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
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c...The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.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
The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreser...The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.展开更多
The plant pathogenic fungus Sclerotinia sclerotiorum is the causative agent of Sclerotinia stem rot(SSR)disease in most dicotyledons.Among the various proteins involved in drug efflux or substance transport,ATP-bindin...The plant pathogenic fungus Sclerotinia sclerotiorum is the causative agent of Sclerotinia stem rot(SSR)disease in most dicotyledons.Among the various proteins involved in drug efflux or substance transport,ATP-binding cassette(ABC)transporters constitute a superfamily of membrane-bound proteins that may play a crucial role in the survival of S.sclerotiorum.However,the expression patterns and functions of ABC transporter genes in S.sclerotiorum remain largely uncharacterized.This study characterized a highly expressed S.sclerotiorum ABC transporter gene during inoculation on host plants,Ss BMR1.Silencing Ss BMR1 resulted in a significant reduction in hyphal growth,infection cushion development,sclerotia formation,and virulence.Moreover,host-induced gene silencing(HIGS)of Ss BMR1 significantly enhanced plant resistance.Transcriptome and metabolomics analyses suggested that Ss BMR1 is involved in antioxidant and toxin transport,thereby influencing fungal defense and cell rescue mechanisms.In comparison to the wild-type strain,Ss BMR1 gene-silenced transformants exhibited a diminished response to extracellar oxidative stress and a decreased exporting of antioxidant glutathione.Tolerance assays further demonstrated the crucial role of Ss BMR1 in conferring resistance to the plant antifungal substances,camalexin and brassinin,as well as certain fungicides.Furthermore,Ss BMR1 gene-silenced transformants showed enhanced repression on virulence when sprayed with camalexin and brassinin on the leaves.Thus,Ss BMR1 likely contributes to virulence by facilitating the export of antioxidant and providing resistance against antifungal agents.The findings of this study provide valuable insights that could contribute to the development of novel management techniques for SSR.展开更多
Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based met...Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based methods,tracking on a single CPU core,or parallelizing the computation across multiple cores via the message passing interface(MPI).Although these approaches work well for single-bunch tracking,scaling them to multiple bunches significantly increases the computational load,which often necessitates the use of a dedicated multi-CPU cluster.To address this challenge,alternative methods leveraging General-Purpose computing on Graphics Processing Units(GPGPU)have been proposed,enabling tracking studies on a standalone desktop personal computer(PC).However,frequent CPU-GPU interactions,including data transfers and synchronization operations during tracking,can introduce communication overheads,potentially reducing the overall effectiveness of GPU-based computations.In this study,we propose a novel approach that eliminates this overhead by performing the entire tracking simulation process exclusively on the GPU,thereby enabling the simultaneous processing of all bunches and their macro-particles.Specifically,we introduce MBTRACK2-CUDA,a Compute Unified Device Architecture(CUDA)ported version of MBTRACK2,which facilitates efficient tracking of single-and multi-bunch collective effects by leveraging the full GPU-resident computation.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
Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and...Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and resources,adding a substantial burden to the healthcare system and patients'families.In this context,chondroitinase ABC,a bacterial enzyme isolated from Proteus vulgaris that is modified to facilitate expression and secretion in mammals,has emerged as a promising therapeutic agent.It works by degrading chondroitin sulfate proteoglycans,cleaving the glycosaminoglycanchains of chondroitin sulfate proteoglycans into soluble disaccharides or tetrasaccharides.Chondroitin sulfate proteoglycans are potent axon growth inhibitors and principal constituents of the extracellular matrix surrounding glial and neuronal cells attached to glycosaminoglycan chains.Chondroitinase ABC has been shown to play an effective role in promoting recovery from acute and chronic spinal cord injury by improving axonal regeneration and sprouting,enhancing the plasticity of perineuronal nets,inhibiting neuronal apoptosis,and modulating immune responses in various animal models.In this review,we introduce the classification and pathological mechanisms of spinal cord injury and discuss the pathophysiological role of chondroitin sulfate proteoglycans in spinal cord injury.We also highlight research advancements in spinal cord injury treatment strategies,with a focus on chondroitinase ABC,and illustrate how improvements in chondroitinase ABC stability,enzymatic activity,and delivery methods have enhanced injured spinal cord repair.Furthermore,we emphasize that combination treatment with chondroitinase ABC further enhances therapeutic efficacy.This review aimed to provide a comprehensive understanding of the current trends and future directions of chondroitinase ABC-based spinal cord injury therapies,with an emphasis on how modern technologies are accelerating the optimization of chondroitinase ABC development.展开更多
Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and v...Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.展开更多
文摘目的探讨基于ABC-X模型的护理干预在轻度认知障碍(mild cognitive impairment,MCI)患者中的应用价值。方法选取100例MCI患者,采用随机数表法将其分为观察组和对照组,各50例。观察组使用基于ABC-X模型的护理干预,对照组使用常规护理干预。比较两组患者干预前和干预4周后的焦虑自评量表(self-rating anxiety scale,SAS)、抑郁自评量表(self-rating depression scale,SDS)、蒙特利尔认知评估(Montreal cognitive assessment,Mo CA)量表、36条简明健康状况调查表(36-item short form health survey,SF-36)评分。结果两组患者在护理干预前SAS、SDS得分、MoCA量表总分、SF-36平均分比较,差异均无统计学意义(均P>0.05)。在干预4周后,观察组患者SAS、SDS得分均显著低于对照组(均P<0.01);Mo CA量表总分、SF-36平均分均显著高于对照组(均P<0.01)。结论在MCI患者中应用基于ABC-X模型的护理干预,能有效缓解其负面情绪,改善认知功能,进而提升其生活质量,因此具备良好的推广应用价值。
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appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.
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.
文摘The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.
基金received financial support from the Natural Science Foundation of Chongqing,China(CSTB2023NSCQMSX0355)the Fundamental Research Funds for the Central Universities,China(SWU120075)the National Natural Science Foundation of China(32372077)。
文摘The plant pathogenic fungus Sclerotinia sclerotiorum is the causative agent of Sclerotinia stem rot(SSR)disease in most dicotyledons.Among the various proteins involved in drug efflux or substance transport,ATP-binding cassette(ABC)transporters constitute a superfamily of membrane-bound proteins that may play a crucial role in the survival of S.sclerotiorum.However,the expression patterns and functions of ABC transporter genes in S.sclerotiorum remain largely uncharacterized.This study characterized a highly expressed S.sclerotiorum ABC transporter gene during inoculation on host plants,Ss BMR1.Silencing Ss BMR1 resulted in a significant reduction in hyphal growth,infection cushion development,sclerotia formation,and virulence.Moreover,host-induced gene silencing(HIGS)of Ss BMR1 significantly enhanced plant resistance.Transcriptome and metabolomics analyses suggested that Ss BMR1 is involved in antioxidant and toxin transport,thereby influencing fungal defense and cell rescue mechanisms.In comparison to the wild-type strain,Ss BMR1 gene-silenced transformants exhibited a diminished response to extracellar oxidative stress and a decreased exporting of antioxidant glutathione.Tolerance assays further demonstrated the crucial role of Ss BMR1 in conferring resistance to the plant antifungal substances,camalexin and brassinin,as well as certain fungicides.Furthermore,Ss BMR1 gene-silenced transformants showed enhanced repression on virulence when sprayed with camalexin and brassinin on the leaves.Thus,Ss BMR1 likely contributes to virulence by facilitating the export of antioxidant and providing resistance against antifungal agents.The findings of this study provide valuable insights that could contribute to the development of novel management techniques for SSR.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT)(No.RS-2022-00143178)the Ministry of Education(MOE)(Nos.2022R1A6A3A13053896 and 2022R1F1A1074616),Republic of Korea.
文摘Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based methods,tracking on a single CPU core,or parallelizing the computation across multiple cores via the message passing interface(MPI).Although these approaches work well for single-bunch tracking,scaling them to multiple bunches significantly increases the computational load,which often necessitates the use of a dedicated multi-CPU cluster.To address this challenge,alternative methods leveraging General-Purpose computing on Graphics Processing Units(GPGPU)have been proposed,enabling tracking studies on a standalone desktop personal computer(PC).However,frequent CPU-GPU interactions,including data transfers and synchronization operations during tracking,can introduce communication overheads,potentially reducing the overall effectiveness of GPU-based computations.In this study,we propose a novel approach that eliminates this overhead by performing the entire tracking simulation process exclusively on the GPU,thereby enabling the simultaneous processing of all bunches and their macro-particles.Specifically,we introduce MBTRACK2-CUDA,a Compute Unified Device Architecture(CUDA)ported version of MBTRACK2,which facilitates efficient tracking of single-and multi-bunch collective effects by leveraging the full GPU-resident computation.
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funded by National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
基金supported by the National Natural Science Foundation of China,No.82002645China Postdoctoral Science Foundation,No.2022M722321Jiangsu Funding Program for Excellent Postdoctoral Talent,No.2022ZB552(all to YH)。
文摘Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and resources,adding a substantial burden to the healthcare system and patients'families.In this context,chondroitinase ABC,a bacterial enzyme isolated from Proteus vulgaris that is modified to facilitate expression and secretion in mammals,has emerged as a promising therapeutic agent.It works by degrading chondroitin sulfate proteoglycans,cleaving the glycosaminoglycanchains of chondroitin sulfate proteoglycans into soluble disaccharides or tetrasaccharides.Chondroitin sulfate proteoglycans are potent axon growth inhibitors and principal constituents of the extracellular matrix surrounding glial and neuronal cells attached to glycosaminoglycan chains.Chondroitinase ABC has been shown to play an effective role in promoting recovery from acute and chronic spinal cord injury by improving axonal regeneration and sprouting,enhancing the plasticity of perineuronal nets,inhibiting neuronal apoptosis,and modulating immune responses in various animal models.In this review,we introduce the classification and pathological mechanisms of spinal cord injury and discuss the pathophysiological role of chondroitin sulfate proteoglycans in spinal cord injury.We also highlight research advancements in spinal cord injury treatment strategies,with a focus on chondroitinase ABC,and illustrate how improvements in chondroitinase ABC stability,enzymatic activity,and delivery methods have enhanced injured spinal cord repair.Furthermore,we emphasize that combination treatment with chondroitinase ABC further enhances therapeutic efficacy.This review aimed to provide a comprehensive understanding of the current trends and future directions of chondroitinase ABC-based spinal cord injury therapies,with an emphasis on how modern technologies are accelerating the optimization of chondroitinase ABC development.
基金supported by the Major Project for the Integration of ScienceEducation and Industry (Grant No.2025ZDZX02)。
文摘Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.
