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
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
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
1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers ...1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers are now able to incorporate intricate features such as delays,stochastic effects,fractional dynamics,variable-order systems,and uncertainty into epidemic models.These advancements not only improve predictive accuracy but also enable deeper insights into disease transmission,control,and policy-making.Tashfeen et al.展开更多
Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise inter...Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.展开更多
Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,w...Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,when coordinated with decoherence-free subspaces,permits additional resilience to the collective dephasing environment.However,the previous scheme[Phys.Rev.Lett.95130501(2005)]of adiabatic holonomic quantum computation in decoherence-free subspaces requires four-body interaction that is challenging in practical implementation.In this work,we put forward a scheme to realize universal adiabatic holonomic quantum computation in decoherence-free subspaces using only realistically available two-body interaction,thereby avoiding the difficulty of implementing four-body interaction.Furthermore,an arbitrary one-qubit gate in our scheme can be realized by a single-shot implementation,which eliminates the need to combine multiple gates for realizing such a gate.展开更多
As the demand for cross-departmental data collaboration continues to grow,traditional encryption methods struggle to balance data privacy with computational efficiency.This paper proposes a cross-departmental privacy-...As the demand for cross-departmental data collaboration continues to grow,traditional encryption methods struggle to balance data privacy with computational efficiency.This paper proposes a cross-departmental privacy-preserving computation framework based on BFV homomorphic encryption,threshold decryption,and blockchain technology.The proposed scheme leverages homomorphic encryption to enable secure computations between sales,finance,and taxation departments,ensuring that sensitive data remains encrypted throughout the entire process.A threshold decryption mechanism is employed to prevent single-point data leakage,while blockchain and IPFS are integrated to ensure verifiability and tamper-proof storage of computation results.Experimental results demonstrate that with 5,000 sample data entries,the framework performs efficiently and is highly scalable in key stages such as sales encryption,cost calculation,and tax assessment,thereby validating its practical feasibility and security.展开更多
The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of ...The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.展开更多
Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a part...Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.展开更多
Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the pro...Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the production of thoracic implants with complex geometries,offering more versatile performance.In this study,we investigated a design based on a spring-like geometry manufactured by laser powder bed fusion(LPBF),as proposed in earlier research.The biomechanical behavior of this design was analyzed using various isolated semi-ring-rib models at different levels of the rib cage.This approach enabled a comprehensive examination,leading to the proposal of several implant configurations that were incorporated into a 3D rib cage model with chest wall defects,to simulate different chest wall reconstruction scenarios.The results revealed that the implant design was too rigid for the second rib level,which therefore was excluded from the proposed implant configurations.In chest wall reconstruction simulations,the maximum stresses observed in all prostheses did not exceed 38%of the implant material's yield stress in the most unfavorable case.Additionally,all the implants showed flexibility compatible with the physiological movements of the human thorax.展开更多
With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of...With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.展开更多
Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,co...Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,conventional approaches,which typically rely on data-driven neural networks to correct optical aberrations caused by manufacturing errors,are constrained by the lack of effective tolerance analysis methods for quantitatively evaluating manufacturing error boundaries.This limitation is crucial for further relaxing manufacturing constraints and providing practical guidance for fabrication.We propose a physics-informed design paradigm for manufacturing-robust imaging systems with computational optics,integrating a physics-informed tolerance analysis methodology for evaluating manufacturing error boundaries and a physics-informed neural network for image reconstruction.With this approach,we achieve a manufacturing-robust imaging system based on an off-axis three-mirror freeform all-aluminum design,delivering a modulation transfer function exceeding 0.34 at the Nyquist frequency(72 lp/mm)in simulation.Notably,this system requires a manufacturing precision of only 0.5λin root mean square(RMS),representing a remarkable 25-fold relaxation compared with the conventional requirement of 0.02λin RMS.Experimental validation further confirmed that the manufacturing-robust imaging system maintains excellent performance in diverse indoor and outdoor environments.Our proposed method paves the way for achieving high-quality imaging without the necessity of high manufacturing precision,enabling practical solutions that are more cost-effective and time-efficient.展开更多
The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases,which are the most common cause of morbidity and mortality worldwide,have gotten a lot of attention and been widely exp...The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases,which are the most common cause of morbidity and mortality worldwide,have gotten a lot of attention and been widely explored in recent decades.Along the way,techniques such as medical imaging,computing modeling,and artificial intelligence(AI)have always played significant roles in above studies.In this article,we illustrated the applications of AI in cardiac electrophysiological research and disease prediction.We summarized general principles of AI and then focused on the roles of AI in cardiac basic and clinical studies incorporating magnetic resonance imaging and computing modeling techniques.The main challenges and perspectives were also analyzed.展开更多
This review provides a comprehensive and systematic examination of Computational Fluid Dynamics(CFD)techniques and methodologies applied to the development of Vertical Axis Wind Turbines(VAWTs).Although VAWTs offer si...This review provides a comprehensive and systematic examination of Computational Fluid Dynamics(CFD)techniques and methodologies applied to the development of Vertical Axis Wind Turbines(VAWTs).Although VAWTs offer significant advantages for urban wind applications,such as omnidirectional wind capture and a compact,ground-accessible design,they face substantial aerodynamic challenges,including dynamic stall,blade-wake interactions,and continuously varying angles of attack throughout their rotation.The review critically evaluates how CFD has been leveraged to address these challenges,detailing the modelling frameworks,simulation setups,mesh strategies,turbulence models,and boundary condition treatments adopted in the literature.Special attention is given to the comparative performance of 2-D vs.3-D simulations,static and dynamic meshing techniques(sliding,overset,morphing),and the impact of near-wall resolution on prediction fidelity.Moreover,this review maps the evolution of CFD tools in capturing key performance indicators including power coefficient,torque,flow separation,and wake dynamics,while highlighting both achievements and current limitations.The synthesis of studies reveals best practices,identifies gaps in simulation fidelity and validation strategies,and outlines critical directions for future research,particularly in high-fidelity modelling and cost-effective simulation of urban-scale VAWTs.By synthesizing insights from over a hundred referenced studies,this review serves as a consolidated resource to advance VAWT design and performance optimization through CFD.These include studies on various aspects such as blade geometry refinement,turbulence modeling,wake interaction mitigation,tip-loss reduction,dynamic stall control,and other aerodynamic and structural improvements.This,in turn,supports their broader integration into sustainable energy systems.展开更多
For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered tr...For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered transition metal oxides(LTMOs),which leverage the synergistic properties of two distinct monophasic LTMOs,have garnered significant attention;however,their efficacy under fast-charging conditions remains underexplored.In this study,we developed a high-throughput computational screening framework to identify optimal dopants that maximize the electrochemical performance of LTMOs.Specifically,we evaluated the efficacy of 32 dopants based on P2/O3-type Mn/Fe-based Na_(x)Mn_(0.5)Fe_(0.5)O_(2)(NMFO)cathode material.Multiphase LTMOs satisfying criteria for thermodynamic and structural stability,minimized phase transitions,and enhanced Na^(+)diffusion were systematically screened for their suitability in fast-charging applications.The analysis identified two dopants,Ti and Zr,which met all predefined screening criteria.Furthermore,we ranked and scored dopants based on their alignment with these criteria,establishing a comprehensive dopant performance database.These findings provide a robust foundation for experimental exploration and offer detailed guidelines for tailoring dopants to optimize fast-charging SIBs.展开更多
Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first c...Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.展开更多
Background and Objective The natural history of type B aortic intramural hematoma(IMH)is highly heterogeneous.A computational fluid dynamics(CFD)model can be utilized to calculate a range of data pertinent to flow dyn...Background and Objective The natural history of type B aortic intramural hematoma(IMH)is highly heterogeneous.A computational fluid dynamics(CFD)model can be utilized to calculate a range of data pertinent to flow dynamics,including flow rates,blood velocity,pressure,and wall shear stress.This study presents a series of CFD simulations that model the dynamic progression from type B aortic IMH to false lumen formation.Methods A 66-year-old male patient presenting with chest and back pain underwent aortic computed tomography angiography(CTA),and a 3D patient-specific model was constructed.To evaluate the hemodynamic environment,the velocity,pressure,time-averaged wall shear stress(TAWSS),and oscillatory shear index(OSI)were calculated.Results A modest quantity of slow flow and recirculation flow was observed in the vicinity of the ulcer-like protrusion(ULP).During the formation of the false lumen,low-velocity blood flow entered the false lumen and resulted in vortex flow.ULPs were located in the region with higher TAWSS,and some high OSIs were found on the ULPs.Conclusion This preliminary study suggests a potential association between the TAWSS or OSI and progression from type B aortic IMH to aortic dissection.展开更多
Thework presents the electronic structure computations and optical spectroscopy studies of half-Heusler ScNiBi and YNiBi compounds.Our first-principles computations of the electronic structures were based on density f...Thework presents the electronic structure computations and optical spectroscopy studies of half-Heusler ScNiBi and YNiBi compounds.Our first-principles computations of the electronic structures were based on density functional theory accounting for spin-orbit coupling.These compounds are computed to be semiconductors.The calculated gap values make ScNiBi and YNiBi valid for thermoelectric and optoelectronic applications and as selective filters.In ScNiBi and YNiBi,an intense peak at the energy of−2 eV is composed of theNi 3d states in the conduction band,and the valence band mostly contains these states with some contributions from the Bi 6p and Sc 3d or Y 4d electronic states.These states participate in the formation of the indirect gap of 0.16 eV(ScNiBi)and 0.18 eV(YNiBi).Within the spectral ellipsometry technique in the interval 0.22–15μm of wavelength,the optical functions of materials are studied,and their dispersion features are revealed.A good matching of the experimental and modeled optical conductivity spectra allowed us to analyze orbital contributions.The abnormally low optical absorption observed in the low-energy region of the spectrum is referred to as the results of band calculations indicating a small density of electronic states near the Fermi energy of these complex materials.展开更多
Food allergy has become a global concern.Spleen tyrosine kinase(SYK)inhibitors are promising therapeutics against allergic disorders.In this study,a total of 300 natural phenolic compounds were firstly subjected to vi...Food allergy has become a global concern.Spleen tyrosine kinase(SYK)inhibitors are promising therapeutics against allergic disorders.In this study,a total of 300 natural phenolic compounds were firstly subjected to virtual screening.Sesamin and its metabolites,sesamin monocatechol(SC-1)and sesamin dicatechol(SC-2),were identified as potential SYK inhibitors,showing high binding affinity and inhibition efficiency towards SYK.Compared with R406(a traditional SYK inhibitor),sesamin,SC-1,and SC-2 had lower binding energy and inhibition constant(Ki)during molecular docking,exhibited higher bioavailability,safety,metabolism/clearance rate,and distribution uniformity ADMET predictions,and showed high stability in occupying the ATP-binding pocket of SYK during molecular dynamics simulations.In anti-dinitrophenyl-immunoglobulin E(Anti-DNP-Ig E)/dinitrophenyl-human serum albumin(DNP-HSA)-stimulated rat basophilic leukemia(RBL-2H3)cells,sesamin in the concentration range of 5-80μmol/L influenced significantly the degranulation and cytokine release,with 54.00%inhibition againstβ-hexosaminidase release and 58.45%decrease in histamine.In BALB/c mice,sesamin could ameliorate Anti-DNP-Ig E/DNP-HSA-induced passive cutaneous anaphylaxis(PCA)and ovalbumin(OVA)-induced active systemic anaphylaxis(ASA)reactions,reduce the levels of allergic mediators(immunoglobulins and pro-inflammatory cytokines),partially correct the imbalance of T helper(Th)cells differentiation in the spleen,and inhibit the phosphorylation of SYK and its downstream signaling proteins,including p38 mitogen-activated protein kinases(p38 MAPK),extracellular signalregulated kinases(ERK),and p65 nuclear factor-κB(p65 NF-κB)in the spleen.Thus,sesamin may be a safe and versatile SYK inhibitor that can alleviate Ig E-mediated food allergies.展开更多
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by the National 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.
文摘1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers are now able to incorporate intricate features such as delays,stochastic effects,fractional dynamics,variable-order systems,and uncertainty into epidemic models.These advancements not only improve predictive accuracy but also enable deeper insights into disease transmission,control,and policy-making.Tashfeen et al.
基金by National Natural Science Foundation of China(No.62306083)the Postdoctoral Science Foundation of Heilongjiang Province of China(LBH-Z22175)the Ministry of Industry and Information Technology。
文摘Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.
基金Project supported by the National Natural Science Foundation of China(Grant No.12305021)。
文摘Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,when coordinated with decoherence-free subspaces,permits additional resilience to the collective dephasing environment.However,the previous scheme[Phys.Rev.Lett.95130501(2005)]of adiabatic holonomic quantum computation in decoherence-free subspaces requires four-body interaction that is challenging in practical implementation.In this work,we put forward a scheme to realize universal adiabatic holonomic quantum computation in decoherence-free subspaces using only realistically available two-body interaction,thereby avoiding the difficulty of implementing four-body interaction.Furthermore,an arbitrary one-qubit gate in our scheme can be realized by a single-shot implementation,which eliminates the need to combine multiple gates for realizing such a gate.
文摘As the demand for cross-departmental data collaboration continues to grow,traditional encryption methods struggle to balance data privacy with computational efficiency.This paper proposes a cross-departmental privacy-preserving computation framework based on BFV homomorphic encryption,threshold decryption,and blockchain technology.The proposed scheme leverages homomorphic encryption to enable secure computations between sales,finance,and taxation departments,ensuring that sensitive data remains encrypted throughout the entire process.A threshold decryption mechanism is employed to prevent single-point data leakage,while blockchain and IPFS are integrated to ensure verifiability and tamper-proof storage of computation results.Experimental results demonstrate that with 5,000 sample data entries,the framework performs efficiently and is highly scalable in key stages such as sales encryption,cost calculation,and tax assessment,thereby validating its practical feasibility and security.
基金supported by the Australian Research Council(Grant No.IC190100020)the Australian Research Council Indus〓〓try Fellowship(Grant No.IE230100435)the National Natural Science Foundation of China(Grant Nos.12032014 and T2488101)。
文摘The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.
文摘Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.
文摘Thoracic reconstructions are essential surgical techniques used to replace severely damaged tissues and restore protection to internal organs.In recent years,advancements in additive manufacturing have enabled the production of thoracic implants with complex geometries,offering more versatile performance.In this study,we investigated a design based on a spring-like geometry manufactured by laser powder bed fusion(LPBF),as proposed in earlier research.The biomechanical behavior of this design was analyzed using various isolated semi-ring-rib models at different levels of the rib cage.This approach enabled a comprehensive examination,leading to the proposal of several implant configurations that were incorporated into a 3D rib cage model with chest wall defects,to simulate different chest wall reconstruction scenarios.The results revealed that the implant design was too rigid for the second rib level,which therefore was excluded from the proposed implant configurations.In chest wall reconstruction simulations,the maximum stresses observed in all prostheses did not exceed 38%of the implant material's yield stress in the most unfavorable case.Additionally,all the implants showed flexibility compatible with the physiological movements of the human thorax.
基金supported by Communication University of China(HG23035)partly supported by the Fundamental Research Funds for the Central Universities(CUC230A013).
文摘With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.
基金supported by the National Natural Science Foundation of China(Grant Nos.62192774,62105243,61925504,6201101335,62020106009,62192770,62192772,62105244,62305250,and 62322217)the Science and Technology Commission of Shanghai Municipality(Grant Nos.17JC1400800,20JC1414600,and 21JC1406100)+1 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100)the Fundamental Research Funds for the Central Universities.
文摘Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,conventional approaches,which typically rely on data-driven neural networks to correct optical aberrations caused by manufacturing errors,are constrained by the lack of effective tolerance analysis methods for quantitatively evaluating manufacturing error boundaries.This limitation is crucial for further relaxing manufacturing constraints and providing practical guidance for fabrication.We propose a physics-informed design paradigm for manufacturing-robust imaging systems with computational optics,integrating a physics-informed tolerance analysis methodology for evaluating manufacturing error boundaries and a physics-informed neural network for image reconstruction.With this approach,we achieve a manufacturing-robust imaging system based on an off-axis three-mirror freeform all-aluminum design,delivering a modulation transfer function exceeding 0.34 at the Nyquist frequency(72 lp/mm)in simulation.Notably,this system requires a manufacturing precision of only 0.5λin root mean square(RMS),representing a remarkable 25-fold relaxation compared with the conventional requirement of 0.02λin RMS.Experimental validation further confirmed that the manufacturing-robust imaging system maintains excellent performance in diverse indoor and outdoor environments.Our proposed method paves the way for achieving high-quality imaging without the necessity of high manufacturing precision,enabling practical solutions that are more cost-effective and time-efficient.
基金the Hainan Provincial Natural Science Foundation of China(No.820RC625)the National Natural Science Foundation of China(No.82060332)。
文摘The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases,which are the most common cause of morbidity and mortality worldwide,have gotten a lot of attention and been widely explored in recent decades.Along the way,techniques such as medical imaging,computing modeling,and artificial intelligence(AI)have always played significant roles in above studies.In this article,we illustrated the applications of AI in cardiac electrophysiological research and disease prediction.We summarized general principles of AI and then focused on the roles of AI in cardiac basic and clinical studies incorporating magnetic resonance imaging and computing modeling techniques.The main challenges and perspectives were also analyzed.
基金funded by Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme(FRGS/1/2024/TK10/UKM/02/7).
文摘This review provides a comprehensive and systematic examination of Computational Fluid Dynamics(CFD)techniques and methodologies applied to the development of Vertical Axis Wind Turbines(VAWTs).Although VAWTs offer significant advantages for urban wind applications,such as omnidirectional wind capture and a compact,ground-accessible design,they face substantial aerodynamic challenges,including dynamic stall,blade-wake interactions,and continuously varying angles of attack throughout their rotation.The review critically evaluates how CFD has been leveraged to address these challenges,detailing the modelling frameworks,simulation setups,mesh strategies,turbulence models,and boundary condition treatments adopted in the literature.Special attention is given to the comparative performance of 2-D vs.3-D simulations,static and dynamic meshing techniques(sliding,overset,morphing),and the impact of near-wall resolution on prediction fidelity.Moreover,this review maps the evolution of CFD tools in capturing key performance indicators including power coefficient,torque,flow separation,and wake dynamics,while highlighting both achievements and current limitations.The synthesis of studies reveals best practices,identifies gaps in simulation fidelity and validation strategies,and outlines critical directions for future research,particularly in high-fidelity modelling and cost-effective simulation of urban-scale VAWTs.By synthesizing insights from over a hundred referenced studies,this review serves as a consolidated resource to advance VAWT design and performance optimization through CFD.These include studies on various aspects such as blade geometry refinement,turbulence modeling,wake interaction mitigation,tip-loss reduction,dynamic stall control,and other aerodynamic and structural improvements.This,in turn,supports their broader integration into sustainable energy systems.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2022R1F1A1074339)。
文摘For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered transition metal oxides(LTMOs),which leverage the synergistic properties of two distinct monophasic LTMOs,have garnered significant attention;however,their efficacy under fast-charging conditions remains underexplored.In this study,we developed a high-throughput computational screening framework to identify optimal dopants that maximize the electrochemical performance of LTMOs.Specifically,we evaluated the efficacy of 32 dopants based on P2/O3-type Mn/Fe-based Na_(x)Mn_(0.5)Fe_(0.5)O_(2)(NMFO)cathode material.Multiphase LTMOs satisfying criteria for thermodynamic and structural stability,minimized phase transitions,and enhanced Na^(+)diffusion were systematically screened for their suitability in fast-charging applications.The analysis identified two dopants,Ti and Zr,which met all predefined screening criteria.Furthermore,we ranked and scored dopants based on their alignment with these criteria,establishing a comprehensive dopant performance database.These findings provide a robust foundation for experimental exploration and offer detailed guidelines for tailoring dopants to optimize fast-charging SIBs.
基金supported by the 2023 Youth Fund for Humanities and Social Sciences Research by the Ministry of Education of the People’s Republic of China(Grant No.23YJC740004).
文摘Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.
文摘Background and Objective The natural history of type B aortic intramural hematoma(IMH)is highly heterogeneous.A computational fluid dynamics(CFD)model can be utilized to calculate a range of data pertinent to flow dynamics,including flow rates,blood velocity,pressure,and wall shear stress.This study presents a series of CFD simulations that model the dynamic progression from type B aortic IMH to false lumen formation.Methods A 66-year-old male patient presenting with chest and back pain underwent aortic computed tomography angiography(CTA),and a 3D patient-specific model was constructed.To evaluate the hemodynamic environment,the velocity,pressure,time-averaged wall shear stress(TAWSS),and oscillatory shear index(OSI)were calculated.Results A modest quantity of slow flow and recirculation flow was observed in the vicinity of the ulcer-like protrusion(ULP).During the formation of the false lumen,low-velocity blood flow entered the false lumen and resulted in vortex flow.ULPs were located in the region with higher TAWSS,and some high OSIs were found on the ULPs.Conclusion This preliminary study suggests a potential association between the TAWSS or OSI and progression from type B aortic IMH to aortic dissection.
文摘Thework presents the electronic structure computations and optical spectroscopy studies of half-Heusler ScNiBi and YNiBi compounds.Our first-principles computations of the electronic structures were based on density functional theory accounting for spin-orbit coupling.These compounds are computed to be semiconductors.The calculated gap values make ScNiBi and YNiBi valid for thermoelectric and optoelectronic applications and as selective filters.In ScNiBi and YNiBi,an intense peak at the energy of−2 eV is composed of theNi 3d states in the conduction band,and the valence band mostly contains these states with some contributions from the Bi 6p and Sc 3d or Y 4d electronic states.These states participate in the formation of the indirect gap of 0.16 eV(ScNiBi)and 0.18 eV(YNiBi).Within the spectral ellipsometry technique in the interval 0.22–15μm of wavelength,the optical functions of materials are studied,and their dispersion features are revealed.A good matching of the experimental and modeled optical conductivity spectra allowed us to analyze orbital contributions.The abnormally low optical absorption observed in the low-energy region of the spectrum is referred to as the results of band calculations indicating a small density of electronic states near the Fermi energy of these complex materials.
基金Incubation Program of Youth Innovation in Shandong ProvinceKey Research and Development Program of Shandong Province(2021TZXD007)。
文摘Food allergy has become a global concern.Spleen tyrosine kinase(SYK)inhibitors are promising therapeutics against allergic disorders.In this study,a total of 300 natural phenolic compounds were firstly subjected to virtual screening.Sesamin and its metabolites,sesamin monocatechol(SC-1)and sesamin dicatechol(SC-2),were identified as potential SYK inhibitors,showing high binding affinity and inhibition efficiency towards SYK.Compared with R406(a traditional SYK inhibitor),sesamin,SC-1,and SC-2 had lower binding energy and inhibition constant(Ki)during molecular docking,exhibited higher bioavailability,safety,metabolism/clearance rate,and distribution uniformity ADMET predictions,and showed high stability in occupying the ATP-binding pocket of SYK during molecular dynamics simulations.In anti-dinitrophenyl-immunoglobulin E(Anti-DNP-Ig E)/dinitrophenyl-human serum albumin(DNP-HSA)-stimulated rat basophilic leukemia(RBL-2H3)cells,sesamin in the concentration range of 5-80μmol/L influenced significantly the degranulation and cytokine release,with 54.00%inhibition againstβ-hexosaminidase release and 58.45%decrease in histamine.In BALB/c mice,sesamin could ameliorate Anti-DNP-Ig E/DNP-HSA-induced passive cutaneous anaphylaxis(PCA)and ovalbumin(OVA)-induced active systemic anaphylaxis(ASA)reactions,reduce the levels of allergic mediators(immunoglobulins and pro-inflammatory cytokines),partially correct the imbalance of T helper(Th)cells differentiation in the spleen,and inhibit the phosphorylation of SYK and its downstream signaling proteins,including p38 mitogen-activated protein kinases(p38 MAPK),extracellular signalregulated kinases(ERK),and p65 nuclear factor-κB(p65 NF-κB)in the spleen.Thus,sesamin may be a safe and versatile SYK inhibitor that can alleviate Ig E-mediated food allergies.
