Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.展开更多
As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.展开更多
Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent noise cross-correlation functions(NCFs)is a fundamental first step.In this study,we introduced a novel CPU-G...Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent noise cross-correlation functions(NCFs)is a fundamental first step.In this study,we introduced a novel CPU-GPU heterogeneous computing framework designed to significantly enhance the efficiency of computing 9-component NCFs from seismic ambient noise data.This framework not only accelerated the computational process by leveraging the Compute Unified Device Architecture(CUDA)but also improved the signal-to-noise ratio(SNR)through innovative stacking techniques,such as time-frequency domain phaseweighted stacking(tf-PWS).We validated the program using multiple datasets,confirming its superior computation speed,improved reliability,and higher signal-to-noise ratios for NCFs.Our comprehensive study provides detailed insights into optimizing the computational processes for noise cross-correlation functions,thereby enhancing the precision and efficiency of ambient noise imaging.展开更多
High computational performance is extremely important for climate system models, especially in ultra-high-resolution model development. In this study, the computational performance of the Finite-volume Atmospheric Mod...High computational performance is extremely important for climate system models, especially in ultra-high-resolution model development. In this study, the computational performance of the Finite-volume Atmospheric Model of the IAP/LASG （FAMIL） was comprehensively evaluated on Tianhe-2, which was the world＇s top-ranked supercomputer from June 2013 to May 2016. The standardized Atmospheric Model Inter-comparison Project （AMIP） type of experiment was carried out that focused on the computational performance of each node as well as the simulation year per day （SYPD）, the running cost speedup, and the scalability of the FAMIL. The results indicated that （1） based on five indexes （CPU usage, percentage of CPU kernel mode that occupies CPU time and of message passing waiting time （CPU SW）, code vectorization （VEC）, average of Gflops （Gflops_ AVE）, and peak of Gflops （Gflops_PK））, FAMIL shows excellent computational performance on every Tianhe-2 computing node; （2） considering SYPD and the cost speedup of FAMIL systematically, the optimal Message Passing Interface （MPI） numbers of processors （MNPs） choice appears when FAMIL use 384 and 1536 MNPs for C96 （100 km） and C384 （25 km）, respectively; and （3） FAMIL shows positive scalability with increased threads to drive the model. Considering the fast network speed and acceleration card in the MIC architecture on Tianhe-2, there is still significant room to improve the computational performance of FAMIL.展开更多
In order to investigate the effects of two mineral admixtures （i. e., fly ash and ground slag）on initial defects existing in concrete microstructures, a high-resolution X-ray micro-CT（ micro-focus computer tomogra...In order to investigate the effects of two mineral admixtures （i. e., fly ash and ground slag）on initial defects existing in concrete microstructures, a high-resolution X-ray micro-CT（ micro-focus computer tomography）is employed to quantitatively analyze the initial defects in four series of highperformance concrete （HPC）specimens with additions of different mineral admixtures. The nigh-resolution 3D images of microstructures and filtered defects are reconstructed by micro- CT software. The size distribution and volume fractions of initial defects are analyzed based on 3D and 2D micro-CT images. The analysis results are verified by experimental results of watersuction tests. The results show that the additions of mineral admixtures in concrete as cementitious materials greatly change the geometrical properties of the microstructures and the spatial features of defects by physical-chemistry actions of these mineral admixtures. This is the major cause of the differences between the mechanical behaviors of HPC with and without mineral admixtures when the water-to-binder ratio and the size distribution of aggregates are constant.展开更多
The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical r...The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.展开更多
Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switchi...Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.展开更多
The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digit...The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.展开更多
[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Method...[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Methods]An Inertsil ODS-C 18 column(250 mm×4.6 mm,5μm)was used.The mobile phase consisted of acetonitrile-0.2% phosphoric acid solution(10:90).The flow rate was 1.0 mL/min.The detection wavelength was 274 nm.The column temperature was 25℃.The injection volume was 10μL.The content of three components,gallic acid,protocatechuic acid,and protocatechualdehyde,was determined in 13 batches of T.asiatica.[Results]Gallic acid showed good linearity within the range of 0.020-6.400μg/mL,protocatechuic acid within 0.201-6.432μg/mL,and protocatechualdehyde within 0.202-6.464μg/mL(r>0.9990).The average recovery rates ranged from 98.61%to 101.17%,with RSD s between 1.21%and 2.69%.[Conclusions]The quantitative determination method established in this study is simple and feasible,and can provide a basis for the quality evaluation of T.asiatica.展开更多
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ...As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.展开更多
Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morpho...Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morphology,good monodispersity and high specific surface area,were used as a stationary phase for high-performance liquid chromatography(HPLC).The single-crystal 3D COFs packed column not only exhibits high efficiency in separating hydrophobic molecules involving substituted benzenes,halogenated benzenes,halogenated nitrobenzenes,aromatic amines,aromatic hydrocarbons(PAHs)and phthalate esters(PAEs),but also achieves baseline separation of acenaphthene and acenaphthylene with similar physical and chemical properties as well as environmental pollutants,which cannot be quickly separated on commercial C18 column and a polycrystalline 3D COFs packed column.Especially,the column efficiency of 17303-24255 plates/m was obtained for PAEs,and the resolution values for acenaphthene and acenaphthylene,and carbamazepine(CBZ)and carbamazepine-10,11-epoxide(CBZEP)were 1.7and 2.2,respectively.This successful application not only confirmed the great potential of the singlecrystal 3D COFs in HPLC separation of the organic molecules,but also facilitates the application of COFs in separation science.展开更多
Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct in...Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct ink writing(DIW)is one of the most popular 3D printing techniques owing to its ability to print multiple materials simultaneously and its high compatibility with printing inks.However,DIW presents significant challenges,particularly in the printing of high-performance polymers.The main challenges are as follows:1.The rigid structures and reaction kinetics of high-performance polymers make developing new inks difficult.2.The limited types of available high-performance polymers underscore the need for new DIW-suitable materials.3.Layer-by-layer stacking weakens interlayer bonding,affecting the mechanical properties of the printed product.4.The accuracy and speed of DIW printing are insufficient for large-scale manufacturing.After introducing the topic,the requirements for DIW printing inks are first reviewed,emphasizing the importance of thixotropic agents.Then,research progress regarding DIW printing of high-performance polymers is comprehensively reviewed according to the requirements of different polymer inks.Additionally,the applications of these materials across various fields are summarized.Finally,the challenges in DIW printing of high-performance polymers,along with corresponding solutions and future development prospects,are discussed in detail.展开更多
Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for t...Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.展开更多
This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,...This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,including PTB7-Th:FOIC,PTB7-Th:O6T-4F,PM6:Y6,and PM6:FM,were systematically tested.When coupled with electron transport layer(ETL)contacts,these blends exhibit exceptional charge separation and extraction,with PM6:Y6 achieving saturation photocurrents up to 16.8 mA cm^(-2) at 1.23 VRHE(oxygen evolution thermodynamic potential).For the first time,a tandem structure utilizing organic photoanodes has been computationally designed and fabricated and the implementation of a double PM6:Y6 photoanode/photovoltaic structure resulted in photogenerated currents exceeding 7mA cm^(-2) at 0 VRHE(hydrogen evolution thermodynamic potential)and anodic current onset potentials as low as-0.5 VRHE.The herein-presented organic-based approach paves the way for further exploration of different blend combinations to target specific oxidative reactions by selecting precise donor/acceptor candidates among the multiple existing ones.展开更多
1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers ...1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers are now able to incorporate intricate features such as delays,stochastic effects,fractional dynamics,variable-order systems,and uncertainty into epidemic models.These advancements not only improve predictive accuracy but also enable deeper insights into disease transmission,control,and policy-making.Tashfeen et al.展开更多
Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise inter...Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.展开更多
Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient int...Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite networks.Specifically,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization efficiency.Additionally,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two sub-problems.In terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational resources.To deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration.Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.展开更多
Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,w...Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,when coordinated with decoherence-free subspaces,permits additional resilience to the collective dephasing environment.However,the previous scheme[Phys.Rev.Lett.95130501(2005)]of adiabatic holonomic quantum computation in decoherence-free subspaces requires four-body interaction that is challenging in practical implementation.In this work,we put forward a scheme to realize universal adiabatic holonomic quantum computation in decoherence-free subspaces using only realistically available two-body interaction,thereby avoiding the difficulty of implementing four-body interaction.Furthermore,an arbitrary one-qubit gate in our scheme can be realized by a single-shot implementation,which eliminates the need to combine multiple gates for realizing such a gate.展开更多
Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is con...Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.展开更多
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
基金supported by the Key Research and Development Program of China(2021YFC3000704)Institute of Geophysics,China Earthquake Administration Grant DQJB23R18+1 种基金the USTC Research Funds of the Double First-Class Initiative(YD2080002012)NSFC Grant(U2239206)。
文摘Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent noise cross-correlation functions(NCFs)is a fundamental first step.In this study,we introduced a novel CPU-GPU heterogeneous computing framework designed to significantly enhance the efficiency of computing 9-component NCFs from seismic ambient noise data.This framework not only accelerated the computational process by leveraging the Compute Unified Device Architecture(CUDA)but also improved the signal-to-noise ratio(SNR)through innovative stacking techniques,such as time-frequency domain phaseweighted stacking(tf-PWS).We validated the program using multiple datasets,confirming its superior computation speed,improved reliability,and higher signal-to-noise ratios for NCFs.Our comprehensive study provides detailed insights into optimizing the computational processes for noise cross-correlation functions,thereby enhancing the precision and efficiency of ambient noise imaging.
基金supported by the National Natural Science Foundation of China[grant number 41675100],[grant number91337110]the Third Tibetan Plateau Scientific Experiment:Observations for Boundary Layer and Troposphere[GYHY201406001]+1 种基金the Key Research Program of Frontier Sciences,Chinese Academy of Science(CAS)(QYZDY-SSW-DQC018)the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund(the 2nd phase)
文摘High computational performance is extremely important for climate system models, especially in ultra-high-resolution model development. In this study, the computational performance of the Finite-volume Atmospheric Model of the IAP/LASG （FAMIL） was comprehensively evaluated on Tianhe-2, which was the world＇s top-ranked supercomputer from June 2013 to May 2016. The standardized Atmospheric Model Inter-comparison Project （AMIP） type of experiment was carried out that focused on the computational performance of each node as well as the simulation year per day （SYPD）, the running cost speedup, and the scalability of the FAMIL. The results indicated that （1） based on five indexes （CPU usage, percentage of CPU kernel mode that occupies CPU time and of message passing waiting time （CPU SW）, code vectorization （VEC）, average of Gflops （Gflops_ AVE）, and peak of Gflops （Gflops_PK））, FAMIL shows excellent computational performance on every Tianhe-2 computing node; （2） considering SYPD and the cost speedup of FAMIL systematically, the optimal Message Passing Interface （MPI） numbers of processors （MNPs） choice appears when FAMIL use 384 and 1536 MNPs for C96 （100 km） and C384 （25 km）, respectively; and （3） FAMIL shows positive scalability with increased threads to drive the model. Considering the fast network speed and acceleration card in the MIC architecture on Tianhe-2, there is still significant room to improve the computational performance of FAMIL.
基金The Scholarship Supported by Ministry of Education of China for Research Abroad(No.3037[2006])the Excellent Doctoral Dissertation Foundation of Southeast University (No.YBTJ-0512)the National Basic Research Program of China(973Program)(No.2009CB623203)
文摘In order to investigate the effects of two mineral admixtures （i. e., fly ash and ground slag）on initial defects existing in concrete microstructures, a high-resolution X-ray micro-CT（ micro-focus computer tomography）is employed to quantitatively analyze the initial defects in four series of highperformance concrete （HPC）specimens with additions of different mineral admixtures. The nigh-resolution 3D images of microstructures and filtered defects are reconstructed by micro- CT software. The size distribution and volume fractions of initial defects are analyzed based on 3D and 2D micro-CT images. The analysis results are verified by experimental results of watersuction tests. The results show that the additions of mineral admixtures in concrete as cementitious materials greatly change the geometrical properties of the microstructures and the spatial features of defects by physical-chemistry actions of these mineral admixtures. This is the major cause of the differences between the mechanical behaviors of HPC with and without mineral admixtures when the water-to-binder ratio and the size distribution of aggregates are constant.
文摘The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.
基金supported in part by China Key Research and Development Program(2016YFA0201800)the National Natural Science Foundation of China(91964104,61974081)。
文摘Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.
文摘The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.
基金Supported by Regional Science Foundation of China,National Natural Science Foundation(No.82160820)General Program of Guizhou Provincial Natural Science Foundation[QianKeHe Foundation-ZK(2023)General153].
文摘[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Methods]An Inertsil ODS-C 18 column(250 mm×4.6 mm,5μm)was used.The mobile phase consisted of acetonitrile-0.2% phosphoric acid solution(10:90).The flow rate was 1.0 mL/min.The detection wavelength was 274 nm.The column temperature was 25℃.The injection volume was 10μL.The content of three components,gallic acid,protocatechuic acid,and protocatechualdehyde,was determined in 13 batches of T.asiatica.[Results]Gallic acid showed good linearity within the range of 0.020-6.400μg/mL,protocatechuic acid within 0.201-6.432μg/mL,and protocatechualdehyde within 0.202-6.464μg/mL(r>0.9990).The average recovery rates ranged from 98.61%to 101.17%,with RSD s between 1.21%and 2.69%.[Conclusions]The quantitative determination method established in this study is simple and feasible,and can provide a basis for the quality evaluation of T.asiatica.
基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62371012in part by the Beijing Natural Science Foundation under Grant 4252001.
文摘As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.
基金the National Natural Science Foundation of China(No.22274021)Natural Science Foundation of Fujian Province(No.2022J01535)for financial support。
文摘Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morphology,good monodispersity and high specific surface area,were used as a stationary phase for high-performance liquid chromatography(HPLC).The single-crystal 3D COFs packed column not only exhibits high efficiency in separating hydrophobic molecules involving substituted benzenes,halogenated benzenes,halogenated nitrobenzenes,aromatic amines,aromatic hydrocarbons(PAHs)and phthalate esters(PAEs),but also achieves baseline separation of acenaphthene and acenaphthylene with similar physical and chemical properties as well as environmental pollutants,which cannot be quickly separated on commercial C18 column and a polycrystalline 3D COFs packed column.Especially,the column efficiency of 17303-24255 plates/m was obtained for PAEs,and the resolution values for acenaphthene and acenaphthylene,and carbamazepine(CBZ)and carbamazepine-10,11-epoxide(CBZEP)were 1.7and 2.2,respectively.This successful application not only confirmed the great potential of the singlecrystal 3D COFs in HPLC separation of the organic molecules,but also facilitates the application of COFs in separation science.
基金supported by National Key Research and Development Program of China(Grant No.2022YFB3809000)Major Science and Technology Project of Gansu Province(Grant No.23ZDGA011)+1 种基金National Natural Science Foundation of China(Grant No.22275199,52105224)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB04701022021).
文摘Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct ink writing(DIW)is one of the most popular 3D printing techniques owing to its ability to print multiple materials simultaneously and its high compatibility with printing inks.However,DIW presents significant challenges,particularly in the printing of high-performance polymers.The main challenges are as follows:1.The rigid structures and reaction kinetics of high-performance polymers make developing new inks difficult.2.The limited types of available high-performance polymers underscore the need for new DIW-suitable materials.3.Layer-by-layer stacking weakens interlayer bonding,affecting the mechanical properties of the printed product.4.The accuracy and speed of DIW printing are insufficient for large-scale manufacturing.After introducing the topic,the requirements for DIW printing inks are first reviewed,emphasizing the importance of thixotropic agents.Then,research progress regarding DIW printing of high-performance polymers is comprehensively reviewed according to the requirements of different polymer inks.Additionally,the applications of these materials across various fields are summarized.Finally,the challenges in DIW printing of high-performance polymers,along with corresponding solutions and future development prospects,are discussed in detail.
基金supported by National Natural Science Foundation of China No.62231012Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001Heilongjiang Province Postdoctoral General Foundation under Grant AUGA4110004923.
文摘Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.
基金partly funded by a BIST Ignite Programme grant from the Barcelona Institute of Science and Technology(Code:MOLOPEC)financial support from LICROX and SOREC2 EUFunded projects(Codes:951843 and 101084326)+7 种基金the BIST Program,and Severo Ochoa Programpartially funded by CEX2019-000910-S(MCIN/AEI/10.13039/501100011033 and PID2020-112650RBI00),Fundació Cellex,Fundació Mir-PuigGeneralitat de Catalunya through CERCAfunding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101081441financial support by the Agencia Estatal de Investigación(grant PRE2018-084881)the financial support by from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101081441support from the MCIN/AEI JdC-F Fellowship(FJC2020-043223-I)the Severo Ochoa Excellence Postdoctoral Fellowship(CEX2019-000910-S).
文摘This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,including PTB7-Th:FOIC,PTB7-Th:O6T-4F,PM6:Y6,and PM6:FM,were systematically tested.When coupled with electron transport layer(ETL)contacts,these blends exhibit exceptional charge separation and extraction,with PM6:Y6 achieving saturation photocurrents up to 16.8 mA cm^(-2) at 1.23 VRHE(oxygen evolution thermodynamic potential).For the first time,a tandem structure utilizing organic photoanodes has been computationally designed and fabricated and the implementation of a double PM6:Y6 photoanode/photovoltaic structure resulted in photogenerated currents exceeding 7mA cm^(-2) at 0 VRHE(hydrogen evolution thermodynamic potential)and anodic current onset potentials as low as-0.5 VRHE.The herein-presented organic-based approach paves the way for further exploration of different blend combinations to target specific oxidative reactions by selecting precise donor/acceptor candidates among the multiple existing ones.
文摘1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers are now able to incorporate intricate features such as delays,stochastic effects,fractional dynamics,variable-order systems,and uncertainty into epidemic models.These advancements not only improve predictive accuracy but also enable deeper insights into disease transmission,control,and policy-making.Tashfeen et al.
基金by National Natural Science Foundation of China(No.62306083)the Postdoctoral Science Foundation of Heilongjiang Province of China(LBH-Z22175)the Ministry of Industry and Information Technology。
文摘Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.
基金supported in part by Sub Project of National Key Research and Development plan in 2020 NO.2020YFC1511704Beijing Information Science and Technology University NO.2020KYNH212,NO.2021CGZH302+1 种基金Beijing Science and Technology Project(Grant No.Z211100004421009)in part by the National Natural Science Foundation of China(Grant No.62301058).
文摘Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite networks.Specifically,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization efficiency.Additionally,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two sub-problems.In terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational resources.To deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration.Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.
基金Project supported by the National Natural Science Foundation of China(Grant No.12305021)。
文摘Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,when coordinated with decoherence-free subspaces,permits additional resilience to the collective dephasing environment.However,the previous scheme[Phys.Rev.Lett.95130501(2005)]of adiabatic holonomic quantum computation in decoherence-free subspaces requires four-body interaction that is challenging in practical implementation.In this work,we put forward a scheme to realize universal adiabatic holonomic quantum computation in decoherence-free subspaces using only realistically available two-body interaction,thereby avoiding the difficulty of implementing four-body interaction.Furthermore,an arbitrary one-qubit gate in our scheme can be realized by a single-shot implementation,which eliminates the need to combine multiple gates for realizing such a gate.
基金supported in part by the Natural Science Foundation of China (62171110,U19B2028 and U20B2070)。
文摘Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.
