The measurements on actual traffic have revealed the existence of meta-stable states with high flow. Such nonlinear phenomena have not been observed in the classic Nagel-Schreckenberg traffic flow model. Here we just ...The measurements on actual traffic have revealed the existence of meta-stable states with high flow. Such nonlinear phenomena have not been observed in the classic Nagel-Schreckenberg traffic flow model. Here we just add a constraint to the classic model by introducing a velocity-dependent randomization. Two typical randomization strategies are adopted in this paper. It is shown that the Matthew effect is a necessary condition to induce traffic meta-stable states, thus shedding a light on the prerequisites for the emergence of hysteresis loop in the fundamental diagrams.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
Apparel exports China:The decline in exports widened from January to September(-2.4%,compared to-0.2%in January-June).Exports to the EU increased(+5.9%),though the growth rate moderated.Exports to the US saw a larger ...Apparel exports China:The decline in exports widened from January to September(-2.4%,compared to-0.2%in January-June).Exports to the EU increased(+5.9%),though the growth rate moderated.Exports to the US saw a larger contraction(January-June:-1.6%,January-September:-8.2%).While exports to ASEAN countries still fell by over 10%(-17.7%),shipments to the Philippines(+6.9%),Indonesia(+19.0%),and Cambodia(+64.9%)demonstrated stronger growth performance within the year.Regarding tariffs,on October 30,China and the US agreed to lower the rates on goods subject to additional duties(effectively reducing the average tariff rate on Chinese imports to the US from about 57%to approximately 47%,though this remains significantly higher than the 19.5%overall average rate applied to other countries).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
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 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
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
Doping metal ions offer a promising strategy to tune the intrinsic and surface properties of BiVO_(4)for enhanced photoelectrochemical(PEC)activity.Given this,experimental and theoretical studies on cadmium(Cd)doping ...Doping metal ions offer a promising strategy to tune the intrinsic and surface properties of BiVO_(4)for enhanced photoelectrochemical(PEC)activity.Given this,experimental and theoretical studies on cadmium(Cd)doping to BiVO_(4)photoanode were studied for PEC water splitting applications.The spectroscopic and PEC results indicate that the substitution of Cd at Bi lattice sites causes the reduction in the valence state of V^(5+)to V4+that creates hole trap states below the Fermi level of BiVO_(4).The introduced hole trap states at the BiVO_(4)surface suppress the charge recombination and provide effective hole transfer sites for the facile water oxidation reactions.The CdBiVO_(4)exhibited significantly higher photocurrent compared to the pristine BiVO_(4)reaching 3.5 mA cm^(-2)(with a hole scavenger)at 1.23 V vs RHE.Furthermore,doping increases the carrier density in the bulk of BiVO_(4)leading to improved charge separation,and charge transfer while reducing the hole transfer resistance at the interface.The Cd-doped BiVO_(4)exhibited a charge separation efficiency of 80%and with a 90%of overall water splitting faradaic efficiency.Importantly,the results of this work propose the advantages of doping metal ions at Bi lattice sites in BiVO_(4)for improved PEC activity.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
Foodborne bacteria produce biofilms and their viable but non-culturable(VBNC)formation,can affect food quality and safety.Studies have shown that these characteristics are regulated by the bacterial quorum sensing(QS)...Foodborne bacteria produce biofilms and their viable but non-culturable(VBNC)formation,can affect food quality and safety.Studies have shown that these characteristics are regulated by the bacterial quorum sensing(QS)system.Quenching the QS system of foodborne bacteria and blocking the expression of the corresponding genes may be an effective way to improve food quality and safety.Therefore,this article reviews the QS systems for foodborne bacteria,the regulatory mechanisms of QS systems in biofilm and VBNC formation and resuscitation,the research progress on quorum sensing inhibitors(QSIs)for Gram-negative and Gram-positive bacteria,and introduces QSIs from various sources.In addition,we have also summarized the current research issues on QS regulation of biofilms and VBNC formation.The systematic study of the QS phenomenon of foodborne bacteria in practical situations,the mechanism of bacterial QS cooperation-cheating,the screening of novel and highly active QSIs,the combination of QSIs and other technologies to improve their bioavailability,and the regulatory network between biofilm and VBNC formation and resuscitation are research directions that need to be paid attention to in the future.展开更多
Making full use of the operator ordering method and the integration within ordered products,we obtain the analytical evolution law of a general quadratic state in the amplitude decay channel,and find that it is determ...Making full use of the operator ordering method and the integration within ordered products,we obtain the analytical evolution law of a general quadratic state in the amplitude decay channel,and find that it is determined not only by the decay rate of the amplitude decay channel but also by the coefficients of the initial quadratic state.Further,the quantum statistical properties of the initial quadratic state for amplitude decay are investigated via its average photon number and photon-counting distribution,and its Wigner distribution function evolution is discussed in detail.展开更多
文摘The measurements on actual traffic have revealed the existence of meta-stable states with high flow. Such nonlinear phenomena have not been observed in the classic Nagel-Schreckenberg traffic flow model. Here we just add a constraint to the classic model by introducing a velocity-dependent randomization. Two typical randomization strategies are adopted in this paper. It is shown that the Matthew effect is a necessary condition to induce traffic meta-stable states, thus shedding a light on the prerequisites for the emergence of hysteresis loop in the fundamental diagrams.
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.
文摘Apparel exports China:The decline in exports widened from January to September(-2.4%,compared to-0.2%in January-June).Exports to the EU increased(+5.9%),though the growth rate moderated.Exports to the US saw a larger contraction(January-June:-1.6%,January-September:-8.2%).While exports to ASEAN countries still fell by over 10%(-17.7%),shipments to the Philippines(+6.9%),Indonesia(+19.0%),and Cambodia(+64.9%)demonstrated stronger growth performance within the year.Regarding tariffs,on October 30,China and the US agreed to lower the rates on goods subject to additional duties(effectively reducing the average tariff rate on Chinese imports to the US from about 57%to approximately 47%,though this remains significantly higher than the 19.5%overall average rate applied to other countries).
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.
基金the support of the Natural Sciences and Engineering Research Council of Canada(NSERC)Tier 1 Canada Research Chair in Green Hydrogen Production,the Québec Ministère de l'Économie,de l'Innovation et de l'Énergie(MEIE)[Développement de catalyseurs et d'électrodes innovants,àfaibles coûts,performants et durables pour la production d'hydrogène vert,funding reference number 00393501]。
文摘Doping metal ions offer a promising strategy to tune the intrinsic and surface properties of BiVO_(4)for enhanced photoelectrochemical(PEC)activity.Given this,experimental and theoretical studies on cadmium(Cd)doping to BiVO_(4)photoanode were studied for PEC water splitting applications.The spectroscopic and PEC results indicate that the substitution of Cd at Bi lattice sites causes the reduction in the valence state of V^(5+)to V4+that creates hole trap states below the Fermi level of BiVO_(4).The introduced hole trap states at the BiVO_(4)surface suppress the charge recombination and provide effective hole transfer sites for the facile water oxidation reactions.The CdBiVO_(4)exhibited significantly higher photocurrent compared to the pristine BiVO_(4)reaching 3.5 mA cm^(-2)(with a hole scavenger)at 1.23 V vs RHE.Furthermore,doping increases the carrier density in the bulk of BiVO_(4)leading to improved charge separation,and charge transfer while reducing the hole transfer resistance at the interface.The Cd-doped BiVO_(4)exhibited a charge separation efficiency of 80%and with a 90%of overall water splitting faradaic efficiency.Importantly,the results of this work propose the advantages of doping metal ions at Bi lattice sites in BiVO_(4)for improved PEC activity.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金financially supported by the National Natural Science Foundation of China(32202191)and(32272279)the Key R&D Project of Shandong Province(2023CXPT007 and 2024CXPT014)the Key R&D Project of Qingdao Science and Technology Plan(24-2-3-4-zyyd-jch).
文摘Foodborne bacteria produce biofilms and their viable but non-culturable(VBNC)formation,can affect food quality and safety.Studies have shown that these characteristics are regulated by the bacterial quorum sensing(QS)system.Quenching the QS system of foodborne bacteria and blocking the expression of the corresponding genes may be an effective way to improve food quality and safety.Therefore,this article reviews the QS systems for foodborne bacteria,the regulatory mechanisms of QS systems in biofilm and VBNC formation and resuscitation,the research progress on quorum sensing inhibitors(QSIs)for Gram-negative and Gram-positive bacteria,and introduces QSIs from various sources.In addition,we have also summarized the current research issues on QS regulation of biofilms and VBNC formation.The systematic study of the QS phenomenon of foodborne bacteria in practical situations,the mechanism of bacterial QS cooperation-cheating,the screening of novel and highly active QSIs,the combination of QSIs and other technologies to improve their bioavailability,and the regulatory network between biofilm and VBNC formation and resuscitation are research directions that need to be paid attention to in the future.
文摘Making full use of the operator ordering method and the integration within ordered products,we obtain the analytical evolution law of a general quadratic state in the amplitude decay channel,and find that it is determined not only by the decay rate of the amplitude decay channel but also by the coefficients of the initial quadratic state.Further,the quantum statistical properties of the initial quadratic state for amplitude decay are investigated via its average photon number and photon-counting distribution,and its Wigner distribution function evolution is discussed in detail.
