Frequent droughts pose considerable threat to global forest carbon uptake,but little is known about the response of forest carbon fluxes in climatic transition zones to seasonal drought.In this study,the responses of ...Frequent droughts pose considerable threat to global forest carbon uptake,but little is known about the response of forest carbon fluxes in climatic transition zones to seasonal drought.In this study,the responses of carbon fluxes to seasonal drought in two natural forests(Quercus aliena var.acute serrata Maxim and Pinus tabuliformis Carr.)in the Baotianman Nature Reserve were investigated.The Q.aliena forest exhibited a high resilience with stable gross primary productivity(GPP).However,ecosystem respiration(Re)significantly declined by 18.4%compared with normal years,leading to an increase in net carbon sequestration capacity of 4.1%.This resilience was attributed to its deep root system accessing soil water(SWC_(50cm))to sustain stomatal openness,coupled with the efficient utilization of photosynthetically active radiation to drive photosynthesis.In contrast,the P.tabuliformis forest,which relied on shallow soil moisture(SWC_(20cm)),experienced simultaneous decreases in both GPP and Re during drought,with a sharply greater decrease in GPP,resulting in low net carbon sink capacity.Further analysis revealed that the Q.aliena forest prioritized carbon assimilation through a deep water-stomatal synergy strategy(anisohydric behavior),whereas the P.tabuliformis forest adopted an isohydric strategy favoring water conservation at the expense of carbon fixation efficiency.These findings highlight distinct mechanisms underlying drought adaptation between forest types,providing critical insight into optimizing forest carbon cycle models and selecting drought-resistant species under the influence of climate change.展开更多
We perform the manifestly covariant quantization of f(R)gravity in the de Donder gauge condition(or harmonic gauge condition)for general coordinate invariance.We explicitly calculate various equal-time commutation rel...We perform the manifestly covariant quantization of f(R)gravity in the de Donder gauge condition(or harmonic gauge condition)for general coordinate invariance.We explicitly calculate various equal-time commutation relations(ETCRs),in particular the ETCR between the metric and its time derivative,and show that it has a nonvanishing and nontrivial expression,whose situation should be contrasted to the previous result in higher-derivative or quadratic gravity where the ETCR was found to be identically vanishing.We also clarify global symmetries,the physical content of f(R)gravity,and clearly show that this theory is manifestly unitary and has a massive scalar and massless graviton as physical modes.展开更多
In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail ...In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.展开更多
Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exp...Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exponentially weighted moving average(SGLGEWMA)control chart,incorporating a Sparse Group Lasso penalty,which is capable of detecting shifts in the covariance matrix across a wide range of data types,including discrete,continuous,and mixed distributions.The proposed approach projects multivariate data into a Euclidean space and then computes an approximate Alt’s likelihood ratio,regularized via the Sparse Group Lasso.The resulting EWMA statistic monitors process shifts.Monte Carlo simulations demonstrate that SGLGEWMA outperforms both the Lasso-based LGShewhart and the Ridge-based RGEWMA control charts under various distributions,with enhanced efficacy in high-dimensional scenarios.Sensitivity analyses are performed on the tuning parameters(λ_(1),λ_(2))and smoothing parameterρ,to evaluate their impact on monitoring performance.Additionally,a simulation study and an illustrative example involving covariance monitoring in wafer semiconductor manufacturing are presented to demonstrate the practical application of the proposed chart.Empirical results confirm that the proposed control chart promptly identifies abnormal fluctuations and issues timely alerts,highlighting both its theoretical significance and practical utility.展开更多
Neurodegenerative disorders,including Alzheimer’s disease(AD),Parkinson’s disease(PD),and amyotrophic lateral sclerosis,impose a considerable social and economic burden on society and have dramatic consequences for ...Neurodegenerative disorders,including Alzheimer’s disease(AD),Parkinson’s disease(PD),and amyotrophic lateral sclerosis,impose a considerable social and economic burden on society and have dramatic consequences for individuals and their families.The majority of existing interventions have been found to be capable of only a slight modification of disease progression or to moderately delay significant functional decline in motor,cognitive,or mental domains.展开更多
Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when ...Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.展开更多
Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimila...Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.展开更多
In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utili...In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.展开更多
Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its ...Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its inverse.However,the high dimensionality and heavy-tailed nature of financial data pose significant challenges to this estimation.In this study,we propose a method to estimate the Gini covariance matrix by introducing a low-rank and sparse correlation structure,as an alternative to the traditional sample covariance matrix.Our approach employs a factor model to capture the low-rank structure,combined with thresholding rules to achieve the final estimation.We demonstrate the consistency of our estimators and validate our approach through simulation experiments and empirical portfolio analyses.Simulation results show that our method is highly applicable across a variety of distributional scenarios.Furthermore,empirical portfolio analysis indicates that our method can construct portfolios with superior performance.展开更多
The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory ...The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory with covariant chiral interactions is a promising ab initio approach to describe both nuclear matter and finite nuclei.In the description of finite nuclei with the current RBHF theory,the covariant chiral interactions have to be localized to make calculations feasible.In order to examine the reliability and validity,in this letter,the RBHF theory with local and nonlocal covariant chiral interactions at leading order is applied to nuclear matter.The low-energy constants in the covariant chiral interactions determined with the local regularization are close to those with the nonlocal regularization.Moreover,the RBHF theory using covariant chiral interactions with local and nonlocal regulators provides an equally good description of the saturation properties of nuclear matter.The present work paves the way for the implementation of covariant chiral interactions in RBHF theory for finite nuclei.展开更多
Background Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental disorder with behavioural symptoms and grey matter volume(GMV)changes.However,previous studies have not fully elucidated the prog...Background Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental disorder with behavioural symptoms and grey matter volume(GMV)changes.However,previous studies have not fully elucidated the progressive and causal GMV changes associated with behavioural symptoms in ADHD.Aims This study aimed to explore the causal relationship between GMV alterations and behavioural symptoms in children and adolescents with ADHD using behaviourcausal structural covariance network(BCaSCN)analysis.Methods Structural magnetic resonance imaging(sMRI)data from 135 children and adolescents with ADHD and182 neurotypical controls(NCs)were analysed.ADHD subtypes were identified based on GMV using a clustering algorithm to address the neuroanatomical heterogeneity.To investigate the causal relationships of GMV changes related to behavioural symptoms,sMRI data were sequentially ordered by ADHD index,inattentive index and hyperactive/impulsive index values to generate pseudotime series data.These data were then analysed using region-of-interest-based BCaSCN analysis to explore potential progressive patterns of GMV change.Results Neuroanatomical subtyping revealed two ADHD subtypes with distinct GMV patterns compared with NCs.BCaSCN analysis showed that ADHD subtype 1 was closely associated with inattentiveness,involving prominent nodes in the frontal regions and cerebellum.In contrast,ADHD subtype 2 was more strongly linked to overall disease severity,with the cerebellum and hippocampus as primary hubs.Conclusions ADHD is associated with heterogeneous changes in GMV corresponding to distinct behavioural domains,highlighting the need for subtype-specific diagnostic and therapeutic strategies.展开更多
本文主要对Hessian度量诱导的截面曲率展开分析。首先阐述Hessian度量和与之相关的Christoffor符号和曲率张量公式。接着介绍当定义域U为锥时,齐次函数的相关概念,以及Clebsch covariant S(f)和截面曲率的联系。利用R3中开子集U和超平面...本文主要对Hessian度量诱导的截面曲率展开分析。首先阐述Hessian度量和与之相关的Christoffor符号和曲率张量公式。接着介绍当定义域U为锥时,齐次函数的相关概念,以及Clebsch covariant S(f)和截面曲率的联系。利用R3中开子集U和超平面M = {f = 1}相切2-平面上一点处的截面曲率可由S(f)和Hessian行列式H(f)表示,其中f为R3上的齐次多项式,得到不变量S(f)为零与Witten-Dijkgraaf-Verlinde-Verlinde (WDVV)方程等价的条件,即f可以表示为两种特殊的形式。This paper primarily analyzes the sectional curvature induced by the Hessian metric. It begins by detailing the Hessian metric and its associated Christoffel symbols and curvature tensor formulas. It then introduces the concept of homogeneous functions when the domain U is a cone, as well as the relationship between the Clebsch covariant S(f) and the sectional curvature. Using an open subset U in R3 and a hypersurface M = {f = 1}, the sectional curvature at a point on a 2-plane tangent to M can be expressed in terms of S(f) and the Hessian matrix H(f), where f is a homogeneous polynomial on R3. The condition for the invariant S(f) to be zero is equivalent to the Witten-Dijkgraaf-Verlinde-Verlinde (WDVV) equations, which implies that f can be represented in two specific forms.展开更多
Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several...Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several approaches have been suggested for gait recognition;nevertheless,the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions,clothing changes,walking speed,and varying camera viewpoints.Furthermore,most existing research focuses on single-person gait recognition;however,counting,tracking,detecting,and recognizing individuals in dual-subject settings with occlusions remains a challenging task.Therefore,this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios.More precisely,in the proposed method,we have designed a deep learning(DL)-based dual-subject gait model(DSG)involving three modules.The first module handles silhouette segmentation,localization,and counting(SLC)using Mask-RCNN with MobileNetV2.The next stage uses a Convolutional block attention module(CBAM)-based Siamese network for frame-level tracking with a modified gallery setting.Following the last,gait recognition based on regionbased deep learning is proposed for dual-subject gait recognition.The proposed method,tested on Shri Mata Vaishno Devi University(SMVDU)-Multi-Gait and Single-Gait datasets,shows strong performance with 94.00%segmentation,58.36%tracking,and 63.04%gait recognition accuracy in dual-subject walk scenarios.展开更多
Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because ...Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because of their working mechanism of transparent forwarding.For the purpose of eliminating the influence of interference,this paper develops an angle reciprocal interference suppression scheme based on the reconstruction of interferenceplus-noise covariance matrix(ARIS-RIN).Firstly,we utilize the reciprocity between the known beam central angle and the unknown signal arrival angle to estimate the angle of arrival(AOA)of desired signal due to the multi-beam coverage.Then,according to the priori known spatial spectrum distribution,the interferenceplus-noise covariance matrix(INCM)is reconstructed by integrating within the range except the direction of desired signal.In order to correct the estimation bias of the first two steps,the worst-case performance optimization technology is adopted in the process of solving the beamforming vector.Numerical simulation results show that the developed scheme:1)has a higher output signal-to-interference-plus-noise ratio(SINR)under arbitrary signal-to-noise ratio(SNR);2)still has good performance under small snapshots;3)is robuster and easier to be realized when comparing with minimum variance distortionless response(MVDR)and the traditional diagonal loading algorithms.展开更多
As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the ...As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the complex relationships.In this paper,we propose a novel method for constructing independence tests for random elements in Hilbert spaces,which includes functional data as a special case.Our approach is using distance covariance of random projections to build a test statistic that is computationally efficient and exhibits strong power performance.We prove the equivalence between testing for independence expressed on the original and the projected covariates,bridging the gap between measures of testing independence in Euclidean spaces and Hilbert spaces.Implementation of the test involves calibration by permutation and combining several p-values from different projections using the false discovery rate method.Simulation studies and real data examples illustrate the finite sample properties of the proposed method under a variety of scenarios.展开更多
The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessar...The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.展开更多
The Mekong Delta in Vietnam is a region that produces rice and emits methane,a potent greenhouse gas.Vietnam’s rice exports,which rank among the top four globally,have a significant impact on the world’s food suppy....The Mekong Delta in Vietnam is a region that produces rice and emits methane,a potent greenhouse gas.Vietnam’s rice exports,which rank among the top four globally,have a significant impact on the world’s food suppy.The Eddy Covariance system,positioned in the rice field,has been recording methane emission rates and bio-meteorological factors.This study presents the findings of three crop seasons(Summer-Autumn 2020(S-A20),Winter-Spring 2021(W-S21),and Spring-Summer 2021(S-S21))from the year 2020 to 2021.The highest CH4 emission value was observed in the S-S21 crop,reaching 4.14μmol s^(-1 )m^(-2).Elevated CH_(4) emission rates were predominantly recorded during the vegetative stage within first 21 days after planting,while lower CH_(4) emissions were observed during the reproductive and ripening stages.This pattern clearly indicates higher methane emissions at the vegetative stage of the growing rice,likely due to the abundance of organic matter in the rice fields.The average CH4 emission rate was 0.1μmol m^(-2) s^(-1).Notably,high methane emissions were recorded when the soil surface temperature was below 33℃.As a results,the S-S21 exhibits the highest methane emission rates compared to other seasons.展开更多
Ocean observations are inherently characterized by irregular temporal and spatial distributions,as well as heterogeneous spatial resolutions and error characteristics arising from the use of diverse observational plat...Ocean observations are inherently characterized by irregular temporal and spatial distributions,as well as heterogeneous spatial resolutions and error characteristics arising from the use of diverse observational platforms and techniques.To enable their application across a broad range of scientific and practical problems,it is essential to map these heterogeneous datasets into temporally and spatially consistent gridded products.Optimal Interpolation remains the most widely adopted algorithm for the mapping of oceanographic data.Two principal implementations of the optimal interpolation algorithm are commonly employed.The first,known as the basic optimal interpolation,is derived from the theory of optimal estimation and involves computationally intensive matrix operations,posing significant challenges when applied to high-dimensional problems.The second,referred to as the point-wise optimal interpolation,reduces computational complexity through point-wise estimation,thereby circumventing high-dimensional operations;however,this approach results in a substantially higher overall computational cost.In this study,a novel optimal interpolation algorithm is proposed that utilizes the Kronecker product to approximate the background error covariance matrix.This formulation enables the decomposition of high-dimensional matrix operations into smaller,computationally tractable sub-problems,thereby improving the scalability of optimal interpolation for large spatial domains with dense observational coverage.Building upon this framework,a multi-scale optimal interpolation method is further developed to enhance the integration of observational datasets with widely varying spatial resolutions,thereby improving the accuracy and applicability of the resulting gridded products.展开更多
In complex environments such as high dynamics and weak signals,a satellite signal compensation method based on prefabricated trajectory assistance and an improved adaptive Kalman filter is proposed for a 155 mm differ...In complex environments such as high dynamics and weak signals,a satellite signal compensation method based on prefabricated trajectory assistance and an improved adaptive Kalman filter is proposed for a 155 mm differential rotating rear-body control-guided projectile to address the situation of satellite signal flickering and loss in projectile navigation systems due to environmental limitations.First,establish the system state and measurement equation when receiving satellite signals normally.Second,a seven-degree-of-freedom external ballistic model is constructed,and the ideal trajectory output from the ballistic model is used to provide the virtual motion state of the projectile,which is input into a filter as a substitute observation when satellite signals are lost.Finally,an adaptive Kalman filter(AKF)is designed,the proposed adaptive Kalman filter can accurately adjust the estimation error covariance matrix and Kalman gain in real-time based on information covariance mismatch.The simulation results show that compared to the classical Kalman filter,it can reduce the average positioning error by more than 38.21%in the case of short-term and full-range loss of satellite signals,providing a new idea for the integrated navigation of projectiles with incomplete information under the condition of satellite signal loss.展开更多
Inspired by the profoundly observed odd-even staggering and the inverted parabolic-like shape in charge radii along calcium isotopic chain,the ground state properties of calcium isotopes are investigated by constraini...Inspired by the profoundly observed odd-even staggering and the inverted parabolic-like shape in charge radii along calcium isotopic chain,the ground state properties of calcium isotopes are investigated by constraining the root-mean-square charge radii under the covariant energy density functionals with effective forces NL3 and PK1.In this work,the pairing correlations are tackled by solving the state-dependent Bardeen-Cooper-Schrieffer equations.The calculated results suggest that the binding energies obtained by the radius constraint method have been slightly changed by about 0.2%.But for charge radii,the corresponding results deriving from NL3 and PK1 forces have been increased by about 1.0%and 2.0%,respectively.This means that the charge radius is a more sensitive quantity in the calibrated protocol.Meanwhile,it is found that the reproduced charge radii of calcium isotopes are attributed to the rather strong isospin dependence of effective potential.The odd-even oscillation behavior can also be presented in the proton Fermi energies along calcium isotopic family,but keep opposite trends with respect to the corresponding binding energies and charge radii.As encountered in charge radii,the weakened odd-even oscillation behavior still emerged from the proton Fermi energies at the neutron numbers N=20 and 28 as well,but not in binding energies.展开更多
基金financially supported by the National Key Research and Development Program of China(2021YFD2200405)the National Natural Science Foundation of China(31930078)special funds for Baotianman Forest Ecosystem Research Station from Chinese Academy of Forestry and Ministry of Science and Technology of China。
文摘Frequent droughts pose considerable threat to global forest carbon uptake,but little is known about the response of forest carbon fluxes in climatic transition zones to seasonal drought.In this study,the responses of carbon fluxes to seasonal drought in two natural forests(Quercus aliena var.acute serrata Maxim and Pinus tabuliformis Carr.)in the Baotianman Nature Reserve were investigated.The Q.aliena forest exhibited a high resilience with stable gross primary productivity(GPP).However,ecosystem respiration(Re)significantly declined by 18.4%compared with normal years,leading to an increase in net carbon sequestration capacity of 4.1%.This resilience was attributed to its deep root system accessing soil water(SWC_(50cm))to sustain stomatal openness,coupled with the efficient utilization of photosynthetically active radiation to drive photosynthesis.In contrast,the P.tabuliformis forest,which relied on shallow soil moisture(SWC_(20cm)),experienced simultaneous decreases in both GPP and Re during drought,with a sharply greater decrease in GPP,resulting in low net carbon sink capacity.Further analysis revealed that the Q.aliena forest prioritized carbon assimilation through a deep water-stomatal synergy strategy(anisohydric behavior),whereas the P.tabuliformis forest adopted an isohydric strategy favoring water conservation at the expense of carbon fixation efficiency.These findings highlight distinct mechanisms underlying drought adaptation between forest types,providing critical insight into optimizing forest carbon cycle models and selecting drought-resistant species under the influence of climate change.
文摘We perform the manifestly covariant quantization of f(R)gravity in the de Donder gauge condition(or harmonic gauge condition)for general coordinate invariance.We explicitly calculate various equal-time commutation relations(ETCRs),in particular the ETCR between the metric and its time derivative,and show that it has a nonvanishing and nontrivial expression,whose situation should be contrasted to the previous result in higher-derivative or quadratic gravity where the ETCR was found to be identically vanishing.We also clarify global symmetries,the physical content of f(R)gravity,and clearly show that this theory is manifestly unitary and has a massive scalar and massless graviton as physical modes.
基金supported by Natural Science Foundation of Hu'nan Province(2024JJ5409)。
文摘In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.
基金Sponsored by the National Natural Science Foundation of China[grant number 12571305]the Natural Science Foundation of Shanghai[grant number 25ZR1401113].
文摘Traditional multivariate parametric control charts often perform inadequately in detecting shifts in the covariance matrix when the data deviate from normality.In this paper,we propose a multivariate nonparametric exponentially weighted moving average(SGLGEWMA)control chart,incorporating a Sparse Group Lasso penalty,which is capable of detecting shifts in the covariance matrix across a wide range of data types,including discrete,continuous,and mixed distributions.The proposed approach projects multivariate data into a Euclidean space and then computes an approximate Alt’s likelihood ratio,regularized via the Sparse Group Lasso.The resulting EWMA statistic monitors process shifts.Monte Carlo simulations demonstrate that SGLGEWMA outperforms both the Lasso-based LGShewhart and the Ridge-based RGEWMA control charts under various distributions,with enhanced efficacy in high-dimensional scenarios.Sensitivity analyses are performed on the tuning parameters(λ_(1),λ_(2))and smoothing parameterρ,to evaluate their impact on monitoring performance.Additionally,a simulation study and an illustrative example involving covariance monitoring in wafer semiconductor manufacturing are presented to demonstrate the practical application of the proposed chart.Empirical results confirm that the proposed control chart promptly identifies abnormal fluctuations and issues timely alerts,highlighting both its theoretical significance and practical utility.
文摘Neurodegenerative disorders,including Alzheimer’s disease(AD),Parkinson’s disease(PD),and amyotrophic lateral sclerosis,impose a considerable social and economic burden on society and have dramatic consequences for individuals and their families.The majority of existing interventions have been found to be capable of only a slight modification of disease progression or to moderately delay significant functional decline in motor,cognitive,or mental domains.
基金supported in part by the National Key Research and Development Program of China(2023YFB3906403)the National Natural Science Foundation of China(62373118,62173105)the Natural Science Foundation of Heilongjiang Province of China(ZD2023F002)
文摘Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.
基金sponsored by the National Natural Science Foundation of China[grant number U2442218]。
文摘Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.
基金supported in part by the National Natural Science Foundation of China(12171124,61933007)the Natural Science Foundation of Heilongjiang Province of China(ZD2022F003)+2 种基金the National High-End Foreign Experts Recruitment Plan of China(G2023012004L)the Royal Society of UKthe Alexander von Humboldt Foundation of Germany
文摘In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.
基金supported by the Postdoctoral Fellowship Program of CPSF(GZC20241651)the National Natural Science Foundation of China(12501391)the Natural Science Foundation of Anhui Province(2408085QA005).
文摘Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its inverse.However,the high dimensionality and heavy-tailed nature of financial data pose significant challenges to this estimation.In this study,we propose a method to estimate the Gini covariance matrix by introducing a low-rank and sparse correlation structure,as an alternative to the traditional sample covariance matrix.Our approach employs a factor model to capture the low-rank structure,combined with thresholding rules to achieve the final estimation.We demonstrate the consistency of our estimators and validate our approach through simulation experiments and empirical portfolio analyses.Simulation results show that our method is highly applicable across a variety of distributional scenarios.Furthermore,empirical portfolio analysis indicates that our method can construct portfolios with superior performance.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.12435006,12435007,12475117,12141501,and 123B2080)the National Key R&D Program of China(Grant No.2024YFE0109803)the National Key Laboratory of Neutron Science and Technology(Grant No.NST202401016)。
文摘The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory with covariant chiral interactions is a promising ab initio approach to describe both nuclear matter and finite nuclei.In the description of finite nuclei with the current RBHF theory,the covariant chiral interactions have to be localized to make calculations feasible.In order to examine the reliability and validity,in this letter,the RBHF theory with local and nonlocal covariant chiral interactions at leading order is applied to nuclear matter.The low-energy constants in the covariant chiral interactions determined with the local regularization are close to those with the nonlocal regularization.Moreover,the RBHF theory using covariant chiral interactions with local and nonlocal regulators provides an equally good description of the saturation properties of nuclear matter.The present work paves the way for the implementation of covariant chiral interactions in RBHF theory for finite nuclei.
基金funded by the Department of Science and Technology of Shandong ProvinceNatural Science Foundation of Shandong Province(ZR2023QH109)Taishan Scholars Program of Shandong Province(TS201712065)。
文摘Background Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental disorder with behavioural symptoms and grey matter volume(GMV)changes.However,previous studies have not fully elucidated the progressive and causal GMV changes associated with behavioural symptoms in ADHD.Aims This study aimed to explore the causal relationship between GMV alterations and behavioural symptoms in children and adolescents with ADHD using behaviourcausal structural covariance network(BCaSCN)analysis.Methods Structural magnetic resonance imaging(sMRI)data from 135 children and adolescents with ADHD and182 neurotypical controls(NCs)were analysed.ADHD subtypes were identified based on GMV using a clustering algorithm to address the neuroanatomical heterogeneity.To investigate the causal relationships of GMV changes related to behavioural symptoms,sMRI data were sequentially ordered by ADHD index,inattentive index and hyperactive/impulsive index values to generate pseudotime series data.These data were then analysed using region-of-interest-based BCaSCN analysis to explore potential progressive patterns of GMV change.Results Neuroanatomical subtyping revealed two ADHD subtypes with distinct GMV patterns compared with NCs.BCaSCN analysis showed that ADHD subtype 1 was closely associated with inattentiveness,involving prominent nodes in the frontal regions and cerebellum.In contrast,ADHD subtype 2 was more strongly linked to overall disease severity,with the cerebellum and hippocampus as primary hubs.Conclusions ADHD is associated with heterogeneous changes in GMV corresponding to distinct behavioural domains,highlighting the need for subtype-specific diagnostic and therapeutic strategies.
文摘本文主要对Hessian度量诱导的截面曲率展开分析。首先阐述Hessian度量和与之相关的Christoffor符号和曲率张量公式。接着介绍当定义域U为锥时,齐次函数的相关概念,以及Clebsch covariant S(f)和截面曲率的联系。利用R3中开子集U和超平面M = {f = 1}相切2-平面上一点处的截面曲率可由S(f)和Hessian行列式H(f)表示,其中f为R3上的齐次多项式,得到不变量S(f)为零与Witten-Dijkgraaf-Verlinde-Verlinde (WDVV)方程等价的条件,即f可以表示为两种特殊的形式。This paper primarily analyzes the sectional curvature induced by the Hessian metric. It begins by detailing the Hessian metric and its associated Christoffel symbols and curvature tensor formulas. It then introduces the concept of homogeneous functions when the domain U is a cone, as well as the relationship between the Clebsch covariant S(f) and the sectional curvature. Using an open subset U in R3 and a hypersurface M = {f = 1}, the sectional curvature at a point on a 2-plane tangent to M can be expressed in terms of S(f) and the Hessian matrix H(f), where f is a homogeneous polynomial on R3. The condition for the invariant S(f) to be zero is equivalent to the Witten-Dijkgraaf-Verlinde-Verlinde (WDVV) equations, which implies that f can be represented in two specific forms.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Convergence Security Core Talent Training Business Support Program(IITP-2025-RS-2023-00266605)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several approaches have been suggested for gait recognition;nevertheless,the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions,clothing changes,walking speed,and varying camera viewpoints.Furthermore,most existing research focuses on single-person gait recognition;however,counting,tracking,detecting,and recognizing individuals in dual-subject settings with occlusions remains a challenging task.Therefore,this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios.More precisely,in the proposed method,we have designed a deep learning(DL)-based dual-subject gait model(DSG)involving three modules.The first module handles silhouette segmentation,localization,and counting(SLC)using Mask-RCNN with MobileNetV2.The next stage uses a Convolutional block attention module(CBAM)-based Siamese network for frame-level tracking with a modified gallery setting.Following the last,gait recognition based on regionbased deep learning is proposed for dual-subject gait recognition.The proposed method,tested on Shri Mata Vaishno Devi University(SMVDU)-Multi-Gait and Single-Gait datasets,shows strong performance with 94.00%segmentation,58.36%tracking,and 63.04%gait recognition accuracy in dual-subject walk scenarios.
基金supported by the National Natural Science Foundation of China under Grants No.61671367 and 62471381the Research Foundation of Science and Technology on Communication Networks Laboratory,and the National Key Laboratory of Wireless Communications Foundation under Grant No.IFN202401.
文摘Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because of their working mechanism of transparent forwarding.For the purpose of eliminating the influence of interference,this paper develops an angle reciprocal interference suppression scheme based on the reconstruction of interferenceplus-noise covariance matrix(ARIS-RIN).Firstly,we utilize the reciprocity between the known beam central angle and the unknown signal arrival angle to estimate the angle of arrival(AOA)of desired signal due to the multi-beam coverage.Then,according to the priori known spatial spectrum distribution,the interferenceplus-noise covariance matrix(INCM)is reconstructed by integrating within the range except the direction of desired signal.In order to correct the estimation bias of the first two steps,the worst-case performance optimization technology is adopted in the process of solving the beamforming vector.Numerical simulation results show that the developed scheme:1)has a higher output signal-to-interference-plus-noise ratio(SINR)under arbitrary signal-to-noise ratio(SNR);2)still has good performance under small snapshots;3)is robuster and easier to be realized when comparing with minimum variance distortionless response(MVDR)and the traditional diagonal loading algorithms.
基金Supported by the Grant of National Science Foundation of China(11971433)Zhejiang Gongshang University“Digital+”Disciplinary Construction Management Project(SZJ2022B004)+1 种基金Institute for International People-to-People Exchange in Artificial Intelligence and Advanced Manufacturing(CCIPERGZN202439)the Development Fund for Zhejiang College of Shanghai University of Finance and Economics(2023FZJJ15).
文摘As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the complex relationships.In this paper,we propose a novel method for constructing independence tests for random elements in Hilbert spaces,which includes functional data as a special case.Our approach is using distance covariance of random projections to build a test statistic that is computationally efficient and exhibits strong power performance.We prove the equivalence between testing for independence expressed on the original and the projected covariates,bridging the gap between measures of testing independence in Euclidean spaces and Hilbert spaces.Implementation of the test involves calibration by permutation and combining several p-values from different projections using the false discovery rate method.Simulation studies and real data examples illustrate the finite sample properties of the proposed method under a variety of scenarios.
基金supported by the National Natural Science Foundation of China(Nos.62120106003 and 62173301)。
文摘The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.
基金funded by Vietnam National University,Ho Chi Minh City(VNU-HCM)under grant number C2022-18-15.
文摘The Mekong Delta in Vietnam is a region that produces rice and emits methane,a potent greenhouse gas.Vietnam’s rice exports,which rank among the top four globally,have a significant impact on the world’s food suppy.The Eddy Covariance system,positioned in the rice field,has been recording methane emission rates and bio-meteorological factors.This study presents the findings of three crop seasons(Summer-Autumn 2020(S-A20),Winter-Spring 2021(W-S21),and Spring-Summer 2021(S-S21))from the year 2020 to 2021.The highest CH4 emission value was observed in the S-S21 crop,reaching 4.14μmol s^(-1 )m^(-2).Elevated CH_(4) emission rates were predominantly recorded during the vegetative stage within first 21 days after planting,while lower CH_(4) emissions were observed during the reproductive and ripening stages.This pattern clearly indicates higher methane emissions at the vegetative stage of the growing rice,likely due to the abundance of organic matter in the rice fields.The average CH4 emission rate was 0.1μmol m^(-2) s^(-1).Notably,high methane emissions were recorded when the soil surface temperature was below 33℃.As a results,the S-S21 exhibits the highest methane emission rates compared to other seasons.
基金The National Key Research and Development Program of China under contract No.2022YFF0801404.
文摘Ocean observations are inherently characterized by irregular temporal and spatial distributions,as well as heterogeneous spatial resolutions and error characteristics arising from the use of diverse observational platforms and techniques.To enable their application across a broad range of scientific and practical problems,it is essential to map these heterogeneous datasets into temporally and spatially consistent gridded products.Optimal Interpolation remains the most widely adopted algorithm for the mapping of oceanographic data.Two principal implementations of the optimal interpolation algorithm are commonly employed.The first,known as the basic optimal interpolation,is derived from the theory of optimal estimation and involves computationally intensive matrix operations,posing significant challenges when applied to high-dimensional problems.The second,referred to as the point-wise optimal interpolation,reduces computational complexity through point-wise estimation,thereby circumventing high-dimensional operations;however,this approach results in a substantially higher overall computational cost.In this study,a novel optimal interpolation algorithm is proposed that utilizes the Kronecker product to approximate the background error covariance matrix.This formulation enables the decomposition of high-dimensional matrix operations into smaller,computationally tractable sub-problems,thereby improving the scalability of optimal interpolation for large spatial domains with dense observational coverage.Building upon this framework,a multi-scale optimal interpolation method is further developed to enhance the integration of observational datasets with widely varying spatial resolutions,thereby improving the accuracy and applicability of the resulting gridded products.
基金funded by the National Natural Science Foundation of China (Grant No. 62471048)Open Fund Project of Beijing Key Laboratory of High Dynamic Navigation TechnologyKey Laboratory Fund Project of Modern Measurement and Control Technology, Ministry of Education
文摘In complex environments such as high dynamics and weak signals,a satellite signal compensation method based on prefabricated trajectory assistance and an improved adaptive Kalman filter is proposed for a 155 mm differential rotating rear-body control-guided projectile to address the situation of satellite signal flickering and loss in projectile navigation systems due to environmental limitations.First,establish the system state and measurement equation when receiving satellite signals normally.Second,a seven-degree-of-freedom external ballistic model is constructed,and the ideal trajectory output from the ballistic model is used to provide the virtual motion state of the projectile,which is input into a filter as a substitute observation when satellite signals are lost.Finally,an adaptive Kalman filter(AKF)is designed,the proposed adaptive Kalman filter can accurately adjust the estimation error covariance matrix and Kalman gain in real-time based on information covariance mismatch.The simulation results show that compared to the classical Kalman filter,it can reduce the average positioning error by more than 38.21%in the case of short-term and full-range loss of satellite signals,providing a new idea for the integrated navigation of projectiles with incomplete information under the condition of satellite signal loss.
基金supported by the Natural Science Foundation of Ningxia Province,China(No.2024AAC03015)the Open Project of Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology,No.NLK2023-05+9 种基金the Central Government Guidance Funds for Local Scientific and Technological Development,China(No.Guike ZY22096024)the Key Laboratory of Beam Technology of Ministry of Education,China(No.BEAM2024G04)the National Natural Science Foundation of China under Grants No.11705118,No.12175151the Major Project of the GuangDong Basic and Applied Basic Research Foundation(2021B0301030006)NT is grateful for the support of the key research and development project of Ningxia(Grant No.2024BEH04090)the Key Laboratory of Beam Technology of Ministry of Education,China(No.BEAM2024G05)the National Natural Science Foundation of China under Grants No.12275025,No.11975096the Fundamental Research Funds for the Central Universities(2020NTST06)supported by the National Key R&D Program of China under Grant No.2023YFA1606401the National Natural Science Foundation of China under Grants No.12135004,No.11635003,No.11961141004.
文摘Inspired by the profoundly observed odd-even staggering and the inverted parabolic-like shape in charge radii along calcium isotopic chain,the ground state properties of calcium isotopes are investigated by constraining the root-mean-square charge radii under the covariant energy density functionals with effective forces NL3 and PK1.In this work,the pairing correlations are tackled by solving the state-dependent Bardeen-Cooper-Schrieffer equations.The calculated results suggest that the binding energies obtained by the radius constraint method have been slightly changed by about 0.2%.But for charge radii,the corresponding results deriving from NL3 and PK1 forces have been increased by about 1.0%and 2.0%,respectively.This means that the charge radius is a more sensitive quantity in the calibrated protocol.Meanwhile,it is found that the reproduced charge radii of calcium isotopes are attributed to the rather strong isospin dependence of effective potential.The odd-even oscillation behavior can also be presented in the proton Fermi energies along calcium isotopic family,but keep opposite trends with respect to the corresponding binding energies and charge radii.As encountered in charge radii,the weakened odd-even oscillation behavior still emerged from the proton Fermi energies at the neutron numbers N=20 and 28 as well,but not in binding energies.
