Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF...Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.展开更多
Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Org...Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion detection.The proposed model is evaluated on the NSL-KDD dataset.The hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world applicability.Therefore,this paper proposes a highly efficient deployment strategy for resource-constrained network edges.The results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)classes.In particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.展开更多
For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target pro...For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed.For stochastic optimization algorithms based on population search methods,the search speed and solution quality are always contradictory.Suppose that the random range of the group search is larger;in that case,the probability of the algorithm converging to the global optimal solution is also greater,but the search speed will inevitably slow.The smaller the random range of the group search is,the faster the search speed will be,but the algorithm will easily fall into local optima.Therefore,our method is intended to utilize heuristic strategies to guide the search direction and extract as much effective information as possible from the search process to guide an optimized search.This method is not only conducive to global search,but also avoids excessive randomness,thereby improving search efficiency.To effectively avoid premature convergence problems,the diversity of the group must be monitored and regulated.In fact,in natural bird flocking systems,the distribution density and diversity of groups are often key factors affecting individual behavior.For example,flying birds can adjust their speed in time to avoid collisions based on the crowding level of the group,while foraging birds will judge the possibility of sharing food based on the density of the group and choose to speed up or escape.The aim of this work was to verify that the proposed optimization method is effective.We compared and analyzed the performances of five algorithms,namely,self-organized particle swarm optimization(PSO)-diversity controlled inertia weight(SOPSO-DCIW),self-organized PSO-diversity controlled acceleration coefficient(SOPSO-DCAC),standard PSO(SPSO),the PSO algorithm with a linear decreasing inertia weight(SPSO-LDIW),and the modified PSO algorithm with a time-varying acceleration constant(MPSO-TVAC).展开更多
The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are ...The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance.In order to improve the intelligence and adaptability of production scheduler,a novel distributed scheduling architecture is proposed,which has the ability to autonomously allocate tasks and handle disturbances.All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines.Firstly,the multi-agent technology is applied to build a self-organizing manufacturing system,enabling each machine to be equipped with the ability of active information interaction and joint-action execution.Secondly,various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents,thereby flexibly achieving global perception of environmental state.To ensure the adaptability and superiority of production decisions in dynamic environment,deep reinforcement learning is applied to build a smart production scheduler:Based on the perceived environment state,the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration.The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed.Compared to heuristic dispatching rules,the proposed method achieves an average performance improvement of 34.0%in three scenarios in terms of order tardiness.The proposed system can provide a new reference for the design of smart manufacturing systems.展开更多
The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nod...The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.展开更多
This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challen...This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challenges inherent in conventional neural network training,an improved self-organizing radial basis function neural network(SRBFNN)with an input-dependent variable structure is developed.Furthermore,a novel selforganizing RBFNN-based observer is introduced to estimate system states across all dimensions.Leveraging the reconstructed states,the proposed adaptive controller effectively compensates for all uncertainties,including estimation errors in the observer,ensuring accurate state tracking with reduced control effort.The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis.Finally,simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.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
Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identifi...Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identified numerous crystal structures with the Li_(3)MX_(6)composition,although many remain unexplored across various chemical systems.In this research,we developed a comprehensive method to examine all conceivable space groups and structures within theLi-M-X system,where M includes In,Ga,and La,and X includes F,Cl,Br,and 1.Our findings revealed two metastable structures:Li_(3)InF_(6)with P3c1 symmetry and Li_(3)InI_(6)with C2/c symmetry,exhibiting ionic conductivities of 0.55 and 2.18mS/cm at 300K,respectively.Notably,the trigonal symmetry of Li3InF6 demonstrates that high ionic conductivities are not limited to monoclinic structures but can also be achieved with trigonal symmetries.The electrochemical stability windows,mechanical properties,and reaction energies of these materials with known cathodes suggest their potential for use in all-solid-state batteries.Additionally,we predicted the stability of novel materials,including Li_(5)InCl_(8),Li_(5)InBr_(8),Li_(5)InI_(8),LiIn_(2)Cl_(9),LiIn_(2)Br_(9),and LiIn_(2)I_(9).展开更多
In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete bounda...In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete boundary unit cells(i.e.,boundary defects)even though the bulk polarization is zero,which challenges the conventional understanding of HOTIs.Here,based on a Kekul´e-distorted honeycomb lattice with incomplete unit cells,we reveal that incomplete unit cells exhibit fractional charges through the analysis of Wannier centers by developing a compensation method and creating the concept of Wannier center domain(WCD)which is the smallest region that one Wannier center occupies.This method compensates for the missing parts of these boundary incomplete unit cells with additional WCDs to make them complete.The compensated WCDs automatically carry the corresponding charge,and this charge together with that of the incomplete unit cell constitutes the total charge of the complete unit cell after compensation.We conclude that the emergence of corner states is attributed to the filling anomaly,which is a fundamental mechanism.Our results refresh the understanding of HOTIs,especially those with structural discontinuities,and provide a novel design for topological states which have application value in producing optical functional devices.展开更多
Surface passivation via two-dimensional(2D)perovskite has emerged as a promising strategy to enhance the performance of perovskite solar cells(PSCs)due to the effective compensation of interfacial states.However,the i...Surface passivation via two-dimensional(2D)perovskite has emerged as a promising strategy to enhance the performance of perovskite solar cells(PSCs)due to the effective compensation of interfacial states.However,the in situ grown 2D perovskite passivation layers typically comprise a mixture of multiple dimensionalities at the interface,where band alignment has only been portrayed qualitatively and empirically.Herein,the interface states for precisely phase-tailored 2D perovskite passivated PSCs are quantitatively investigated.In comparison to traditional passivation molecules,2D perovskite layers based on 4-trifluoromethyl-phenylethylammonium iodide(CF3PEAI)exhibit an increased work function,introducing desirable downward band bending to eliminate the Schottky Barrier.Furthermore,precisely phase-tailored 2D layers could modulate the interface trap density and energetics.The n=1 film delivers optimal performance with a hole extraction efficiency of 95.1%.The optimized n-i-p PSCs in the two-step method significantly improve PCE to 25.40%,along with enhanced photostability and negligible hysteresis.It highlights that tailoring in the composition and phase distribution of the 2D perovskite layer could modulate the interface states at the 2D/3D interface.展开更多
The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the S...The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the STT missile is designed based on nonlinear model predictive control(NMPC)using Taylor series expansion,after which,via a neural network(NN),unknown functions are approximated.The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system.Specifically,to estimate the missile states such as normal acceleration and its derivatives for the future,originally the Taylor series states expansion was gained to any specified order,based on their receding horizons.To address the problem of prediction error,an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer.Out of the gains resulting from the analytic solution,as developed for the problem of prediction error,the selection of the proposed observer gain was optimally conducted to meet the stability condition.Thus,combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance,which needed only estimated normal acceleration and its derivatives.Meanwhile,no angular velocity measurement or wind angle estimation was required.Ultimately,the proposed technique was found effective,as confirmed by the qualitative simulation results.展开更多
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ...Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.展开更多
The development of collinear resonance ionization spectroscopy for studying the nuclear structure of nickel isotopes far from the stability line relies on high-efficiency two-color two-step photoionization pathways.We...The development of collinear resonance ionization spectroscopy for studying the nuclear structure of nickel isotopes far from the stability line relies on high-efficiency two-color two-step photoionization pathways.We systematically investigated the even-parity autoionization spectrum of atomic nickel through resonance ionization mass spectrometry(RIMS).Fifteen intense single-color photoionization lines and corresponding transitions in the 300-325 nm range were identified and excluded as potential interference peaks for subsequent two-color studies.Fifty-one even-parity autoionization states in the 64000-66800 cm^(-1)range were identified for the first time by scanning from five intermediate excited states of the3d^(8)(^(3)F)4s4p(^(3)P^(o))configuration.Forty-eight of these states were assigned unique total angular momentum quantum numbers(J)based on electric dipole transition selection rules.The autoionization state at 64437.77 cm^(-1)was identified as an optimal final state for enhancing photoionization efficiency in two-color two-step pathways.This study provides comprehensive datasets of even-parity autoionization states of nickel,supporting both the advancement of collinear resonance ionization spectroscopy for exotic nickel isotopes and theoretical modeling of autoionization states.The datasets are openly available at https://doi.org/10.57760/sciencedb.j00113.00280.展开更多
Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prereq...Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.展开更多
The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network sele...The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.展开更多
When two layers of graphene are stacked with a twist angle of approximately 1.1°,strong interlayer coupling gives rise to a pair of flat bands in twisted bilayer graphene(TBG),resulting in pronounced electron–el...When two layers of graphene are stacked with a twist angle of approximately 1.1°,strong interlayer coupling gives rise to a pair of flat bands in twisted bilayer graphene(TBG),resulting in pronounced electron–electron interactions.At half filling of the flat bands,TBG exhibits correlated insulating states.Here,we investigate the electrical transport properties of heterostructures composed of TBG and the antiferromagnetic insulator chromium oxychloride(CrOCl),and propose a strategy to modulate the correlated insulating states in TBG.During the transition from a conventional phase to a strong interfacial coupling phase,kink-like features are observed in the charge neutrality point(CNP),correlated insulating state,and band insulating state.Under a perpendicular magnetic field,the system exhibits broadened quantum Hall plateaus in the strong interfacial coupling regime.Electrons localized in the CrOCl layer screen the bottom gate,rendering the carrier density in TBG less sensitive to variations in the bottom gate voltage.These phenomena are well captured by a charge-transfer model between TBG and CrOCl.Our results provide insights into the control of electronic correlations and topological states in graphene moirésystems via interfacial charge coupling.展开更多
BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this ...BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this relationship remain unclear.AIM To investigate emotion regulation habits impact on students negative emotions during lockdown,using the coronavirus disease 2019 pandemic as a case example.METHODS During the coronavirus disease 2019 lockdown,an online cross-sectional survey was conducted at a Chinese university.Emotional states were assessed using the Depression,Anxiety,and Stress Scale-21(DASS-21),while demographic data and emotion regulation habits were collected concurrently.Data analysis was performed using SPSS version 27.0 and includedχ^(2)-tests for intergroup comparisons,Spearman’s rank-order correlation coefficient analysis to examine associations,and stepwise linear regression modeling to explore the relationships between emotion regulation habits and emotional states.Statistical significance was set atα=0.05.RESULTS Among the 494 valid questionnaires analyzed,the prevalence rates of negative emotional states were as follows:Depression(65.0%),anxiety(69.4%),and stress(50.8%).DASS-21 scores(mean±SD)demonstrated significant symptomatology:Total(48.77±34.88),depression(16.21±12.18),anxiety(14.90±11.91),and stress(17.64±12.07).Significant positive intercorrelations were observed among all DASS-21 subscales(P<0.01).Regression analysis identified key predictors of negative emotions(P<0.05):Risk factors included late-night frequency and academic pressure,while protective factors were the frequency of parental contact and the number of same-gender friends.Additionally,compensatory spending and binge eating positively predicted all negative emotion scores(β>0,P<0.01),whereas appropriate recreational activities negatively predicted these scores(β<0,P<0.01).CONCLUSION High negative emotion prevalence occurred among confined students.Recreational activities were protective,while compensatory spending and binge eating were risk factors,necessitating guided emotion regulation.展开更多
The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonal...The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations,particularly at finite temperatures.In this work,we propose a new algorithm for obtaining thermal pure quantum states,which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures.We implement this algorithm in our open-source C++template library,Physica.Combining the improved algorithm with state-of-the-art software engineering,our implementation achieves high performance and numerical stability.As an example,we demonstrate that for the 4×4 Hubbard model,our method runs approximately 10~3times faster than HΦ3.5.2.Moreover,the accessible temperature range is extended down toβ=32 across arbitrary doping levels.These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.展开更多
The methanol oxidation reaction(MOR)to formic acid offers a promising alternative to the anodic oxygen evolution reaction(OER)in water electrolysis.However,the development of efficient and cost-effective catalysts rem...The methanol oxidation reaction(MOR)to formic acid offers a promising alternative to the anodic oxygen evolution reaction(OER)in water electrolysis.However,the development of efficient and cost-effective catalysts remains a primary challenge.In this study,an enhancement in catalytic MOR performance is achieved through the incorporation of Mn atoms with unsaturated t_(2g)orbitals into Ni_(3)Se_(4).Comprehensive experimental analyses and theoretical calculations reveal that substituting Ni with Mn induces strong electron-withdrawing effects,effectively modulating the local coordination environment of the metal centers.The presence of Mn also elongates Ni–Se(O)bonds,which reduces eg orbital occupancy and modifies the spin state of the material.Electrochemical measurements demonstrate that electrodes based on this optimized material exhibit a high spin state and deliver excellent catalytic activity,achieving a MOR current density up to∼190 mA cm^(−2)at 1.6 V.This performance enhancement is attributed to the favorable electronic configuration and reduced reaction energy barriers associated with the high-spin state.展开更多
基金supported by the National Natural Science Foundation of China(7092100160574058)+1 种基金the Key International Cooperation Programs of Hunan Provincial Science & Technology Department (2009WK2009)the General Program of Hunan Provincial Education Department(11C0023)
文摘Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.
基金Researcher Supporting Project number(RSPD2025R582),King Saud University,Riyadh,Saudi Arabia.
文摘Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion detection.The proposed model is evaluated on the NSL-KDD dataset.The hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world applicability.Therefore,this paper proposes a highly efficient deployment strategy for resource-constrained network edges.The results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)classes.In particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.
文摘For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed.For stochastic optimization algorithms based on population search methods,the search speed and solution quality are always contradictory.Suppose that the random range of the group search is larger;in that case,the probability of the algorithm converging to the global optimal solution is also greater,but the search speed will inevitably slow.The smaller the random range of the group search is,the faster the search speed will be,but the algorithm will easily fall into local optima.Therefore,our method is intended to utilize heuristic strategies to guide the search direction and extract as much effective information as possible from the search process to guide an optimized search.This method is not only conducive to global search,but also avoids excessive randomness,thereby improving search efficiency.To effectively avoid premature convergence problems,the diversity of the group must be monitored and regulated.In fact,in natural bird flocking systems,the distribution density and diversity of groups are often key factors affecting individual behavior.For example,flying birds can adjust their speed in time to avoid collisions based on the crowding level of the group,while foraging birds will judge the possibility of sharing food based on the density of the group and choose to speed up or escape.The aim of this work was to verify that the proposed optimization method is effective.We compared and analyzed the performances of five algorithms,namely,self-organized particle swarm optimization(PSO)-diversity controlled inertia weight(SOPSO-DCIW),self-organized PSO-diversity controlled acceleration coefficient(SOPSO-DCAC),standard PSO(SPSO),the PSO algorithm with a linear decreasing inertia weight(SPSO-LDIW),and the modified PSO algorithm with a time-varying acceleration constant(MPSO-TVAC).
基金supported by the Scientific Research Foundation of Nanjing Institute of Technology(No.YKJ202425)the National Natural Science Foundation of China(No.72301130).
文摘The production mode of manufacturing industry presents characteristics of multiple varieties,small-batch and personalization,leading to frequent disturbances in workshop.Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance.In order to improve the intelligence and adaptability of production scheduler,a novel distributed scheduling architecture is proposed,which has the ability to autonomously allocate tasks and handle disturbances.All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines.Firstly,the multi-agent technology is applied to build a self-organizing manufacturing system,enabling each machine to be equipped with the ability of active information interaction and joint-action execution.Secondly,various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents,thereby flexibly achieving global perception of environmental state.To ensure the adaptability and superiority of production decisions in dynamic environment,deep reinforcement learning is applied to build a smart production scheduler:Based on the perceived environment state,the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration.The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed.Compared to heuristic dispatching rules,the proposed method achieves an average performance improvement of 34.0%in three scenarios in terms of order tardiness.The proposed system can provide a new reference for the design of smart manufacturing systems.
基金supported by the National Natural Science Foundation of China(No.62401597)the Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Scientific Research Project of National University of Defense Technology,China(No.ZK22-02)。
文摘The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.
基金supported in part by the National Natural Science Foundation of China(62033008,62188101,62173343,62073339)the Natural Science Foundation of Shandong Province of China(ZR2024MF072,ZR2022ZD34)the Research Fund for the Taishan Scholar Project of Shandong Province of China.
文摘This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challenges inherent in conventional neural network training,an improved self-organizing radial basis function neural network(SRBFNN)with an input-dependent variable structure is developed.Furthermore,a novel selforganizing RBFNN-based observer is introduced to estimate system states across all dimensions.Leveraging the reconstructed states,the proposed adaptive controller effectively compensates for all uncertainties,including estimation errors in the observer,ensuring accurate state tracking with reduced control effort.The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis.Finally,simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.
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.
基金supported by the Higher Education and Science Committee of Armenia in the frames of the research projects 20TTSG-2F010, 23AA-2F033 and ANSEF (EN-matsc-2660) grant.
文摘Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identified numerous crystal structures with the Li_(3)MX_(6)composition,although many remain unexplored across various chemical systems.In this research,we developed a comprehensive method to examine all conceivable space groups and structures within theLi-M-X system,where M includes In,Ga,and La,and X includes F,Cl,Br,and 1.Our findings revealed two metastable structures:Li_(3)InF_(6)with P3c1 symmetry and Li_(3)InI_(6)with C2/c symmetry,exhibiting ionic conductivities of 0.55 and 2.18mS/cm at 300K,respectively.Notably,the trigonal symmetry of Li3InF6 demonstrates that high ionic conductivities are not limited to monoclinic structures but can also be achieved with trigonal symmetries.The electrochemical stability windows,mechanical properties,and reaction energies of these materials with known cathodes suggest their potential for use in all-solid-state batteries.Additionally,we predicted the stability of novel materials,including Li_(5)InCl_(8),Li_(5)InBr_(8),Li_(5)InI_(8),LiIn_(2)Cl_(9),LiIn_(2)Br_(9),and LiIn_(2)I_(9).
基金supported by the Natural Science Basic Research Program of Shaanxi Province (Grant Nos.2024JC-JCQN-06 and2025JC-QYCX-006)the National Natural Science Foundation of China (Grant No.12474337)Chinese Academy of Sciences Project (Grant Nos.E4BA270100,E4Z127010F,E4Z6270100,and E53327020D)。
文摘In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete boundary unit cells(i.e.,boundary defects)even though the bulk polarization is zero,which challenges the conventional understanding of HOTIs.Here,based on a Kekul´e-distorted honeycomb lattice with incomplete unit cells,we reveal that incomplete unit cells exhibit fractional charges through the analysis of Wannier centers by developing a compensation method and creating the concept of Wannier center domain(WCD)which is the smallest region that one Wannier center occupies.This method compensates for the missing parts of these boundary incomplete unit cells with additional WCDs to make them complete.The compensated WCDs automatically carry the corresponding charge,and this charge together with that of the incomplete unit cell constitutes the total charge of the complete unit cell after compensation.We conclude that the emergence of corner states is attributed to the filling anomaly,which is a fundamental mechanism.Our results refresh the understanding of HOTIs,especially those with structural discontinuities,and provide a novel design for topological states which have application value in producing optical functional devices.
基金supported by the National Natural Science Foundation of China(Nos.62304111,62304110,22579136)the National Key Research and Development Program of China(2024YFE0201800)+6 种基金the China Postdoctoral Science Foundation(No.2024M761492)the Project of State Key Laboratory of Organic Electronics and Information Displays(Nos.GDX2022010009,GZR2023010046)the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY223053)the Science and Technology Project of Jiangsu(Science and Technology Cooperation Project of HongKong,Macao and Taiwan,No.BZ2023059)Shaanxi Fundamental Science Research Project for Mathematics and Physics(No.22jSY015)Young Talent Fund of Xi'an Association for Science and Technology(No.959202313020)Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems(No.2023B1212010003).
文摘Surface passivation via two-dimensional(2D)perovskite has emerged as a promising strategy to enhance the performance of perovskite solar cells(PSCs)due to the effective compensation of interfacial states.However,the in situ grown 2D perovskite passivation layers typically comprise a mixture of multiple dimensionalities at the interface,where band alignment has only been portrayed qualitatively and empirically.Herein,the interface states for precisely phase-tailored 2D perovskite passivated PSCs are quantitatively investigated.In comparison to traditional passivation molecules,2D perovskite layers based on 4-trifluoromethyl-phenylethylammonium iodide(CF3PEAI)exhibit an increased work function,introducing desirable downward band bending to eliminate the Schottky Barrier.Furthermore,precisely phase-tailored 2D layers could modulate the interface trap density and energetics.The n=1 film delivers optimal performance with a hole extraction efficiency of 95.1%.The optimized n-i-p PSCs in the two-step method significantly improve PCE to 25.40%,along with enhanced photostability and negligible hysteresis.It highlights that tailoring in the composition and phase distribution of the 2D perovskite layer could modulate the interface states at the 2D/3D interface.
文摘The objective of the current study is to investigate an adaptive predictive observer-based autopilot for a skid-to-turn(STT)missile model with uncertainties and unknown dynamic equations.A predictive control for the STT missile is designed based on nonlinear model predictive control(NMPC)using Taylor series expansion,after which,via a neural network(NN),unknown functions are approximated.The present study also evaluates an adaptive optimal observer of a new strategy-based nonlinear system.Specifically,to estimate the missile states such as normal acceleration and its derivatives for the future,originally the Taylor series states expansion was gained to any specified order,based on their receding horizons.To address the problem of prediction error,an analytic solution was prepared that led to a closed form regarding the nonlinear optimal observer.Out of the gains resulting from the analytic solution,as developed for the problem of prediction error,the selection of the proposed observer gain was optimally conducted to meet the stability condition.Thus,combining the adaptive predictive autopilot and the adaptive optimal observer scheme was implemented to secure the performance,which needed only estimated normal acceleration and its derivatives.Meanwhile,no angular velocity measurement or wind angle estimation was required.Ultimately,the proposed technique was found effective,as confirmed by the qualitative simulation results.
基金financial support provided by the Natural Science Foundation of Hebei Province,China(No.E2024105036)the Tangshan Talent Funding Project,China(Nos.B202302007 and A2021110015)+1 种基金the National Natural Science Foundation of China(No.52264042)the Australian Research Council(No.IH230100010)。
文摘Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.
基金supported by the China National Nuclear Corporation Basic Research Project(Grant No.CNNC-JCYJ-202327)。
文摘The development of collinear resonance ionization spectroscopy for studying the nuclear structure of nickel isotopes far from the stability line relies on high-efficiency two-color two-step photoionization pathways.We systematically investigated the even-parity autoionization spectrum of atomic nickel through resonance ionization mass spectrometry(RIMS).Fifteen intense single-color photoionization lines and corresponding transitions in the 300-325 nm range were identified and excluded as potential interference peaks for subsequent two-color studies.Fifty-one even-parity autoionization states in the 64000-66800 cm^(-1)range were identified for the first time by scanning from five intermediate excited states of the3d^(8)(^(3)F)4s4p(^(3)P^(o))configuration.Forty-eight of these states were assigned unique total angular momentum quantum numbers(J)based on electric dipole transition selection rules.The autoionization state at 64437.77 cm^(-1)was identified as an optimal final state for enhancing photoionization efficiency in two-color two-step pathways.This study provides comprehensive datasets of even-parity autoionization states of nickel,supporting both the advancement of collinear resonance ionization spectroscopy for exotic nickel isotopes and theoretical modeling of autoionization states.The datasets are openly available at https://doi.org/10.57760/sciencedb.j00113.00280.
基金Project supported by the National Natural Science Foundation of China(Grant No.61573266)the Natural Science Basic Research Program of Shaanxi(Grant No.2021JM-133)the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University(Grant No.YJSJ25009)。
文摘Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.
基金National Key Research and Development Program of China(No.2022YFB3903404,2024YFC3015403)National Natural Science Foundation of China(NSFC No.42271431,42271425)Hubei Province Major Science and Technology Innovation Program(2024BAA011)。
文摘The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.52225207 and 52350001)the Shanghai Pilot Program for Basic Research–Fudan University 21TQ1400100(Grant No.21TQ006)the Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)。
文摘When two layers of graphene are stacked with a twist angle of approximately 1.1°,strong interlayer coupling gives rise to a pair of flat bands in twisted bilayer graphene(TBG),resulting in pronounced electron–electron interactions.At half filling of the flat bands,TBG exhibits correlated insulating states.Here,we investigate the electrical transport properties of heterostructures composed of TBG and the antiferromagnetic insulator chromium oxychloride(CrOCl),and propose a strategy to modulate the correlated insulating states in TBG.During the transition from a conventional phase to a strong interfacial coupling phase,kink-like features are observed in the charge neutrality point(CNP),correlated insulating state,and band insulating state.Under a perpendicular magnetic field,the system exhibits broadened quantum Hall plateaus in the strong interfacial coupling regime.Electrons localized in the CrOCl layer screen the bottom gate,rendering the carrier density in TBG less sensitive to variations in the bottom gate voltage.These phenomena are well captured by a charge-transfer model between TBG and CrOCl.Our results provide insights into the control of electronic correlations and topological states in graphene moirésystems via interfacial charge coupling.
文摘BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this relationship remain unclear.AIM To investigate emotion regulation habits impact on students negative emotions during lockdown,using the coronavirus disease 2019 pandemic as a case example.METHODS During the coronavirus disease 2019 lockdown,an online cross-sectional survey was conducted at a Chinese university.Emotional states were assessed using the Depression,Anxiety,and Stress Scale-21(DASS-21),while demographic data and emotion regulation habits were collected concurrently.Data analysis was performed using SPSS version 27.0 and includedχ^(2)-tests for intergroup comparisons,Spearman’s rank-order correlation coefficient analysis to examine associations,and stepwise linear regression modeling to explore the relationships between emotion regulation habits and emotional states.Statistical significance was set atα=0.05.RESULTS Among the 494 valid questionnaires analyzed,the prevalence rates of negative emotional states were as follows:Depression(65.0%),anxiety(69.4%),and stress(50.8%).DASS-21 scores(mean±SD)demonstrated significant symptomatology:Total(48.77±34.88),depression(16.21±12.18),anxiety(14.90±11.91),and stress(17.64±12.07).Significant positive intercorrelations were observed among all DASS-21 subscales(P<0.01).Regression analysis identified key predictors of negative emotions(P<0.05):Risk factors included late-night frequency and academic pressure,while protective factors were the frequency of parental contact and the number of same-gender friends.Additionally,compensatory spending and binge eating positively predicted all negative emotion scores(β>0,P<0.01),whereas appropriate recreational activities negatively predicted these scores(β<0,P<0.01).CONCLUSION High negative emotion prevalence occurred among confined students.Recreational activities were protective,while compensatory spending and binge eating were risk factors,necessitating guided emotion regulation.
基金Fu-Zhou Chen for helpful discussions.The work is partly supported by the National Key Research and Development Program of China(Grant No.2022YFA1402704)the National Natural Science Foundation of China(Grant No.12247101)。
文摘The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations,particularly at finite temperatures.In this work,we propose a new algorithm for obtaining thermal pure quantum states,which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures.We implement this algorithm in our open-source C++template library,Physica.Combining the improved algorithm with state-of-the-art software engineering,our implementation achieves high performance and numerical stability.As an example,we demonstrate that for the 4×4 Hubbard model,our method runs approximately 10~3times faster than HΦ3.5.2.Moreover,the accessible temperature range is extended down toβ=32 across arbitrary doping levels.These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.
基金financially supported by the Sichuan Science and Technology Program (Grant No. 2025NSFSC0139)the China Postdoctoral Science Foundation (Grant No.2023MD734228)+10 种基金funding from Generalitat de Catalunya 2021SGR00457supported by MCIN with funding from European Union NextGenerationEU(PRTR-C17.I1)by Generalitat de Catalunya (In-CAEM Project)the support from the project AMaDE(PID2023-149158OB-C43)funded by MCIN/AEI/10.13039/501100011033/by “ERDF A way of making Europe”by the “European Union”supported by the Severo Ochoa program from Spanish MCIN/AEI (Grant No.:CEX2021-001214-S)funded by the CERCA Programme/Generalitat de Catalunyaperformed in the framework of Universitat Autònoma de Barcelona Materials Science PhD programfunding from the CSC-UAB PhD scholarship program. ICN2 is founding member of e-DREAM[87]
文摘The methanol oxidation reaction(MOR)to formic acid offers a promising alternative to the anodic oxygen evolution reaction(OER)in water electrolysis.However,the development of efficient and cost-effective catalysts remains a primary challenge.In this study,an enhancement in catalytic MOR performance is achieved through the incorporation of Mn atoms with unsaturated t_(2g)orbitals into Ni_(3)Se_(4).Comprehensive experimental analyses and theoretical calculations reveal that substituting Ni with Mn induces strong electron-withdrawing effects,effectively modulating the local coordination environment of the metal centers.The presence of Mn also elongates Ni–Se(O)bonds,which reduces eg orbital occupancy and modifies the spin state of the material.Electrochemical measurements demonstrate that electrodes based on this optimized material exhibit a high spin state and deliver excellent catalytic activity,achieving a MOR current density up to∼190 mA cm^(−2)at 1.6 V.This performance enhancement is attributed to the favorable electronic configuration and reduced reaction energy barriers associated with the high-spin state.
