This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic diff...This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic differential equations.Then we obtain a comparison theorem in one-dimensional situation.展开更多
Differential Code Bias(DCB)is the time delays between two different GNSS signals,which is crucial for GNSS positioning.Previous studies have shown that it can be significantly affected by the flex power operations in ...Differential Code Bias(DCB)is the time delays between two different GNSS signals,which is crucial for GNSS positioning.Previous studies have shown that it can be significantly affected by the flex power operations in satellites.This study proposes a 15-min short-term DCB estimation method to analyze flex power's impact on DCB variations.The method jointly estimates satellite DCB,receiver DCB,and ionospheric parameters using over 300 MGEX stations.We examined three representative flex power events in 2024,achieving average internal RMS values of 0.042 ns and 0.0068 ns for inter-frequency and intra-frequency scenarios respectively.Results show that intra-frequency DCB exhibits clear shift biases synchronized with flex power state transitions while maintaining stability within 0.20 ns during nontransition periods.No definitive impact on inter-frequency DCB was observed at current estimation precision levels.展开更多
AIM:To identify differentially expressed genes(DEGs)in rabbits with traumatic proliferative vitreoretinopathy(PVR)using high-throughput sequencing(HTS).METHODS:Thirty-six rabbits were randomly allocated to the control...AIM:To identify differentially expressed genes(DEGs)in rabbits with traumatic proliferative vitreoretinopathy(PVR)using high-throughput sequencing(HTS).METHODS:Thirty-six rabbits were randomly allocated to the control group and the PVR group induced by scleral puncture.On the 28th day following modeling,fundus B-ultrasound and fundus photography were performed on all rabbits,and hematoxylin-eosin(HE)staining was conducted on retinal tissues.RNA sequencing(RNA-Seq)combined with bioinformatics analysis was used to screen PVRassociated DEGs.Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were carried out for the identified DEGs.S100A6,EDNRB and CEBPD were randomly selected for quantitative reverse transcription polymerase chain reaction(RT-qPCR)validation to verify the reliability of the RNA-Seq results.RESULTS:Fundus B-ultrasound,fundus photography and retinal HE staining confirmed the successful establishment of the traumatic PVR rabbit model.A total of 1587 DEGs were screened,of which 1094 were significantly up-regulated and 493 were significantly down-regulated.GO function enrichment analysis showed that these DEGs were mainly enriched in immune response,extracellular region and inflammatory response.KEGG pathway enrichment analysis showed that DEGs were mainly involved in the cytokine-cytokine receptor interaction and hematopoietic cell lineage pathway.RT-qPCR results showed that S100A6,CEBPD and EDNRB were significantly increased in PVR group.CONCLUSION:A large number of genes exhibit significant differential expression in rabbits with traumatic PVR,among which S100A6,CEBPD and EDNRB may play an important role in traumatic PVR.展开更多
The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location re...The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.展开更多
With the popularization of smart devices,Location-Based Services(LBS)greatly facilitates users’life,but at the same time brings the risk of users’location privacy leakage.Existing location privacy protection methods...With the popularization of smart devices,Location-Based Services(LBS)greatly facilitates users’life,but at the same time brings the risk of users’location privacy leakage.Existing location privacy protection methods are deficient,failing to reasonably allocate the privacy budget for non-outlier location points and ignoring the critical location information that may be contained in the outlier points,leading to decreased data availability and privacy exposure problems.To address these problems,this paper proposes a Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering(MLDP).The method first utilizes the DBSCAN clustering algorithm to classify location points into non-outliers and outliers.For non-outliers,the scoring function is designed by combining geographic information and semantic information,and the privacy budget is allocated according to the heat intensity of the hotspot area;for outliers,the scoring function is constructed to allocate the privacy budget based on their correlation with the hotspot area.By comprehensively considering the geographic information,semantic information,and correlation with hotspot areas of the location points,a reasonable privacy budget is assigned to each location point,andfinallynoise is added throughthe Laplacemechanismto realizeprivacyprotection.Experimental results on tworeal trajectory datasets,Geolife and T-Drive,show that the MLDP approach significantly improves data availability while effectively protecting location privacy.Compared with the comparison methods,the maximum available data ratio of MLDP is 1.Moreover,compared with the RandomNoise method,its execution time is 0.056–0.061 s longer,and the logRE is 0.12951–0.62194 lower;compared with KemeansDP,QTK-DP,DPK-F,IDP-SC,and DPK-Means-up methods,it saves 0.114–0.296 s in execution time,and the logRE is 0.01112–0.38283 lower.展开更多
This paper proposes a two-phase game guidance strategy for the three-body confrontation scenario according to the linear quadratic differential game method,which includes an Attacker,an Interceptor,and a Target.The in...This paper proposes a two-phase game guidance strategy for the three-body confrontation scenario according to the linear quadratic differential game method,which includes an Attacker,an Interceptor,and a Target.The interception probabilities between the Attacker and the Interceptor-Target team are estimated using the probability density function.The desired zero-effort miss distances associated with the interception probabilities for the three-body conflict are acquired by virtue of the gradient descent method.The game combat is divided into two phases by introducing the switching time.In Phase 1,a differential game strategy is developed to guide the zero-control miss distance between the Attacker and the Interceptor-Target into the desired position,which guarantees that the Attacker has the maximizing probability of intercepting the Target and the minimizing probability of being captured by the Interceptor.In Phase 2,a differential game guidance strategy is proposed to ensure that the Attacker evades the pursuit of the Interceptor and intercepts the Target at the preset impact angle.Finally,numerical simulation verifies the effectiveness of the two-stage game guidance strategy.展开更多
The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differ...The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.展开更多
Poly(phenylene oxide)(PPO)exhibits excellent dielectric properties,making it an ideal substrate for high-frequency,high-speed copper-clad laminates.The phenolic hydroxyl group at the end of PPO plays a key role in its...Poly(phenylene oxide)(PPO)exhibits excellent dielectric properties,making it an ideal substrate for high-frequency,high-speed copper-clad laminates.The phenolic hydroxyl group at the end of PPO plays a key role in its reactivity.Accurately quantifying the phenolic hydroxyl content in PPO is essential but challenging.In this study,we proposed a method for measuring the phenolic hydroxyl content of PPO using differential UV absorption spectroscopy.In alkaline solutions,the phenolic hydroxyl in PPO completely ionizes to form phenoxide ions,leading to a significant increase in UV absorbance at approximately 250 and 300 nm.Notably,the differential UV absorbance at approximately 300 nm was directly proportional to the phenolic hydroxyl concentration.Using 2,6-dimethylphenol as a standard,a calibration curve was established to relate the phenolic hydroxyl concentration to differential UV absorbance at approximately 300 nm,providing a precise and straightforward method for phenolic hydroxyl quantification in PPO with distinct advantages over conventional techniques.展开更多
In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order relia...In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.展开更多
[Objective]As wireless power transfer(WPT)technology is increasingly deployed in scenarios such as electric vehicles,metallic foreign objects in the WPT area may cause local overheating and energy loss.Existing method...[Objective]As wireless power transfer(WPT)technology is increasingly deployed in scenarios such as electric vehicles,metallic foreign objects in the WPT area may cause local overheating and energy loss.Existing methods still suffer from poor edge/corner sensitivity,misjudgment due to fixed thresholds,and limited ability to extract position information.This work proposes a wireless power transfer-foreign object detection(WPT-FOD)method based on channel differential response and a dynamic-threshold corner-enhancement strategy,aiming to improve detection sensitivity,localization accuracy,and robustness without altering the overall coil layout.[Method]A multi-channel detection coil array is designed,and the self-inductance disturbance response of each channel coil is modeled.A channel-difference mapping mechanism is introduced to build a 2-D sensitivity matrix to characterize spatial position correlation.A corner-enhancement algorithm is developed to weight and amplify the collaborative response of adjacent channels,and a dynamic threshold adjustment mechanism is integrated to adapt to varying interference levels.Validation is carried out on a self-built 64-channel FOD platform by moving a typical metallic foreign object across central,edge,and corner regions,and by conducting comparative tests under different interference intensities.[Result]When a typical metallic foreign object moves to corner regions,the self-inductance disturbance of the detection coil increases from less than 0.02μH to more than 0.06μH,significantly enhancing the discrimination capability at corners.Under varying interference strengths,the dynamic threshold mechanism reduces the number of false positives from 13 to 2,demonstrating good environmental adaptability and stability.[Conclusion]By combining channel differential response,corner enhancement,and dynamic thresholding,the proposed WPT-FOD effectively mitigates edge/corner blind spots and fixed-threshold misjudgment,while providing localization capability and robustness.It markedly improves the accuracy of metallic foreign object detection in WPT systems and offers a feasible path and method reference for the safe application and engineering deployment of WPT systems.展开更多
To meet the high energy efficiency requirements of solar-powered Unmanned Aerial Vehicles(UAVs),this paper proposes an optimization design framework for a rudder/differential thrust joint control strategy based on inc...To meet the high energy efficiency requirements of solar-powered Unmanned Aerial Vehicles(UAVs),this paper proposes an optimization design framework for a rudder/differential thrust joint control strategy based on incremental nonlinear dynamic inversion,and this collaborative control strategy is further integrated into top-level trajectory optimization.In this framework,the additional aerodynamic forces and moments caused by the asymmetry of the propeller slipstream are precisely modeled.The results demonstrate that through a rational allocation between rudder control and differential thrust control,the extra flight power caused by horizontal turns can be reduced by 44.5%,and the overall average flight power decreases by 6.2%.In energyoptimal trajectory design,the introduction of differential thrust control contributes to minimizing unfavorable segments in the flight trajectory,resulting in increased solar energy absorption and reduced flight energy consumption.The results indicate that the average net residual power increases by 7.3%.The effectiveness of differential thrust control in enhancing the energy performance of solar-powered UAVs is verified in this research.展开更多
By investigating the evolution of shale gas generation,storage,adjustment and accumulation under different structural settings in superimposed basins,this study elucidates the differential accumulation mechanisms of s...By investigating the evolution of shale gas generation,storage,adjustment and accumulation under different structural settings in superimposed basins,this study elucidates the differential accumulation mechanisms of shale gas.An improved evaluation method of shale gas content evolution in superimposed basins is proposed.This method incorporates the coupling effect of key geological factors such as temperature,pressure,organic matter abundance,maturity,and pore characteristics on the content and occurrence state of shale gas,as well as the configuration relationship between shale gas generation and storage throughout geological history.Using this approach,the gas evolution histories of the Longmaxi Formation shales in wells N201 and PY1 are reconstructed under varying geological conditions.The Longmaxi Formation shales in these wells are dominated by typeⅠkerogen,with original total organic carbon(TOC_(o))contents of 6.20 wt% and 4.92 wt%,respectively,indicating differences in the initial material basis for gas generation.At the maximum burial depth of approximately 5000 m,the Longmaxi Formation shale in well N201 exhibits a formation pressure coefficient of 2.05,an organic matter maturity of 2.2%,and organic pores accounting for 68%of the total porosity.The gas generation quantity(Q_(g))reaches 19.24 m^(3)/t,while the gas storage capacity(Q_(s))is 4.30 m^(3)/t.The actual total gas content(Q_(a)),constrained by Q_(s),is 4.30 m^(3)/t,with free gas comprising 94%.Following relatively moderate tectonic uplift,the Q_(a) in well N201 decreases to 4.03 m^(3)/t,with free gas accounting for 63%.In contrast,the Longmaxi Formation shale in well PY1 reached a maximum burial depth of 6300 m,associated with a formation pressure coefficient of 1.62,organic matter maturity of 2.5%,and organic pore proportion of 67%.Here,Q_(g) is 16.87 m^(3)/t,and both Q_(s) and Q_(a) are 3.65 m^(3)/t,with free gas accounting for 98%.After intense tectonic uplift,Q_(a) declines to 2.72 m^(3)/t,and the proportion of free gas drops to51%.Finally,a four-stage differential accumulation model of shale gas is established:Slow gas generation and only adsorbed gas occur in stageⅠ,which is primarily controlled by TOC content;both adsorbed gas and free gas present in stageⅡ,with free gas becoming dominant;rapid gas generation and free gas predominance are controlled by temperature and porosity in stageⅢ;and gas adjustment and accumulation are primarily controlled by temperature and pressure in stageⅣ.展开更多
Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy ...Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy and efficiency in solving complex problems.Thus,the use of machine learning to predict welding de-formations is a novel approach.In this study,laser welding experiments were conducted on a TC4 titanium alloy to establish a welding deformation dataset.The deep neural network(DNN)and convolutional neural network(CNN)models were designed and constructed,with average prediction errors of 0.85 mm and 0.94 mm on the validation set,respectively.To further optimize the network parameters,a differential evolution algorithm was employed through mutation,crossover,and selection.The results indicated that after optimization,the pre-diction errors of the DNN and CNN models reduced to 0.75 mm and 0.85 mm,respectively.These represent accuracy improvements of 14.8%and 9.6%,respectively.The optimized models exhibited superior predictive performances for the validation set.展开更多
Selective Laser Melting(SLM),an advanced metal additive manufacturing technology,offers high precision and personalized customization advantages.However,selecting reasonable SLM parameters is challenging due to comple...Selective Laser Melting(SLM),an advanced metal additive manufacturing technology,offers high precision and personalized customization advantages.However,selecting reasonable SLM parameters is challenging due to complex relationships.This study proposes a method for identifying the optimal process window by combining the simulation model with an optimization algorithm.JAYA is guided by the principle of preferential behavior towards best solutions and avoidance of worst ones,but it is prone to premature convergence thus leading to insufficient global search.To overcome limitations,this research proposes a Differential Evolution-framed JAYA algorithm(DEJAYA).DEJAYA incorporates four key enhancements to improve the flexibility of the original algorithm,which include DE framework design,horizontal crossover operator,longitudinal crossover operator,and global greedy strategy.The effectiveness of DEJAYA is rigorously evaluated by a suite of 23 distinct benchmark functions.Furthermore,the numerical simulation establishes AlSi10Mg single-track formation models,and DEJAYA successfully identified the optimal process window for this problem.Experimental results validate that DEJAYA effectively guides SLM parameter selection for AlSi10Mg.展开更多
Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving g...Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs).展开更多
Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that devi...Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
The rapid development and widespread adoption of massive open online courses(MOOCs)have indeed had a significant impact on China’s education curriculum.However,the problem of fake reviews and ratings on the platform ...The rapid development and widespread adoption of massive open online courses(MOOCs)have indeed had a significant impact on China’s education curriculum.However,the problem of fake reviews and ratings on the platform has seriously affected the authenticity of course evaluations and user trust,requiring effective anomaly detection techniques for screening.The textual characteristics of MOOCs reviews,such as varying lengths and diverse emotional tendencies,have brought complexity to text analysis.Traditional rule-based analysis methods are often inadequate in dealing with such unstructured data.We propose a Differential Privacy-Enabled Text Convolutional Neural Network(DP-TextCNN)framework,aiming to achieve high-precision identification of outliers in MOOCs course reviews and ratings while protecting user privacy.This framework leverages the advantages of Convolutional Neural Networks(CNN)in text feature extraction and combines differential privacy techniques.It balances data privacy protection with model performance by introducing controlled random noise during the data preprocessing stage.By embedding differential privacy into the model training process,we ensure the privacy security of the framework when handling sensitive data,while maintaining a high recognition accuracy.Experimental results indicate that the DP-TextCNN framework achieves an exceptional accuracy of over 95%in identifying fake reviews on the dataset,this outcome not only verifies the applicability of differential privacy techniques in TextCNN but also underscores its potential in handling sensitive educational data.Additionally,we analyze the specific impact of differential privacy parameters on framework performance,offering theoretical support and empirical analysis to strike an optimal balance between privacy protection and framework efficiency.展开更多
Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase ...Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase contrast(DPC),a non-interferometric QPI technique,requires only four intensity images under asymmetric illumination to recover the phase of a sample,offering the advantages of being label-free,non-coherent and highly robust.Its phase reconstruction result relies on precise modeling of the phase transfer function(PTF).However,in real optical systems,the PTF will deviate from its theoretical ideal due to the unknown wavefront aberrations,which will lead to significant artifacts and distortions in the reconstructed phase.We propose an aberration-corrected DPC(ACDPC)method that utilizes three intensity images under annular illumination to jointly retrieve the aberration and the phase,achieving high-quality QPI with minimal raw data.By employing three annular illuminations precisely matched to the numerical aperture of the objective lens,the object information is transmitted into the acquired intensity with a high signal-to-noise ratio.Phase retrieval is achieved by an iterative deconvolution algorithm that uses simulated annealing to estimate the aberration and further employs regularized deconvolution to reconstruct the phase,ultimately obtaining a refined complex pupil function and an aberration-corrected quantitative phase.We demonstrate that ACDPC is robust to multi-order aberrations without any priori knowledge,and can effectively retrieve and correct system aberrations to obtain high-quality quantitative phase.Experimental results show that ACDPC can clearly reproduce subcellular structures such as vesicles and lipid droplets with higher resolution than conventional DPC,which opens up new possibilities for more accurate subcellular structure analysis in cell biology.展开更多
基金Supported by the National Natural Science Foundation of China(12001074)the Research Innovation Program of Graduate Students in Hunan Province(CX20220258)+1 种基金the Research Innovation Program of Graduate Students of Central South University(1053320214147)the Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110025)。
文摘This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic differential equations.Then we obtain a comparison theorem in one-dimensional situation.
基金the funds from the Key Laboratory of Smart Earth(KF2023YB01-07)Shanghai Collaborative Innovation Fund(XTCX-KJ-2024-17)the National Natural Science Foundation of China(42388102,62303311,and 62231010)。
文摘Differential Code Bias(DCB)is the time delays between two different GNSS signals,which is crucial for GNSS positioning.Previous studies have shown that it can be significantly affected by the flex power operations in satellites.This study proposes a 15-min short-term DCB estimation method to analyze flex power's impact on DCB variations.The method jointly estimates satellite DCB,receiver DCB,and ionospheric parameters using over 300 MGEX stations.We examined three representative flex power events in 2024,achieving average internal RMS values of 0.042 ns and 0.0068 ns for inter-frequency and intra-frequency scenarios respectively.Results show that intra-frequency DCB exhibits clear shift biases synchronized with flex power state transitions while maintaining stability within 0.20 ns during nontransition periods.No definitive impact on inter-frequency DCB was observed at current estimation precision levels.
基金Supported by the National Natural Science Foundation of China(No.82260210)the Natural Science Foundation of Jiangxi Province(No.20252BAC250122)+2 种基金the Science and Technology Planning Project of Jiangxi Provincial Health Commission(No.202510055)Jiangxi Province Graduate Innovation Special Fund(No.YC2025-B211)Jiangxi Association for Science Education(No.2025KXJYS019).
文摘AIM:To identify differentially expressed genes(DEGs)in rabbits with traumatic proliferative vitreoretinopathy(PVR)using high-throughput sequencing(HTS).METHODS:Thirty-six rabbits were randomly allocated to the control group and the PVR group induced by scleral puncture.On the 28th day following modeling,fundus B-ultrasound and fundus photography were performed on all rabbits,and hematoxylin-eosin(HE)staining was conducted on retinal tissues.RNA sequencing(RNA-Seq)combined with bioinformatics analysis was used to screen PVRassociated DEGs.Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were carried out for the identified DEGs.S100A6,EDNRB and CEBPD were randomly selected for quantitative reverse transcription polymerase chain reaction(RT-qPCR)validation to verify the reliability of the RNA-Seq results.RESULTS:Fundus B-ultrasound,fundus photography and retinal HE staining confirmed the successful establishment of the traumatic PVR rabbit model.A total of 1587 DEGs were screened,of which 1094 were significantly up-regulated and 493 were significantly down-regulated.GO function enrichment analysis showed that these DEGs were mainly enriched in immune response,extracellular region and inflammatory response.KEGG pathway enrichment analysis showed that DEGs were mainly involved in the cytokine-cytokine receptor interaction and hematopoietic cell lineage pathway.RT-qPCR results showed that S100A6,CEBPD and EDNRB were significantly increased in PVR group.CONCLUSION:A large number of genes exhibit significant differential expression in rabbits with traumatic PVR,among which S100A6,CEBPD and EDNRB may play an important role in traumatic PVR.
基金supported by the Natural Science Foundation of Fujian Province of China(2025J01380)National Natural Science Foundation of China(No.62471139)+3 种基金the Major Health Research Project of Fujian Province(2021ZD01001)Fujian Provincial Units Special Funds for Education and Research(2022639)Fujian University of Technology Research Start-up Fund(GY-S24002)Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare(GY-H-24179).
文摘The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.
基金supported in part by the National Natural Science Foundation of China(Grant No.61971291)the Basic Scientific Research Project of the Liaoning Provincial Department of Education(LJ212410144013)+2 种基金the Leading Talent of the‘Xing Liao Ying Cai Plan’(XLYC2202013)the Shenyang Natural Science Foundation(22-315-6-10)the Guangxuan Scholar of Shenyang Ligong University(SYLUGXXZ202205).
文摘With the popularization of smart devices,Location-Based Services(LBS)greatly facilitates users’life,but at the same time brings the risk of users’location privacy leakage.Existing location privacy protection methods are deficient,failing to reasonably allocate the privacy budget for non-outlier location points and ignoring the critical location information that may be contained in the outlier points,leading to decreased data availability and privacy exposure problems.To address these problems,this paper proposes a Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering(MLDP).The method first utilizes the DBSCAN clustering algorithm to classify location points into non-outliers and outliers.For non-outliers,the scoring function is designed by combining geographic information and semantic information,and the privacy budget is allocated according to the heat intensity of the hotspot area;for outliers,the scoring function is constructed to allocate the privacy budget based on their correlation with the hotspot area.By comprehensively considering the geographic information,semantic information,and correlation with hotspot areas of the location points,a reasonable privacy budget is assigned to each location point,andfinallynoise is added throughthe Laplacemechanismto realizeprivacyprotection.Experimental results on tworeal trajectory datasets,Geolife and T-Drive,show that the MLDP approach significantly improves data availability while effectively protecting location privacy.Compared with the comparison methods,the maximum available data ratio of MLDP is 1.Moreover,compared with the RandomNoise method,its execution time is 0.056–0.061 s longer,and the logRE is 0.12951–0.62194 lower;compared with KemeansDP,QTK-DP,DPK-F,IDP-SC,and DPK-Means-up methods,it saves 0.114–0.296 s in execution time,and the logRE is 0.01112–0.38283 lower.
基金co-supported by the National Natural Science Foundation of China(No.62273119)。
文摘This paper proposes a two-phase game guidance strategy for the three-body confrontation scenario according to the linear quadratic differential game method,which includes an Attacker,an Interceptor,and a Target.The interception probabilities between the Attacker and the Interceptor-Target team are estimated using the probability density function.The desired zero-effort miss distances associated with the interception probabilities for the three-body conflict are acquired by virtue of the gradient descent method.The game combat is divided into two phases by introducing the switching time.In Phase 1,a differential game strategy is developed to guide the zero-control miss distance between the Attacker and the Interceptor-Target into the desired position,which guarantees that the Attacker has the maximizing probability of intercepting the Target and the minimizing probability of being captured by the Interceptor.In Phase 2,a differential game guidance strategy is proposed to ensure that the Attacker evades the pursuit of the Interceptor and intercepts the Target at the preset impact angle.Finally,numerical simulation verifies the effectiveness of the two-stage game guidance strategy.
基金supported by the National Key R&D Program of China under Grant No.2023YFA1008702the National Natural Science Foundation of China under Grant No.12571300。
文摘The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.
基金the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01072)the Institute of Zhejiang University-Quzhou for their financial support。
文摘Poly(phenylene oxide)(PPO)exhibits excellent dielectric properties,making it an ideal substrate for high-frequency,high-speed copper-clad laminates.The phenolic hydroxyl group at the end of PPO plays a key role in its reactivity.Accurately quantifying the phenolic hydroxyl content in PPO is essential but challenging.In this study,we proposed a method for measuring the phenolic hydroxyl content of PPO using differential UV absorption spectroscopy.In alkaline solutions,the phenolic hydroxyl in PPO completely ionizes to form phenoxide ions,leading to a significant increase in UV absorbance at approximately 250 and 300 nm.Notably,the differential UV absorbance at approximately 300 nm was directly proportional to the phenolic hydroxyl concentration.Using 2,6-dimethylphenol as a standard,a calibration curve was established to relate the phenolic hydroxyl concentration to differential UV absorbance at approximately 300 nm,providing a precise and straightforward method for phenolic hydroxyl quantification in PPO with distinct advantages over conventional techniques.
基金National Natural Science Foundation of China(No.52375236)Fundamental Research Funds for the Central Universities of China(No.23D110316)。
文摘In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.
文摘[Objective]As wireless power transfer(WPT)technology is increasingly deployed in scenarios such as electric vehicles,metallic foreign objects in the WPT area may cause local overheating and energy loss.Existing methods still suffer from poor edge/corner sensitivity,misjudgment due to fixed thresholds,and limited ability to extract position information.This work proposes a wireless power transfer-foreign object detection(WPT-FOD)method based on channel differential response and a dynamic-threshold corner-enhancement strategy,aiming to improve detection sensitivity,localization accuracy,and robustness without altering the overall coil layout.[Method]A multi-channel detection coil array is designed,and the self-inductance disturbance response of each channel coil is modeled.A channel-difference mapping mechanism is introduced to build a 2-D sensitivity matrix to characterize spatial position correlation.A corner-enhancement algorithm is developed to weight and amplify the collaborative response of adjacent channels,and a dynamic threshold adjustment mechanism is integrated to adapt to varying interference levels.Validation is carried out on a self-built 64-channel FOD platform by moving a typical metallic foreign object across central,edge,and corner regions,and by conducting comparative tests under different interference intensities.[Result]When a typical metallic foreign object moves to corner regions,the self-inductance disturbance of the detection coil increases from less than 0.02μH to more than 0.06μH,significantly enhancing the discrimination capability at corners.Under varying interference strengths,the dynamic threshold mechanism reduces the number of false positives from 13 to 2,demonstrating good environmental adaptability and stability.[Conclusion]By combining channel differential response,corner enhancement,and dynamic thresholding,the proposed WPT-FOD effectively mitigates edge/corner blind spots and fixed-threshold misjudgment,while providing localization capability and robustness.It markedly improves the accuracy of metallic foreign object detection in WPT systems and offers a feasible path and method reference for the safe application and engineering deployment of WPT systems.
基金co-supported by the Aeronautical Science Foundation of China(No.2022Z006051003)the Frontier Cross Fund Project of Beihang University,China(No.YWF-23-Q-1013)。
文摘To meet the high energy efficiency requirements of solar-powered Unmanned Aerial Vehicles(UAVs),this paper proposes an optimization design framework for a rudder/differential thrust joint control strategy based on incremental nonlinear dynamic inversion,and this collaborative control strategy is further integrated into top-level trajectory optimization.In this framework,the additional aerodynamic forces and moments caused by the asymmetry of the propeller slipstream are precisely modeled.The results demonstrate that through a rational allocation between rudder control and differential thrust control,the extra flight power caused by horizontal turns can be reduced by 44.5%,and the overall average flight power decreases by 6.2%.In energyoptimal trajectory design,the introduction of differential thrust control contributes to minimizing unfavorable segments in the flight trajectory,resulting in increased solar energy absorption and reduced flight energy consumption.The results indicate that the average net residual power increases by 7.3%.The effectiveness of differential thrust control in enhancing the energy performance of solar-powered UAVs is verified in this research.
基金funded by the Sinopec Science and Technology Project(No.P23132)the AAPG Foundation Grants-inAid Program(No.18644937)。
文摘By investigating the evolution of shale gas generation,storage,adjustment and accumulation under different structural settings in superimposed basins,this study elucidates the differential accumulation mechanisms of shale gas.An improved evaluation method of shale gas content evolution in superimposed basins is proposed.This method incorporates the coupling effect of key geological factors such as temperature,pressure,organic matter abundance,maturity,and pore characteristics on the content and occurrence state of shale gas,as well as the configuration relationship between shale gas generation and storage throughout geological history.Using this approach,the gas evolution histories of the Longmaxi Formation shales in wells N201 and PY1 are reconstructed under varying geological conditions.The Longmaxi Formation shales in these wells are dominated by typeⅠkerogen,with original total organic carbon(TOC_(o))contents of 6.20 wt% and 4.92 wt%,respectively,indicating differences in the initial material basis for gas generation.At the maximum burial depth of approximately 5000 m,the Longmaxi Formation shale in well N201 exhibits a formation pressure coefficient of 2.05,an organic matter maturity of 2.2%,and organic pores accounting for 68%of the total porosity.The gas generation quantity(Q_(g))reaches 19.24 m^(3)/t,while the gas storage capacity(Q_(s))is 4.30 m^(3)/t.The actual total gas content(Q_(a)),constrained by Q_(s),is 4.30 m^(3)/t,with free gas comprising 94%.Following relatively moderate tectonic uplift,the Q_(a) in well N201 decreases to 4.03 m^(3)/t,with free gas accounting for 63%.In contrast,the Longmaxi Formation shale in well PY1 reached a maximum burial depth of 6300 m,associated with a formation pressure coefficient of 1.62,organic matter maturity of 2.5%,and organic pore proportion of 67%.Here,Q_(g) is 16.87 m^(3)/t,and both Q_(s) and Q_(a) are 3.65 m^(3)/t,with free gas accounting for 98%.After intense tectonic uplift,Q_(a) declines to 2.72 m^(3)/t,and the proportion of free gas drops to51%.Finally,a four-stage differential accumulation model of shale gas is established:Slow gas generation and only adsorbed gas occur in stageⅠ,which is primarily controlled by TOC content;both adsorbed gas and free gas present in stageⅡ,with free gas becoming dominant;rapid gas generation and free gas predominance are controlled by temperature and porosity in stageⅢ;and gas adjustment and accumulation are primarily controlled by temperature and pressure in stageⅣ.
基金Supported by Defense Industrial Technology Development Program of China(Grant No.JCKY2021605B015).
文摘Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy and efficiency in solving complex problems.Thus,the use of machine learning to predict welding de-formations is a novel approach.In this study,laser welding experiments were conducted on a TC4 titanium alloy to establish a welding deformation dataset.The deep neural network(DNN)and convolutional neural network(CNN)models were designed and constructed,with average prediction errors of 0.85 mm and 0.94 mm on the validation set,respectively.To further optimize the network parameters,a differential evolution algorithm was employed through mutation,crossover,and selection.The results indicated that after optimization,the pre-diction errors of the DNN and CNN models reduced to 0.75 mm and 0.85 mm,respectively.These represent accuracy improvements of 14.8%and 9.6%,respectively.The optimized models exhibited superior predictive performances for the validation set.
文摘Selective Laser Melting(SLM),an advanced metal additive manufacturing technology,offers high precision and personalized customization advantages.However,selecting reasonable SLM parameters is challenging due to complex relationships.This study proposes a method for identifying the optimal process window by combining the simulation model with an optimization algorithm.JAYA is guided by the principle of preferential behavior towards best solutions and avoidance of worst ones,but it is prone to premature convergence thus leading to insufficient global search.To overcome limitations,this research proposes a Differential Evolution-framed JAYA algorithm(DEJAYA).DEJAYA incorporates four key enhancements to improve the flexibility of the original algorithm,which include DE framework design,horizontal crossover operator,longitudinal crossover operator,and global greedy strategy.The effectiveness of DEJAYA is rigorously evaluated by a suite of 23 distinct benchmark functions.Furthermore,the numerical simulation establishes AlSi10Mg single-track formation models,and DEJAYA successfully identified the optimal process window for this problem.Experimental results validate that DEJAYA effectively guides SLM parameter selection for AlSi10Mg.
基金supported by the National Key Research and Development Program of China(2023YFF0612900,2023YFF0612902)the Natural Science Foundation of Beijing,China(4254086)+3 种基金the National Natural Science Foundation of China(62472032)the Open Project Funding of Key Laboratory of Mobile Application Innovation and Governance Technology,Ministry of Industry and Information Technology(2023IFS080601-K)the Beijing Institute of Technology Research Fund Program for Young Scholarsthe Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)。
文摘Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs).
基金supported in part by the National Natural Science Foundation of China(No.52467008)Gansu Provincial Depatment of Education Youth Doctoral Suppo Project(2024QB-051).
文摘Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
文摘The rapid development and widespread adoption of massive open online courses(MOOCs)have indeed had a significant impact on China’s education curriculum.However,the problem of fake reviews and ratings on the platform has seriously affected the authenticity of course evaluations and user trust,requiring effective anomaly detection techniques for screening.The textual characteristics of MOOCs reviews,such as varying lengths and diverse emotional tendencies,have brought complexity to text analysis.Traditional rule-based analysis methods are often inadequate in dealing with such unstructured data.We propose a Differential Privacy-Enabled Text Convolutional Neural Network(DP-TextCNN)framework,aiming to achieve high-precision identification of outliers in MOOCs course reviews and ratings while protecting user privacy.This framework leverages the advantages of Convolutional Neural Networks(CNN)in text feature extraction and combines differential privacy techniques.It balances data privacy protection with model performance by introducing controlled random noise during the data preprocessing stage.By embedding differential privacy into the model training process,we ensure the privacy security of the framework when handling sensitive data,while maintaining a high recognition accuracy.Experimental results indicate that the DP-TextCNN framework achieves an exceptional accuracy of over 95%in identifying fake reviews on the dataset,this outcome not only verifies the applicability of differential privacy techniques in TextCNN but also underscores its potential in handling sensitive educational data.Additionally,we analyze the specific impact of differential privacy parameters on framework performance,offering theoretical support and empirical analysis to strike an optimal balance between privacy protection and framework efficiency.
基金supported by the National Natural Science Foundation of China(62305162,62227818,62361136588)China Postdoctoral Science Foundation(2023TQ0160,2023M731683)+5 种基金Nanjing University of Science and Technology independent research project(30923010305)National Key Research and Development Program of China(2024YFE0101300)Biomedical Competition Foundation of Jiangsu Province(BE2022847)Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039)Fundamental Research Funds for the Central Universities(2023102001)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201,JSGPCXZNGZ202401)。
文摘Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase contrast(DPC),a non-interferometric QPI technique,requires only four intensity images under asymmetric illumination to recover the phase of a sample,offering the advantages of being label-free,non-coherent and highly robust.Its phase reconstruction result relies on precise modeling of the phase transfer function(PTF).However,in real optical systems,the PTF will deviate from its theoretical ideal due to the unknown wavefront aberrations,which will lead to significant artifacts and distortions in the reconstructed phase.We propose an aberration-corrected DPC(ACDPC)method that utilizes three intensity images under annular illumination to jointly retrieve the aberration and the phase,achieving high-quality QPI with minimal raw data.By employing three annular illuminations precisely matched to the numerical aperture of the objective lens,the object information is transmitted into the acquired intensity with a high signal-to-noise ratio.Phase retrieval is achieved by an iterative deconvolution algorithm that uses simulated annealing to estimate the aberration and further employs regularized deconvolution to reconstruct the phase,ultimately obtaining a refined complex pupil function and an aberration-corrected quantitative phase.We demonstrate that ACDPC is robust to multi-order aberrations without any priori knowledge,and can effectively retrieve and correct system aberrations to obtain high-quality quantitative phase.Experimental results show that ACDPC can clearly reproduce subcellular structures such as vesicles and lipid droplets with higher resolution than conventional DPC,which opens up new possibilities for more accurate subcellular structure analysis in cell biology.
