The main purpose of this paper is to try to find all entire solutions of the Fermat type difference-differential equation[p1(z)f(z+c)]^(2)+[p2(z)f(z)+p3(z)f′(z)]^(2)=p(z);or[p1(z)f(z)]^(2)+[p2(z)f′(z)+p3(z)f(z+c)]^(...The main purpose of this paper is to try to find all entire solutions of the Fermat type difference-differential equation[p1(z)f(z+c)]^(2)+[p2(z)f(z)+p3(z)f′(z)]^(2)=p(z);or[p1(z)f(z)]^(2)+[p2(z)f′(z)+p3(z)f(z+c)]^(2)=p(z)or[p1(z)f′(z)]^(2)+[p2(z)f(z+c)+p3(z)f(z)]^(2)=p(z);where c is a nonzero complex number,p1;p2 and p3 are polynomials in C satisfying p1p3■0;and p is a nonzero irreducible polynomial in C.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
When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error...In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error between the numerical solution and the exact solution is obtained,and then compared with the error formed by the difference method,it is concluded that the Lagrange interpolation method is more effective in solving the variable coefficient ordinary differential equation.展开更多
In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval...In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval is singular or all four endpoints are regulars are the special cases).And these extensions yield"new"self-adjoint operators,which involve interactions between the two intervals.展开更多
This paper studies the Smoluchowski–Kramers approximation for a discrete-time dynamical system modeled as the motion of a particle in a force field.We show that the approximation holds for the drift-implicit Euler–M...This paper studies the Smoluchowski–Kramers approximation for a discrete-time dynamical system modeled as the motion of a particle in a force field.We show that the approximation holds for the drift-implicit Euler–Maruyama discretization and derive its convergence rate.In particular,the solution of the discretized system converges to the solution of the first-order limit equation in the mean-square sense,and this convergence is independent of the order in which the mass parameterμand the step size h tend to zero.展开更多
In this article,we study the meromorphic solutions of the following non-linear differential equation■where n and k are integers with n≥k≥3,P_(d)(z,f)is a differential polynomial in f of degree d≤n−1,p′js andα′j...In this article,we study the meromorphic solutions of the following non-linear differential equation■where n and k are integers with n≥k≥3,P_(d)(z,f)is a differential polynomial in f of degree d≤n−1,p′js andα′js are non-zero constants.We obtain the expressions of meromorphic solutions of the above equations under some restrictions onα′js.Some examples are given to illustrate the possibilities of our results.展开更多
This study aims to explore the clinical and CT imaging features of brucellosis spondylitis(BS)and to strengthen the clinical cognition to reduce misdiagnosis and mistreatment.In this study,clinical and CT imaging data...This study aims to explore the clinical and CT imaging features of brucellosis spondylitis(BS)and to strengthen the clinical cognition to reduce misdiagnosis and mistreatment.In this study,clinical and CT imaging data of 106 patients with BS diagnosed in the Second Hospital of Hohhot and the Third Hospital of Baotou were collected from March 2023 to September 2024 and retrospectively analyzed by clinical manifestations,CT imaging,and disease regression.In 106 patients,58.5%had fever,98.1%had malaise,96.2%had excessive sweating,81.1%had lumbosacral pain,79.3%had limitation of limb movement,76.4%had constipation,and 6.6%had urinary retention.For imaging manifestations,the involvement of lumbar,thoracic and cervical vertebrae were 80.2%,16.9%and 1.9%,respectively.Lesions<1.0 cm,1.0–2.0 cm,2.0–3.0 cm,and>3.0 cm were found in 49.4%,29.6%,19.4%,and 1.6%,respectively.In 106 patients,CT showed round,irregular or worm-like areas of bone destruction,with coexisting osteophytes in 61.5%and no signs of dead bone or pedicle destruction.Interdiscal destruction,spinal canal abscess,ligament injury,and signs of lumbar major muscle compression were rare,accounting for 11.7%,6.6%,4.7%,and 3.8%,respectively.Regarding regression,106 patients with BS treated with antimicrobial therapy or antimicrobial+surgery had a good prognosis.In conclusion,BS has its own characteristics in clinical and imaging aspects and it is easy to distinguish from other common causes of spondylitis bone damage.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
With the ongoing digitalization and intelligence of power systems,there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting.Neve...With the ongoing digitalization and intelligence of power systems,there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting.Nevertheless,power data often contains sensitive information,making it a critical industry challenge to efficiently utilize this data while ensuring privacy.Traditional Federated Learning(FL)methods can mitigate data leakage by training models locally instead of transmitting raw data.Despite this,FL still has privacy concerns,especially gradient leakage,which might expose users’sensitive information.Therefore,integrating Differential Privacy(DP)techniques is essential for stronger privacy protection.Even so,the noise from DP may reduce the performance of federated learning models.To address this challenge,this paper presents an explainability-driven power data privacy federated learning framework.It incorporates DP technology and,based on model explainability,adaptively adjusts privacy budget allocation and model aggregation,thus balancing privacy protection and model performance.The key innovations of this paper are as follows:(1)We propose an explainability-driven power data privacy federated learning framework.(2)We detail a privacy budget allocation strategy:assigning budgets per training round by gradient effectiveness and at model granularity by layer importance.(3)We design a weighted aggregation strategy that considers the SHAP value and model accuracy for quality knowledge sharing.(4)Experiments show the proposed framework outperforms traditional methods in balancing privacy protection and model performance in power load forecasting tasks.展开更多
In response to the shortcomings of the common encoders in the industry,of which the photoelectric encoders have a poor anti-interference ability in harsh industrial environments with water,oil,dust,or strong vibration...In response to the shortcomings of the common encoders in the industry,of which the photoelectric encoders have a poor anti-interference ability in harsh industrial environments with water,oil,dust,or strong vibrations and the magnetic encoders are too sensitive to magnetic field density,this paper designs a new differential encoder based on the grating eddy-current measurement principle,abbreviated as differential grating eddy-current encoder(DGECE).The grating eddy-current of DGECE consists of a circular array of trapezoidal reflection conductors and 16 trapezoidal coils with a special structure to form a differential relationship,which are respectively located on the code plate and the readout plate designed by a printed circuit board.The differential structure of DGECE corrects the common mode interference and the amplitude distortion due to the assembly to some extent,possesses a certain anti-interference capability,and greatly simplifies the regularization algorithm of the original data.By means of the corresponding readout circuit and demodulation algorithm,the DGECE can convert the periodic impedance variation of 16 coils into an angular output within the 360°cycle.Due to its simple manufacturing process and certain interference immunity,DGECE is easy to be integrated and mass-produced as well as applicable in the industrial spindles,especially in robot joints.This paper presents the measurement principle,implementation methods,and results of the experiment of the DGECE.The experimental results show that the accuracy of the DGECE can reach 0.237%and the measurement standard deviation can reach±0.14°within360°cycle.展开更多
Interstitial hypertension and extracellular matrix(ECM)barriers imposed by cancer-associated fibroblasts(CAFs)at the tumor site significantly impede the retention of intratumorally administered oncolytic viruses(OVs)a...Interstitial hypertension and extracellular matrix(ECM)barriers imposed by cancer-associated fibroblasts(CAFs)at the tumor site significantly impede the retention of intratumorally administered oncolytic viruses(OVs)as well as their efficacy in infecting and eradicating tumor cells.Herein,a stable,controllable,and easily prepared hydrogel was developed for employing a differential release strategy to deliver OVs.The oncolytic herpes simplex virus-2(oH2)particles were loaded within sodium alginate(ALG),together with the small molecule drug PT-100 targeting CAFs.The rapid release of PT-100 functions as an anti-CAFs agent,reducing ECM,and alleviating interstitial pressure at the tumor site.Consequently,the delayed release of oH2 could more effectively invade and eradicate tumor cells while also facilitating enhanced infiltration of immune cells into the tumor microenvironment,thereby establishing an immunologically favorable milieu against tumors.This approach holds significant potential for achieving highly efficient oncolytic virus therapy with minimal toxicity,particularly in tumors rich in stromal components.展开更多
Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensi...Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensing location data.In the application of mobile crowdsensing,most location privacy protection studies do not consider the temporal correlations between locations,so they are vulnerable to various inference attacks,and there is the problem of low data availability.In order to solve the above problems,this paper proposes a dynamic differential location privacy data publishing framework(DDLP)that protects privacy while publishing locations continuously.Firstly,the corresponding Markov transition matrices are established according to different times of historical trajectories,and then the protection location set is generated based on the current location at each timestamp.Moreover,using the exponential mechanism in differential privacy perturbs the true location by designing the utility function.Finally,experiments on the real-world trajectory dataset show that our method not only provides strong privacy guarantees,but also outperforms existing methods in terms of data availability and computational efficiency.展开更多
This paper presents a novel element differential method for modeling cracks in piezoelectric materials,aiming to simulate fracture behaviors and predict the fracture parameter known as the J-integral accurately.The me...This paper presents a novel element differential method for modeling cracks in piezoelectric materials,aiming to simulate fracture behaviors and predict the fracture parameter known as the J-integral accurately.The method leverages an efficient collocation technique to satisfy traction and electric charge equilibrium on the crack surface,aligning internal nodes with piezoelectric governing equations without needing integration or variational principles.It combines the strengths of the strong form collocation and finite element methods.The J-integral is derived analytically using the equivalent domain integral method,employing Green's formula and Gauss's divergence theorem to transform line integrals into area integrals for solving two-dimensional piezoelectric material problems.The accuracy of the method is validated through comparison with three typical examples,and it offers fracture prevention strategies for engineering piezoelectric structures under different electrical loading patterns.展开更多
Deep learning’s widespread dependence on large datasets raises privacy concerns due to the potential presence of sensitive information.Differential privacy stands out as a crucial method for preserving privacy,garner...Deep learning’s widespread dependence on large datasets raises privacy concerns due to the potential presence of sensitive information.Differential privacy stands out as a crucial method for preserving privacy,garnering significant interest for its ability to offer robust and verifiable privacy safeguards during data training.However,classic differentially private learning introduces the same level of noise into the gradients across training iterations,which affects the trade-off between model utility and privacy guarantees.To address this issue,an adaptive differential privacy mechanism was proposed in this paper,which dynamically adjusts the privacy budget at the layer-level as training progresses to resist member inference attacks.Specifically,an equal privacy budget is initially allocated to each layer.Subsequently,as training advances,the privacy budget for layers closer to the output is reduced(adding more noise),while the budget for layers closer to the input is increased.The adjustment magnitude depends on the training iterations and is automatically determined based on the iteration count.This dynamic allocation provides a simple process for adjusting privacy budgets,alleviating the burden on users to tweak parameters and ensuring that privacy preservation strategies align with training progress.Extensive experiments on five well-known datasets indicate that the proposed method outperforms competing methods in terms of accuracy and resilience against membership inference attacks.展开更多
Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving dat...Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing.Despite its privacy-preserving advantages,FL remains vulnerable to backdoor attacks,where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process.While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL,they often incur a significant degradation in the performance of the aggregated models on benign tasks.To address this limitation,we propose a novel backdoor defense mechanism based on differential privacy.Our approach first utilizes the inherent out-of-distribution characteristics of backdoor samples to identify and exclude malicious model updates that significantly deviate from benign models.By filtering out models that are clearly backdoor-infected before applying differential privacy,our method reduces the required noise level for differential privacy,thereby enhancing model robustness while preserving performance.Experimental evaluations on the CIFAR10 and FEMNIST datasets demonstrate that our method effectively limits the backdoor accuracy to below 15%across various backdoor scenarios while maintaining high main task accuracy.展开更多
Background:Exercise induces molecular changes that involve multiple organs and tissues.Moreover,these changes are modulated by various exercise parameters—such as intensity,frequency,mode,and duration—as well as by ...Background:Exercise induces molecular changes that involve multiple organs and tissues.Moreover,these changes are modulated by various exercise parameters—such as intensity,frequency,mode,and duration—as well as by clinical features like gender,age,and body mass index(BMI),each eliciting distinct biological effects.To assist exercise researchers in understanding these changes from a comprehensive perspective that includes multiple organs,diverse exercise regimens,and a range of clinical features,we developed Exercise Regulated Genes Database(ExerGeneDB),a database of exercise-regulated differential genes.Methods:ExerGeneDB aggregated publicly available exercise-related sequencing datasets and subjected them to uniform quality control and preprocessing.The data,encompassing a variety of types,were organized into a specialized database of exercise-regulated genes.Notably,Exer-GeneDB conducted differential analyses on this collected data,leveraging curated clinical information and accounting for important factors such as gender,age,and BMI.Results:ExerGeneDB has assembled 1692 samples from rats and mice as well as 4492 human samples.It contains data from various tissues and organs,such as skeletal muscle,blood,adipose tissue,intestine,heart,liver,spleen,lungs,kidneys,brain,spinal cord,bone marrow,and bones.ExerGeneDB features bulk ribonucleic acid sequencing(RNA-seq)(including non-coding RNA(ncRNA)and protein-coding RNA),microarray(including ncRNA and protein-coding RNA),and single cell RNA-seq data.Conclusion:ExerGeneDB compiles and re-analyzes exercise-related data with a focus on clinical information.This has culminated in the crea-tion of an interactive database for exercise regulation genes.The website for ExerGeneDB can be found at:https://exergenedb.com.展开更多
Federated learning effectively alleviates privacy and security issues raised by the development of artificial intelligence through a distributed training architecture.Existing research has shown that attackers can com...Federated learning effectively alleviates privacy and security issues raised by the development of artificial intelligence through a distributed training architecture.Existing research has shown that attackers can compromise user privacy and security by stealing model parameters.Therefore,differential privacy is applied in federated learning to further address malicious issues.However,the addition of noise and the update clipping mechanism in differential privacy jointly limit the further development of federated learning in privacy protection and performance optimization.Therefore,we propose an adaptive adjusted differential privacy federated learning method.First,a dynamic adaptive privacy budget allocation strategy is proposed,which flexibly adjusts the privacy budget within a given range based on the client’s data volume and training requirements,thereby alleviating the loss of privacy budget and the magnitude of model noise.Second,a longitudinal clipping differential privacy strategy is proposed,which based on the differences in factors that affect parameter updates,uses sparse methods to trim local updates,thereby reducing the impact of privacy pruning steps on model accuracy.The two strategies work together to ensure user privacy while the effect of differential privacy on model accuracy is reduced.To evaluate the effectiveness of our method,we conducted extensive experiments on benchmark datasets,and the results showed that our proposed method performed well in terms of performance and privacy protection.展开更多
Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms ...Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms are used to solve complex problems(e.g.,real-world artificial neural network(ANN)training problems).To resolve this issue,this paper proposes a framework based on an efficient elite centroid operator.It continuously monitors the current state of the population.Once stagnation is detected,two dedicated operators,centroid-based mutation(CM)and centroid-based crossover(CX),are executed to replace the classical mutation and binomial crossover operations in DE.CM and CX are centred on the elite centroid composed of multiple elite individuals,constituting a framework consisting of elitism centroid-based operations(CMX)to improve the performance of the individuals who fall into stagnation.In CM,elite centroid provide evolutionary direction for stagnant individuals,and in CX,elite plasmoids address the limitation that stagnant individuals can only obtain limited information about the population.The CMX framework is simple enough to easily incorporate into both classically well-known DEs with constant population sizes and state-of-the-art DEs with varying populations.Numerical experiments on benchmark functions show that the proposed CMX method can significantly enhance the classical DE algorithm and its advanced variants in solving the stagnation problem and improving performance.展开更多
This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-preci...This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-precision laser interferometric displacement measurement.A stable power supply module is designed to provide low-noise voltage to the entire circuit.An analog circuit system is constructed,including key circuits such as photoelectric sensors,I-V amplification,zero adjustment,fully differential amplification,and amplitude modulation filtering.To acquire and process signals,the PMAC Acc24E3 data acquisition card is selected,which realizes phase demodulation through reversible square wave counting,inverts displacement information,and a visual interface for the host computer is designed.Experimental verification shows that the designed system achieves micrometer-level measurement accuracy within a range of 0-10mm,with a maximum measurement error of less than 1.2μm,a maximum measurement speed of 6m/s,and a resolution better than 0.158μm.展开更多
基金Supported by the National Natural Science Foundation of China(11871260,11761050)the Jiangxi Natural Science Foundation(#20232ACB201005)+1 种基金the Shandong Natural Science Foundation(#ZR2024MA024)Doctoral Startup Fund of Jiangxi Science and Technology Normal University(#2021BSQD30).
文摘The main purpose of this paper is to try to find all entire solutions of the Fermat type difference-differential equation[p1(z)f(z+c)]^(2)+[p2(z)f(z)+p3(z)f′(z)]^(2)=p(z);or[p1(z)f(z)]^(2)+[p2(z)f′(z)+p3(z)f(z+c)]^(2)=p(z)or[p1(z)f′(z)]^(2)+[p2(z)f(z+c)+p3(z)f(z)]^(2)=p(z);where c is a nonzero complex number,p1;p2 and p3 are polynomials in C satisfying p1p3■0;and p is a nonzero irreducible polynomial in C.
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%.
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
文摘In order to solve the problem of the variable coefficient ordinary differen-tial equation on the bounded domain,the Lagrange interpolation method is used to approximate the exact solution of the equation,and the error between the numerical solution and the exact solution is obtained,and then compared with the error formed by the difference method,it is concluded that the Lagrange interpolation method is more effective in solving the variable coefficient ordinary differential equation.
基金Supported by NSFC (No.12361027)NSF of Inner Mongolia (No.2018MS01021)+1 种基金NSF of Shandong Province (No.ZR2020QA009)Science and Technology Innovation Program for Higher Education Institutions of Shanxi Province (No.2024L533)。
文摘In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval is singular or all four endpoints are regulars are the special cases).And these extensions yield"new"self-adjoint operators,which involve interactions between the two intervals.
基金supported by the PhD Research Startup Foundation of Hubei University of Economics(Grand No.XJ23BS42).
文摘This paper studies the Smoluchowski–Kramers approximation for a discrete-time dynamical system modeled as the motion of a particle in a force field.We show that the approximation holds for the drift-implicit Euler–Maruyama discretization and derive its convergence rate.In particular,the solution of the discretized system converges to the solution of the first-order limit equation in the mean-square sense,and this convergence is independent of the order in which the mass parameterμand the step size h tend to zero.
基金supported by the National Natural Science Foundation of China(No.12001117)the Guangdong Basic and Applied Basic Research Foundation(No.2021A1515110654).
文摘In this article,we study the meromorphic solutions of the following non-linear differential equation■where n and k are integers with n≥k≥3,P_(d)(z,f)is a differential polynomial in f of degree d≤n−1,p′js andα′js are non-zero constants.We obtain the expressions of meromorphic solutions of the above equations under some restrictions onα′js.Some examples are given to illustrate the possibilities of our results.
文摘This study aims to explore the clinical and CT imaging features of brucellosis spondylitis(BS)and to strengthen the clinical cognition to reduce misdiagnosis and mistreatment.In this study,clinical and CT imaging data of 106 patients with BS diagnosed in the Second Hospital of Hohhot and the Third Hospital of Baotou were collected from March 2023 to September 2024 and retrospectively analyzed by clinical manifestations,CT imaging,and disease regression.In 106 patients,58.5%had fever,98.1%had malaise,96.2%had excessive sweating,81.1%had lumbosacral pain,79.3%had limitation of limb movement,76.4%had constipation,and 6.6%had urinary retention.For imaging manifestations,the involvement of lumbar,thoracic and cervical vertebrae were 80.2%,16.9%and 1.9%,respectively.Lesions<1.0 cm,1.0–2.0 cm,2.0–3.0 cm,and>3.0 cm were found in 49.4%,29.6%,19.4%,and 1.6%,respectively.In 106 patients,CT showed round,irregular or worm-like areas of bone destruction,with coexisting osteophytes in 61.5%and no signs of dead bone or pedicle destruction.Interdiscal destruction,spinal canal abscess,ligament injury,and signs of lumbar major muscle compression were rare,accounting for 11.7%,6.6%,4.7%,and 3.8%,respectively.Regarding regression,106 patients with BS treated with antimicrobial therapy or antimicrobial+surgery had a good prognosis.In conclusion,BS has its own characteristics in clinical and imaging aspects and it is easy to distinguish from other common causes of spondylitis bone damage.
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.
文摘With the ongoing digitalization and intelligence of power systems,there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting.Nevertheless,power data often contains sensitive information,making it a critical industry challenge to efficiently utilize this data while ensuring privacy.Traditional Federated Learning(FL)methods can mitigate data leakage by training models locally instead of transmitting raw data.Despite this,FL still has privacy concerns,especially gradient leakage,which might expose users’sensitive information.Therefore,integrating Differential Privacy(DP)techniques is essential for stronger privacy protection.Even so,the noise from DP may reduce the performance of federated learning models.To address this challenge,this paper presents an explainability-driven power data privacy federated learning framework.It incorporates DP technology and,based on model explainability,adaptively adjusts privacy budget allocation and model aggregation,thus balancing privacy protection and model performance.The key innovations of this paper are as follows:(1)We propose an explainability-driven power data privacy federated learning framework.(2)We detail a privacy budget allocation strategy:assigning budgets per training round by gradient effectiveness and at model granularity by layer importance.(3)We design a weighted aggregation strategy that considers the SHAP value and model accuracy for quality knowledge sharing.(4)Experiments show the proposed framework outperforms traditional methods in balancing privacy protection and model performance in power load forecasting tasks.
基金the Biomedical Science and Technology Support Special Project of Shanghai Science and Technology Committee(No.20S31908300)。
文摘In response to the shortcomings of the common encoders in the industry,of which the photoelectric encoders have a poor anti-interference ability in harsh industrial environments with water,oil,dust,or strong vibrations and the magnetic encoders are too sensitive to magnetic field density,this paper designs a new differential encoder based on the grating eddy-current measurement principle,abbreviated as differential grating eddy-current encoder(DGECE).The grating eddy-current of DGECE consists of a circular array of trapezoidal reflection conductors and 16 trapezoidal coils with a special structure to form a differential relationship,which are respectively located on the code plate and the readout plate designed by a printed circuit board.The differential structure of DGECE corrects the common mode interference and the amplitude distortion due to the assembly to some extent,possesses a certain anti-interference capability,and greatly simplifies the regularization algorithm of the original data.By means of the corresponding readout circuit and demodulation algorithm,the DGECE can convert the periodic impedance variation of 16 coils into an angular output within the 360°cycle.Due to its simple manufacturing process and certain interference immunity,DGECE is easy to be integrated and mass-produced as well as applicable in the industrial spindles,especially in robot joints.This paper presents the measurement principle,implementation methods,and results of the experiment of the DGECE.The experimental results show that the accuracy of the DGECE can reach 0.237%and the measurement standard deviation can reach±0.14°within360°cycle.
基金supported by the National Key R&D Program of China(No.2022YFC2403401)the National Natural Science Foundation of China(Nos.82073368,82303766)+2 种基金the Liaoning Revitalization Talents Program(No.XLYC2007071)the China Postdoctoral Science Foundation(No.2023M743908)the Joint Program of Science and Technology Program of Liaoning Province(No.2023JH2/101700094).
文摘Interstitial hypertension and extracellular matrix(ECM)barriers imposed by cancer-associated fibroblasts(CAFs)at the tumor site significantly impede the retention of intratumorally administered oncolytic viruses(OVs)as well as their efficacy in infecting and eradicating tumor cells.Herein,a stable,controllable,and easily prepared hydrogel was developed for employing a differential release strategy to deliver OVs.The oncolytic herpes simplex virus-2(oH2)particles were loaded within sodium alginate(ALG),together with the small molecule drug PT-100 targeting CAFs.The rapid release of PT-100 functions as an anti-CAFs agent,reducing ECM,and alleviating interstitial pressure at the tumor site.Consequently,the delayed release of oH2 could more effectively invade and eradicate tumor cells while also facilitating enhanced infiltration of immune cells into the tumor microenvironment,thereby establishing an immunologically favorable milieu against tumors.This approach holds significant potential for achieving highly efficient oncolytic virus therapy with minimal toxicity,particularly in tumors rich in stromal components.
基金supported by the Inner Mongolia Natural Science Foundation(Grant No.2023MS06022)the University Youth Science and Technology Talent Development Project(Innovation Group Development Plan)of Inner Mongolia A.R.of China(Grant No.NMGIRT2318)+1 种基金the“Inner Mongolia Science and Technology Achievement Transfer and Transformation Demonstration Zone,University Collaborative Innovation Base,and University Entrepreneurship Training Base”Construction Project(Supercomputing Power Project)(Grant No.21300-231510)the Engineering Research Center of Ecological Big Data,Ministry of Education.
文摘Mobile crowdsensing(MCS)has become an effective paradigm to facilitate urban sensing.However,mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensing location data.In the application of mobile crowdsensing,most location privacy protection studies do not consider the temporal correlations between locations,so they are vulnerable to various inference attacks,and there is the problem of low data availability.In order to solve the above problems,this paper proposes a dynamic differential location privacy data publishing framework(DDLP)that protects privacy while publishing locations continuously.Firstly,the corresponding Markov transition matrices are established according to different times of historical trajectories,and then the protection location set is generated based on the current location at each timestamp.Moreover,using the exponential mechanism in differential privacy perturbs the true location by designing the utility function.Finally,experiments on the real-world trajectory dataset show that our method not only provides strong privacy guarantees,but also outperforms existing methods in terms of data availability and computational efficiency.
基金Financial support of this work by the Technology Development program of China(Grant No.2022204B003)National Natural Science Foundation of China(12272083 and 12172078)the Fundamental Research Funds for the Central Universities(DUT24YJ136)is gratefully acknowledged.
文摘This paper presents a novel element differential method for modeling cracks in piezoelectric materials,aiming to simulate fracture behaviors and predict the fracture parameter known as the J-integral accurately.The method leverages an efficient collocation technique to satisfy traction and electric charge equilibrium on the crack surface,aligning internal nodes with piezoelectric governing equations without needing integration or variational principles.It combines the strengths of the strong form collocation and finite element methods.The J-integral is derived analytically using the equivalent domain integral method,employing Green's formula and Gauss's divergence theorem to transform line integrals into area integrals for solving two-dimensional piezoelectric material problems.The accuracy of the method is validated through comparison with three typical examples,and it offers fracture prevention strategies for engineering piezoelectric structures under different electrical loading patterns.
基金supported by the National Natural Science Foundation of China(Grant No.62462022)the Hainan Province Science and Technology Special Fund(Grants No.ZDYF2022GXJS229).
文摘Deep learning’s widespread dependence on large datasets raises privacy concerns due to the potential presence of sensitive information.Differential privacy stands out as a crucial method for preserving privacy,garnering significant interest for its ability to offer robust and verifiable privacy safeguards during data training.However,classic differentially private learning introduces the same level of noise into the gradients across training iterations,which affects the trade-off between model utility and privacy guarantees.To address this issue,an adaptive differential privacy mechanism was proposed in this paper,which dynamically adjusts the privacy budget at the layer-level as training progresses to resist member inference attacks.Specifically,an equal privacy budget is initially allocated to each layer.Subsequently,as training advances,the privacy budget for layers closer to the output is reduced(adding more noise),while the budget for layers closer to the input is increased.The adjustment magnitude depends on the training iterations and is automatically determined based on the iteration count.This dynamic allocation provides a simple process for adjusting privacy budgets,alleviating the burden on users to tweak parameters and ensuring that privacy preservation strategies align with training progress.Extensive experiments on five well-known datasets indicate that the proposed method outperforms competing methods in terms of accuracy and resilience against membership inference attacks.
文摘Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing.Despite its privacy-preserving advantages,FL remains vulnerable to backdoor attacks,where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process.While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL,they often incur a significant degradation in the performance of the aggregated models on benign tasks.To address this limitation,we propose a novel backdoor defense mechanism based on differential privacy.Our approach first utilizes the inherent out-of-distribution characteristics of backdoor samples to identify and exclude malicious model updates that significantly deviate from benign models.By filtering out models that are clearly backdoor-infected before applying differential privacy,our method reduces the required noise level for differential privacy,thereby enhancing model robustness while preserving performance.Experimental evaluations on the CIFAR10 and FEMNIST datasets demonstrate that our method effectively limits the backdoor accuracy to below 15%across various backdoor scenarios while maintaining high main task accuracy.
基金supported by grants from the National Natural Science Foundation of China(82225005, 82020108002 to JX,82200321 to QZ)Science and Technology Commission of ShanghaiMunicipality(23410750100,20DZ2255400,, 21XD1421300 to JX)+1 种基金the“Dawn”Program of Shanghai Educa-tion Commission(19SG34 to JX)Shanghai Sailing Program(21YF1413200 to QZ).
文摘Background:Exercise induces molecular changes that involve multiple organs and tissues.Moreover,these changes are modulated by various exercise parameters—such as intensity,frequency,mode,and duration—as well as by clinical features like gender,age,and body mass index(BMI),each eliciting distinct biological effects.To assist exercise researchers in understanding these changes from a comprehensive perspective that includes multiple organs,diverse exercise regimens,and a range of clinical features,we developed Exercise Regulated Genes Database(ExerGeneDB),a database of exercise-regulated differential genes.Methods:ExerGeneDB aggregated publicly available exercise-related sequencing datasets and subjected them to uniform quality control and preprocessing.The data,encompassing a variety of types,were organized into a specialized database of exercise-regulated genes.Notably,Exer-GeneDB conducted differential analyses on this collected data,leveraging curated clinical information and accounting for important factors such as gender,age,and BMI.Results:ExerGeneDB has assembled 1692 samples from rats and mice as well as 4492 human samples.It contains data from various tissues and organs,such as skeletal muscle,blood,adipose tissue,intestine,heart,liver,spleen,lungs,kidneys,brain,spinal cord,bone marrow,and bones.ExerGeneDB features bulk ribonucleic acid sequencing(RNA-seq)(including non-coding RNA(ncRNA)and protein-coding RNA),microarray(including ncRNA and protein-coding RNA),and single cell RNA-seq data.Conclusion:ExerGeneDB compiles and re-analyzes exercise-related data with a focus on clinical information.This has culminated in the crea-tion of an interactive database for exercise regulation genes.The website for ExerGeneDB can be found at:https://exergenedb.com.
基金funded by the Science and Technology Project of State Grid Corporation of China(Research on the theory and method of multiparty encrypted computation in the edge fusion environment of power IoT,No.5700-202358592A-3-2-ZN)the National Natural Science Foundation of China(Grant Nos.62272056,62372048,62371069).
文摘Federated learning effectively alleviates privacy and security issues raised by the development of artificial intelligence through a distributed training architecture.Existing research has shown that attackers can compromise user privacy and security by stealing model parameters.Therefore,differential privacy is applied in federated learning to further address malicious issues.However,the addition of noise and the update clipping mechanism in differential privacy jointly limit the further development of federated learning in privacy protection and performance optimization.Therefore,we propose an adaptive adjusted differential privacy federated learning method.First,a dynamic adaptive privacy budget allocation strategy is proposed,which flexibly adjusts the privacy budget within a given range based on the client’s data volume and training requirements,thereby alleviating the loss of privacy budget and the magnitude of model noise.Second,a longitudinal clipping differential privacy strategy is proposed,which based on the differences in factors that affect parameter updates,uses sparse methods to trim local updates,thereby reducing the impact of privacy pruning steps on model accuracy.The two strategies work together to ensure user privacy while the effect of differential privacy on model accuracy is reduced.To evaluate the effectiveness of our method,we conducted extensive experiments on benchmark datasets,and the results showed that our proposed method performed well in terms of performance and privacy protection.
基金funded by National Special Project Number for International Cooperation under Grant 2015DFR11050the Applied Science and Technology Research and Development Special Fund Project of Guangdong Province under Grant 2016B010126004.
文摘Differential evolution(DE)algorithms are simple and efficient evolutionary algorithms that performwell in various optimization problems.Unfortunately,they inevitably stagnate when differential evolutionary algorithms are used to solve complex problems(e.g.,real-world artificial neural network(ANN)training problems).To resolve this issue,this paper proposes a framework based on an efficient elite centroid operator.It continuously monitors the current state of the population.Once stagnation is detected,two dedicated operators,centroid-based mutation(CM)and centroid-based crossover(CX),are executed to replace the classical mutation and binomial crossover operations in DE.CM and CX are centred on the elite centroid composed of multiple elite individuals,constituting a framework consisting of elitism centroid-based operations(CMX)to improve the performance of the individuals who fall into stagnation.In CM,elite centroid provide evolutionary direction for stagnant individuals,and in CX,elite plasmoids address the limitation that stagnant individuals can only obtain limited information about the population.The CMX framework is simple enough to easily incorporate into both classically well-known DEs with constant population sizes and state-of-the-art DEs with varying populations.Numerical experiments on benchmark functions show that the proposed CMX method can significantly enhance the classical DE algorithm and its advanced variants in solving the stagnation problem and improving performance.
文摘This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-precision laser interferometric displacement measurement.A stable power supply module is designed to provide low-noise voltage to the entire circuit.An analog circuit system is constructed,including key circuits such as photoelectric sensors,I-V amplification,zero adjustment,fully differential amplification,and amplitude modulation filtering.To acquire and process signals,the PMAC Acc24E3 data acquisition card is selected,which realizes phase demodulation through reversible square wave counting,inverts displacement information,and a visual interface for the host computer is designed.Experimental verification shows that the designed system achieves micrometer-level measurement accuracy within a range of 0-10mm,with a maximum measurement error of less than 1.2μm,a maximum measurement speed of 6m/s,and a resolution better than 0.158μm.
