With respect to oceanic fluid dynamics,certain models have appeared,e.g.,an extended time-dependent(3+1)-dimensional shallow water wave equation in an ocean or a river,which we investigate in this paper.Using symbolic...With respect to oceanic fluid dynamics,certain models have appeared,e.g.,an extended time-dependent(3+1)-dimensional shallow water wave equation in an ocean or a river,which we investigate in this paper.Using symbolic computation,we find out,on one hand,a set of bilinear auto-Backlund transformations,which could connect certain solutions of that equation with other solutions of that equation itself,and on the other hand,a set of similarity reductions,which could go from that equation to a known ordinary differential equation.The results in this paper depend on all the oceanic variable coefficients in that equation.展开更多
This paper explores the rich structure of peakon and pseudo-peakon solutions for a class of higher-order b-family equations,referred to as the J-th b-family(J-bF)equations.We propose several conjectures concerning the...This paper explores the rich structure of peakon and pseudo-peakon solutions for a class of higher-order b-family equations,referred to as the J-th b-family(J-bF)equations.We propose several conjectures concerning the weak solutions of these equations,including a b-independent pseudo-peakon solution,a b-independent peakon solution,and a b-dependent peakon solution.These conjectures are analytically verified for J≤14 and/or J≤9 using the symbolic computation system MAPLE,which includes a built-in definition of the higher-order derivatives of the sign function.The b-independent pseudo-peakon solution is a 3rd-order pseudo-peakon for general arbitrary constants,with higher-order pseudo-peakons derived under specific parameter constraints.Additionally,we identify both b-independent and b-dependent peakon solutions,highlighting their distinct properties and the nuanced relationship between the parameters b and J.The existence of these solutions underscores the rich dynamical structure of the J-bF equations and generalizes previous results for lower-order equations.Future research directions include higher-order generalizations,rigorous proofs of the conjectures,interactions between different types of peakons and pseudo-peakons,stability analysis,and potential physical applications.These advancements significantly contribute to the understanding of peakon systems and their broader implications in mathematics and physics.展开更多
We present a new perspective on the P vs NP problem by demonstrating that its answer is inherently observer-dependent in curved spacetime, revealing an oversight in the classical formulation of computational complexit...We present a new perspective on the P vs NP problem by demonstrating that its answer is inherently observer-dependent in curved spacetime, revealing an oversight in the classical formulation of computational complexity theory. By incorporating general relativistic effects into complexity theory through a gravitational correction factor, we prove that problems can transition between complexity classes depending on the observer’s reference frame and local gravitational environment. This insight emerges from recognizing that the definition of polynomial time implicitly assumes a universal time metric, an assumption that breaks down in curved spacetime due to gravitational time dilation. We demonstrate the existence of gravitational phase transitions in problem complexity, where an NP-complete problem in one reference frame becomes polynomially solvable in another frame experiencing extreme gravitational time dilation. Through rigorous mathematical formulation, we establish a gravitationally modified complexity theory that extends classical complexity classes to incorporate observer-dependent effects, leading to a complete framework for understanding how computational complexity transforms across different spacetime reference frames. This finding parallels other self-referential insights in mathematics and physics, such as Gödel’s incompleteness theorems and Einstein’s relativity, suggesting a deeper connection between computation, gravitation, and the nature of mathematical truth.展开更多
The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches l...The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches lack built-in privacy mechanisms,exposing sensitive data to risks,which motivates the development of Privacy-Preserving Machine Learning(PPML)methods.Despite significant advances in PPML,a comprehensive and focused exploration of Secure Multi-Party Computing(SMPC)within this context remains underdeveloped.This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML,offering a structured overviewof current techniques,challenges,and future directions.Using a semi-systematicmapping studymethodology,this paper surveys recent literature spanning SMPC protocols,PPML frameworks,implementation approaches,threat models,and performance metrics.Emphasis is placed on identifying trends,technical limitations,and comparative strengths of leading SMPC-based methods.Our findings reveal thatwhile SMPCoffers strong cryptographic guarantees for privacy,challenges such as computational overhead,communication costs,and scalability persist.The paper also discusses critical vulnerabilities,practical deployment issues,and variations in protocol efficiency across use cases.展开更多
Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of Things.But existing schem...Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of Things.But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content.Additionally,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption costs.In this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy.Dictionary attacks are thwarted by providing unauthorized users with invalid values.The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers.Additionally,users can verify the correctness of multiple outsourced decryption results simultaneously.Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.展开更多
Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing be...Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing benefits from its ability of processing data in parallel,the communication burden between different servers is incurred,thereby the computation process is detained.Recent researches have applied coding in distributed computing to reduce the communication burden,where repetitive computation is utilized to enable multicast opportunities so that the same coded information can be reused across different servers.To handle the computation tasks in practical heterogeneous systems,we propose a novel coding scheme to effectively mitigate the "straggling effect" in distributed computing.We assume that there are two types of servers in the system and the only difference between them is their computational capabilities,the servers with lower computational capabilities are called stragglers.Given any ratio of fast servers to slow servers and any gap of computational capabilities between them,we achieve approximately the same computation time for both fast and slow servers by assigning different amounts of computation tasks to them,thus reducing the overall computation time.Furthermore,we investigate the informationtheoretic lower bound of the inter-communication load and show that the lower bound is within a constant multiplicative gap to the upper bound achieved by our scheme.Various simulations also validate the effectiveness of the proposed scheme.展开更多
In this study,the flow characteristics around a group of three piers arranged in tandem were investigated both numerically and experimentally.The simulation utilised the volume of fluid(VOF)model in conjunction with t...In this study,the flow characteristics around a group of three piers arranged in tandem were investigated both numerically and experimentally.The simulation utilised the volume of fluid(VOF)model in conjunction with the k–ɛmethod(i.e.,for flow turbulence representations),implemented through the ANSYS FLUENT software,to model the free-surface flow.The simulation results were validated against laboratory measurements obtained using an acoustic Doppler velocimeter.The comparative analysis revealed discrepancies between the simulated and measured maximum velocities within the investigated flow field.However,the numerical results demonstrated a distinct vortex-induced flow pattern following the first pier and throughout the vicinity of the entire pier group,which aligned reasonably well with experimental data.In the heavily narrowed spaces between the piers,simulated velocity profiles were overestimated in the free-surface region and underestimated in the areas near the bed to the mid-stream when compared to measurements.These discrepancies diminished away from the regions with intense vortices,indicating that the employed model was capable of simulating relatively less disturbed flow turbulence.Furthermore,velocity results from both simulations and measurements were compared based on velocity distributions at three different depth ratios(0.15,0.40,and 0.62)to assess vortex characteristic around the piers.This comparison revealed consistent results between experimental and simulated data.This research contributes to a deeper understanding of flow dynamics around complex interactive pier systems,which is critical for designing stable and sustainable hydraulic structures.Furthermore,the insights gained from this study provide valuable information for engineers aiming to develop effective strategies for controlling scour and minimizing destructive vortex effects,thereby guiding the design and maintenance of sustainable infrastructure.展开更多
The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,com...The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine(EVM)-based smart contract platforms due to their distributed security environment limitations.Existing off-chain methods often result in a significant reduction in contract execution efficiency,thus a platform software development kit interface implementation method has become a feasible way to reduce overheads,but this method cannot verify operation correctness and may leak sensitive user data.To solve the above problems,we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations,especially matrix operations,which can guarantee operation correctness and user privacy while ensuring computational efficiency.In this scheme,a verifiable interaction process is designed to verify the computation process and results,and a matrix blinding technology is introduced to protect sensitive user data in the calculation process.The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.展开更多
With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heurist...With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains,such as machine learning,process control,and engineering design,showcasing their capability to address complex optimization problems.The Stochastic Fractal Search(SFS)algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials.Since its introduction by Hamid Salimi in 2015,SFS has garnered significant attention from researchers and has been applied to diverse optimization problems acrossmultiple disciplines.Its popularity can be attributed to several factors,including its simplicity,practical computational efficiency,ease of implementation,rapid convergence,high effectiveness,and ability to address singleandmulti-objective optimization problems,often outperforming other established algorithms.This review paper offers a comprehensive and detailed analysis of the SFS algorithm,covering its standard version,modifications,hybridization,and multi-objective implementations.The paper also examines several SFS applications across diverse domains,including power and energy systems,image processing,machine learning,wireless sensor networks,environmental modeling,economics and finance,and numerous engineering challenges.Furthermore,the paper critically evaluates the SFS algorithm’s performance,benchmarking its effectiveness against recently published meta-heuristic algorithms.In conclusion,the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.展开更多
In this paper,a variable-coefficient modified Kadomtsev-Petviashvili(vcm KP)system is investigated by modeling the propagation of electromagnetic waves in an isotropic charge-free infinite ferromagnetic thin film and ...In this paper,a variable-coefficient modified Kadomtsev-Petviashvili(vcm KP)system is investigated by modeling the propagation of electromagnetic waves in an isotropic charge-free infinite ferromagnetic thin film and nonlinear waves in plasma physics and electrodynamics.Painlevéanalysis is given out,and an auto-B?cklund transformation is constructed via the truncated Painlevéexpansion.Based on the auto-B?cklund transformation,analytic solutions are given,including the solitonic,periodic and rational solutions.Using the Lie symmetry approach,infinitesimal generators and symmetry groups are presented.With the Lagrangian,the vcm KP equation is shown to be nonlinearly self-adjoint.Moreover,conservation laws for the vcm KP equation are derived by means of a general conservation theorem.Besides,the physical characteristics of the influences of the coefficient parameters on the solutions are discussed graphically.Those solutions have comprehensive implications for the propagation of solitary waves in nonuniform backgrounds.展开更多
Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.H...Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.展开更多
Hydrogen peroxide(H_(2)O_(2))is an eco-friendly chemical with widespread industrial applications.However,the commercial anthraquinone process for H_(2)O_(2) production is energy-intensive and environmentally harmful,h...Hydrogen peroxide(H_(2)O_(2))is an eco-friendly chemical with widespread industrial applications.However,the commercial anthraquinone process for H_(2)O_(2) production is energy-intensive and environmentally harmful,highlighting the need for more sustainable alternatives.The electrochemical production of H_(2)O_(2) via the two-electron water oxidation reaction(2e^(−)WOR)presents a promising route but is often hindered by low efficiency and selectivity,due to the competition with the oxygen evolution reaction.In this study,we employed high-throughput computational screening and microkinetic modeling to design a series of efficient 2e^(−)WOR electrocatalysts from a library of 240 single-metal-embedded nitrogen heterocycle aromatic molecules(M-NHAMs).These catalysts,primarily comprising post-transition metals,such as Cu,Ni,Zn,and Pd,exhibit high activity for H_(2)O_(2) conversion with a limiting potential approaching the optimal value of 1.76 V.Additionally,they exhibit excellent selectivity,with Faradaic efficiencies exceeding 80%at overpotentials below 300 mV.Structure-performance analysis reveals that the d-band center and magnetic moment of the metal center correlated strongly with the oxygen adsorption free energy(ΔGO*),suggesting these parameters as key catalytic descriptors for efficient screening and performance optimization.This study contributes to the rational design of highly efficient and selective electrocatalysts for electrochemical production of H_(2)O_(2),offering a sustainable solution for green energy and industrial applications.展开更多
Unmanned Aerial Vehicles(UAVs)are increasingly recognized for their pivotal role in military and civilian applications,serving as essential technology for transmitting,evaluating,and gathering information.Unfortunatel...Unmanned Aerial Vehicles(UAVs)are increasingly recognized for their pivotal role in military and civilian applications,serving as essential technology for transmitting,evaluating,and gathering information.Unfortunately,this crucial process often occurs through unsecured wireless connections,exposing it to numerous cyber-physical attacks.Furthermore,UAVs’limited onboard computing resources make it challenging to perform complex cryptographic operations.The main aim of constructing a cryptographic scheme is to provide substantial security while reducing the computation and communication costs.This article introduces an efficient and secure cross-domain Authenticated Key Agreement(AKA)scheme that uses Hyperelliptic Curve Cryptography(HECC).The HECC,a modified version of ECC with a smaller key size of 80 bits,is well-suited for use in UAVs.In addition,the proposed scheme is employed in a cross-domain environment that integrates a Public Key Infrastructure(PKI)at the receiving end and a Certificateless Cryptosystem(CLC)at the sending end.Integrating CLC with PKI improves network security by restricting the exposure of encryption keys only to the message’s sender and subsequent receiver.A security study employing ROM and ROR models,together with a comparative performance analysis,shows that the proposed scheme outperforms comparable existing schemes in terms of both efficiency and security.展开更多
Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structur...Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.展开更多
Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on trai...Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on training methodologies,hardware implementations,and practical applications.We analyze four major training paradigms:ANN-to-SNN conversion,direct gradient-based training,spike-timing-dependent plasticity(STDP),and hybrid approaches.Our review encompasses major specialized hardware platforms:Intel Loihi,IBM TrueNorth,SpiNNaker,and BrainScaleS,analyzing their capabilities and constraints.We survey applications spanning computer vision,robotics,edge computing,and brain-computer interfaces,identifying where SNN provide compelling advantages.Our comparative analysis reveals SNN offer significant energy efficiency improvements(1000-10000×reduction)and natural temporal processing,while facing challenges in scalability and training complexity.We identify critical research directions including improved gradient estimation,standardized benchmarking protocols,and hardware-software co-design approaches.This survey provides researchers and practitioners with a comprehensive understanding of current SNN capabilities,limitations,and future prospects.展开更多
Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method...Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method for representing observational uncertainty and develops a two-step approximate Bayesian computation(ABC)framework using time-series data.Within the ABC framework,Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps,respectively.A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty,resulting in rapid convergence and accurate parameter estimation with minimal iterations.The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis.The results affirm the efficiency,robustness,and practical applicability of the proposed method.展开更多
Jumping robots are highly capable of overcoming obstacles.However,their explosive force,short duration,and variable trajectories pose significant challenges in achieving stable landings in complex environments.Traditi...Jumping robots are highly capable of overcoming obstacles.However,their explosive force,short duration,and variable trajectories pose significant challenges in achieving stable landings in complex environments.Traditional approaches rely heavily on sophisticated algorithms and electronic sensor feedback systems to ensure landing stability,which increases the implementation complexity.Inspired by the process by which humans complete jumps and achieve stable landings in complex environments,this study proposes a novel landing control method for jumping robots.By designing a mechanically coupled perception-control structure based on mechanical logic computing,the robot simulates the real-time transmission of neural signals triggered by the ground reaction force(GRF)in human reflex loops,thereby simplifying traditional control approaches.Through the collaboration of a flexible mechanical spine and a bistable foot module,the robot achieves an average height of 16.8 cm and a distance of 25.36 cm in consecutive stable jumps.It also demonstrates reliable landing performance on challenging terrain including slopes and cobblestone surfaces.This paper proposes a novel landing control method for jumping robots that simplifies traditional control approaches.The method enables stable landings on complex terrain through a mechanically coupled perception-control structure.This approach has potential applications in tasks requiring mobility over uneven terrain,such as search and rescue.展开更多
High-pressure water jet technology has emerged as a highly effective method for removing industrial-scale deposits from pipelines,offering a clean,efficient,and environmentally sustainable alternative to conventional ...High-pressure water jet technology has emerged as a highly effective method for removing industrial-scale deposits from pipelines,offering a clean,efficient,and environmentally sustainable alternative to conventional mechanical or chemical cleaning techniques.Among the many parameters influencing its performance,the geometry of the nozzle plays a decisive role in governing jet coherence,impact pressure distribution,and overall cleaning efficiency.In this study,a comprehensive numerical and experimental investigation is conducted to elucidate the influence of nozzle geometry on the behavior of high-pressure water jets.Using Computational Fluid Dynamics(CFD)simulations based on the Volume of Fluid(VOF)approach,the jet dynamics and impingement characteristics of three representative nozzle configurations—flat,conical,and tapered—are systematically analyzed.Particular attention is devoted to the tapered nozzle,where variations in the outlet diameter are explored to determine their effect on flow structure,jet stability,and impact performance.The numerical predictions are rigorously validated against experimental measurements,demonstrating excellent quantitative agreement and confirming the robustness of the computational model.Results show that the tapered nozzle,characterized by its elongated conical transition section,promotes a more stable jet core and superior efflux performance compared to flat and conical geometries.Furthermore,the exit diameter is found to exert a profound influence on jet development.At an inlet pressure of 130 MPa,increasing the tapered nozzle's outlet diameter from 0.8 mm to 1.2 mm enlarges the coherent core region,enhances jet stability,and improves hydraulic energy utilization.Under these conditions,the total impact pressure on the target surface increases by 33.14%,while the overall cleaning efficiency improves by 40.44%.展开更多
With increasing demand for data circulation,ensuring data security and privacy is paramount,specifically protecting privacy while maximizing utility.Blockchain,while decentralized and transparent,faces challenges in p...With increasing demand for data circulation,ensuring data security and privacy is paramount,specifically protecting privacy while maximizing utility.Blockchain,while decentralized and transparent,faces challenges in privacy protection and data verification,especially for sensitive data.Existing schemes often suffer from inefficiency and high overhead.We propose a privacy protection scheme using BGV homomorphic encryption and Pedersen Secret Sharing.This scheme enables secure computation on encrypted data,with Pedersen sharding and verifying the private key,ensuring data consistency and immutability.The blockchain framework manages key shards,verifies secrets,and aids security auditing.This approach allows for trusted computation without revealing the underlying data.Preliminary results demonstrate the scheme's feasibility in ensuring data privacy and security,making data available but not visible.This study provides an effective solution for data sharing and privacy protection in blockchain applications.展开更多
基金financially supported by the Scientific Research Foundation of North China University of Technology(Grant Nos.11005136024XN147-87 and 110051360024XN151-86).
文摘With respect to oceanic fluid dynamics,certain models have appeared,e.g.,an extended time-dependent(3+1)-dimensional shallow water wave equation in an ocean or a river,which we investigate in this paper.Using symbolic computation,we find out,on one hand,a set of bilinear auto-Backlund transformations,which could connect certain solutions of that equation with other solutions of that equation itself,and on the other hand,a set of similarity reductions,which could go from that equation to a known ordinary differential equation.The results in this paper depend on all the oceanic variable coefficients in that equation.
基金supported by the National Natural Science Foundations of China(Grant Nos.12235007,12271324,and 11975131)。
文摘This paper explores the rich structure of peakon and pseudo-peakon solutions for a class of higher-order b-family equations,referred to as the J-th b-family(J-bF)equations.We propose several conjectures concerning the weak solutions of these equations,including a b-independent pseudo-peakon solution,a b-independent peakon solution,and a b-dependent peakon solution.These conjectures are analytically verified for J≤14 and/or J≤9 using the symbolic computation system MAPLE,which includes a built-in definition of the higher-order derivatives of the sign function.The b-independent pseudo-peakon solution is a 3rd-order pseudo-peakon for general arbitrary constants,with higher-order pseudo-peakons derived under specific parameter constraints.Additionally,we identify both b-independent and b-dependent peakon solutions,highlighting their distinct properties and the nuanced relationship between the parameters b and J.The existence of these solutions underscores the rich dynamical structure of the J-bF equations and generalizes previous results for lower-order equations.Future research directions include higher-order generalizations,rigorous proofs of the conjectures,interactions between different types of peakons and pseudo-peakons,stability analysis,and potential physical applications.These advancements significantly contribute to the understanding of peakon systems and their broader implications in mathematics and physics.
文摘We present a new perspective on the P vs NP problem by demonstrating that its answer is inherently observer-dependent in curved spacetime, revealing an oversight in the classical formulation of computational complexity theory. By incorporating general relativistic effects into complexity theory through a gravitational correction factor, we prove that problems can transition between complexity classes depending on the observer’s reference frame and local gravitational environment. This insight emerges from recognizing that the definition of polynomial time implicitly assumes a universal time metric, an assumption that breaks down in curved spacetime due to gravitational time dilation. We demonstrate the existence of gravitational phase transitions in problem complexity, where an NP-complete problem in one reference frame becomes polynomially solvable in another frame experiencing extreme gravitational time dilation. Through rigorous mathematical formulation, we establish a gravitationally modified complexity theory that extends classical complexity classes to incorporate observer-dependent effects, leading to a complete framework for understanding how computational complexity transforms across different spacetime reference frames. This finding parallels other self-referential insights in mathematics and physics, such as Gödel’s incompleteness theorems and Einstein’s relativity, suggesting a deeper connection between computation, gravitation, and the nature of mathematical truth.
文摘The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches lack built-in privacy mechanisms,exposing sensitive data to risks,which motivates the development of Privacy-Preserving Machine Learning(PPML)methods.Despite significant advances in PPML,a comprehensive and focused exploration of Secure Multi-Party Computing(SMPC)within this context remains underdeveloped.This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML,offering a structured overviewof current techniques,challenges,and future directions.Using a semi-systematicmapping studymethodology,this paper surveys recent literature spanning SMPC protocols,PPML frameworks,implementation approaches,threat models,and performance metrics.Emphasis is placed on identifying trends,technical limitations,and comparative strengths of leading SMPC-based methods.Our findings reveal thatwhile SMPCoffers strong cryptographic guarantees for privacy,challenges such as computational overhead,communication costs,and scalability persist.The paper also discusses critical vulnerabilities,practical deployment issues,and variations in protocol efficiency across use cases.
基金supported in part by the National Key R&D Program of China(Grant No.2019YFB2101700)the National Natural Science Foundation of China(Grant No.62272102,No.62172320,No.U21A20466)+4 种基金the Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province(Grant No.ZCL21015)the Qinghai Key R&D and Transformation Projects(Grant No.2021-GX-112)the Natural Science Foundation of Nanjing University of Posts and Telecommunications(Grant No.NY222141)the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant(No.22KJB520029)Henan Key Laboratory of Network Cryptography Technology(No.LNCT2022-A10)。
文摘Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of Things.But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content.Additionally,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption costs.In this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy.Dictionary attacks are thwarted by providing unauthorized users with invalid values.The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers.Additionally,users can verify the correctness of multiple outsourced decryption results simultaneously.Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.
基金supported by NSF China(No.T2421002,62061146002,62020106005)。
文摘Distributed computing is an important topic in the field of wireless communications and networking,and its high efficiency in handling large amounts of data is particularly noteworthy.Although distributed computing benefits from its ability of processing data in parallel,the communication burden between different servers is incurred,thereby the computation process is detained.Recent researches have applied coding in distributed computing to reduce the communication burden,where repetitive computation is utilized to enable multicast opportunities so that the same coded information can be reused across different servers.To handle the computation tasks in practical heterogeneous systems,we propose a novel coding scheme to effectively mitigate the "straggling effect" in distributed computing.We assume that there are two types of servers in the system and the only difference between them is their computational capabilities,the servers with lower computational capabilities are called stragglers.Given any ratio of fast servers to slow servers and any gap of computational capabilities between them,we achieve approximately the same computation time for both fast and slow servers by assigning different amounts of computation tasks to them,thus reducing the overall computation time.Furthermore,we investigate the informationtheoretic lower bound of the inter-communication load and show that the lower bound is within a constant multiplicative gap to the upper bound achieved by our scheme.Various simulations also validate the effectiveness of the proposed scheme.
文摘In this study,the flow characteristics around a group of three piers arranged in tandem were investigated both numerically and experimentally.The simulation utilised the volume of fluid(VOF)model in conjunction with the k–ɛmethod(i.e.,for flow turbulence representations),implemented through the ANSYS FLUENT software,to model the free-surface flow.The simulation results were validated against laboratory measurements obtained using an acoustic Doppler velocimeter.The comparative analysis revealed discrepancies between the simulated and measured maximum velocities within the investigated flow field.However,the numerical results demonstrated a distinct vortex-induced flow pattern following the first pier and throughout the vicinity of the entire pier group,which aligned reasonably well with experimental data.In the heavily narrowed spaces between the piers,simulated velocity profiles were overestimated in the free-surface region and underestimated in the areas near the bed to the mid-stream when compared to measurements.These discrepancies diminished away from the regions with intense vortices,indicating that the employed model was capable of simulating relatively less disturbed flow turbulence.Furthermore,velocity results from both simulations and measurements were compared based on velocity distributions at three different depth ratios(0.15,0.40,and 0.62)to assess vortex characteristic around the piers.This comparison revealed consistent results between experimental and simulated data.This research contributes to a deeper understanding of flow dynamics around complex interactive pier systems,which is critical for designing stable and sustainable hydraulic structures.Furthermore,the insights gained from this study provide valuable information for engineers aiming to develop effective strategies for controlling scour and minimizing destructive vortex effects,thereby guiding the design and maintenance of sustainable infrastructure.
基金supported in part by the National Natural Science Foundation of China under Grant 62272007,U23B2002in part by the Excellent Young Talents Project of the Beijing Municipal University Teacher Team Construction Support Plan under Grant BPHR202203031+1 种基金in part by the Yunnan Key Laboratory of Blockchain Application Technology under Grant 2021105AG070005(YNB202102)in part by the Open Topics of Key Laboratory of Blockchain Technology and Data Security,The Ministry of Industry and Information Technology of the People’s Republic of China under Grant 20243222。
文摘The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine(EVM)-based smart contract platforms due to their distributed security environment limitations.Existing off-chain methods often result in a significant reduction in contract execution efficiency,thus a platform software development kit interface implementation method has become a feasible way to reduce overheads,but this method cannot verify operation correctness and may leak sensitive user data.To solve the above problems,we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations,especially matrix operations,which can guarantee operation correctness and user privacy while ensuring computational efficiency.In this scheme,a verifiable interaction process is designed to verify the computation process and results,and a matrix blinding technology is introduced to protect sensitive user data in the calculation process.The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.
基金supported by Prince Sattam bin Abdulaziz University for funding this research work through the project number(2024/RV/06).
文摘With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains,such as machine learning,process control,and engineering design,showcasing their capability to address complex optimization problems.The Stochastic Fractal Search(SFS)algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials.Since its introduction by Hamid Salimi in 2015,SFS has garnered significant attention from researchers and has been applied to diverse optimization problems acrossmultiple disciplines.Its popularity can be attributed to several factors,including its simplicity,practical computational efficiency,ease of implementation,rapid convergence,high effectiveness,and ability to address singleandmulti-objective optimization problems,often outperforming other established algorithms.This review paper offers a comprehensive and detailed analysis of the SFS algorithm,covering its standard version,modifications,hybridization,and multi-objective implementations.The paper also examines several SFS applications across diverse domains,including power and energy systems,image processing,machine learning,wireless sensor networks,environmental modeling,economics and finance,and numerous engineering challenges.Furthermore,the paper critically evaluates the SFS algorithm’s performance,benchmarking its effectiveness against recently published meta-heuristic algorithms.In conclusion,the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.
文摘In this paper,a variable-coefficient modified Kadomtsev-Petviashvili(vcm KP)system is investigated by modeling the propagation of electromagnetic waves in an isotropic charge-free infinite ferromagnetic thin film and nonlinear waves in plasma physics and electrodynamics.Painlevéanalysis is given out,and an auto-B?cklund transformation is constructed via the truncated Painlevéexpansion.Based on the auto-B?cklund transformation,analytic solutions are given,including the solitonic,periodic and rational solutions.Using the Lie symmetry approach,infinitesimal generators and symmetry groups are presented.With the Lagrangian,the vcm KP equation is shown to be nonlinearly self-adjoint.Moreover,conservation laws for the vcm KP equation are derived by means of a general conservation theorem.Besides,the physical characteristics of the influences of the coefficient parameters on the solutions are discussed graphically.Those solutions have comprehensive implications for the propagation of solitary waves in nonuniform backgrounds.
基金supported by the U.S.Army Research Laboratory through their award#W911NF-22-2-0040the Ministry of Education,Youth and Sports of the Czech Republic through the e-INFRA CZ(ID:90254).
文摘Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.
基金supported by the National Natural Science Foundation of China (22209061 and 22462006)Start-up Fund for Senior Talents in Jiangsu University (21JDG060)the Fundamental Research Funds for the Central Universities (20720220009)
文摘Hydrogen peroxide(H_(2)O_(2))is an eco-friendly chemical with widespread industrial applications.However,the commercial anthraquinone process for H_(2)O_(2) production is energy-intensive and environmentally harmful,highlighting the need for more sustainable alternatives.The electrochemical production of H_(2)O_(2) via the two-electron water oxidation reaction(2e^(−)WOR)presents a promising route but is often hindered by low efficiency and selectivity,due to the competition with the oxygen evolution reaction.In this study,we employed high-throughput computational screening and microkinetic modeling to design a series of efficient 2e^(−)WOR electrocatalysts from a library of 240 single-metal-embedded nitrogen heterocycle aromatic molecules(M-NHAMs).These catalysts,primarily comprising post-transition metals,such as Cu,Ni,Zn,and Pd,exhibit high activity for H_(2)O_(2) conversion with a limiting potential approaching the optimal value of 1.76 V.Additionally,they exhibit excellent selectivity,with Faradaic efficiencies exceeding 80%at overpotentials below 300 mV.Structure-performance analysis reveals that the d-band center and magnetic moment of the metal center correlated strongly with the oxygen adsorption free energy(ΔGO*),suggesting these parameters as key catalytic descriptors for efficient screening and performance optimization.This study contributes to the rational design of highly efficient and selective electrocatalysts for electrochemical production of H_(2)O_(2),offering a sustainable solution for green energy and industrial applications.
文摘Unmanned Aerial Vehicles(UAVs)are increasingly recognized for their pivotal role in military and civilian applications,serving as essential technology for transmitting,evaluating,and gathering information.Unfortunately,this crucial process often occurs through unsecured wireless connections,exposing it to numerous cyber-physical attacks.Furthermore,UAVs’limited onboard computing resources make it challenging to perform complex cryptographic operations.The main aim of constructing a cryptographic scheme is to provide substantial security while reducing the computation and communication costs.This article introduces an efficient and secure cross-domain Authenticated Key Agreement(AKA)scheme that uses Hyperelliptic Curve Cryptography(HECC).The HECC,a modified version of ECC with a smaller key size of 80 bits,is well-suited for use in UAVs.In addition,the proposed scheme is employed in a cross-domain environment that integrates a Public Key Infrastructure(PKI)at the receiving end and a Certificateless Cryptosystem(CLC)at the sending end.Integrating CLC with PKI improves network security by restricting the exposure of encryption keys only to the message’s sender and subsequent receiver.A security study employing ROM and ROR models,together with a comparative performance analysis,shows that the proposed scheme outperforms comparable existing schemes in terms of both efficiency and security.
基金supported by the National Natural Science Foundation of China (Grant Nos.12102021,12372105,12172026,and 12225201)the Fundamental Research Funds for the Central Universities and the Academic Excellence Foundation of BUAA for PhD Students.
文摘Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.
文摘Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on training methodologies,hardware implementations,and practical applications.We analyze four major training paradigms:ANN-to-SNN conversion,direct gradient-based training,spike-timing-dependent plasticity(STDP),and hybrid approaches.Our review encompasses major specialized hardware platforms:Intel Loihi,IBM TrueNorth,SpiNNaker,and BrainScaleS,analyzing their capabilities and constraints.We survey applications spanning computer vision,robotics,edge computing,and brain-computer interfaces,identifying where SNN provide compelling advantages.Our comparative analysis reveals SNN offer significant energy efficiency improvements(1000-10000×reduction)and natural temporal processing,while facing challenges in scalability and training complexity.We identify critical research directions including improved gradient estimation,standardized benchmarking protocols,and hardware-software co-design approaches.This survey provides researchers and practitioners with a comprehensive understanding of current SNN capabilities,limitations,and future prospects.
基金supported by the National Natural Science Foundation of China(Grant No.U23B20105).
文摘Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method for representing observational uncertainty and develops a two-step approximate Bayesian computation(ABC)framework using time-series data.Within the ABC framework,Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps,respectively.A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty,resulting in rapid convergence and accurate parameter estimation with minimal iterations.The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis.The results affirm the efficiency,robustness,and practical applicability of the proposed method.
基金Supported by New Chongqing Innovative Young Talent Project(Grant No.2024NSCQ-qncxX0468)Natural Science Foundation of Chongqing(Grant No.CSTB2022NSCQ-MSX1283)Dreams Foundation of Jianghuai Advanced Technology Center(Grant No.2023-ZM01Z007).
文摘Jumping robots are highly capable of overcoming obstacles.However,their explosive force,short duration,and variable trajectories pose significant challenges in achieving stable landings in complex environments.Traditional approaches rely heavily on sophisticated algorithms and electronic sensor feedback systems to ensure landing stability,which increases the implementation complexity.Inspired by the process by which humans complete jumps and achieve stable landings in complex environments,this study proposes a novel landing control method for jumping robots.By designing a mechanically coupled perception-control structure based on mechanical logic computing,the robot simulates the real-time transmission of neural signals triggered by the ground reaction force(GRF)in human reflex loops,thereby simplifying traditional control approaches.Through the collaboration of a flexible mechanical spine and a bistable foot module,the robot achieves an average height of 16.8 cm and a distance of 25.36 cm in consecutive stable jumps.It also demonstrates reliable landing performance on challenging terrain including slopes and cobblestone surfaces.This paper proposes a novel landing control method for jumping robots that simplifies traditional control approaches.The method enables stable landings on complex terrain through a mechanically coupled perception-control structure.This approach has potential applications in tasks requiring mobility over uneven terrain,such as search and rescue.
基金the Natural Science Foundation of Shandong Province,China(No.ZR2021QE157).
文摘High-pressure water jet technology has emerged as a highly effective method for removing industrial-scale deposits from pipelines,offering a clean,efficient,and environmentally sustainable alternative to conventional mechanical or chemical cleaning techniques.Among the many parameters influencing its performance,the geometry of the nozzle plays a decisive role in governing jet coherence,impact pressure distribution,and overall cleaning efficiency.In this study,a comprehensive numerical and experimental investigation is conducted to elucidate the influence of nozzle geometry on the behavior of high-pressure water jets.Using Computational Fluid Dynamics(CFD)simulations based on the Volume of Fluid(VOF)approach,the jet dynamics and impingement characteristics of three representative nozzle configurations—flat,conical,and tapered—are systematically analyzed.Particular attention is devoted to the tapered nozzle,where variations in the outlet diameter are explored to determine their effect on flow structure,jet stability,and impact performance.The numerical predictions are rigorously validated against experimental measurements,demonstrating excellent quantitative agreement and confirming the robustness of the computational model.Results show that the tapered nozzle,characterized by its elongated conical transition section,promotes a more stable jet core and superior efflux performance compared to flat and conical geometries.Furthermore,the exit diameter is found to exert a profound influence on jet development.At an inlet pressure of 130 MPa,increasing the tapered nozzle's outlet diameter from 0.8 mm to 1.2 mm enlarges the coherent core region,enhances jet stability,and improves hydraulic energy utilization.Under these conditions,the total impact pressure on the target surface increases by 33.14%,while the overall cleaning efficiency improves by 40.44%.
基金supported by the National Key Research and Development Plan in China(Grant No.2020YFB1005500)。
文摘With increasing demand for data circulation,ensuring data security and privacy is paramount,specifically protecting privacy while maximizing utility.Blockchain,while decentralized and transparent,faces challenges in privacy protection and data verification,especially for sensitive data.Existing schemes often suffer from inefficiency and high overhead.We propose a privacy protection scheme using BGV homomorphic encryption and Pedersen Secret Sharing.This scheme enables secure computation on encrypted data,with Pedersen sharding and verifying the private key,ensuring data consistency and immutability.The blockchain framework manages key shards,verifies secrets,and aids security auditing.This approach allows for trusted computation without revealing the underlying data.Preliminary results demonstrate the scheme's feasibility in ensuring data privacy and security,making data available but not visible.This study provides an effective solution for data sharing and privacy protection in blockchain applications.
