We present a concise review of the vector charmonium state ψ(4230), which was originally labelled as Y(4260)in the literature. As one of the earliest candidates for a quantum chromodynamics exotic states, its interpr...We present a concise review of the vector charmonium state ψ(4230), which was originally labelled as Y(4260)in the literature. As one of the earliest candidates for a quantum chromodynamics exotic states, its interpretation has initiated various ideas about possible manifestations of non-perturbative mechanisms in the charmonium mass regime. In this short article we briefly review the experimental status of ψ(4230) and discuss possible theoretical interpretations. We will focus on four broadly investigated scenarios, i.e., tetraquark, hybrid, hadro-quarkonium,and hadronic molecule, and highlight the key issues based on these approaches. Crucial experimental observables,e.g., mass position, lineshapes, di-lepton decay width Γ_(ee), production rates in B meson decays, dominant hadronic decay patterns, and the potential 1^(-+)and 0^(--)exotic partners, are assessed, which can provide crucial structure information for understanding this mysterious state.展开更多
Objective:To collate and summarize phenotypic insecticide susceptibility data of Indian malaria vectors from 2017 to 2024,focusing on insecticides used in adult vector control,dichloro diphenyl trichloroethane,malathi...Objective:To collate and summarize phenotypic insecticide susceptibility data of Indian malaria vectors from 2017 to 2024,focusing on insecticides used in adult vector control,dichloro diphenyl trichloroethane,malathion,deltamethrin,alpha-cypermethrin,and permethrin to identify resistance patterns to different classes of insecticides.Methods:The data included information on vector species,location of the study(state/district),insecticide tested,mortality percentage,and susceptibility classification based on the World Health Organization interpretation criteria.Retrospective data were collected from peer-reviewed publications(2017-2024)and up to June 2025.The data were collated for five major malaria vector species,namely Anopheles(An.)culicifacies,An.fluviatilis,An.stephensi,An.baimaii,and An.minimus.Results:Insecticide susceptibility data were available from 86 districts across 16 Indian states for 40615 mosquitoes.The majority of the data was on An.culicifacies(n=28308),followed by An.stephensi(n=5611),An.fluviatilis(n=5967),An.baimaii(n=365),and An.minimus(n=364).Intensity bioassays revealed low to moderate resistance levels in An.culicifacies populations from selected districts in 3 states,Odisha,Madhya Pradesh,and Chhattisgarh against deltamethrin and alpha-cypermethrin.Conclusions:This review highlights spatial and species-level variations in insecticide susceptibility among Indian malaria vectors.The low to moderate intensity suggested that it may not yet be severe enough to cause operational failure with current vector control interventions.Continued monitoring of insecticide resistance,as well as the use of new-generation insecticides and interventions,is suggested to sustain vector control efficacy and manage insecticide resistance in malaria vectors to support India’s malaria elimination.展开更多
The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a...The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique(BO-SVR with a Gaussian kernel)was proposed.Unlike,traditional approaches that use the internal resistance,and battery capacity as input parameters,this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval,as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error,R^(2),root mean squared error,and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models,with a reduced RMSE of 0.0082,thus demonstrating its potential to predict the SoH more accurately.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
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.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
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates ...The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.展开更多
Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme ...Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.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
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.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
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
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
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
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
A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily s...A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology.展开更多
基金supported in part by the National Natural Science Foundation of China (Grant Nos.12375073 and 12235018)。
文摘We present a concise review of the vector charmonium state ψ(4230), which was originally labelled as Y(4260)in the literature. As one of the earliest candidates for a quantum chromodynamics exotic states, its interpretation has initiated various ideas about possible manifestations of non-perturbative mechanisms in the charmonium mass regime. In this short article we briefly review the experimental status of ψ(4230) and discuss possible theoretical interpretations. We will focus on four broadly investigated scenarios, i.e., tetraquark, hybrid, hadro-quarkonium,and hadronic molecule, and highlight the key issues based on these approaches. Crucial experimental observables,e.g., mass position, lineshapes, di-lepton decay width Γ_(ee), production rates in B meson decays, dominant hadronic decay patterns, and the potential 1^(-+)and 0^(--)exotic partners, are assessed, which can provide crucial structure information for understanding this mysterious state.
文摘Objective:To collate and summarize phenotypic insecticide susceptibility data of Indian malaria vectors from 2017 to 2024,focusing on insecticides used in adult vector control,dichloro diphenyl trichloroethane,malathion,deltamethrin,alpha-cypermethrin,and permethrin to identify resistance patterns to different classes of insecticides.Methods:The data included information on vector species,location of the study(state/district),insecticide tested,mortality percentage,and susceptibility classification based on the World Health Organization interpretation criteria.Retrospective data were collected from peer-reviewed publications(2017-2024)and up to June 2025.The data were collated for five major malaria vector species,namely Anopheles(An.)culicifacies,An.fluviatilis,An.stephensi,An.baimaii,and An.minimus.Results:Insecticide susceptibility data were available from 86 districts across 16 Indian states for 40615 mosquitoes.The majority of the data was on An.culicifacies(n=28308),followed by An.stephensi(n=5611),An.fluviatilis(n=5967),An.baimaii(n=365),and An.minimus(n=364).Intensity bioassays revealed low to moderate resistance levels in An.culicifacies populations from selected districts in 3 states,Odisha,Madhya Pradesh,and Chhattisgarh against deltamethrin and alpha-cypermethrin.Conclusions:This review highlights spatial and species-level variations in insecticide susceptibility among Indian malaria vectors.The low to moderate intensity suggested that it may not yet be severe enough to cause operational failure with current vector control interventions.Continued monitoring of insecticide resistance,as well as the use of new-generation insecticides and interventions,is suggested to sustain vector control efficacy and manage insecticide resistance in malaria vectors to support India’s malaria elimination.
基金supported by the Royal Academy of Engineering,UK,under the scheme of Distinguished International Associates(DIA-2424-5-134).
文摘The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique(BO-SVR with a Gaussian kernel)was proposed.Unlike,traditional approaches that use the internal resistance,and battery capacity as input parameters,this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval,as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error,R^(2),root mean squared error,and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models,with a reduced RMSE of 0.0082,thus demonstrating its potential to predict the SoH more accurately.
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 Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
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 grants PID2020-120308RB-I00 and PID2023-147802OB-I00 funded by MICIU/AEI/10.13039/501100011033FEDER,UE,by Aligning Science Across Parkinson’s(ref.ASAP-020505)through the Michael J.Fox Foundation for Parkinson’s Research+1 种基金by CiberNed Intramural Collaborative Projects(ref.PI2020/09)by the Spanish Fundación Mutua Madrile?a de Investigación Médica(to JLL)。
文摘The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.
文摘Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.
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 China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
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.
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 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.
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.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104190,12104189,12204312)the Natural Science Foundation of Jiangsu Province(Grant No.BK20210874)+2 种基金General project of Natural Science Research in Colleges And Universities of Jiangsu Province(Grant No.20KJB140008)the Jiangxi Provincial Natural Science Foundation(Grant Nos.20224BAB211014 and 20232BAB201042)Key Laboratory of Tian Qin Project(Sun Yat-sen University)。
文摘A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology.
