge Computing(MEC)is employed to support resource and priority-aware IoT applications by implementing Virtual Network Function(VNF)sequences within Service Function Chaining(SFC).This paper proposes the use of Deep Reinforcement Learning(DRL)combined with Graph Neural Networks(GNN)to enhance network processing,performance,and resource pooling capabilities.GNN facilitates feature extraction through Message-Passing Neural Network(MPNN)mechanisms.Together with DRL,Deep Q-Networks(DQN)are utilized to dynamically allocate resources based on IoT network priorities and demands.Our focus is on minimizing delay times for VNF instance execution,ensuring effective resource placement,and allocation in SFC deployments,offering flexibility to adapt to real-time changes in priority and workload.Simulation results demonstrate that our proposed scheme outperforms reference models in terms of reward,delay,delivery,service drop ratios,and average completion ratios,proving its potential for IoT applications.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.51978672 and 52308335)the Natural Science Funding of Hunan Province(Grant No.2023JJ41054)the Natural Science Research Project of Anhui Educational Committee(Grant No.2023AH051170)。
文摘Understanding the reinforcement effect of the newly developed prestressed reinforcement components(PRCs)(a system composed of prestressed steel bars(PSBs),protective sleeves,lateral pressure plates(LPPs),and anchoring elements)is technically significant for the rational design of prestressed subgrade.A three-dimensional finite element model was established and verified based on a novel static model test and utilized to systematically analyze the influence of prestress levels and reinforcement modes on the reinforcement effect of the subgrade.The results show that the PRCs provide additional confining pressure to the subgrade through the diffusion effect of the prestress,which can therefore effectively improve the service performance of the subgrade.Compared to the unreinforced conventional subgrades,the settlements of prestressreinforced subgrades are reduced.The settlement attenuation rate(Rs)near the LPPs is larger than that at the subgrade center,and increasing the prestress positively contributes to the stability of the subgrade structure.In the multi-row reinforcement mode,the reinforcement effect of PRCs can extend from the reinforced area to the unreinforced area.In addition,as the horizontal distance from the LPPs increases,the additional confining pressure converted by the PSBs and LPPs gradually diminishes when spreading to the core load bearing area of the subgrade,resulting in a decrease in the Rs.Under the singlerow reinforcement mode,PRCs can be strategically arranged according to the local areas where subgrade defects readily occurred or observed,to obtain the desired reinforcement effect.Moreover,excessive prestress should not be applied near the subgrade shoulder line to avoid the shear failure of the subgrade shoulder.PRCs can be flexibly used for preventing and treating various subgrade defects of newly constructed or existing railway lines,achieving targeted and classified prevention,and effectively improving the bearing performance and deformation resistance of the subgrade.The research results are instructive for further elucidating the prestress reinforcement effect of PRCs on railway subgrades.
基金National Natural Science Foundation of China (Nos.U2013603,51827901,and 52403383)Program for Guangdong Introducing Innovative and Entrepreneurial Teams (No.2019ZT08G315)+1 种基金Institute of New Energy and Low-Carbon Technology (Sichuan University)State Key Laboratory of Coal Mine Disaster Dynamics and Control of Chongqing University。
文摘Lunar core samples are the key materials for accurately assessing and developing lunar resources.However,the difficulty of maintaining borehole stability in the lunar coring process limits the depth of lunar coring.Here,a strategy of using a reinforcement fluid that undergoes a phase transition spontaneously in a vacuum environment to reinforce the borehole is proposed.Based on this strategy,a reinforcement liquid suitable for a wide temperature range and a high vacuum environment was developed.A feasibility study on reinforcing the borehole with the reinforcement liquid was carried out,and it is found that the cohesion of the simulated lunar soil can be increased from 2 to 800 kPa after using the reinforcement liquid.Further,a series of coring experiments are conducted using a selfdeveloped high vacuum(vacuum degree of 5 Pa)and low-temperature(between-30 and 50℃)simulation platform.It is confirmed that the high-boiling-point reinforcement liquid pre-placed in the drill pipe can be released spontaneously during the drilling process and finally complete the reinforcement of the borehole.The reinforcement effect of the borehole is better when the solute concentration is between0.15 and 0.25 g/mL.
基金Supported by the Scientific Research Project of Shanghai Municipal Health Commission(No.202140416)the Clinical Research Boosting Program of the Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine(No.JYLJ202117).
文摘AIM:To investigate the refractive and the histological changes in guinea pig eyes after posterior scleral reinforcement with scleral allografts.METHODS:Four-week-old guinea pigs were implanted with scleral allografts,and the changes of refraction,corneal curvature and axis length were monitored for 51d.The effects of methylprednisolone(MPS)on refraction parameters were also evaluated.And the microstructure and ultra-microstructure of eyes were observed on the 9d and 51d after operation.Repeated-measures analysis of variance and one-way analysis of variance were used.RESULTS:The refraction outcome of the implanted eye decreased after operation,and the refraction change of the 3 mm scleral allografts group was significantly different with control group(P=0.005)and the sham surgical group(P=0.004).After the application of MPS solution,the reduction of refraction outcome was statistically suppressed(P=0.008).The inflammatory encapsulation appeared 9d after surgery.On 51d after operation,the loose implanted materials were absorbed,while the adherent implanted materials with MPS group were still tightly attached to the recipient’s eyeball.CONCLUSION:After implantation of scleral allografts,the refraction of guinea pig eyes fluctuated from a decrease to an increase.The outcome of the scleral allografts is affected by implantation methods and the inflammatory response.Stability of the material can be improved by MPS.
文摘This paper explores how reinforcement learning(RL)can improve intelligent education systems.RL helps make learning personal,flexible,and efficient by choosing actions based on student needs and rewards like better scores or engagement.We study its use in custom learning paths,smart testing,and teacher support,showing how it beats old methods that don’t adapt.The paper also suggests future ideas—like better RL tools,teamwork learning,and mixing RL with big language models—while noting fairness challenges.Using pretend data with 1000 students,we test RL’s power to plan learning step by step.Results show RL can lift learning by 2025%in areas like tutoring and class focus.This work gives a clear plan for using RL to make education smarter and fairer,pointing to a bright future for adaptive learning.
基金supported by Henan Province Science and Technology Research Funding Project(No.222102320129)the Key Research Project of Henan Higher Education Institutions(Grant Nos.22A560004,22A56005).
文摘As an evaluation index,the natural frequency has the advantages of easy acquisition and quantitative evaluation.In this paper,the natural frequency is used to evaluate the performance of external cable reinforced bridges.Numerical examples show that compared with the natural frequencies of first-order modes,the natural frequencies of higher-order modes are more sensitive and can reflect the damage situation and external cable reinforcement effect of T-beam bridges.For damaged bridges,as the damage to the T-beam increases,the natural frequency value of the bridge gradually decreases.When the degree of local damage to the beam reaches 60%,the amplitude of natural frequency change exceeds 10%for the first time.The natural frequencies of the firstorder vibration mode and higher-order vibration mode can be selected as indexes for different degrees of the damaged T-beam bridges.For damaged bridges reinforced with external cables,the traditional natural frequency of the first-order vibration mode cannot be used as the index,which is insensitive to changes in prestress of the external cable.Some natural frequencies of higher-order vibration modes can be selected as indexes,which can reflect the reinforcement effect of externally prestressed damaged T-beam bridges,and its numerical value increases with the increase of external prestressed cable force.
文摘With the development of modern society,people put forward higher requirements for building safety,which makes the construction project face new challenges.Reinforced concrete frame structure as a common engineering type,although the construction technology has been relatively mature,but its earthquake collapse ability still needs to be strengthened.This paper analyzes the specific factors that affect the seismic collapse ability of reinforced concrete frame structure,summarizes the previous research results,and puts forward innovative application of fiber-reinforced polymer(FRP)composite materials,play the role of smart materials,improve the isolation and energy dissipation devices,etc.,to promote the continuous optimization of reinforced concrete frame structure design,and show better seismic performance.
基金funded by the National Key Research and Development Program of China under Grant 2019YFB1803301Beijing Natural Science Foundation (L202002)。
文摘Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading strategies.However,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy performance.In order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy consumption.Firstly,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov property.Secondly,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission delays.Thirdly,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real ones.Numerical results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12305043 and 12165016)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220511)+1 种基金the Project of Undergraduate Scientific Research(Grant No.22A684)the support from the Jiangsu Specially-Appointed Professor Program。
文摘Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered driven separately by social reinforcement or higher-order interactions.However,due to the limitations of empirical data and theoretical analysis,how the higher-order network structure affects the explosive information spreading under the role of social reinforcement has not been fully explored.In this work,we propose an information-spreading model by considering the social reinforcement in real and synthetic higher-order networks,describable as hypergraphs.Depending on the average group size(hyperedge cardinality)and node membership(hyperdegree),we observe two different spreading behaviors:(i)The spreading progress is not sensitive to social reinforcement,resulting in the information localized in a small part of nodes;(ii)a strong social reinforcement will promote the large-scale spread of information and induce an explosive transition.Moreover,a large average group size and membership would be beneficial to the appearance of the explosive transition.Further,we display that the heterogeneity of the node membership and group size distributions benefit the information spreading.Finally,we extend the group-based approximate master equations to verify the simulation results.Our findings may help us to comprehend the rapidly information-spreading phenomenon in modern society.
文摘Squat reinforced concrete(RC)shear walls are essential structural elements in low-rise buildings,valued for their high strength and stiffness.However,research on their seismic behavior remains limited,as most studies focus on tall,slender walls,which exhibit distinct failure mechanisms and deformation characteristics.This study addresses this gap by conducting an extensive review of existing research on the seismic performance of squat RC shear walls.Experimental studies,analytical models,and numerical simulations are examined to provide insights into key factors affecting wall behavior during seismic events,including material properties,wall geometry,reinforcement detailing,and loading conditions.The review aims to support safer design practices by identifying current knowledge gaps and offering guidance on areas needing further investigation.The findings are expected to aid researchers and practitioners in refining seismic design codes,ultimately contributing to the development of more resilient squat RC shear walls for earthquake-prone regions.This research underscores the importance of improving structural resilience to enhance the safety and durability of buildings.
基金supported by National Natural Science Foundation of China(Project No.51878156)EPC Innovation Consulting Project for Longkou Nanshan LNG Phase I Receiving Terminal(Z2000LGENT0399).
文摘To mitigate the challenges in managing the damage level of reinforced concrete(RC)pier columns subjected to cyclic reverse loading,this study conducted a series of cyclic reverse tests on RC pier columns.By analyzing the outcomes of destructive testing on various specimens and fine-tuning the results with the aid of the IMK(Ibarra Medina Krawinkler)recovery model,the energy dissipation capacity coefficient of the pier columns were able to be determined.Furthermore,utilizing the calibrated damage model parameters,the damage index for each specimen were calculated.Based on the obtained damage levels,three distinct pre-damage conditions were designed for the pier columns:minor damage,moderate damage,and severe damage.The study then predicted the variations in hysteresis curves and damage indices under cyclic loading conditions.The experimental findings reveal that the displacement at the top of the pier columns can serve as a reliable indicator for controlling the damage level of pier columns post-loading.Moreover,the calibrated damage index model exhibits proficiency in accurately predicting the damage level of RC pier columns under cyclic loading.
基金supported in part by the Shenzhen Basic Research Project under Grant JCYJ20220531103008018 and Grant 20200812112423002in part by the Guangdong Basic Research Program under Grant 2019A1515110358,2021A1515012097in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University (No.2021D16)。
文摘In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the UAV.To solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based approaches.Specifically,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL approach.The simulation results show that both approaches can solve the proposed optimization problem and obtain good results.Moreover,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.
文摘Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
基金the support provided by the National Natural Science Foundation of China(Grant Nos.52278336 and 42302032)Guangdong Basic and Applied Research Foundation(Grant Nos.2023B1515020061).
文摘Granite residual soil (GRS) is a type of weathering soil that can decompose upon contact with water, potentially causing geological hazards. In this study, cement, an alkaline solution, and glass fiber were used to reinforce GRS. The effects of cement content and SiO_(2)/Na2O ratio of the alkaline solution on the static and dynamic strengths of GRS were discussed. Microscopically, the reinforcement mechanism and coupling effect were examined using X-ray diffraction (XRD), micro-computed tomography (micro-CT), and scanning electron microscopy (SEM). The results indicated that the addition of 2% cement and an alkaline solution with an SiO_(2)/Na2O ratio of 0.5 led to the densest matrix, lowest porosity, and highest static compressive strength, which was 4994 kPa with a dynamic impact resistance of 75.4 kN after adding glass fiber. The compressive strength and dynamic impact resistance were a result of the coupling effect of cement hydration, a pozzolanic reaction of clay minerals in the GRS, and the alkali activation of clay minerals. Excessive cement addition or an excessively high SiO_(2)/Na2O ratio in the alkaline solution can have negative effects, such as the destruction of C-(A)-S-H gels by the alkaline solution and hindering the production of N-A-S-H gels. This can result in damage to the matrix of reinforced GRS, leading to a decrease in both static and dynamic strengths. This study suggests that further research is required to gain a more precise understanding of the effects of this mixture in terms of reducing our carbon footprint and optimizing its properties. The findings indicate that cement and alkaline solution are appropriate for GRS and that the reinforced GRS can be used for high-strength foundation and embankment construction. The study provides an analysis of strategies for mitigating and managing GRS slope failures, as well as enhancing roadbed performance.
基金supported in part by the National Natural Science Foundation of China under Grants 62303098 and 62173073in part by China Postdoctoral Science Foundation under Grant 2022M720679+1 种基金in part by the Central University Basic Research Fund of China under Grant N2304021in part by the Liaoning Provincial Science and Technology Plan Project-Technology Innovation Guidance of the Science and Technology Department under Grant 2023JH1/10400011.
文摘Despite its immense potential,the application of digital twin technology in real industrial scenarios still faces numerous challenges.This study focuses on industrial assembly lines in sectors such as microelectronics,pharmaceuticals,and food packaging,where precision and speed are paramount,applying digital twin technology to the robotic assembly process.The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments.Based on this system,a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed,significantly enhancing learning efficiency through an improved intermediate reward mechanism.Experiments on communication and interaction between the digital twin system and the physical robot validate the effectiveness of this method.The system not only enhances the integration of digital twin technology,deep reinforcement learning and robotics,offering an efficient solution for path planning and target grasping inDelta robots,but also underscores the transformative potential of digital twin technology in intelligent manufacturing,with extensive applicability across diverse industrial domains.
基金funded in part by the National Key Research and Development of China Project (2020YFB1807204)in part by National Natural Science Foundation of China (U2001213 and 61971191)+1 种基金in part by the Beijing Natural Science Foundation under Grant L201011in part by the key project of Natural Science Foundation of Jiangxi Province (20202ACBL202006)。
文摘In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources.Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs.In this paper,we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy.To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle(V2V)user pairs,we propose a data-driven Multi-Agent Deep Reinforcement Learning(MADRL)resource allocation scheme for the HVN.Simulation results demonstrate that compared to existing algorithms,the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission,while providing close-to-limit performance.
基金supported by the Science and Technology Project of State Grid Sichuan Electric Power Company Chengdu Power Supply Company under Grant No.521904240005.
文摘This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.
文摘Newly built tunnels often encounter a series of defects within the first few years of operation.If not promptly addressed and reinforced,these defects threaten the tunnel's durability and stability and bring severe challenges to its safe operation.This study aims to explore reinforcement techniques for addressing defects in newly built tunnels.The research begins with an analysis of common defects found in newly built tunnels,followed by a case study of the Jinfeng Tunnel in Chongqing,examining the post-construction defects.The actual reinforcement strategies and methods employed for the tunnel are then discussed.Finally,based on the research findings,this study provides insights and references for tunnel operation and construction units in China,aiming to enhance the overall quality of tunnel engineering in the country,align with sustainable development goals,and promote further improvements at a macro level.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72101046 and 61672128)。
文摘Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.
基金financially supported by National Natural Science Foundation of China(Project No.51878156,received by Wenwei Wang).
文摘To meticulously dissect the cracking issue in the transverse diaphragm concrete,situated at the anchor point of a colossal large-span,single cable plane cable-stayed bridge,this research paper adopts an innovative layered modeling analysis methodology for numerical simulations.The approach is structured into three distinct layers,each tailored to address specific aspects of the cracking phenomenon.The foundational first layer model operates under the assumption of linear elasticity,adhering to the Saint Venant principle.It narrows its focus to the crucial zone between the Bp20 transverse diaphragm and the central axis of pier 4’s support,encompassing the critically cracked diaphragm beneath the N1 cable anchor.This layer provides a preliminary estimate of potential cracking zones within the concrete,serving as a baseline for further analysis.The second layer model builds upon this foundation by incorporating material plasticity into its considerations.It pinpoints its investigation to the immediate vicinity of the cracked transverse diaphragm associated with the N1 cable,aiming to capture the intricate material behavior under stress.This layer’s predictions of crack locations and patterns exhibit a remarkable alignment with actual detection results,confirming its precision and reliability.The third and most intricate layer delves deep into the heart of the matter,examining the cracked transverse diaphragm precisely where the cable force attains its maximum intensity.By leveraging advanced extended finite element technology,this layer offers an unprecedented level of detail in tracing the progression of concrete cracks.Its findings reveal a close correlation between predicted and observed crack widths,validating the model’s proficiency in simulating real-world cracking dynamics.Crucially,the boundary conditions for each layer are meticulously aligned with those of the overarching model,ensuring consistency and integrity throughout the analysis.These results not only enrich our understanding of the cracking mechanisms but also underscore the efficacy of reinforcing cracked concrete sections with external steel plates.In conclusion,this study represents a significant contribution to the field of bridge engineering,offering both theoretical insights and practical solutions for addressing similar challenges.
基金supported by Institute of Information&Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00167197,Development of Intelligent 5G/6G Infrastructure Technology for the Smart City)in part by the National Research Foundation of Korea(NRF),Ministry of Education,through the Basic Science Research Program under Grant NRF-2020R1I1A3066543+1 种基金in part by BK21 FOUR(Fostering Outstanding Universities for Research)under Grant 5199990914048in part by the Soonchunhyang University Research Fund.
文摘Recently,Network Functions Virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G era.NFV decomposes the network resource paradigm,demonstrating the efficient utilization of Network Functions(NFs)to enable configurable service priorities and resource demands.Telecommunications Service Providers(TSPs)face challenges in network utilization,as the vast amounts of data generated by the Internet of Things(IoT)overwhelm existing infrastructures.IoT applications,which generate massive volumes of diverse data and require real-time communication,contribute to bottlenecks and congestion.In this context,Multiaccess Edge Computing(MEC)is employed to support resource and priority-aware IoT applications by implementing Virtual Network Function(VNF)sequences within Service Function Chaining(SFC).This paper proposes the use of Deep Reinforcement Learning(DRL)combined with Graph Neural Networks(GNN)to enhance network processing,performance,and resource pooling capabilities.GNN facilitates feature extraction through Message-Passing Neural Network(MPNN)mechanisms.Together with DRL,Deep Q-Networks(DQN)are utilized to dynamically allocate resources based on IoT network priorities and demands.Our focus is on minimizing delay times for VNF instance execution,ensuring effective resource placement,and allocation in SFC deployments,offering flexibility to adapt to real-time changes in priority and workload.Simulation results demonstrate that our proposed scheme outperforms reference models in terms of reward,delay,delivery,service drop ratios,and average completion ratios,proving its potential for IoT applications.
