Preweaning period is a critical window for rumen microbial regulation of average daily gain in Holstein heifer calves

Background Rumen bacterial groups can affect growth performance,such as average daily gain(ADG),feed intake,and efficiency.The study aimed to investigate the inter-relationship of rumen bacterial composition,rumen fermentation indicators,serum indicators,and growth performance of Holstein heifer calves with different ADG.Twelve calves were chosen from a trail with 60 calves and divided into higher ADG(HADG,high pre-and post-weaning ADG,n=6)and lower ADG(LADG,low pre-and post-weaning ADG,n=6)groups to investigate differences in bacterial composition and functions and host phenotype.Results During the preweaning period,the relative abundances of propionate producers,including g_norank_f_Butyricicoccaceae,g_Pyramidobacter,and g_norank_f_norank_o_Clostridia_vadin BB60_group,were higher in HADG calves(LDA>2,P<0.05).Enrichment of these bacteria resulted in increased levels of propionate,a gluconeogenic precursor,in preweaning HADG calves(adjusted P<0.05),which consequently raised serum glucose concentrations(adjusted P<0.05).In contrast,the relative abundances of rumen bacteria in post-weaning HADG calves did not exert this effect.Moreover,no significant differences were observed in rumen fermentation parameters and serum indices between the two groups.Conclusions The findings of this study revealed that the preweaning period is the window of opportunity for rumen bacteria to regulate the ADG of calves.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Libration-Generated Average Convection in a Rotating Flat Layer with Horizontal Axis

The study of average convection in a rotating cavity subjected to modulated rotation is an interesting area for the development of both fundamental and applied science.This phenomenon finds application in the field of mass transfer and fluid flow control,relevant examples being crystal growth under reduced gravity and fluid mixing in microfluidic devices for cell cultures.In this study,the averaged flow generated by the oscillating motion of a fluid in a planar layer rotating about a horizontal axis is experimentally investigated.The boundaries of the layer are maintained at constant temperatures,while the lateral cylindrical wall is thermally insulated.It is demonstrated that libration results in intense oscillatory fluid motion,which in turn produces a time-averaged flow.For the first time,quantitative measures for the instantaneous velocity field are obtained using the Particle Image Velocimetry technique.It is revealed that the flow has the form of counter-rotating vortices.The vortex circulations sense changes during a libration cycle.An increase in the rotation rate and amplitude of the cavity libration results in an increase in the flow intensity.The heat transfer and time-averaged velocity are examined accordingly as a function of the dimensionless oscillation frequency,and resonant excitation of heat transfer and average oscillation velocity are revealed.The threshold curve for the onset of the averaged convection is identified in the plane of control parameters(dimensionless rotational velocity and pulsation Reynolds number).It is found that an increase in the dimensionless rotational velocity has a stabilizing effect on the onset of convection.

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

Multiple Matching Attenuation Based on Curvelet Domain Extended Filtering

The paper develops a multiple matching attenuation method based on extended filtering in the curvelet domain,which combines the traditional Wiener filtering method with the matching attenuation method in curvelet domain.Firstly,the method uses the predicted multiple data to generate the Hilbert transform records,time derivative records and time derivative records of Hilbert transform.Then,the above records are transformed into the curvelet domain and multiple matching attenuation based on least squares extended filtering is performed.Finally,the attenuation results are transformed back into the time-space domain.Tests on the model data and field data show that the method proposed in the paper effectively suppress the multiples while preserving the primaries well.Furthermore,it has higher accuracy in eliminating multiple reflections,which is more suitable for the multiple attenuation tasks in the areas with complex structures compared to the time-space domain extended filtering method and the conventional curvelet transform method.

Perfect 1-k Matchings of Bipartite Graphs

Let k be a positive integer and G a bipartite graph with bipartition (X,Y). A perfect 1-k matching is an edge subset M of G such that each vertex in Y is incident with exactly one edge in M and each vertex in X is incident with exactly k edges in M. A perfect 1-k matching is an optimal semi-matching related to the load-balancing problem, where a semi-matching is an edge subset M such that each vertex in Y is incident with exactly one edge in M, and a vertex in X can be incident with an arbitrary number of edges in M. In this paper, we give three sufficient and necessary conditions for the existence of perfect 1-k matchings and for the existence of 1-k matchings covering | X |−dvertices in X, respectively, and characterize k-elementary bipartite graph which is a graph such that the subgraph induced by all k-allowed edges is connected, where an edge is k-allowed if it is contained in a perfect 1-k matching.

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

Distributed Matching Theory-Based Task Re-Allocating for Heterogeneous Multi-UAV Edge Computing

Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

Heparin-binding protein as a predictor of mortality in patients with diabetes mellitus and community-acquired pneumonia in intensive care unit:a propensity score matched study

BACKGROUND:Patients with diabetes mellitus(DM)are vulnerable to community-acquired pneumonia(CAP),which have a high mortality rate.We aimed to investigate the value of heparin-binding protein(HBP)as a prognostic marker of mortality in patients with DM and CAP.METHODS:This retrospective study included CAP patients who were tested for HBP at intensive care unit(ICU)admission from January 2019 to April 2020.Patients were allocated to the DM or non-DM group and paired with propensity score matching.Baseline characteristics and clinical outcomes up to 90 days were evaluated.The primary outcome was the 10-day mortality.Receiver operating characteristic(ROC)curves,Kaplan-Meier analysis,and Cox regression were used for statistical analysis.RESULTS:Among 152 enrolled patients,60 pairs were successfully matched.There was no significant difference in 10-day mortality,while more patients in the DM group died within 28 d(P=0.024)and 90 d(P=0.008).In the DM group,HBP levels at ICU admission were higher in 10-day non-survivors than in 10-day survivors(median 182.21[IQR:55.43-300]ng/ml vs.median 66.40[IQR:34.13-107.85]ng/mL,P=0.019),and HBP levels could predict the 10-day mortality with an area under the ROC curve of 0.747.The cut-off value,sensitivity,and specificity were 160.6 ng/mL,66.7%,and 90.2%,respectively.Multivariate Cox regression analysis indicated that HBP was an independent prognostic factor for 10-day(HR 7.196,95%CI:1.596-32.455,P=0.01),28-day(HR 4.381,95%CI:1.449-13.245,P=0.009),and 90-day mortality(HR 4.581,95%CI:1.637-12.819,P=0.004)in patients with DM.CONCLUSION:Plasma HBP at ICU admission was associated with the 10-day,28-day,and 90-day mortality,and might be a prognostic factor in patients with DM and CAP.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

Reshaping Li–Mg hybrid batteries:Epitaxial electrodeposition and spatial confinement on MgMOF substrates via the lattice‐matching strategy

The emergence of Li–Mg hybrid batteries has been receiving attention,owing to their enhanced electrochemical kinetics and reduced overpotential.Nevertheless,the persistent challenge of uneven Mg electrodeposition remains a significant impediment to their practical integration.Herein,we developed an ingenious approach that centered around epitaxial electrocrystallization and meticulously controlled growth of magnesium crystals on a specialized MgMOF substrate.The chosen MgMOF substrate demonstrated a robust affinity for magnesium and showed minimal lattice misfit with Mg,establishing the crucial prerequisites for successful heteroepitaxial electrocrystallization.Moreover,the incorporation of periodic electric fields and successive nanochannels within the MgMOF structure created a spatially confined environment that considerably promoted uniform magnesium nucleation at the molecular scale.Taking inspiration from the“blockchain”concept prevalent in the realm of big data,we seamlessly integrated a conductive polypyrrole framework,acting as a connecting“chain,”to interlink the“blocks”comprising the MgMOF cavities.This innovative design significantly amplified charge‐transfer efficiency,thereby increasing overall electrochemical kinetics.The resulting architecture(MgMOF@PPy@CC)served as an exceptional host for heteroepitaxial Mg electrodeposition,showcasing remarkable electrostripping/plating kinetics and excellent cycling performance.Surprisingly,a symmetrical cell incorporating the MgMOF@PPy@CC electrode demonstrated impressive stability even under ultrahigh current density conditions(10mAcm^(–2)),maintaining operation for an extended 1200 h,surpassing previously reported benchmarks.Significantly,on coupling the MgMOF@PPy@CC anode with a Mo_(6)S_(8) cathode,the assembled battery showed an extended lifespan of 10,000 cycles at 70 C,with an outstanding capacity retention of 96.23%.This study provides a fresh perspective on the rational design of epitaxial electrocrystallization driven by metal–organic framework(MOF)substrates,paving the way toward the advancement of cuttingedge batteries.

Matching Dyeing and Properties of Silk Fabrics with Natural Edible Pigments

The silk fabrics were matching dyed with three natural edible pigments(red rice red,ginger yellow and gardenia blue).By investigating the dyeing rates and lifting properties of these pigments,it was observed that their compatibilities were excellent in the dyeing process:dye dosage 2.5%(omf),mordant alum dosage 2.0%(omf),dyeing temperature 80℃and dyeing time 40 min.The silk fabrics dyed with secondary colors exhibited vibrant and vivid color owing to the remarkable lightness and chroma of ginger yellow.However,gardenia blue exhibited multiple absorption peaks in the visible light range,resulting in significantly lower lightness and chroma for the silk fabrics dyed with tertiary colors,thus making it suitable only for matte-colored fabrics with low chroma levels.In addition,the silk fabrics dyed with these three pigments had a color fastness that exceeded grade 3 in resistance to perspiration,soap washing and light exposure,indicating acceptable wearing properties.The dyeing process described in this research exhibited a wide range of potential applications in matching dyeing of protein-based textiles with natural colorants.

A Study on Short Text Matching Method Based on KS-BERT Algorithm

To improve the accuracy of short text matching,a short text matching method with knowledge and structure enhancement for BERT(KS-BERT)was proposed in this study.This method first introduced external knowledge to the input text,and then sent the expanded text to both the context encoder BERT and the structure encoder GAT to capture the contextual relationship features and structural features of the input text.Finally,the match was determined based on the fusion result of the two features.Experiment results based on the public datasets BQ_corpus and LCQMC showed that KS-BERT outperforms advanced models such as ERNIE 2.0.This Study showed that knowledge enhancement and structure enhancement are two effective ways to improve BERT in short text matching.In BQ_corpus,ACC was improved by 0.2%and 0.3%,respectively,while in LCQMC,ACC was improved by 0.4%and 0.9%,respectively.

Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

Spectral matching based remote sensing identification of two main crop rotation patterns in a large irrigation district

The rapid identification of planting patterns for major crops in a large irrigated district has vital importance for irrigation management,water fee collection,and crop yield estimation.In this study,the OTSU algorithm and Mean-Shift algorithm were employed to automatically determine threshold values for mapping two main rotated crop patterns at the pixel scale.A time series analysis was conducted to extract the spatial distribution of rice-wheat and wheat-maize rotations in the Chuanhang irrigation district from 2016 to 2020.The results demonstrate that both threshold segmentation algorithms are reliable in extracting the spatial distribution of the crops,with an overall accuracy exceeding 80%.Additionally,both Kappa coefficients surpass 0.7,indicating better performance by OTSU method.Over the period from 2016 to 2020,the area occupied by rice-wheat rotation cropping ranged from 12500 to 14400 hm 2;whereas wheat-maize rotation cropping exhibited smaller and more variable areas ranging from 19730 to 34070 hm 2.These findings highlight how remote sensing-based approaches can provide reliable support for rapidly and accurately identifying the spatial distribution of main crop rotation patterns within a large irrigation district.

Optimizing Average Electric Power During the Charging of Lithium-Ion Batteries Through the Taguchi Method

In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries faces a significant challenge owing to the need to increase average electric power during charging. This challenge results from the direct influence of the power level on the rate of chemical reactions occurring in the battery electrodes. In this study, the Taguchi optimization method was used to enhance the average electric power during the charging process of lithium-ion batteries. The Taguchi technique is a statistical strategy that facilitates the systematic and efficient evaluation of numerous experimental variables. The proposed method involved varying seven input factors, including positive electrode thickness, positive electrode material, positive electrode active material volume fraction, negative electrode active material volume fraction, separator thickness, positive current collector thickness, and negative current collector thickness. Three levels were assigned to each control factor to identify the optimal conditions and maximize the average electric power during charging. Moreover, a variance assessment analysis was conducted to validate the results obtained from the Taguchi analysis. The results revealed that the Taguchi method was an eff ective approach for optimizing the average electric power during the charging of lithium-ion batteries. This indicates that the positive electrode material, followed by the separator thickness and the negative electrode active material volume fraction, was key factors significantly infl uencing the average electric power during the charging of lithium-ion batteries response. The identification of optimal conditions resulted in the improved performance of lithium-ion batteries, extending their potential in various applications. Particularly, lithium-ion batteries with average electric power of 16 W and 17 W during charging were designed and simulated in the range of 0-12000 s using COMSOL Multiphysics software. This study efficiently employs the Taguchi optimization technique to develop lithium-ion batteries capable of storing a predetermined average electric power during the charging phase. Therefore, this method enables the battery to achieve complete charging within a specific timeframe tailored to a specificapplication. The implementation of this method can save costs, time, and materials compared with other alternative methods, such as the trial-and-error approach.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.