Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and m...Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).展开更多
The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charg...The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.展开更多
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and use...As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have sugg...Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have suggested that multiple factors may contribute to its pathogenesis.PD is characterized by a movement disorder that primarily affects motor control;pathologically,the disease is marked by the presence of Lewy bodies (LBs) in the brain.展开更多
Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,com...Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,computational efficiency,and regulatory compliance.Traditional approaches,such as differential privacy,homomorphic encryption,and secure multi-party computation,often fail to balance performance and privacy,rendering them unsuitable for resource-constrained healthcare AIoT environments.This paper introduces LMSA(Lightweight Multi-Key Secure Aggregation),a novel framework designed to address these challenges and enable efficient,secure federated learning across distributed healthcare institutions.LMSA incorporates three key innovations:(1)a lightweight multikey management system leveraging Diffie-Hellman key exchange and SHA3-256 hashing,achieving O(n)complexity with AES(Advanced Encryption Standard)-256-level security;(2)a privacy-preserving aggregation protocol employing hardware-accelerated AES-CTR(CounTeR)encryption andmodular arithmetic for securemodel weight combination;and(3)a resource-optimized implementation utilizing AES-NI(New Instructions)instructions and efficient memory management for real-time operations on constrained devices.Experimental evaluations using the National Institutes of Health(NIH)Chest X-ray dataset demonstrate LMSA’s ability to train multi-label thoracic disease prediction models with Vision Transformer(ViT),ResNet-50,and MobileNet architectures across distributed healthcare institutions.Memory usage analysis confirmed minimal overhead,with ViT(327.30 MB),ResNet-50(89.87 MB),and MobileNet(8.63 MB)maintaining stable encryption times across communication rounds.LMSA ensures robust security through hardware acceleration,enabling real-time diagnostics without compromising patient confidentiality or regulatory compliance.Future research aims to optimize LMSA for ultra-low-power devices and validate its scalability in heterogeneous,real-world environments.LMSA represents a foundational advancement for privacy-conscious healthcare AI applications,bridging the gap between privacy and performance.展开更多
Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregatio...Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.展开更多
The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained...The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.展开更多
Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the a...Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the aggregated flexible region of load clusters managed by load aggregators is the crucial basis of power system scheduling for the system operator.This is because the aggregation result affects the qual-ity of the scheduling schemes.A stringent computation based on the Minkowski sum is NP-hard,whereas existing approximation meth-ods that use a special type of polytope exhibit limited adaptability when aggregating heterogeneous loads.This study proposes a stringent internal approximation method based on the convex hull of multiple layers of maximum volume boxes and embeds it into a day-ahead scheduling optimization model.The numerical results indicate that the aggregation accuracy can be improved compared with methods based on one type of special polytope,including boxes,zonotopes,and homothets.Hence,the reliability and economy of the power sys-tem scheduling can be enhanced.展开更多
To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installa...To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.展开更多
Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cott...Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.展开更多
Parkinson's disease(PD),a prevalent neurodegenerative disorder,is chara cterized by the loss of dopaminergic neurons and the aggregation ofα-synuclein protein into Lewy bodies.While the current standards of thera...Parkinson's disease(PD),a prevalent neurodegenerative disorder,is chara cterized by the loss of dopaminergic neurons and the aggregation ofα-synuclein protein into Lewy bodies.While the current standards of therapy have been successful in providing some symptom relief,they fail to address the underlying pathophysiology of PD and as a result,they have no effect on disease progression.展开更多
Red blood cells(RBCs)are the most abundant human blood cells.RBC aggregation and deformation strongly determine blood viscosity which impacts hemorheology and microcirculation.In turn,RBC properties depend on di®...Red blood cells(RBCs)are the most abundant human blood cells.RBC aggregation and deformation strongly determine blood viscosity which impacts hemorheology and microcirculation.In turn,RBC properties depend on di®erent endogenous and exogenous factors.One such factor is nitric oxide(NO),which is mainly produced by endothelial cells(EC)from L-arginine amino acid in the circulatory system.Since the mechanisms of the RBC-endothelium interplay are not clear up to date and considering its possible clinical importance,the aims of this study are to investigate in vitro:(1)The effect of L-arginine induced NO on RBC aggregation and adhesion to endothelium;(2)the NO e®ect on RBC aggregation and deformation induced by L-arginine and sodium nitroprusside without the presence of endothelium in the samples.The RBC aggregation and adhesion to a monolayer of EC were studied using optical tweezers(OT).The RBC deformability and aggregation without endothelium in the samples were studied using the flow chamber method and Myrenne aggregometer.We confirmed that NO increases deformability and decreases aggregation of RBCs.We showed that the soluble guanylate cyclase pathway appears to be the only NO signaling pathway involved.In the samples with the endothelium,the "bell-shaped"dependence of RBC aggregation force on L-arginine concentration was observed,which improves our knowledge about the process of NO production by endothelium.Additionally,data related to L-arginine accumulation by endothelium were obtained:Necessity of the presence of extracellular L-arginine stated by other authors was put under question.In our study,NO decreased the RBC-endothelium adhesion,however,the tendency appeared to be weak and was not confirmed in another set of experiments.To our knowledge,this is the first attempt to measure the forces of RBC adhesion to endothelium monolayer with OT.展开更多
Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also th...Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.展开更多
Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collect...Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collectively known as tauopathies.They comprise frontotemporal dementia,Pick’s disease,or corticobasal degeneration,among others.The symptomatology varies with the specific tau protein variant involved and the affected brain region or cell type.However,they share a common neuropathological hallmark-the formation of proteinaceous deposits named neurofibrillary tangles.Neurofibrillary tangles,primarily composed of aggregated tau(Zhang et al.,2022),disrupt normal neuronal functions,leading to cell death and cognitive decline.展开更多
The bioreduction of graphene oxide(GO)using environmentally functional bacteria such as Shewanella represents a green approach to produce reduced graphene oxide(rGO).This process differs from the chemical reduction th...The bioreduction of graphene oxide(GO)using environmentally functional bacteria such as Shewanella represents a green approach to produce reduced graphene oxide(rGO).This process differs from the chemical reduction that involves instantaneous molecular reactions.In bioreduction,the contact of bacterial cells and GO is considered the rate-limiting step.To reveal how the bacteria-GO integration regulates rGO production,the comparative experiments of GO and three Shewanella strains were carried out.Fourier-transform infrared spectroscopy,X-ray photoelectron spectroscopy,Raman spectroscopy,and atomic force microscopy were used to characterize the reduction degree and the aggregation degree.The results showed that a spontaneous aggregation of GO and Shewanella into the condensed entity occurred within 36 h.A positive linear correlation was established,linking three indexes of the aggregation potential,the bacterial reduction ability,and the reduction degree(ID/IG)comprehensively.展开更多
This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online ide...This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.展开更多
The heat transfer between two corresponding plates,disks,and concentric pipes has many applications,including water cleansing and lubrication.Furthermore,TiO_(2)-water-based nanofluids are used widely because it is us...The heat transfer between two corresponding plates,disks,and concentric pipes has many applications,including water cleansing and lubrication.Furthermore,TiO_(2)-water-based nanofluids are used widely because it is useful for operating and controlling the temperature,especially in photovoltaic technology and solar panels.Motivated by these applications,the current study is based on the nanoparticle aggregation effect on magnetohydrodynamics(MHD)flow via rotating parallel plates with the chemical reaction.To achieve maximum heat transportation,the Bruggeman model is used to adapt the Maxwell model.Also,melting and thermal radiation effects are considered in the modeling to discuss heat transport.The Runge-Kutta-Fehlberg 4th−5th order method is used to attain numerical solutions.The main focus of this study is to see the thermodynamic behavior considering several aspects of nanoparticle aggregation.The heat transfer rate between the parallel plates is enhanced by improving the thermophoresis,radiation,and Brownian motion parameters.The rise in Schmidt number and chemical reaction rate parameter decreases the concentration distribution.This study will be helpful in enhancing the thermal efficiency of photovoltaic technology in solar plates,water purifying,thermal management of electronic devices,designing effective cooling systems,and other sustainable technologies.展开更多
With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most...With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.展开更多
A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linea...A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.展开更多
文摘Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
基金supported by the National Key R&D Program of China(No.2023YFB2703700)the National Natural Science Foundation of China(Nos.U21A20465,62302457,62402444,62172292)+4 种基金the Fundamental Research Funds of Zhejiang Sci-Tech University(Nos.23222092-Y,22222266-Y)the Program for Leading Innovative Research Team of Zhejiang Province(No.2023R01001)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ24F020008,LQ24F020012)the Foundation of State Key Laboratory of Public Big Data(No.[2022]417)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01119).
文摘As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
基金supported by the Innovation and Technology Commission (ITCPD/17-9)the Hong Kong Research Grants Council,China (GRF16104517)(to KKKC)。
文摘Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have suggested that multiple factors may contribute to its pathogenesis.PD is characterized by a movement disorder that primarily affects motor control;pathologically,the disease is marked by the presence of Lewy bodies (LBs) in the brain.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2022R1C1C2012463).
文摘Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,computational efficiency,and regulatory compliance.Traditional approaches,such as differential privacy,homomorphic encryption,and secure multi-party computation,often fail to balance performance and privacy,rendering them unsuitable for resource-constrained healthcare AIoT environments.This paper introduces LMSA(Lightweight Multi-Key Secure Aggregation),a novel framework designed to address these challenges and enable efficient,secure federated learning across distributed healthcare institutions.LMSA incorporates three key innovations:(1)a lightweight multikey management system leveraging Diffie-Hellman key exchange and SHA3-256 hashing,achieving O(n)complexity with AES(Advanced Encryption Standard)-256-level security;(2)a privacy-preserving aggregation protocol employing hardware-accelerated AES-CTR(CounTeR)encryption andmodular arithmetic for securemodel weight combination;and(3)a resource-optimized implementation utilizing AES-NI(New Instructions)instructions and efficient memory management for real-time operations on constrained devices.Experimental evaluations using the National Institutes of Health(NIH)Chest X-ray dataset demonstrate LMSA’s ability to train multi-label thoracic disease prediction models with Vision Transformer(ViT),ResNet-50,and MobileNet architectures across distributed healthcare institutions.Memory usage analysis confirmed minimal overhead,with ViT(327.30 MB),ResNet-50(89.87 MB),and MobileNet(8.63 MB)maintaining stable encryption times across communication rounds.LMSA ensures robust security through hardware acceleration,enabling real-time diagnostics without compromising patient confidentiality or regulatory compliance.Future research aims to optimize LMSA for ultra-low-power devices and validate its scalability in heterogeneous,real-world environments.LMSA represents a foundational advancement for privacy-conscious healthcare AI applications,bridging the gap between privacy and performance.
基金funded by the Deanship of Scientific Research,the Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia under the project(KFU250420).
文摘Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.
基金Funds for High-Level Talents Programof Xi’an International University(Grant No.XAIU202411).
文摘The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.
基金supported by State Grid science and technology projects“Research on energy and power sup-ply and demand interactive simulation technology for new power system(5100-202257028A-1-1-ZN)”.
文摘Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the aggregated flexible region of load clusters managed by load aggregators is the crucial basis of power system scheduling for the system operator.This is because the aggregation result affects the qual-ity of the scheduling schemes.A stringent computation based on the Minkowski sum is NP-hard,whereas existing approximation meth-ods that use a special type of polytope exhibit limited adaptability when aggregating heterogeneous loads.This study proposes a stringent internal approximation method based on the convex hull of multiple layers of maximum volume boxes and embeds it into a day-ahead scheduling optimization model.The numerical results indicate that the aggregation accuracy can be improved compared with methods based on one type of special polytope,including boxes,zonotopes,and homothets.Hence,the reliability and economy of the power sys-tem scheduling can be enhanced.
基金supported by the Fujian University of Technology under Grant GYZ20016,GY-Z18183,and GY-Z19005partially supported by the National Science and Technology Council under Grant NSTC 113-2221-E-224-056-.
文摘To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.
基金supported by the National Natural Science Foundation of China(32071968)the Jiangsu Agricultural Science and Technology Innovation Fund,China(CX(22)2015))the Jiangsu Collaborative Innovation Center for Modern Crop Production,China。
文摘Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.
基金the financial support received from the Michael J.Fox Foundation through the Target Advancement Program Grant Award (Grant No.MJFF-000649) (to HK)。
文摘Parkinson's disease(PD),a prevalent neurodegenerative disorder,is chara cterized by the loss of dopaminergic neurons and the aggregation ofα-synuclein protein into Lewy bodies.While the current standards of therapy have been successful in providing some symptom relief,they fail to address the underlying pathophysiology of PD and as a result,they have no effect on disease progression.
基金supported by the Russian Science Foundation Grant No.22-15-00120.
文摘Red blood cells(RBCs)are the most abundant human blood cells.RBC aggregation and deformation strongly determine blood viscosity which impacts hemorheology and microcirculation.In turn,RBC properties depend on di®erent endogenous and exogenous factors.One such factor is nitric oxide(NO),which is mainly produced by endothelial cells(EC)from L-arginine amino acid in the circulatory system.Since the mechanisms of the RBC-endothelium interplay are not clear up to date and considering its possible clinical importance,the aims of this study are to investigate in vitro:(1)The effect of L-arginine induced NO on RBC aggregation and adhesion to endothelium;(2)the NO e®ect on RBC aggregation and deformation induced by L-arginine and sodium nitroprusside without the presence of endothelium in the samples.The RBC aggregation and adhesion to a monolayer of EC were studied using optical tweezers(OT).The RBC deformability and aggregation without endothelium in the samples were studied using the flow chamber method and Myrenne aggregometer.We confirmed that NO increases deformability and decreases aggregation of RBCs.We showed that the soluble guanylate cyclase pathway appears to be the only NO signaling pathway involved.In the samples with the endothelium,the "bell-shaped"dependence of RBC aggregation force on L-arginine concentration was observed,which improves our knowledge about the process of NO production by endothelium.Additionally,data related to L-arginine accumulation by endothelium were obtained:Necessity of the presence of extracellular L-arginine stated by other authors was put under question.In our study,NO decreased the RBC-endothelium adhesion,however,the tendency appeared to be weak and was not confirmed in another set of experiments.To our knowledge,this is the first attempt to measure the forces of RBC adhesion to endothelium monolayer with OT.
基金supported by the National Key R&D Program of China(Nos.2019YFD0901204,2019YFD 0901205).
文摘Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.
基金funded by European Union Horizon 2020 research and innovation programme under GA 952334(PhasAGE)the Spanish Ministry of Science and Innovation(PID2019-105017RB-I00)by ICREA,ICREA Academia 2015,and 2020(to SV).
文摘Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collectively known as tauopathies.They comprise frontotemporal dementia,Pick’s disease,or corticobasal degeneration,among others.The symptomatology varies with the specific tau protein variant involved and the affected brain region or cell type.However,they share a common neuropathological hallmark-the formation of proteinaceous deposits named neurofibrillary tangles.Neurofibrillary tangles,primarily composed of aggregated tau(Zhang et al.,2022),disrupt normal neuronal functions,leading to cell death and cognitive decline.
基金supported by the National Natural Science Foundation of China(22178293)the Natural Science Foundation of Fujian Province of China(2022J01022)。
文摘The bioreduction of graphene oxide(GO)using environmentally functional bacteria such as Shewanella represents a green approach to produce reduced graphene oxide(rGO).This process differs from the chemical reduction that involves instantaneous molecular reactions.In bioreduction,the contact of bacterial cells and GO is considered the rate-limiting step.To reveal how the bacteria-GO integration regulates rGO production,the comparative experiments of GO and three Shewanella strains were carried out.Fourier-transform infrared spectroscopy,X-ray photoelectron spectroscopy,Raman spectroscopy,and atomic force microscopy were used to characterize the reduction degree and the aggregation degree.The results showed that a spontaneous aggregation of GO and Shewanella into the condensed entity occurred within 36 h.A positive linear correlation was established,linking three indexes of the aggregation potential,the bacterial reduction ability,and the reduction degree(ID/IG)comprehensively.
基金supported by the State Grid Science&Technology Project(5100-202114296A-0-0-00).
文摘This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.
基金Large research project(RGP2/159/45)supported by the Deanship of Research and Graduate Studies at King Khalid University,Saudi Arabia。
文摘The heat transfer between two corresponding plates,disks,and concentric pipes has many applications,including water cleansing and lubrication.Furthermore,TiO_(2)-water-based nanofluids are used widely because it is useful for operating and controlling the temperature,especially in photovoltaic technology and solar panels.Motivated by these applications,the current study is based on the nanoparticle aggregation effect on magnetohydrodynamics(MHD)flow via rotating parallel plates with the chemical reaction.To achieve maximum heat transportation,the Bruggeman model is used to adapt the Maxwell model.Also,melting and thermal radiation effects are considered in the modeling to discuss heat transport.The Runge-Kutta-Fehlberg 4th−5th order method is used to attain numerical solutions.The main focus of this study is to see the thermodynamic behavior considering several aspects of nanoparticle aggregation.The heat transfer rate between the parallel plates is enhanced by improving the thermophoresis,radiation,and Brownian motion parameters.The rise in Schmidt number and chemical reaction rate parameter decreases the concentration distribution.This study will be helpful in enhancing the thermal efficiency of photovoltaic technology in solar plates,water purifying,thermal management of electronic devices,designing effective cooling systems,and other sustainable technologies.
基金supported in part by National Natural Science Foundation of China(Nos.62102311,62202377,62272385)in part by Natural Science Basic Research Program of Shaanxi(Nos.2022JQ-600,2022JM-353,2023-JC-QN-0327)+2 种基金in part by Shaanxi Distinguished Youth Project(No.2022JC-47)in part by Scientific Research Program Funded by Shaanxi Provincial Education Department(No.22JK0560)in part by Distinguished Youth Talents of Shaanxi Universities,and in part by Youth Innovation Team of Shaanxi Universities.
文摘With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.
基金Project supported by the National Natural Science Foundation of China (Nos.12072007,12072006,12132001,and 52192632)the Ningbo Natural Science Foundation of Zhejiang Province of China (No.202003N4018)the Defense Industrial Technology Development Program of China (Nos.JCKY2019205A006,JCKY2019203A003,and JCKY2021204A002)。
文摘A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.
