This comprehensive study investigates the formation and evolution of intermetallic compounds during the solidification process of magnesium alloys using advanced micro X-ray computed tomography.By analyzing both commo...This comprehensive study investigates the formation and evolution of intermetallic compounds during the solidification process of magnesium alloys using advanced micro X-ray computed tomography.By analyzing both common industrial Mg-Al-Zn alloys and a novel rare earth-containing Mg-Ni-Gd-Y alloy,we aim to characterize the nucleation,growth,and distribution of Al-Mn and eutectic intermetallics across various stages of solidification.The non destructive imaging technique employed in this research provides high-resolution,three-dimensional insights into the microstructural development,allowing for a detailed examination of the morphology,spatial arrangement,and interconnectivity of intermetallic phases.This approach overcomes limitations of traditional two-dimensional metallographic methods,offering a more comprehensive understanding of the complex three-dimensional structures formed during solidification.展开更多
The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an ...The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an X-ray computed tomography(X-ray CT)was used,i e,the tomography system of Zeiss Xradia 510 versa.The results indicate that X-CT can monitor the development process and distribution characteristics of the internal cracks of cement pastes under ESA with attack time.In addition,the C3A content in the cement significantly affects the damage mode of cement paste specimens during sulfate erosion.The damage of ordinary Portland cement(OPC)pastes subjected to sulfate attack with high C3A content are severe,while the damage of sulfate resistant Portland cement(SRPC)pastes is much smaller than that of OPC pastes.Furthermore,a quadratic function describes the correlation between the crack volume fraction and development depth for two cement pastes immermed in sulfate solution.展开更多
Understanding the mechanisms of gas transport and the resulting preferential pathways formation through bentonite-based barriers is essential for their performance evaluation.In this experimental study,gas migration w...Understanding the mechanisms of gas transport and the resulting preferential pathways formation through bentonite-based barriers is essential for their performance evaluation.In this experimental study,gas migration within a heterogenous mixture of MX80 bentonite pellets and powder with a ratio of 80/20 in dry mass was investigated.A novel X-ray transparent constant volume cell has been developed to assess the effect of gas pressure,material heterogeneities,and water vapor gas saturation on breakthrough pressure and gas pathways.The new cell allows to perform high-resolution X-ray computed micro-tomography(X-ray μCT)scans to track microstructural changes during different phases of saturation and gas injection.Experimental results showed that the gas breakthrough occurred when the pressure was raised to 3 MPa.This is slightly higher than the expected swelling pressure(2.9 MPa)of the bentonite sample.Each gas injection was followed by a long resaturation phase restoring material homogeneity at μCT resolution scale(16 mm).However,the elapsed time needed for gas to breakthrough at 3 MPa diminished at each subsequent injection test.X-ray μCT results also revealed the opening of the specimen/cell wall interface during gas passage.This opening expanded as the injection pressure increased.The gas flow along the interface was associated with the development of dilatant pathways inside the sample,although they did not reach the outlet surface.It was observed that the water vapor gas saturation had no effect on the breakthrough pressure.These findings enhance the understanding of the complex mechanisms underlying microstructural evolution and gas pathway development within the highly heterogeneous mixture.The experimental outcomes highlight the effectiveness of X-ray μCT to improve quality protocols for engineering design and safety assessments of engineered barriers.展开更多
Chaotic microcavities play a crucial role in several research areas,including the study of unidirectional microlasers,nonlinear optics,sensing,quantum chaos,and non-Hermitian physics.To date,most theoretical and exper...Chaotic microcavities play a crucial role in several research areas,including the study of unidirectional microlasers,nonlinear optics,sensing,quantum chaos,and non-Hermitian physics.To date,most theoretical and experimental explorations have focused on two-dimensional(2D)chaotic dielectric microcavities,but there have been minimal studies on three-dimensional(3D)ones because precise geometrical information of a 3D microcavity can be difficult to obtain.Here,we image 3D microcavities with submicron resolution using X-ray microcomputed tomography(μCT),enabling nondestructive imaging that preserves the sample for subsequent use.By analyzing the ray dynamics of a typical deformed microsphere,we demonstrate that a sufficient deformation along all three dimensions can lead to chaotic ray trajectories over extended time scales.Notably,using the X-rayμCT reconstruction results,the phase space chaotic ray dynamics of a deformed microsphere are accurately established.X-rayμCT could become a unique platform for the characterization of such deformed 3D microcavities by providing a precise means for determining the degree of deformation necessary for potential applications in ray chaos and quantum chaos.展开更多
Methane in-situ explosive fracturing technology produces shale debris particles within fracture channels,enabling a self-propping effect that enhances the fracture network conductivity and long-term stability.This stu...Methane in-situ explosive fracturing technology produces shale debris particles within fracture channels,enabling a self-propping effect that enhances the fracture network conductivity and long-term stability.This study employs X-ray computed tomography(CT)and digital volume correlation(DVC)to investigate the microstructural evolution and hydromechanical responses of shale self-propped fracture under varying confining pressures,highlighting the critical role of shale particles in maintaining fracture conductivity.Results indicate that the fracture aperture in the self-propped sample is significantly larger than in the unpropped sample throughout the loading process,with shale particles tending to crush rather than embedded into the matrix,thus maintaining flow pathways.As confining pressure increases,contact areas between fracture surfaces and particles expand,enhancing the system's stability and compressive resistance.Geometric analyses show flow paths becoming increasingly concentrated and branched under high stress.This resulted in a significant reduction in connectivity,restricting fracture permeability and amplifying the nonlinear gas flow behavior.This study introduces a permeability-strain recovery zone and a novel sensitivity parameter m,delineating stress sensitivity boundaries for permeability and normal strain,with m-value increasing with stress,revealing four characteristic regions.These findings offer theoretical support for optimizing fracturing techniques to enhance resource extraction efficiency.展开更多
Correctly tracking the evolution of spatial heterogeneity of local degree of saturation(Sr)in unsaturated soils is essential to explain the seepage phenomenon,which is crucial to assessing slope stability.Several meth...Correctly tracking the evolution of spatial heterogeneity of local degree of saturation(Sr)in unsaturated soils is essential to explain the seepage phenomenon,which is crucial to assessing slope stability.Several methods exist for quantifying the heterogeneity of local S_(r).However,a comprehensive comparison of these methods in terms of accuracy,relative advantages,and disadvantages is currently lacking.This paper presents a comparative analysis of local Sr obtained at multiple scales,ranging from the element scale to the slice,representative element volume(REV),pore,and voxel scales.The spatial heterogeneity of Sr in an unsaturated glass beads specimen at different matric suctions was visualised and quantified by multiscale X-ray micro-focus computed tomography image-based analysis methods.Local Sr obtained at different scales displayed a comparable trend along the sample depth,yet the REV-scale method showed a much scattered and discontinuous distribution.In contrast,the pore-scale method detected a distinct two-clustered,bimodal distribution of S_(r).The pore-scale method has the highest integrated resolution,as it has the highest spatial resolution(i.e.number of data points)and provides more information(i.e.number of extractable physical parameters).This method thus provides a more effective approach for tracking the spatial heterogeneity of S_(r).Based on this method,pore-scale water retention curves were determined,offering new quantitative means to characterise pore water heterogeneity and explainwater drainage processes such as hysteresis at the pore scale.展开更多
Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing o...Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.展开更多
The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing r...The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.展开更多
This study aims to investigate the responses of a perovskite-based direct-conversion dual-layer flat-panel detector(DL-FPD)numerically.To this end,the X-ray sensitivity,spatial resolution quantified by the modulation ...This study aims to investigate the responses of a perovskite-based direct-conversion dual-layer flat-panel detector(DL-FPD)numerically.To this end,the X-ray sensitivity,spatial resolution quantified by the modulation transfer function(MTF),and detective quantum efficiency(DQE)of the DL-FPD are evaluated numerically using a linear cascade model.In addition,both the single-crystal(SC)and polycrystalline(PC)structures of MAPbI_(3)are investigated,along with various other key parameters such as the material thickness,electric field strength,X-ray beam spectrum,and electronic readout noise.The results demonstrate that SC perovskite consistently exhibits better performance than PC perovskite owing to fewer material defects.Increasing the layer thickness may decrease the MTF,but can also enhance the sensitivity and DQE.Moreover,appropriately increasing the external electric field within the material can improve the sensitivity,MTF,and DQE.Finally,reducing the electronic readout noise can significantly enhance the DQE for low-dose imaging.This study demonstrates the potential of high-quality dual-energy X-ray imaging using direct-conversion perovskite DL-FPDs.展开更多
With the development of the semiconductor industry below the 7 nm scale,critical dimension small-angle X-ray scattering(CD-SAXS)has emerged as a powerful tool for quantitatively measuring nanoscale deviations.In this ...With the development of the semiconductor industry below the 7 nm scale,critical dimension small-angle X-ray scattering(CD-SAXS)has emerged as a powerful tool for quantitatively measuring nanoscale deviations.In this study,the effects of X-ray beam size and photon energy on the accuracy of critical dimension measurements were investigated.Critical dimensions measured using beams with different spot sizes showed different deviations from the expected values.Beam sizes that were either too large or too small did not improve confidence intervals.As the incident energy increased,the X-ray transmission rate increased,while the scattering cross section decreased,resulting in a gradual decrease in the signal-to-noise ratio of the diffraction peaks,which reduced the accuracy of the CD-SAXS measurements.An optimal accuracy was obtained at 12 keV with a smaller beam size.Using an effective trapezoid model,the results yielded an average pitch of 100.4±0.2 nm,width of 49.8±0.2 nm,height of 130.0±0.2 nm,and a sidewall angle below 1.1°±0.1°.These results provide crucial guidance for the future development of CD-SAXS laboratories and the construction of X-ray machines as well as robust support for research in related fields.展开更多
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca...Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.展开更多
The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality....The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality.Synthetic aperture x-ray ghost imaging(SAXGI)is invented to achieve megapixel XGI with limited measurements,which is expected to implement XGI simultaneously with large field of view and low radiation exposure.In this paper,we experimentally investigate the effect of measurements reduction on the spatial resolution and image quality of SAXGI with standard sample and biomedical specimen.The results with a resolution chart demonstrated that at 360 measurements,SAXGI successfully retrieved the sample image of 1960×1960 pixels with spatial resolution of 4μm.With measurement reduction,the spatial resolution deteriorates but the sparser structures are still discernable.Even with measurements reduced to 10,a spatial resolution of 10μm can still be achieved by SAXGI.A biomedical sample of a fish specimen is employed to evaluate the method and the fish image of 2000×1000 pixels with an SSIM of 0.962 is reconstructed by SAXGI with 770measurements,corresponding to an accumulative exposure reduction of more than 2 times.With the measurements reduced to 10 which corresponds to 1/160 of the accumulative radiation exposure for conventional radiology,bulky structure like the fish skeleton can still be definitely discerned and the SSIM for the reconstructed image still retained 0.9179.Results of this paper demonstrate that measurements reduction is practicable for the radiation exposure reduction of the sample,which implicates that SAXGI with limited measurements is an efficient solution for low dose radiology.展开更多
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
Rotational computed laminography(CL)has broad application potential in three-dimensional imaging of plate-like objects because it only requires X-rays to pass through the tested object in the thickness direction durin...Rotational computed laminography(CL)has broad application potential in three-dimensional imaging of plate-like objects because it only requires X-rays to pass through the tested object in the thickness direction during the imaging process.In this study,a rectangular cross-section field-of-view rotational CL(RC-CL)is proposed for circuit board imaging.Compared to other rotational CL systems,the field of view is the largest and most suitable for rectangular circuit boards.Meanwhile,as the imaging geometry of RC-CL is significantly different from that of cone-beam CT,the Feldkamp-Davis-Kress(FDK)reconstruction algorithm cannot be used directly.However,transferring the projection data to fit into the CBCT geometry using two-dimensional interpolation introduces interpolation errors.Therefore,an FDK-type analytical reconstruction algorithm applicable to RC-CL was developed.The effectiveness of the method was validated through numerical experiments,and the influence of the tilt angle on the reconstruction results was analyzed.Finally,the RC-CL technique was applied to real defect detection research on circuit boards.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
A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-d...A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-dimensional cusp boundary from a two-dimensional X-ray image because the detected X-ray signals will be integrated along the line of sight.In this work,a global magnetohydrodynamic code was used to simulate the X-ray images and photon count images,assuming an interplanetary magnetic field with a pure Bz component.The assumption of an elliptic cusp boundary at a given altitude was used to trace the equatorward and poleward boundaries of the cusp from a simulated X-ray image.The average discrepancy was less than 0.1 RE.To reduce the influence of instrument effects and cosmic X-ray backgrounds,image denoising was considered before applying the method above to SXI photon count images.The cusp boundaries were reasonably reconstructed from the noisy X-ray image.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
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and el...As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.展开更多
基金Project(2023YFB4606200)supported by the National Key Research and Development Program of ChinaProject(2023-SSRF-HZ-503114-2)supported by Shanghai Synchrotron Radiation Facility,Instrument BL16U2,China。
文摘This comprehensive study investigates the formation and evolution of intermetallic compounds during the solidification process of magnesium alloys using advanced micro X-ray computed tomography.By analyzing both common industrial Mg-Al-Zn alloys and a novel rare earth-containing Mg-Ni-Gd-Y alloy,we aim to characterize the nucleation,growth,and distribution of Al-Mn and eutectic intermetallics across various stages of solidification.The non destructive imaging technique employed in this research provides high-resolution,three-dimensional insights into the microstructural development,allowing for a detailed examination of the morphology,spatial arrangement,and interconnectivity of intermetallic phases.This approach overcomes limitations of traditional two-dimensional metallographic methods,offering a more comprehensive understanding of the complex three-dimensional structures formed during solidification.
基金Funded by Chinese National Natural Science Foundation of China(No.U2006224)。
文摘The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an X-ray computed tomography(X-ray CT)was used,i e,the tomography system of Zeiss Xradia 510 versa.The results indicate that X-CT can monitor the development process and distribution characteristics of the internal cracks of cement pastes under ESA with attack time.In addition,the C3A content in the cement significantly affects the damage mode of cement paste specimens during sulfate erosion.The damage of ordinary Portland cement(OPC)pastes subjected to sulfate attack with high C3A content are severe,while the damage of sulfate resistant Portland cement(SRPC)pastes is much smaller than that of OPC pastes.Furthermore,a quadratic function describes the correlation between the crack volume fraction and development depth for two cement pastes immermed in sulfate solution.
基金funding from the European Union's Horizon 2020 research and innovation program European Joint Program on RadioactiveWaste Management(EURAD)(2019e2024)WP-Gas‘Mechanistic understanding of gas transport in clay materials’under Grant agreement No.847593.
文摘Understanding the mechanisms of gas transport and the resulting preferential pathways formation through bentonite-based barriers is essential for their performance evaluation.In this experimental study,gas migration within a heterogenous mixture of MX80 bentonite pellets and powder with a ratio of 80/20 in dry mass was investigated.A novel X-ray transparent constant volume cell has been developed to assess the effect of gas pressure,material heterogeneities,and water vapor gas saturation on breakthrough pressure and gas pathways.The new cell allows to perform high-resolution X-ray computed micro-tomography(X-ray μCT)scans to track microstructural changes during different phases of saturation and gas injection.Experimental results showed that the gas breakthrough occurred when the pressure was raised to 3 MPa.This is slightly higher than the expected swelling pressure(2.9 MPa)of the bentonite sample.Each gas injection was followed by a long resaturation phase restoring material homogeneity at μCT resolution scale(16 mm).However,the elapsed time needed for gas to breakthrough at 3 MPa diminished at each subsequent injection test.X-ray μCT results also revealed the opening of the specimen/cell wall interface during gas passage.This opening expanded as the injection pressure increased.The gas flow along the interface was associated with the development of dilatant pathways inside the sample,although they did not reach the outlet surface.It was observed that the water vapor gas saturation had no effect on the breakthrough pressure.These findings enhance the understanding of the complex mechanisms underlying microstructural evolution and gas pathway development within the highly heterogeneous mixture.The experimental outcomes highlight the effectiveness of X-ray μCT to improve quality protocols for engineering design and safety assessments of engineered barriers.
基金support from the Okinawa Institute of Science and Technology Graduate University(OIST),the China Scholarship Council(CSC)(Grant No.202306680004)the Korea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Korean government(MSIT)(Grant Nos.RS-2024-00403036 and RS-202500521202)+2 种基金support from the Japan Society for the Promotion of Science(JSPS)KAKENHI through Grant-in-Aid for Scientific Research(C)(Grant No.23K04617)Grant-in-Aid for Early-Career Scientists(Grant No.22K14621)Grant-in-Aid for JSPS fellows(Grant No.25KJ2244)。
文摘Chaotic microcavities play a crucial role in several research areas,including the study of unidirectional microlasers,nonlinear optics,sensing,quantum chaos,and non-Hermitian physics.To date,most theoretical and experimental explorations have focused on two-dimensional(2D)chaotic dielectric microcavities,but there have been minimal studies on three-dimensional(3D)ones because precise geometrical information of a 3D microcavity can be difficult to obtain.Here,we image 3D microcavities with submicron resolution using X-ray microcomputed tomography(μCT),enabling nondestructive imaging that preserves the sample for subsequent use.By analyzing the ray dynamics of a typical deformed microsphere,we demonstrate that a sufficient deformation along all three dimensions can lead to chaotic ray trajectories over extended time scales.Notably,using the X-rayμCT reconstruction results,the phase space chaotic ray dynamics of a deformed microsphere are accurately established.X-rayμCT could become a unique platform for the characterization of such deformed 3D microcavities by providing a precise means for determining the degree of deformation necessary for potential applications in ray chaos and quantum chaos.
基金financially supported by the National Key Research and Development Program of China (No.2020YFA0711800)the National Science Fund for Distinguished Young Scholars (No.51925404)+2 种基金the Graduate Innovation Program of China University of Mining and Technology (No.2023WLKXJ149)the Fundamental Research Funds for the Central Universities (No.2023XSCX040)the Postgraduate Research Practice Innovation Program of Jiangsu Province (No.KYCX23_2864)。
文摘Methane in-situ explosive fracturing technology produces shale debris particles within fracture channels,enabling a self-propping effect that enhances the fracture network conductivity and long-term stability.This study employs X-ray computed tomography(CT)and digital volume correlation(DVC)to investigate the microstructural evolution and hydromechanical responses of shale self-propped fracture under varying confining pressures,highlighting the critical role of shale particles in maintaining fracture conductivity.Results indicate that the fracture aperture in the self-propped sample is significantly larger than in the unpropped sample throughout the loading process,with shale particles tending to crush rather than embedded into the matrix,thus maintaining flow pathways.As confining pressure increases,contact areas between fracture surfaces and particles expand,enhancing the system's stability and compressive resistance.Geometric analyses show flow paths becoming increasingly concentrated and branched under high stress.This resulted in a significant reduction in connectivity,restricting fracture permeability and amplifying the nonlinear gas flow behavior.This study introduces a permeability-strain recovery zone and a novel sensitivity parameter m,delineating stress sensitivity boundaries for permeability and normal strain,with m-value increasing with stress,revealing four characteristic regions.These findings offer theoretical support for optimizing fracturing techniques to enhance resource extraction efficiency.
基金support provided by the research funds from the Hong Kong Research Grants Council(Grant Nos.16206623,N_HKUST603/22,and C6006-20G).
文摘Correctly tracking the evolution of spatial heterogeneity of local degree of saturation(Sr)in unsaturated soils is essential to explain the seepage phenomenon,which is crucial to assessing slope stability.Several methods exist for quantifying the heterogeneity of local S_(r).However,a comprehensive comparison of these methods in terms of accuracy,relative advantages,and disadvantages is currently lacking.This paper presents a comparative analysis of local Sr obtained at multiple scales,ranging from the element scale to the slice,representative element volume(REV),pore,and voxel scales.The spatial heterogeneity of Sr in an unsaturated glass beads specimen at different matric suctions was visualised and quantified by multiscale X-ray micro-focus computed tomography image-based analysis methods.Local Sr obtained at different scales displayed a comparable trend along the sample depth,yet the REV-scale method showed a much scattered and discontinuous distribution.In contrast,the pore-scale method detected a distinct two-clustered,bimodal distribution of S_(r).The pore-scale method has the highest integrated resolution,as it has the highest spatial resolution(i.e.number of data points)and provides more information(i.e.number of extractable physical parameters).This method thus provides a more effective approach for tracking the spatial heterogeneity of S_(r).Based on this method,pore-scale water retention curves were determined,offering new quantitative means to characterise pore water heterogeneity and explainwater drainage processes such as hysteresis at the pore scale.
基金supported in part by the National Natural Science Foundation of China under Grant 52307134the Fundamental Research Funds for the Central Universities(xzy012025022)。
文摘Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.
文摘The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.
基金supported in part by the National Natural Science Foundation of China(Nos.12305349,12235006,12027812)Shenzhen Science and Technology Program(No.JSGGKQTD20210831174329010)Guangdong Basic and Applied Basic Research Foundation(No.2021TQ06Y108).
文摘This study aims to investigate the responses of a perovskite-based direct-conversion dual-layer flat-panel detector(DL-FPD)numerically.To this end,the X-ray sensitivity,spatial resolution quantified by the modulation transfer function(MTF),and detective quantum efficiency(DQE)of the DL-FPD are evaluated numerically using a linear cascade model.In addition,both the single-crystal(SC)and polycrystalline(PC)structures of MAPbI_(3)are investigated,along with various other key parameters such as the material thickness,electric field strength,X-ray beam spectrum,and electronic readout noise.The results demonstrate that SC perovskite consistently exhibits better performance than PC perovskite owing to fewer material defects.Increasing the layer thickness may decrease the MTF,but can also enhance the sensitivity and DQE.Moreover,appropriately increasing the external electric field within the material can improve the sensitivity,MTF,and DQE.Finally,reducing the electronic readout noise can significantly enhance the DQE for low-dose imaging.This study demonstrates the potential of high-quality dual-energy X-ray imaging using direct-conversion perovskite DL-FPDs.
基金supported by the National Natural Science Foundation of China(No.12175295)the National Key R&D Program of China(2021YFA1601000)the Shanghai Municipal Science and Technology Major Project。
文摘With the development of the semiconductor industry below the 7 nm scale,critical dimension small-angle X-ray scattering(CD-SAXS)has emerged as a powerful tool for quantitatively measuring nanoscale deviations.In this study,the effects of X-ray beam size and photon energy on the accuracy of critical dimension measurements were investigated.Critical dimensions measured using beams with different spot sizes showed different deviations from the expected values.Beam sizes that were either too large or too small did not improve confidence intervals.As the incident energy increased,the X-ray transmission rate increased,while the scattering cross section decreased,resulting in a gradual decrease in the signal-to-noise ratio of the diffraction peaks,which reduced the accuracy of the CD-SAXS measurements.An optimal accuracy was obtained at 12 keV with a smaller beam size.Using an effective trapezoid model,the results yielded an average pitch of 100.4±0.2 nm,width of 49.8±0.2 nm,height of 130.0±0.2 nm,and a sidewall angle below 1.1°±0.1°.These results provide crucial guidance for the future development of CD-SAXS laboratories and the construction of X-ray machines as well as robust support for research in related fields.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB3608300in part by the National Nature Science Foundation of China(NSFC)under Grants 62404050,U2341218,62574056,62204052。
文摘Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2022YFA1603601,2021YFF0601203,and 2021YFA1600703)。
文摘The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality.Synthetic aperture x-ray ghost imaging(SAXGI)is invented to achieve megapixel XGI with limited measurements,which is expected to implement XGI simultaneously with large field of view and low radiation exposure.In this paper,we experimentally investigate the effect of measurements reduction on the spatial resolution and image quality of SAXGI with standard sample and biomedical specimen.The results with a resolution chart demonstrated that at 360 measurements,SAXGI successfully retrieved the sample image of 1960×1960 pixels with spatial resolution of 4μm.With measurement reduction,the spatial resolution deteriorates but the sparser structures are still discernable.Even with measurements reduced to 10,a spatial resolution of 10μm can still be achieved by SAXGI.A biomedical sample of a fish specimen is employed to evaluate the method and the fish image of 2000×1000 pixels with an SSIM of 0.962 is reconstructed by SAXGI with 770measurements,corresponding to an accumulative exposure reduction of more than 2 times.With the measurements reduced to 10 which corresponds to 1/160 of the accumulative radiation exposure for conventional radiology,bulky structure like the fish skeleton can still be definitely discerned and the SSIM for the reconstructed image still retained 0.9179.Results of this paper demonstrate that measurements reduction is practicable for the radiation exposure reduction of the sample,which implicates that SAXGI with limited measurements is an efficient solution for low dose radiology.
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 Key Research and Development Program of China(No.2022YFF0607802)。
文摘Rotational computed laminography(CL)has broad application potential in three-dimensional imaging of plate-like objects because it only requires X-rays to pass through the tested object in the thickness direction during the imaging process.In this study,a rectangular cross-section field-of-view rotational CL(RC-CL)is proposed for circuit board imaging.Compared to other rotational CL systems,the field of view is the largest and most suitable for rectangular circuit boards.Meanwhile,as the imaging geometry of RC-CL is significantly different from that of cone-beam CT,the Feldkamp-Davis-Kress(FDK)reconstruction algorithm cannot be used directly.However,transferring the projection data to fit into the CBCT geometry using two-dimensional interpolation introduces interpolation errors.Therefore,an FDK-type analytical reconstruction algorithm applicable to RC-CL was developed.The effectiveness of the method was validated through numerical experiments,and the influence of the tilt angle on the reconstruction results was analyzed.Finally,the RC-CL technique was applied to real defect detection research on circuit boards.
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funded by the National Natural Science Foundation of China(NNSFC)under Grant Numbers 42322408,42188101,and 42441809Additional support was provided by the Climbing Program of the National Space Science Center(NSSC,Grant No.E4PD3005)as well as the Specialized Research Fund for State Key Laboratories of China.
文摘A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-dimensional cusp boundary from a two-dimensional X-ray image because the detected X-ray signals will be integrated along the line of sight.In this work,a global magnetohydrodynamic code was used to simulate the X-ray images and photon count images,assuming an interplanetary magnetic field with a pure Bz component.The assumption of an elliptic cusp boundary at a given altitude was used to trace the equatorward and poleward boundaries of the cusp from a simulated X-ray image.The average discrepancy was less than 0.1 RE.To reduce the influence of instrument effects and cosmic X-ray backgrounds,image denoising was considered before applying the method above to SXI photon count images.The cusp boundaries were reasonably reconstructed from the noisy X-ray image.
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supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051,ZR2025QB50)+6 种基金Guangdong Basic and Applied Basic Research Foundation(2025A1515011191)the Shanghai Sailing Program(23YF1402200,23YF1402400)funded by Basic Research Program of Jiangsu(BK20240424)Open Research Fund of State Key Laboratory of Crystal Materials(KF2406)Taishan Scholar Foundation of Shandong Province(tsqn202408006,tsqn202507058)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University。
文摘As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.
