On the Mean Difference Variance in Random Samples of Student’s Variables

The purpose of this paper is to obtain the expression of the sample mean difference variance of the Student’s distributive model. In the 2007 the study of the mean difference variance, after some decades, was resumed by Campobasso [1]. Using the Nair’s [2] and Lomnicki’s general results [3], he obtained the variance of sample mean difference for different distributive models (Laplace’s, triangular, power, logit, Pareto’s and Gumbel’s model). In addition he extended the knowledge comparing to the ones already known for the other distributive model (normal, rectangular and exponential model).

Characteristics and demographic factors of traditional Chinese medicine constitution types among elderly individuals in China:A national multistage cluster random study Author links open overlay panel

Objective To reveal the distribution characteristics and demographic factors of traditional Chinese medicine(TCM)constitution among elderly individuals in China.Methods Elderly individuals from seven regions in China were selected as samples in this study using a multistage cluster random sampling method.The basic information questionnaire and Constitution in Chinese Medicine Questionnaire(Elderly Edition)were used.Descriptive statistical analysis,chi-squared tests,and binary logistic regression analysis were used.Results The single balanced constitution(BC)accounted for 23.9%.The results of the major TCM constitution types showed that BC(43.2%)accounted for the largest proportion and unbalanced constitutions ranged from 0.9%to 15.7%.East China region(odds ratio[OR]=2.097;95%confidence interval[CI],1.912 to 2.301),married status(OR=1.341;95%CI,1.235 to 1.457),and managers(OR=1.254;95%CI,1.044 to 1.505)were significantly associated with BC.Age>70 years was associated with qi-deficiency constitution and blood stasis constitution(BSC).Female sex was significantly associated with yang-deficiency constitution(OR=1.646;95%CI,1.52 to 1.782).Southwest region was significantly associated with phlegm-dampness constitution(OR=1.809;95%CI,1.569 to 2.086).North China region was significantly associated with inherited special constitution(OR=2.521;95%CI,1.569 to 4.05).South China region(OR=2.741;95%CI,1.997 to 1.3.763),Central China region(OR=8.889;95%CI,6.676 to 11.835),senior middle school education(OR=2.442;95%CI,1.932 to 3.088),and managers(OR=1.804;95%CI,1.21 to 2.69)were significantly associated with BSC.Conclusions This study defined the distribution characteristics and demographic factors of TCM constitution in the elderly population.Adjusting and improving unbalanced constitutions,which are correlated with diseases,can help promote healthy aging through the scientific management of these demographic factors.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

Ground target localization of unmanned aerial vehicle based on scene matching

In order to improve target localization precision,accuracy,execution efficiency,and application range of the unmanned aerial vehicle(UAV)based on scene matching,a ground target localization method for unmanned aerial vehicle based on scene matching(GTLUAVSM)is proposed.The sugges-ted approach entails completing scene matching through a feature matching algorithm.Then,multi-sensor registration is optimized by robust estimation based on homologous registration.Finally,basemap generation and model solution are utilized to improve basemap correspondence and accom-plish aerial image positioning.Theoretical evidence and experimental verification demonstrate that GTLUAVSM can improve localization accuracy,speed,and precision while minimizing reliance on task equipment.

Quality Assessment of Attribute Data in GIS Based on Simple Random Sampling

On the basis of the principles of simple random sampling, the statistical model of rate of disfigurement (RD) is put forward and described in detail. According to the definition of simple random sampling for the attribute data in GIS, the mean and variance of the RD are deduced as the characteristic value of the statistical model in order to explain the feasibility of the accuracy measurement of the attribute data in GIS by using the RD. Moreover, on the basis of the mean and variance of the RD, the quality assessment method for attribute data of vector maps during the data collecting is discussed. The RD spread graph is also drawn to see whether the quality of the attribute data is under control. The RD model can synthetically judge the quality of attribute data, which is different from other measurement coefficients that only discuss accuracy of classification.

On the Impact of Bootstrap in Stratified Random Sampling

In general the accuracy of mean estimator can be improved by stratified random sampling. In this paper, we provide an idea different from empirical methods that the accuracy can be more improved through bootstrap resampling method under some conditions. The determination of sample size by bootstrap method is also discussed, and a simulation is made to verify the accuracy of the proposed method. The simulation results show that the sample size based on bootstrapping is smaller than that based on central limit theorem.

Analysis of Methodology for the Application of Stratified Random Sampling with Optimum Allocation： The Case Study of Forest Bioenergy

In this paper, analysis of methodology was realized for the application of stratified random sampling with optimum allocation in the case of a subject of research which concerns the rural population and presents high differentiations among the three strata in which this population could be classified. The rural population of Evros Prefecture （Greece） with criterion the mean altitude of settlements was classified in three strata, the mountainous, semi-mountainous and fiat population for the estimation of mean consumption of forest fuelwood for covering of heating and cooking needs in households of these three strata. The analysis of this methodology includes： （1） the determination of total size of sample for entire the rural population and its allocation to the various strata; （2） the investigation of effectiveness of stratification with the technique of analysis of variance （One-Way ANOVA）; （3） the conduct of sampling research with the realization of face-to-face interviews in selected households and （4） the control of forms of the questionnaire and the analysis of data by using the statistical package for social sciences, SPSS for Windows. All data for the analysis of this methodology and its practical application were taken by the pilot sampling which was realized in each stratum. Relative paper was not found by the review of literature.

A Pseudo Random Number Generator under Windows Using Refined Descriptive Sampling

In this paper, we propose a software component under Windows that generates pseudo random numbers using RDS （Refined Descriptive Sampling） as required by the simulation. RDS is regarded as the best sampling method as shown in the literature. In order to validate the proposed component, its implementation is proposed on approximating integrals. The simulation results from RDS using ＂RDSRnd＂ generator were compared to those obtained using the generator ＂Rnd＂ included in the Pascal programming language under Windows. The best results are given by the proposed software component.

Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach

In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.

General Classes of Variance Estimators in Simple Random Sampling Using Multi-auxiliary Variables

Srivastava and Jhajj [ 1 6] proposed a class of estimators for estimating population variance using multi auxiliary variables in simple random sampling and they utilized the means and variances of auxiliary variables. In this paper, we adapted this class and motivated by Searle [13], and we suggested more generalized class of estimators for estimating the population variance in simple random sampling. The expressions for the mean square error of proposed class have been derived in general form. Besides obtaining the minimized MSE of the proposed and adapted class, it is shown that the adapted classis the special case of the proposed class. Moreover, these theoretical findings are supported by an empirical study of original data.

Bias Correction Technique for Estimating Quantiles of Finite Populations under Simple Random Sampling without Replacement

In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators is presented. It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.

Lymph Diseases Prediction Using Random Forest and Particle Swarm Optimization

This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two major phases: feature selection and classification. In the first stage, a number of discriminative features out of 18 were selected using PSO and several feature selection techniques to reduce the features dimension. In the second stage, we applied the random forest ensemble classification scheme to diagnose lymphatic diseases. While making experiments with the selected features, we used original and resampled distributions of the dataset to train random forest classifier. Experimental results demonstrate that the proposed method achieves a remark-able improvement in classification accuracy rate.

Research on color image matching method based on feature point compensation in dark light environment

Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.

Characterizing prediction errors of a new tree height model for cut-to-length Pinus radiata stems through the Burr TypeⅫdistribution

Unlike height-diameter equations for standing trees commonly used in forest resources modelling,tree height models for cut-to-length(CTL)stems tend to produce prediction errors whose distributions are not conditionally normal but are rather leptokurtic and heavy-tailed.This feature was merely noticed in previous studies but never thoroughly investigated.This study characterized the prediction error distribution of a newly developed such tree height model for Pin us radiata(D.Don)through the three-parameter Burr TypeⅫ(BⅫ)distribution.The model’s prediction errors(ε)exhibited heteroskedasticity conditional mainly on the small end relative diameter of the top log and also on DBH to a minor extent.Structured serial correlations were also present in the data.A total of 14 candidate weighting functions were compared to select the best two for weightingεin order to reduce its conditional heteroskedasticity.The weighted prediction errors(εw)were shifted by a constant to the positive range supported by the BXII distribution.Then the distribution of weighted and shifted prediction errors(εw+)was characterized by the BⅫdistribution using maximum likelihood estimation through 1000 times of repeated random sampling,fitting and goodness-of-fit testing,each time by randomly taking only one observation from each tree to circumvent the potential adverse impact of serial correlation in the data on parameter estimation and inferences.The nonparametric two sample Kolmogorov-Smirnov(KS)goodness-of-fit test and its closely related Kuiper’s(KU)test showed the fitted BⅫdistributions provided a good fit to the highly leptokurtic and heavy-tailed distribution ofε.Random samples generated from the fitted BⅫdistributions ofεw+derived from using the best two weighting functions,when back-shifted and unweighted,exhibited distributions that were,in about97 and 95%of the 1000 cases respectively,not statistically different from the distribution ofε.Our results for cut-tolength P.radiata stems represented the first case of any tree species where a non-normal error distribution in tree height prediction was described by an underlying probability distribution.The fitted BXII prediction error distribution will help to unlock the full potential of the new tree height model in forest resources modelling of P.radiata plantations,particularly when uncertainty assessments,statistical inferences and error propagations are needed in research and practical applications through harvester data analytics.

CFSA-Net:Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

Traditional models for semantic segmentation in point clouds primarily focus on smaller scales.However,in real-world applications,point clouds often exhibit larger scales,leading to heavy computational and memory requirements.The key to handling large-scale point clouds lies in leveraging random sampling,which offers higher computational efficiency and lower memory consumption compared to other sampling methods.Nevertheless,the use of random sampling can potentially result in the loss of crucial points during the encoding stage.To address these issues,this paper proposes cross-fusion self-attention network(CFSA-Net),a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.At the core of this network is the incorporation of random sampling alongside a local feature extraction module based on cross-fusion self-attention(CFSA).This module effectively integrates long-range contextual dependencies between points by employing hierarchical position encoding(HPC).Furthermore,it enhances the interaction between each point’s coordinates and feature information through cross-fusion self-attention pooling,enabling the acquisition of more comprehensive geometric information.Finally,a residual optimization(RO)structure is introduced to extend the receptive field of individual points by stacking hierarchical position encoding and cross-fusion self-attention pooling,thereby reducing the impact of information loss caused by random sampling.Experimental results on the Stanford Large-Scale 3D Indoor Spaces(S3DIS),Semantic3D,and SemanticKITTI datasets demonstrate the superiority of this algorithm over advanced approaches such as RandLA-Net and KPConv.These findings underscore the excellent performance of CFSA-Net in large-scale 3D semantic segmentation.

Climate Change Perceptions , Impacts and Adaptation Strategies of F arm Households in Potohar Region of Punjab, Pakistan

Climate change has become a global phenomenon and is adversely affecting agricultural development across the globe.Developing countries like Pakistan where 18.9%of the GDP(gross domestic product)came from the agriculture sector and also 42%of the labor force involved in agriculture.They are directly and indirectly affected by climate change due to an increase in the frequency and intensity of climatic extreme events such as floods,droughts and extreme weather events.In this paper,we have focused on the impact of climate change on farm households and their adaptation strategies to cope up the climatic extremes.For this purpose,we have selected farm households by using multistage stratified random sampling from four districts of the Potohar region i.e.Attock,Rawalpindi,Jhelum and Chakwal.These districts were selected by dividing the Potohar region into rain-fed areas.We have employed logistic regression to assess the determinants of adaptation to climate change and its impact.We have also calculated the marginal effect of each independent variable of the logistic regression to measure the immediate rate of change in the model.In order to check the significance of our suggested model,we have used hypothesis testing.

A New Random Sampling Method and Its Application in Improving Progressive BKZ Algorithm

Random sampling algorithm was proposed firstly by Schnorr in 2003 to find short lattice vectors,as an alternative to enumeration.The follow-up developments in random sampling were mainly proposed by Fukase and Kashiwabara in 2015 and Aono and Nguyen in 2017.Although they extended the sampling space compared to Schnorr's work through the natural number representation,they did not show how to sample specifically in practice and what vectors should be sampled,in order to find short enough lattice vectors.In this paper,the authors firstly introduce a practical random sampling algorithm under some reasonable assumptions which can find short enough lattice vectors efficiently.Then,as an application of this new random sampling algorithm,the authors show that it can improve the performance of progressive BKZ algorithm in practice.Finally,the authors solve the Darmstadt's Lattice Challenge and get a series of new records in the dimension from 500 to 825,using the improved progressive BKZ algorithm.

基于最佳几何约束和RANSAC的特征匹配算法

为解决特征点匹配的质量与计算效率不能兼得的问题,研究了一种基于最佳几何约束和RANSAC(random sample consensus)的特征点匹配方法。采用KNN(k-nearest neighbor)算法对提取到的特征点完成初始匹配,根据匹配点对连接线长度相等、斜率相同的特点,基于统计排序策略构建最佳几何约束,剔除明显错误匹配。利用RANSAC算法进行二次过滤,确保特征匹配点对的正确率,同时给出实验结果加以验证。结果表明:在正常光照下,与Lowe’s算法和GMS算法相比,该算法匹配到的点对数有了明显增加,同时很大程度上保证了特征点的质量。

国家卫生统计与专题调查样本地区的实验设计——社会经济多变量综合因子作为分层抽样的标识

本次研究旨在通过分层整群随机抽样抽取一个经济而有效的国家卫生统计和专题调查的地区样本。采用人口普查分地区社会经济、文化教育和健康等多个指标的资料,用主成分因子分析法确定抽样分层的标识,在此基础上用K-MEANS聚类分析法对总体聚类分层;采用分层整群随机抽样技术抽取多组不同样本容量的样本,分别计算出每个样本各变量的统计量;通过各样本变量的统计量对代表总体参数精确度的比较和各样本变量的统计量与总体参数分布拟合度检验确定最佳样本容量和样本地区。结果表明,抽取60个县(市)可以代表全国,至少90个县(市)可以代表全国不同类型地区。

随机化检验在内部预试验自适应设计样本量调整中的应用

目的:探讨随机化检验(Randomization test)在内部预试验IPS(Internal Pilot Study)自适应设计样本量调整中对I型错误和检验效能的影响.方法:利用蒙特-卡罗(MonteCarlo)法模拟样本量较小时的IPS样本量调整,分别采用随机化检验和t检验分析最后数据并比较二者对I型错误、检验效能值的影响.结果:重计算的第二阶段样本量波动性较大,t检验不能很好地抑制I型错误,随机化检验能较好的抑制I型错误,检验效能略有降低.结论:在临床试验样本量较小的情况下,内部预试验盲态下样本量调整后随机化检验能保护I型错误不增大,同时保证检验效能亦满足要求.