AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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.

Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

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.

Navigating the pathways:TAR-DNA-binding-protein-43 aggregation,axonal transport,and local synthesis in amyotrophic lateral sclerosis pathology

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).

Impacts of Aggregation Methods and Trophospecies Number on the Structure and Function of Marine Food Webs

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.

RBC aggregation,deformation and adhesion to endothelium:Role of nitric oxide derived from L-Arginine and sodium nitroprusside

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.

A novel approach to Parkinson's disease treatment with a potentially dual-acting therapeutic agent that targetsα-synuclein aggregation and neuron death

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.

Aggregation-regulated bioreduction process of graphene oxide by Shewanella bacteria

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.

A non-probabilistic reliability topology optimization method based on aggregation function and matrix multiplication considering buckling response constraints

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.

Modelling the temporal-varied nonlinear velocity profile of debris flow using a stratification aggregation algorithm in 3D-HBP-SPH framework

Estimation of velocity profile within mud depth is a long-standing and essential problem in debris flow dynamics.Until now,various velocity profiles have been proposed based on the fitting analysis of experimental measurements,but these are often limited by the observation conditions,such as the number of configured sensors.Therefore,the resulting linear velocity profiles usually exhibit limitations in reproducing the temporal-varied and nonlinear behavior during the debris flow process.In this study,we present a novel approach to explore the debris flow velocity profile in detail upon our previous 3D-HBPSPH numerical model,i.e.,the three-dimensional Smoothed Particle Hydrodynamic model incorporating the Herschel-Bulkley-Papanastasiou rheology.Specifically,we propose a stratification aggregation algorithm for interpreting the details of SPH particles,which enables the recording of temporal velocities of debris flow at different mud depths.To analyze the velocity profile,we introduce a logarithmic-based nonlinear model with two key parameters,that a controlling the shape of velocity profile and b concerning its temporal evolution.We verify the proposed velocity profile and explore its sensitivity using 34 sets of velocity data from three individual flume experiments in previous literature.Our results demonstrate that the proposed temporalvaried nonlinear velocity profile outperforms the previous linear profiles.

Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

Small molecules to target tau amyloid aggregation

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.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

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.

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.

Differentially Private Support Vector Machines with Knowledge Aggregation

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.

Click reaction-induced in situ nanoparticle aggregation for cancer imaging and treatment

With their high drug-loading capacity and enhanced permeability and retention(EPR)effects,nanoparticles possess significant potential for the diagnosis and treatment of tumors.However,unlike active targeting,the complex tumor microenvironment influences the passive accumulation of nanoparticles in tumor areas.Hence,it is necessary to actively control the behavior of nanoparticles when they enter the tumor microenvironment.By utilizing biocompatible and efficient click reactions,the aggregation of nanoparticles at the tumor site can be controlled,thereby enhancing nanoparticle accumulation at the target location with improved imaging signals and enhanced tumor-inhibitory effects.Herein,we introduce and classify in situ nanoparticle aggregation for biomedical imaging and therapeutic applications induced by four types of common click reactions:coppercatalyzed azide–alkyne cycloaddition(CuAAC),strain-promoted azide–alkyne cycloaddition(SPAAC),click condensation between 2-cyanobenzothiazole(CBT)and cysteine(Cys),and inverse electron-demand Diels–Alder(iEDDA).Furthermore,we summarize the main strategies of these click reaction-based nanoparticle aggregation approaches.Finally,we discuss the advantages and disadvantages of click reaction-triggered aggregation and analyze future trends.

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

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.

Deaggregation of long chain alkyl β-naphthoic acid ester with β-CD and α-CD:A mimic way to remove excessive fat from fatty liver

The deaggregating ability of β-CD and α-CD against the aggregated n-hexadecyl β-naphthoate （A16） and n-dodecyl β-naphthoate （A12） depended not only on the aggregating tendency of A16 and A12 but also on the initial concentration of the aggregated A16 or A12. The inclusive ability of β-CD with the substrates is greater than that of α-CD under hydrophobiclipophilic interaction.

Web Data Aggregation in MOLAP:Approach,Language,and Implementation

This paper investigates the Web data aggregation issues in multidimensional on-line analytical processing （MOLAP） and presents a rule-driven aggregation approach. The core of the approach is defining aggregate rules. To define the rules for reading warehouse data and computing aggregates, a rule definition language - array aggregation language （AAL） is developed. This language treats an array as a function from indexes to values and provides syntax and semantics based on monads. External functions can be called in aggregation rules to specify array reading, writing, and aggregating. Based on the features of AAL, array operations are unified as function operations, which can be easily expressed and automatically evaluated. To implement the aggregation approach, a processor for computing aggregates over the base cube and for materializing them in the data warehouse is built, and the component structure and working principle of the aggregation processor are introduced.

The features of soil aggregation and its eco-environmental effects under different subalpine forests on the east slope of Gongga Mountain, China

Structural properties of forest soils have important hydro-ecological function and can influence the soil water-physical characters and soil erosion. The experimental soil samples were obtained in surface horizon (0-10 cm) from different subalpine forest types on east slope of Gongga Mountain in the upriver area of Yangtze River China in May 2002. The soil bulk density, porosity, stable infiltration rate, aggregate distribution and particle-size distribution were analyzed by the routine methods in room, and the features and effects on eco-environment of soil aggregation were studied. The results showed that the structure of soil under mixed mature forest is in the best condition and can clearly enhance the eco-environmental function of soil, and the soil structure under the clear-cutting forest is the worst, the others are ranked between them. The study results can offer a basic guidance for the eco-environmental construction in the upper reaches of Yangtze River.