The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability ...The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.展开更多
Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-targe...Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the“therapeutic module”of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.展开更多
In recent years,universal adversarial per-turbation(UAP)has attracted the attention of many re-searchers due to its good generalization.However,in order to generate an appropriate UAP,current methods usually require e...In recent years,universal adversarial per-turbation(UAP)has attracted the attention of many re-searchers due to its good generalization.However,in order to generate an appropriate UAP,current methods usually require either accessing the original dataset or meticulously constructing optimization functions and proxy datasets.In this paper,we aim to elimi-nate any dependency on proxy datasets and explore a method for generating Universal Adversarial Pertur-bations(UAP)on a single image.After revisiting re-search on UAP,we discovered that the key to gener-ating UAP lies in the accumulation of Individual Ad-versarial Perturbation(IAP)gradient,which prompted us to study the method of accumulating gradients from an IAP.We designed a simple and effective process to generate UAP,which only includes three steps:pre-cessing,generating an IAP and scaling the perturba-tions.Through our proposed process,any IAP gener-ated on an image can be constructed into a UAP with comparable performance,indicating that UAP can be generated free of data.Extensive experiments on var-ious classifiers and attack approaches demonstrate the superiority of our method on efficiency and aggressiveness.展开更多
Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly effi...Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.展开更多
Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),i...Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),in particular the resonant portion,is found to provide the dominant contribution to the total toroidal torque under the slow plasma flow regime in ITER.While the electromagnetic torque always opposes the plasma flow,the toroidal torque associated with the Reynolds stress enhances the plasma flow independent of the flow direction.A peculiar double-peak structure for the net NTV torque is robustly computed for ITER,as the toroidal rotation frequency is scanned near the zero value.This structure is found to be ultimately due to a non-monotonic behavior of the wave-particle resonance integral(over the particle pitch angle)in the superbanana plateau NTV regime in ITER.These findings are qualitatively insensitive to variations of a range of factors including the wall resistivity,the plasma pedestal flow and the assumed frequency of the rotating RMP field.展开更多
The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typic...The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension.展开更多
Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulati...Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulations such as the General Data Protection Regulation(GDPR),which explicitly restrict the processing of data that can re-identify individuals.Although existing anonymization approaches such as the AnonymizingAutoEncoder(AAE)can reduce the risk of re-identification,they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task(e.g.,human activity recognition).This study proposes a novel sensor data anonymization method based onAdversarial Perturbations(AP)to address these limitations.By generating minimal yet targeted noise,the proposed method significantly degrades the accuracy of identity classification while retaining essential features for multiple tasks such as activity,gender,or device-position recognition.Moreover,to enhance robustness against frequency-domain analysis,additional models trained on transformed(e.g.,short-time Fourier transform(STFT))representations are incorporated into the perturbation process.A multi-task formulation is introduced that selectively suppresses person-identifying features while reinforcing those relevant to other desired tasks without retraining large autoencoder-based architectures.The proposed framework is,to our knowledge,the first AP-based anonymization technique that(i)defends simultaneously against time-and frequency-domain attacks and(ii)allows per-task trade-off control on a single forward-back-propagation run,enabling real-time,on-device deployment on commodity hardware.On three public datasets,the proposed method reduces person-identification accuracy from 60–90%to near-chance levels(≤5%)while preserving the original activity-recognition F1 both in the time and frequency domains.Compared with the baseline AAE,the proposed method improves downstream task F1 and lowers waveform mean squared error,demonstrating a better privacy-utility trade-off without additional model retraining.These findings underscore the effectiveness and flexibility of AP in privacy-preserving sensor-data processing,offering a practical solution that safeguards user identity while retaining rich,application-critical information.展开更多
Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-ra...Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.展开更多
Hydraulic fracturing then fluid circulation in enhanced geothermal system(EGS)reservoirs have been shown to induce seismicity remote from the stimulation-potentially generated by the distal projection of thermoporoela...Hydraulic fracturing then fluid circulation in enhanced geothermal system(EGS)reservoirs have been shown to induce seismicity remote from the stimulation-potentially generated by the distal projection of thermoporoelastic stresses.We explore this phenomenon by evaluating stress perturbations resulting from stimulation of a single stage of hydraulic fracturing that is followed by thermal depletion of a prismatic zone adjacent to the hydraulic fracture.We use Coulomb failure stress to assess the effect of resulting stress perturbations on instability on adjacent critically-stressed faults.Results show that hydraulic fracturing in a single stage is capable of creating stress perturbations at distances to 1000 m that reach 10^(-5)-10^(-4)MPa.At a closer distance,the magnitude of stress perturbations increases even further.The stress perturbation induced by temperature depletion could also reach 10^(-3)-10^(-2)MPa within 1000 m-much higher than that by hydraulic fracturing.Considering that a critical change in Coulomb failure stress for fault instability is 10^(-2)MPa,a single stage of hydraulic fracturing and thermal drawdown are capable of reactivating critically-stressed faults at distances within 200 m and 1000 m,respectively.These results have important implications for understanding the distribution and magnitudes of stress perturbations driven by thermoporoelastic effects and the associated seismicity during the simulation and early production of EGS reservoirs.展开更多
A large number of runaway electrons(REs)generated during disruption can cause significant damage to next-generation large-scale tokamaks.The influence of three-dimensional(3D)helical magnetic perturbations on the supp...A large number of runaway electrons(REs)generated during disruption can cause significant damage to next-generation large-scale tokamaks.The influence of three-dimensional(3D)helical magnetic perturbations on the suppression of RE generation was explored using a set of 3D helical coils in J-TEXT tokamak,which can excite m/n=-2/2 helical magnetic perturbations.Experimental evidence shows that the-2/2 magnetic perturbations caused by the opposite coil current direct plasma toward the high-field side,simultaneously enhancing the magnetic fluctuations,which would enhance the radial loss of REs and even prevent RE generation.On the other hand,-2/2 magnetic perturbations can also reduce the cooling time during the disruption phase and generate a population of high-energy REs,which can interact with high-frequency magnetic fluctuations and in turn suppress RE generation.The critical helical coil current was found to correlate with electron density,requiring higher coil currents at higher densities.According to the statistical analysis of RE generation at different electron densities,the applied-2/2 magnetic perturbations can increase the magnetic fluctuations to the same level at lower electron densities,which can decrease the threshold electron density for RE suppression.This will be beneficial for RE mitigation in future large tokamak devices.展开更多
Making exact approximations to solve equations distinguishes applied mathematicians from pure mathematicians, physicists, and engineers. Perturbation problems, both regular and singular, are pervasive in diverse field...Making exact approximations to solve equations distinguishes applied mathematicians from pure mathematicians, physicists, and engineers. Perturbation problems, both regular and singular, are pervasive in diverse fields of applied mathematics and engineering. This research paper provides a comprehensive overview of algebraic methods for solving perturbation problems, featuring a comparative analysis of their strengths and limitations. Serving as a valuable resource for researchers and practitioners, it offers insights and guidance for tackling perturbation problems in various disciplines, facilitating the advancement of applied mathematics and engineering.展开更多
Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial pertur...Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.展开更多
Time scale is a new and powerful tool for dealing with complex dynamics problems. The main result of this study is the exact invariants and adiabatic invariants of the generalized Birkhoffian system and the constraine...Time scale is a new and powerful tool for dealing with complex dynamics problems. The main result of this study is the exact invariants and adiabatic invariants of the generalized Birkhoffian system and the constrained Birkhoffian system on time scales. Firstly, we establish the differential equations of motion for the above two systems and give the corresponding Noether symmetries and exact invariants. Then, the perturbation to the Noether symmetries and the adiabatic invariants for the systems mentioned above under the action of slight disturbance are investigated, respectively. Finally, two examples are provided to show the practicality of the findings.展开更多
Pharmacological perturbation studies based on protein-level signatures are fundamental for drug discovery. In the present study, we used a mass spectrometry (MS)-based proteomic platform to profile the whole proteome ...Pharmacological perturbation studies based on protein-level signatures are fundamental for drug discovery. In the present study, we used a mass spectrometry (MS)-based proteomic platform to profile the whole proteome of the breast cancer MCF7 cell line under stress induced by 78 bioactive compounds. The integrated analysis of perturbed signal abundance revealed the connectivity between phenotypic behaviors and molecular features in cancer cells. Our data showed functional relevance in exploring the novel pharmacological activity of phenolic xanthohumol, as well as the noncanonical targets of clinically approved tamoxifen, lovastatin, and their derivatives. Furthermore, the rational design of synergistic inhibition using a combination of histone methyltransferase and topoisomerase was identified based on their complementary drug fingerprints. This study provides rich resources for the proteomic landscape of drug responses for precision therapeutic medicine.展开更多
Cement density monitoring plays a vital role in evaluating the quality of cementing projects,which is of great significance to the development of oil and gas.However,the presence of inhomogeneous cement distribution a...Cement density monitoring plays a vital role in evaluating the quality of cementing projects,which is of great significance to the development of oil and gas.However,the presence of inhomogeneous cement distribution and casing eccentricity in horizontal wells often complicates the accurate evaluation of cement azimuthal density.In this regard,this paper proposes an algorithm to calculate the cement azimuthal density in horizontal wells using a multi-detector gamma-ray detection system.The spatial dynamic response functions are simulated to obtain the influence of cement density on gamma-ray counts by the perturbation theory,and the contribution of cement density in six sectors to the gamma-ray recorded by different detectors is obtained by integrating the spatial dynamic response functions.Combined with the relationship between gamma-ray counts and cement density,a multi-parameter calculation equation system is established,and the regularized Newton iteration method is employed to invert casing eccentricity and cement azimuthal density.This approach ensures the stability of the inversion process while simultaneously achieving an accuracy of 0.05 g/cm^(3) for the cement azimuthal density.This accuracy level is ten times higher compared to density accuracy calculated using calibration equations.Overall,this algorithm enhances the accuracy of cement azimuthal density evaluation,provides valuable technical support for the monitoring of cement azimuthal density in the oil and gas industry.展开更多
The effects of equilibrium toroidal rotation during edge-localized mode(ELM)mitigated by resonant magnetic perturbation(RMP)are studied with the experimental equilibria of the EAST tokamak based on the four-field mode...The effects of equilibrium toroidal rotation during edge-localized mode(ELM)mitigated by resonant magnetic perturbation(RMP)are studied with the experimental equilibria of the EAST tokamak based on the four-field model in the BOUT++code.As the two main parameters to determine the toroidal rotation profiles,the rotation shear and magnitudes were separately scanned to investigate their roles in the impact of RMPs on peeling-ballooning(P-B)modes.On one hand,the results show that strong toroidal rotation shear is favorable for the enhancement of the self-generated E×B shearing rate<ω_(E×B)>with RMPs,leading to significant ELM mitigation with RMP in the stronger toroidal rotation shear region.On the other hand,toroidal rotation magnitudes may affect ELM mitigation by changing the penetration of the RMPs,more precisely the resonant components.RMPs can lead to a reduction in the pedestal energy loss by enhancing the multimode coupling in the turbulence transport phase.The shielding effects on RMPs increase with the toroidal rotation magnitude,leading to the enhancement of the multimode coupling with RMPs to be significantly weakened.Hence,the reduction in pedestal energy loss by RMPs decreased with the rotation magnitude.In brief,the results show that toroidal rotation plays a dual role in ELM mitigation with RMP by changing the shielding effects of plasma by rotation magnitude and affecting<ω_(E×B)>by rotation shear.In the high toroidal rotation region,toroidal rotation shear is usually strong and hence plays a dominant role in the influence of RMP on P-B modes,whereas in the low rotation region,toroidal rotation shear is weak and has negligible impact on P-B modes,and the rotation magnitude plays a dominant role in the influence of RMPs on the P-B modes by changing the field penetration.Therefore,the dual role of toroidal rotation leads to stronger ELM mitigation with RMP,which may be achieved both in the low toroidal rotation region and the relatively high rotation region that has strong rotational shear.展开更多
Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distorti...Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://github.com/kuijiang94/PerTeRNet.展开更多
Let X be a Banach space and let P:X→X be a bounded linear operator.Using an algebraic inequality on the spectrum of P,we give a new sufficient condition that guarantees the existence of(I-P)^(-1) as a bounded linear ...Let X be a Banach space and let P:X→X be a bounded linear operator.Using an algebraic inequality on the spectrum of P,we give a new sufficient condition that guarantees the existence of(I-P)^(-1) as a bounded linear operator on X,and a bound on its spectral radius is also obtained.This generalizes the classic Banach lemma.We apply the result to the perturbation analysis of general bounded linear operators on X with commutative perturbations.展开更多
The neutron radiation field has vital applications in areas such as biomedicine,geology,radiation safety,and many others for neutron detection and neutron metrology.Correcting neutron fluence rate perturbation accurat...The neutron radiation field has vital applications in areas such as biomedicine,geology,radiation safety,and many others for neutron detection and neutron metrology.Correcting neutron fluence rate perturbation accurately is an important yet challenging problem.This study proposes a correction method that analyzes three physical processes.This method,which transforms the detection process from point detection to area detection,is based on a novel physical model and has been validated through theoretical analyses,experiments,and simulations.According to the average differences between the calculated and experimental results,the new method(1.67%)demonstrated better accuracy than the traditional simulation(2.17%).In a closed thermal neutron radiation field,the detector or strong neutron absorption material significantly perturbs the neutron fluence rate,whereas its impact on the energy spectrum shape and neutron directionality is relatively minor.Furthermore,based on the calculation results of the perturbation rate formula for medium materials with different compositions and sizes,the larger the volume and capture cross section of the medium,the higher the perturbation rate generated in the closed radiation field.展开更多
The scalar-free black hole could be unstable against the scalar field perturbation when it is coupled to a Gauss–Bonnet(GB)invariant in a special form.It is known that the tachyonic instability in this scenario is tr...The scalar-free black hole could be unstable against the scalar field perturbation when it is coupled to a Gauss–Bonnet(GB)invariant in a special form.It is known that the tachyonic instability in this scenario is triggered by the sufficiently strong GB coupling.In this paper,we focus on the time domain analysis of massive scalar field perturbation around the Schwarzschild de-Sitter black hole in Einstein-scalar–Gauss–Bonnet gravity.By analyzing the scalar field propagation,we find that the scalar field will finally grow when the GB coupling is large enough.This could lead to the instability of the background black hole.Furthermore,we demonstrate how the mass of the scalar field and the GB coupling strength affect the onset of tachyonic instability.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42005005 and 42030607)the Science and Technology Department of Shaanxi Province(Grant No.2024JC-YBQN-0248)+2 种基金the Education Department of Shaanxi Province(Grant No.23JK0686)a Xi'an Science and Technology Project(Grant No.22GXFW0131)the Young Talent fund of the University Association for Science and Technology in Shaanxi(Grant No.20210706)。
文摘The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.
基金supported by the National Natural Science Foundation of China(Grant Nos.:32271292,31872723,32200778,and 22377089)the Jiangsu Students Innovation and Entrepre-neurship Training Program,China(Program No.:202210285081Z)+6 种基金the Project of MOE Key Laboratory of Geriatric Diseases and Immunology,China(Project No.:JYN202404)Proj-ect Funded by the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions,Natural Science Foundation of Jiangsu Province,China(Project No.:BK20220494)Suzhou Medical and Health Technology Innovation Project,China(Grant No.:SKY2022107)the Clinical Research Center of Neuro-logical Disease in The Second Affiliated Hospital of Soochow University,China(Grant No.:ND2022A04)State Key Laboratory of Drug Research(Grant No.:SKLDR-2023-KF-05)Jiangsu Shuang-chuang Program for Doctor,Young Science Talents Promotion Project of Jiangsu Science and Technology Association(Program No.:TJ-2023-019)Young Science Talents Promotion Project of Suzhou Science and Technology Association,Suzhou International Joint Laboratory for Diagnosis and Treatment of Brain Diseases,and startup funding(Grant Nos.:NH21500221,NH21500122,and NH21500123)to Qifei Cong.
文摘Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the“therapeutic module”of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.
基金supported in part by the Natural Science Foundation of China under Grant 62372395in part by the Research Foundation of Education Bureau of Hunan Province under Grant No.24A0105in part by the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.CX20230546).
文摘In recent years,universal adversarial per-turbation(UAP)has attracted the attention of many re-searchers due to its good generalization.However,in order to generate an appropriate UAP,current methods usually require either accessing the original dataset or meticulously constructing optimization functions and proxy datasets.In this paper,we aim to elimi-nate any dependency on proxy datasets and explore a method for generating Universal Adversarial Pertur-bations(UAP)on a single image.After revisiting re-search on UAP,we discovered that the key to gener-ating UAP lies in the accumulation of Individual Ad-versarial Perturbation(IAP)gradient,which prompted us to study the method of accumulating gradients from an IAP.We designed a simple and effective process to generate UAP,which only includes three steps:pre-cessing,generating an IAP and scaling the perturba-tions.Through our proposed process,any IAP gener-ated on an image can be constructed into a UAP with comparable performance,indicating that UAP can be generated free of data.Extensive experiments on var-ious classifiers and attack approaches demonstrate the superiority of our method on efficiency and aggressiveness.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.41930971,42330111,and 42405061)the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(Earth Lab).
文摘Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.
基金funded by National Natural Science Foundation of China(NSFC)(Nos.12075053,11505021 and 11975068)by National Key R&D Program of China(No.2022YFE 03060002)+1 种基金by Fundamental Research Funds for the Central Universities(No.2232024G-10)supported by the U.S.DoE Office of Science(No.DE-FG02–95ER54309)。
文摘Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),in particular the resonant portion,is found to provide the dominant contribution to the total toroidal torque under the slow plasma flow regime in ITER.While the electromagnetic torque always opposes the plasma flow,the toroidal torque associated with the Reynolds stress enhances the plasma flow independent of the flow direction.A peculiar double-peak structure for the net NTV torque is robustly computed for ITER,as the toroidal rotation frequency is scanned near the zero value.This structure is found to be ultimately due to a non-monotonic behavior of the wave-particle resonance integral(over the particle pitch angle)in the superbanana plateau NTV regime in ITER.These findings are qualitatively insensitive to variations of a range of factors including the wall resistivity,the plasma pedestal flow and the assumed frequency of the rotating RMP field.
基金supported by the National Natural Science Foundation of China (Grant No. 42288101, 42375062, 42476192, 42275158)the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab)the GHfund C (202407036001)
文摘The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension.
基金supported in part by the Japan Society for the Promotion of Science(JSPS)KAKENHI Grant-in-Aid for Scientific Research(C)under Grants 23K11164.
文摘Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulations such as the General Data Protection Regulation(GDPR),which explicitly restrict the processing of data that can re-identify individuals.Although existing anonymization approaches such as the AnonymizingAutoEncoder(AAE)can reduce the risk of re-identification,they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task(e.g.,human activity recognition).This study proposes a novel sensor data anonymization method based onAdversarial Perturbations(AP)to address these limitations.By generating minimal yet targeted noise,the proposed method significantly degrades the accuracy of identity classification while retaining essential features for multiple tasks such as activity,gender,or device-position recognition.Moreover,to enhance robustness against frequency-domain analysis,additional models trained on transformed(e.g.,short-time Fourier transform(STFT))representations are incorporated into the perturbation process.A multi-task formulation is introduced that selectively suppresses person-identifying features while reinforcing those relevant to other desired tasks without retraining large autoencoder-based architectures.The proposed framework is,to our knowledge,the first AP-based anonymization technique that(i)defends simultaneously against time-and frequency-domain attacks and(ii)allows per-task trade-off control on a single forward-back-propagation run,enabling real-time,on-device deployment on commodity hardware.On three public datasets,the proposed method reduces person-identification accuracy from 60–90%to near-chance levels(≤5%)while preserving the original activity-recognition F1 both in the time and frequency domains.Compared with the baseline AAE,the proposed method improves downstream task F1 and lowers waveform mean squared error,demonstrating a better privacy-utility trade-off without additional model retraining.These findings underscore the effectiveness and flexibility of AP in privacy-preserving sensor-data processing,offering a practical solution that safeguards user identity while retaining rich,application-critical information.
基金supported by the joint funds of the Chinese National Natural Science Foundation(NSFC)(Grant No.U2242213)the funds of the NSFC(Grant No.42341209)+2 种基金the National Key Research and Development(R&D)Program of the Ministry of Science and Technology of China(Grant No.2021YFC3000902)the National Science Foundation for Young Scholars(Grant No.42205166)the Joint Research Project for Meteorological Capacity Improvement(Grant No.22NLTSQ008)。
文摘Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.
基金funded by the National Natural Science Foundation of China(Grant Nos.42107163 and 42320104003)support from the G.Albert Shoemaker endowment.
文摘Hydraulic fracturing then fluid circulation in enhanced geothermal system(EGS)reservoirs have been shown to induce seismicity remote from the stimulation-potentially generated by the distal projection of thermoporoelastic stresses.We explore this phenomenon by evaluating stress perturbations resulting from stimulation of a single stage of hydraulic fracturing that is followed by thermal depletion of a prismatic zone adjacent to the hydraulic fracture.We use Coulomb failure stress to assess the effect of resulting stress perturbations on instability on adjacent critically-stressed faults.Results show that hydraulic fracturing in a single stage is capable of creating stress perturbations at distances to 1000 m that reach 10^(-5)-10^(-4)MPa.At a closer distance,the magnitude of stress perturbations increases even further.The stress perturbation induced by temperature depletion could also reach 10^(-3)-10^(-2)MPa within 1000 m-much higher than that by hydraulic fracturing.Considering that a critical change in Coulomb failure stress for fault instability is 10^(-2)MPa,a single stage of hydraulic fracturing and thermal drawdown are capable of reactivating critically-stressed faults at distances within 200 m and 1000 m,respectively.These results have important implications for understanding the distribution and magnitudes of stress perturbations driven by thermoporoelastic effects and the associated seismicity during the simulation and early production of EGS reservoirs.
基金supported by the National Magnetic Confinement Fusion Energy R&D Program of China (Nos.2018YFE0309103 and 2019YFE03010004)National Natural Science Foundation of China (Nos.12475222,12205122,and 51821005)Hubei International Science and Technology Cooperation Projects (No.2022EHB003)。
文摘A large number of runaway electrons(REs)generated during disruption can cause significant damage to next-generation large-scale tokamaks.The influence of three-dimensional(3D)helical magnetic perturbations on the suppression of RE generation was explored using a set of 3D helical coils in J-TEXT tokamak,which can excite m/n=-2/2 helical magnetic perturbations.Experimental evidence shows that the-2/2 magnetic perturbations caused by the opposite coil current direct plasma toward the high-field side,simultaneously enhancing the magnetic fluctuations,which would enhance the radial loss of REs and even prevent RE generation.On the other hand,-2/2 magnetic perturbations can also reduce the cooling time during the disruption phase and generate a population of high-energy REs,which can interact with high-frequency magnetic fluctuations and in turn suppress RE generation.The critical helical coil current was found to correlate with electron density,requiring higher coil currents at higher densities.According to the statistical analysis of RE generation at different electron densities,the applied-2/2 magnetic perturbations can increase the magnetic fluctuations to the same level at lower electron densities,which can decrease the threshold electron density for RE suppression.This will be beneficial for RE mitigation in future large tokamak devices.
文摘Making exact approximations to solve equations distinguishes applied mathematicians from pure mathematicians, physicists, and engineers. Perturbation problems, both regular and singular, are pervasive in diverse fields of applied mathematics and engineering. This research paper provides a comprehensive overview of algebraic methods for solving perturbation problems, featuring a comparative analysis of their strengths and limitations. Serving as a valuable resource for researchers and practitioners, it offers insights and guidance for tackling perturbation problems in various disciplines, facilitating the advancement of applied mathematics and engineering.
基金supported by the Joint Funds of the Chinese National Natural Science Foundation (NSFC)(Grant No.U2242213)the National Key Research and Development (R&D)Program of the Ministry of Science and Technology of China(Grant No. 2021YFC3000902)the National Science Foundation for Young Scholars (Grant No. 42205166)。
文摘Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.
基金Supported by the National Natural Science Foundation of China (12172241, 12272248, 11972241, 12002228)Qing Lan Project of Colleges and Universities in Jiangsu Province。
文摘Time scale is a new and powerful tool for dealing with complex dynamics problems. The main result of this study is the exact invariants and adiabatic invariants of the generalized Birkhoffian system and the constrained Birkhoffian system on time scales. Firstly, we establish the differential equations of motion for the above two systems and give the corresponding Noether symmetries and exact invariants. Then, the perturbation to the Noether symmetries and the adiabatic invariants for the systems mentioned above under the action of slight disturbance are investigated, respectively. Finally, two examples are provided to show the practicality of the findings.
基金supported by the Natural Science Foundation of China(Grant Nos.:22225702 and 32322048)the National Key R&D Program of China(Grant No.:2020YFE0202200)+8 种基金the Shanghai Academic/Technology Research Leader Program,China(Grant No.:22XD1420900)Guangdong High-level New R&D Institute,China(Grant No.:2019B090904008)Guangdong High-level Innovative Research Institute,China(Grant No.:2021B0909050003)the Shanghai Rising-Star Program,China(Grant No.:22QA1411100)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.:2021276)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology,China(Grant No.:2022QNRC001)the open fund of State Key Laboratory of Pharmaceutical Biotechnology,Nanjing University,China(Grant No.:KF-202201)We also thank the support of the Innovative Research Team of High-Level Local Universities in Shanghai,China(Grant No.:SHSMU-ZDCX20212700)Sanofi scholarship program.
文摘Pharmacological perturbation studies based on protein-level signatures are fundamental for drug discovery. In the present study, we used a mass spectrometry (MS)-based proteomic platform to profile the whole proteome of the breast cancer MCF7 cell line under stress induced by 78 bioactive compounds. The integrated analysis of perturbed signal abundance revealed the connectivity between phenotypic behaviors and molecular features in cancer cells. Our data showed functional relevance in exploring the novel pharmacological activity of phenolic xanthohumol, as well as the noncanonical targets of clinically approved tamoxifen, lovastatin, and their derivatives. Furthermore, the rational design of synergistic inhibition using a combination of histone methyltransferase and topoisomerase was identified based on their complementary drug fingerprints. This study provides rich resources for the proteomic landscape of drug responses for precision therapeutic medicine.
基金The authors would like to acknowledge the support of the National Natural Science Foundation of China(41974127,42174147).References。
文摘Cement density monitoring plays a vital role in evaluating the quality of cementing projects,which is of great significance to the development of oil and gas.However,the presence of inhomogeneous cement distribution and casing eccentricity in horizontal wells often complicates the accurate evaluation of cement azimuthal density.In this regard,this paper proposes an algorithm to calculate the cement azimuthal density in horizontal wells using a multi-detector gamma-ray detection system.The spatial dynamic response functions are simulated to obtain the influence of cement density on gamma-ray counts by the perturbation theory,and the contribution of cement density in six sectors to the gamma-ray recorded by different detectors is obtained by integrating the spatial dynamic response functions.Combined with the relationship between gamma-ray counts and cement density,a multi-parameter calculation equation system is established,and the regularized Newton iteration method is employed to invert casing eccentricity and cement azimuthal density.This approach ensures the stability of the inversion process while simultaneously achieving an accuracy of 0.05 g/cm^(3) for the cement azimuthal density.This accuracy level is ten times higher compared to density accuracy calculated using calibration equations.Overall,this algorithm enhances the accuracy of cement azimuthal density evaluation,provides valuable technical support for the monitoring of cement azimuthal density in the oil and gas industry.
基金supported by the National MCF Energy R&D Program of China(Nos.2019YFE03090400 and 2019YFE03030004)National Natural Science Foundation of China(Nos.12375222 and 11775154)National Key R&D Program of China(Nos.2017YFE0301203 and 2017YFE0301101)。
文摘The effects of equilibrium toroidal rotation during edge-localized mode(ELM)mitigated by resonant magnetic perturbation(RMP)are studied with the experimental equilibria of the EAST tokamak based on the four-field model in the BOUT++code.As the two main parameters to determine the toroidal rotation profiles,the rotation shear and magnitudes were separately scanned to investigate their roles in the impact of RMPs on peeling-ballooning(P-B)modes.On one hand,the results show that strong toroidal rotation shear is favorable for the enhancement of the self-generated E×B shearing rate<ω_(E×B)>with RMPs,leading to significant ELM mitigation with RMP in the stronger toroidal rotation shear region.On the other hand,toroidal rotation magnitudes may affect ELM mitigation by changing the penetration of the RMPs,more precisely the resonant components.RMPs can lead to a reduction in the pedestal energy loss by enhancing the multimode coupling in the turbulence transport phase.The shielding effects on RMPs increase with the toroidal rotation magnitude,leading to the enhancement of the multimode coupling with RMPs to be significantly weakened.Hence,the reduction in pedestal energy loss by RMPs decreased with the rotation magnitude.In brief,the results show that toroidal rotation plays a dual role in ELM mitigation with RMP by changing the shielding effects of plasma by rotation magnitude and affecting<ω_(E×B)>by rotation shear.In the high toroidal rotation region,toroidal rotation shear is usually strong and hence plays a dominant role in the influence of RMP on P-B modes,whereas in the low rotation region,toroidal rotation shear is weak and has negligible impact on P-B modes,and the rotation magnitude plays a dominant role in the influence of RMPs on the P-B modes by changing the field penetration.Therefore,the dual role of toroidal rotation leads to stronger ELM mitigation with RMP,which may be achieved both in the low toroidal rotation region and the relatively high rotation region that has strong rotational shear.
基金supported by the National Natural Science Foundation of China (U23B2009, 62376201, 423B2104)Open Foundation (ZNXX2023MSO2, HBIR202311)。
文摘Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://github.com/kuijiang94/PerTeRNet.
基金Supported by the National Natural Science Foundation of China(12001142).
文摘Let X be a Banach space and let P:X→X be a bounded linear operator.Using an algebraic inequality on the spectrum of P,we give a new sufficient condition that guarantees the existence of(I-P)^(-1) as a bounded linear operator on X,and a bound on its spectral radius is also obtained.This generalizes the classic Banach lemma.We apply the result to the perturbation analysis of general bounded linear operators on X with commutative perturbations.
基金supported by the Fundamental Research Funds of the National Institute of MetrologyChina(No.AKYZZ2113)+1 种基金National Key Research and Development Program of China(No.2017YFF0206205)the Team Building Program of Nanjing University(No.1480604114)。
文摘The neutron radiation field has vital applications in areas such as biomedicine,geology,radiation safety,and many others for neutron detection and neutron metrology.Correcting neutron fluence rate perturbation accurately is an important yet challenging problem.This study proposes a correction method that analyzes three physical processes.This method,which transforms the detection process from point detection to area detection,is based on a novel physical model and has been validated through theoretical analyses,experiments,and simulations.According to the average differences between the calculated and experimental results,the new method(1.67%)demonstrated better accuracy than the traditional simulation(2.17%).In a closed thermal neutron radiation field,the detector or strong neutron absorption material significantly perturbs the neutron fluence rate,whereas its impact on the energy spectrum shape and neutron directionality is relatively minor.Furthermore,based on the calculation results of the perturbation rate formula for medium materials with different compositions and sizes,the larger the volume and capture cross section of the medium,the higher the perturbation rate generated in the closed radiation field.
基金partly supported by Natural Science Foundation of China under Grant No.12375054the Natural Science Foundation of Jiangsu Province under Grant No.BK20211601。
文摘The scalar-free black hole could be unstable against the scalar field perturbation when it is coupled to a Gauss–Bonnet(GB)invariant in a special form.It is known that the tachyonic instability in this scenario is triggered by the sufficiently strong GB coupling.In this paper,we focus on the time domain analysis of massive scalar field perturbation around the Schwarzschild de-Sitter black hole in Einstein-scalar–Gauss–Bonnet gravity.By analyzing the scalar field propagation,we find that the scalar field will finally grow when the GB coupling is large enough.This could lead to the instability of the background black hole.Furthermore,we demonstrate how the mass of the scalar field and the GB coupling strength affect the onset of tachyonic instability.
