A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

The clutch size,incubation behavior of Reeves’s Pheasant(Syrmaticus reevesii) and their responses to ambient temperature and precipitation

Weather conditions play a pivotal role in embryo development and parental incubation costs,potentially impacting the clutch size and incubation behavior of birds.Understanding these effects is crucial for bird conservation.Reeves’ s Pheasant(Syrmaticus reevesii) is a threatened species endemic to China,which is characterized by female-only incubation.However,there is a lack of information regarding the impact of weather conditions on clutch size and incubation behavior in this species.Using satellite tracking,we tracked 27 wild female Reeves’ s Pheasants from 2020 to 2023 in Hubei Province,China.We explored their clutch size and incubation behavior,as well as their responses to ambient temperature and precipitation.Clutch size averaged 7.75 ±1.36,had an association with average ambient temperature and average daily precipitation during the egglaying period,and was potentially linked to female breeding attempts.Throughout the incubation period,females took an average of 0.73 ±0.46 recesses every 24 h,with an average recess duration of 100.80 ±73.37 min and an average nest attendance of 92.98 ±5.27%.They showed a unimodal recess pattern in which nest departures peaked primarily between 13:00 and 16:00.Furthermore,females rarely left nests when daily precipitation was high.Recess duration and nest attendance were influenced by the interaction between daily mean ambient temperature and daily precipitation,as well as day of incubation.Additionally,there was a positive correlation between clutch size and recess duration.These results contribute valuable insights into the lifehistory features of this endangered species.

New indices to balanceα-diversity against tree size inequality

The number and composition of species in a community can be quantified withα-diversity indices,including species richness(R),Simpson’s index(D),and the Shannon-Wiener index(H΄).In forest communities,there are large variations in tree size among species and individu-als of the same species,which result in differences in eco-logical processes and ecosystem functions.However,tree size inequality(TSI)has been largely neglected in studies using the available diversity indices.The TSI in the diameter at breast height(DBH)data for each of 99920 m×20 m forest census quadrats was quantified using the Gini index(GI),a measure of the inequality of size distribution.The generalized performance equation was used to describe the rotated and right-shifted Lorenz curve of the cumulative proportion of DBH and the cumulative proportion of number of trees per quadrat.We also examined the relationships ofα-diversity indices with the GI using correlation tests.The generalized performance equation effectively described the rotated and right-shifted Lorenz curve of DBH distributions,with most root-mean-square errors(990 out of 999 quadrats)being<0.0030.There were significant positive correlations between each of threeα-diversity indices(i.e.,R,D,and H’)and the GI.Nevertheless,the total abundance of trees in each quadrat did not significantly influence the GI.This means that the TSI increased with increasing spe-cies diversity.Thus,two new indices are proposed that can balanceα-diversity against the extent of TSI in the com-munity:(1−GI)×D,and(1−GI)×H’.These new indices were significantly correlated with the original D and H΄,and did not increase the extent of variation within each group of indices.This study presents a useful tool for quantifying both species diversity and the variation in tree sizes in forest communities,especially in the face of cumulative species loss under global climate change.

Changes of China’s Status in the Global System and Its Influencing Factors:A Multiple Contact Networks Perspective

Clarifying China’s position in the global system is an important logical basis for developing national diplomacy.Although much research has been done on China’s development status,most studies have been based on country comparisons or institutional en-vironment.In today’s networked era in which the global economy,trade,personnel,and information are closely connected,studies on China’s global position and its status changes and influencing factors in multiple contact networks are still insufficient.In this study,from the perspective of diverse global contact networks,we constructed economic,cultural,and political influence indices to explore the changes and influencing factors on China’s status in the global system from 2005 to 2018.The results show that during the study period,China’s global influence in the fields of economic ties,cultural exchanges,and political contacts increased significantly,but its influ-ence in the fields of cultural exchanges and political contacts lagged far economic ties.The pattern of China’s economic influence on various economies around the world has shown a transformation from an‘upright pyramid’to an‘inverted pyramid’structure.The proportion of these economies in low-influence zones has decreased from more than 60%in 2005 to less than 20%in 2018.China’s cultural and political influence on various economies around the world has increased significantly;however,for the former,the percentage of high-influence areas is still less than 20%,whereas for the latter the percentage of these economies in medium-and high-influence areas is still less than 50%.Analyses such as a scatter plot matrix show that geographical proximity,economic globalization,close cooperation with developing countries,and a proactive and peaceful foreign policy are important factors in improving China’s status in the diverse global network system.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

基于D-S证据理论的农作物气候品质预测方法研究:以晚熟杂交柑橘春见为例

【目的】基于多源气象数据构建果实品质(糖含量等级)预测模型,为科学评价果实气候品质及深入挖掘农产品气候资源提供科学依据。【方法】以晚熟柑橘春见果实为研究对象,利用多源数据融合技术、人工神经网络(BP神经网络、RBF神经网络和Elman神经网络)和D-S证据理论,包括气象数据质量控制、特征选取、特征级融合、决策级融合4个步骤,构建基于多源气象数据的果实品质(糖含量等级)预测模型。【结果】春见果实品质预测模型采用BP神经网络预测结果总体准确率为87.50%,平均绝对误差(MAE)为0.150,均方根误差(RMSE)为0.447;RBF神经网络预测结果总体准确率为85.00%,MAE为0.175,RMSE为0.474;Elman神经网络预测结果总体准确率为87.50%,MAE为0.150,RMSE为0.447;D-S证据理论决策融合总体预测准确率达95.20%,分别较BP神经网络、RBF神经网络和Elman神经网络提升7.7百分点、10.2百分点和7.7百分点,MAE和RMSE分别为0.040和0.214,均明显降低。【结论】D-S证据理论决策融合后的果实品质预测准确率相比单一神经网络预测更高、误差更小。

基于加权D-S证据理论的旋翼故障诊断

旋翼作为直升机的升力面和操作面,其健康状态对直升机的安全至关重要。旋翼故障诊断技术仍是直升机健康与使用监测系统(Health and usage monitoring system, HUMS)领域的薄弱环节,开发旋翼故障诊断技术具有重要价值。基于信息融合技术,首先分析了旋翼故障的诊断机理,建立了旋翼故障模型,通过流固耦合仿真获取了不同故障下桨叶、轮毂和机身的故障特征信息,生成数据集进行网络训练和验证。然后,利用遗传算法反向传播(Genetic algorithm-backpropagation, GA-BP)优化神经网络诊断3种类型的直升机旋翼故障,即后缘调整片误调、变距拉杆误调和桨叶质量不平衡。3个逐级神经网络分别对旋翼故障类型、故障位置和故障程度进行了诊断识别。最后采用加权的Dempster-Shafer(D-S)证据理论对旋翼故障进行诊断和分析。结果证明基于改进D-S证据理论的旋翼故障诊断方法能够成功应用到旋翼故障诊断中,并具有良好的识别效果。

基于BAS算法优化的电弧增材制造焊道尺寸预测

目的为提高实际应用中电弧增材制造对工艺参数的选取效率及成形形貌的控制效果,建立高效且精准的成形尺寸预测模型,实现对焊道尺寸的合理预测。方法在单层单道CMT电弧增材制造实验的基础上,建立基于天牛须搜索算法(Beetle Antennae Search,BAS)优化BP神经网络的焊道尺寸预测模型,利用BAS算法实现对BP神经网络初始权值和阈值的优化,可以实现预测不同工艺参数(焊接速度、送丝速度、干伸长)下焊道的成形尺寸(熔宽、余高)。利用试验验证BAS-BP预测模型的性能,与现有模型进行对比,结果结果表明该模型具有较高精度的预测效果,能够有效映射工艺参数与焊道尺寸之间的非线性关系,印证了该模型具有良好的拟合和泛化能力,同时其对焊道熔宽和余高的预测误差分别不超过0.2、0.12 mm,预测平均误差率均不超过6%,相对于其他预测模型表现出较好的准确性和稳定性。结论BAS-BP神经网络预测模型的输出误差较小,网络训练收敛速度加快,避免了过拟合及欠拟合的风险,有效提高了预测模型的泛化能力和预测精度,可以实现一定工艺参数范围内的焊道尺寸预测,为后续电弧增材的实时预测及控制参数应用提供了技术支持。

Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Alzheimer′s disease using network pharma⁃cology approach and in vitro experimental validation

OBJECTIVE To explore the new indications and key mechanism of Bazi Bushen capsule(BZBS)by network pharmacology and in vitro experiment.METHODS The potential tar⁃get profiles of the components of BZBS were pre⁃dicted.Subsequently,new indications for BZBS were predicted by disease ontology(DO)enrich⁃ment analysis and initially validated by GO and KEGG pathway enrichment analysis.Further⁃more,the therapeutic target of BZBS acting on AD signaling pathway were identified by intersec⁃tion analysis.Two Alzheimer′s disease(AD)cell models,BV-2 and SH-SY5Y,were used to pre⁃liminarily verify the anti-AD efficacy and mecha⁃nism of BZBS in vitro.RESULTS In total,1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula,and 1320 BZBS targets with high confidence were predicted.Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treat⁃ment of AD.In vitro experiments showed that BZ⁃BS could significantly reduce the release of TNF-αand IL-6 and the expression of COX-2 and PSEN1 in Aβ25-35-induced BV-2 cells.BZBS reduced the apoptosis rate of Aβ25-35 induced SH-SY5Y cells,significantly increased mitochon⁃drial membrane potential,reduced the expres⁃sion of Caspase3 active fragment and PSEN1,and increased the expression of IDE.CONCLU⁃SIONS BZBS formula has a potential use in the treatment of AD,which is achieved through regu⁃lation of ERK1/2,NF-κB signaling pathways,and GSK-3β/β-catenin signaling pathway.Further⁃more,the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action.The study lays a foun⁃dation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.

Long-lasting,reinforced electrical networking in a high-loading Li_(2)S cathode for high-performance lithium–sulfur batteries

Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein,a high-loading Li_(2)S-based cathode with micrometric Li_(2)S particles composed of two-dimensional graphene(Gr)and one-dimensional carbon nanotubes(CNTs)in a compact geometry is developed,and the role of CNTs in stable cycling of high-capacity Li–S batteries is emphasized.In a dimensionally combined carbon matrix,CNTs embedded within the Gr sheets create robust and sustainable electron diffusion pathways while suppressing the passivation of the active carbon surface.As a unique point,during the first charging process,the proposed cathode is fully activated through the direct conversion of Li_(2)S into S_(8) without inducing lithium polysulfide formation.The direct conversion of Li_(2)S into S_(8) in the composite cathode is ubiquitously investigated using the combined study of in situ Raman spectroscopy,in situ optical microscopy,and cryogenic transmission electron microscopy.The composite cathode demonstrates unprecedented electrochemical properties even with a high Li_(2)S loading of 10 mg cm^(–2);in particular,the practical and safe Li–S full cell coupled with a graphite anode shows ultra-long-term cycling stability over 800 cycles.

Modeling of grain size in isothermal compression of Ti-6Al-4V alloy using fuzzy neural network

Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K, the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s-1, and the height reductions of 20%-60% with an interval of 10%. After compression, the effect of the processing parameters including deformation temperature, strain rate, and height reduction on the flow stress and the microstructure was investigated. The grain size of primary a phase was measured using an OLYMPUS PMG3 microscope with the quantitative metallography SISC IAS V8.0 image analysis software. A model of grain size in isothermal compression of Ti-6A1-4V alloy was developed using fuzzy neural net- work （FNN） with back-propagation （BP） learning algorithm. The maximum difference and the average difference between the predicted and the experimental grain sizes of primary a phase are 13.31% and 7.62% for the sampled data, and 16.48% and 6.97% for the non-sampled data, respectively. It can be concluded that the present model with high prediction precision can be used to predict the grain size in isothermal compression of Ti-6Al-4V alloy.

Growing actin networks regulated by obstacle size and shape

Growing actin networks provide the driving force for the motility of cells and intracellular pathogens. Based on the molecular-level processes of actin polymerization, branching, capping, and depolymerization, we have developed a modeling framework to simulate the stochastic and cooperative behaviors of growing actin networks in propelling obstacles, with an emphasis on the size and shape effects on work capacity and filament orientation in the growing process. Our results show that the characteristic size of obstacles changes the protrusion power per unit length, without influencing the orientation distribution of actin filaments in growing networks. In contrast, the geometry of obstacles has a profound effect on filament patterning, which influences the orientation of filaments differently when the drag coefficient of environment is small, intermediate, or large. We also discuss the role of various parameters, such as the aspect ratio of obstacles, branching rate, and capping rate, in affecting the protrusion power of network growth.

Inverse of Rensch’s Rule of Allometry for Sexual Size Dimorphism in Bufo andrewsi

Difference in body size between males and females(sexual size dimorphism:SSD)and its variation are a common phenomenon in animal kingdom.Rensch’s rule predicts that the degree of SSD variation increases with the enlarged mean body size when males are larger than females and decreases when females are larger than males.Here,whether the patterns of variations in SSD in the Andrew’s toad(Bufo andrewsi)follow Rensch’s rule was tested using unpublished data from 14 populations and published data from 17 populations.Results show the reduced major axis regression of log10(male size)on log10(female size)across all populations displayed a significant hyperallometric relationship,which was consistent with inverse Rensch’s rule(the degree of SSD increased with enlarged mean body size).SSD could also be explained by sexual age difference(SAD)due to a positive SSD–SAD relationship among all populations.The findings suggest that the occurrence of inverse Rensch’s rule in B.andrewsi is likely to be a result of fecundity selection on increased reproductive investments in larger females.

Multi-dimensional size effects and representative elements for nonpersistent fractured rock masses: A perspective of geometric parameter distribution

This study takes a fractured rock mass in the Datengxia Hydropower Station,China as an example to analyze the size effects and determine the representative elementary sizes.A novel method considering geometric parameter distributions is proposed in this work.The proposed method can quickly and simply determine the size effects and representative elementary sizes.Specifically,geometric parameter distributions,including fracture frequency,size and orientation,are generated on the basis of the Bernoulli trial and Monte Carlo simulation.The distributions are assessed using the coefficient of variation(CV),and the acceptable variations for CV(5%,10%and 20%)are used to determine representative elementary sizes.Generally,the representative element of rock masses is the representative elementary volume(REV).The present study extends the representative element to other dimensions,i.e.representative elementary length(REL)and representative elementary area(REA)for one and two dimensions,respectively.REL and REA are useful in studying the size effects of one-(1D)and twodimensional(2D)characteristics of rock masses.The relationships among multi-dimensional representative elementary sizes are established.The representative elementary sizes reduce with the increase in the dimensions,and REA and REV can be deduced by REL.Therefore,the proposed method can quickly and simply determine REL and further estimate REA and REV,which considerably improves the efficiency of rock mass analysis.

Temporal change of plankton size structure preserved by Lugol's solution:a FlowCAM study

Plankton size structure is crucial for understanding marine ecosystem dynamics and the associated biogeochemical processes.A fixation step by acid Lugol’s solution has been commonly employed to preserve plankton samples in the field.However,the acid Lugol’s solution can bias the estimation of size structure and the preserved plankton size structure can vary with time.Here,we explore the impact of sample storage time on the size-structure of the plankton community preserved by Lugol’s solution.Two short-term experiments and one long-term experiment were conducted to explore the change of plankton community size structure with the storage time:covering from a week to a month,and to nearly seven months based on particle-size data obtained by continuous Flow Cytometer and Microscope(FlowCAM)measurements.We found a linear change of plankton size with the storage time in short-term periods(less than 3 months)with a decrease of the slope but an increase of the intercept for the normalized biomass size spectrum(NBS S).However,there were opposite trends for NBSS with increasing slope but decreasing intercept after3 months.The potential causes of the distinct patterns of the NBSS parameters are addressed in terms of the interplay between particle aggregation and fragmentation.We found large changes in plankton biovolume and abundance among different size classes,which may indicate a distinct effect of acid Lugol’s solution on various plankton size classes.The mechanism driving temporal change in the size-structure of the Lugolfixed plankton community was further discussed in terms of particle aggregation and fragmentation.Finally,we emphasize that the effect of storage time should be taken into account when interpreting or comparing data of plankton community acquired from samples with various storage durations.

Wireless Network Security Using Load Balanced Mobile Sink Technique

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most significant resources in the WSN are battery power and security.Clustering stra-tegies improve the power factor and secure the WSN environment.It takes more electricity to forward data in a WSN.Though numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less security.These possibilities arise due to the cluster head’s limited life span.These cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster interactions.The proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of CH.In addition,the LCLBM was added with an assistant cluster head(ACH)for load balancing.Power consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating LCLBM.CEOMS will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of WSNs.According to simulatedfind-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

Study on the Action Mechanism of Euphorbia peplus in the Treatment of Alzheimer s Disease Based on UPLC-Q-TOF-MS/MS Combined with Network Pharmacology and Molecular Docking

[Objectives]Based on UPLC-Q-TOF-MS/MS,network pharmacology and molecular docking techniques,the mechanism of Euphorbia peplus in the treatment of Alzheimer s disease(AD)was studied.[Methods]The UPLC-Q-TOF-MS/MS technique was used to rapidly analyze the chemical components of E.peplus.Active components and potential targets of E.peplus were retrieved from TCMSP and Swiss Target Prediction database,and AD targets were screened using GeneCards database.The targets of E.peplus in the treatment of AD were obtained.The PPI network was constructed using String platform,and the network topology of Cytoscape software was used to compute and screen key targets,and the GO and KEGG pathway enrichment analysis was carried out in Metascape database to construct the"component-target-pathway-disease"network.Molecular docking was used to predict the binding properties of active ingredients and targets.[Results]The results of UPLC-Q-TOF-MS/MS showed that 83 compounds were identified from E.peplus,including 19 terpenoids,10 phenolic acids and phenols,16 flavonoids,2 phenylpropanoids,4 coumarins,1 alkaloid,1 anthraquinone and 30 other compounds.The results of network pharmacological analysis showed that 82 active ingredients were screened,and 279 common targets were identified for the treatment of AD,among which the key targets were ALB(albumin),GAPDH(glyceraldehyde triphosphate dehydrogenase),TNF(tumor necrosis factor),AKT1(serine/threonine protein kinase 1),and IL6(interleukin-6).KEGG enrichment analysis showed that key signaling pathways include cancer pathways,lipid and atherosclerosis,Alzheimer s disease,insulin resistance,serotonergic synapses,calcium signaling pathway,cAMP signaling pathway and other signaling pathways.Molecular docking results showed that 14-deoxyandrographolide,dehydroandrographolide,licochalcone B,apigenin and naringenin may be the key components of E.peplus in the treatment of AD.[Conclusions]The results suggest that E.peplus can be used to treat Alzheimer s disease through multi-component,multi-target and multi-pathway.