An Analysis-of-Variance (ANOVA) Approach for Sib-pair Linkage Analysis of Complex Ordinal Human Diseases

Sib-pair linkage analysis of complex human diseases has become a method of choice in modern human genetic studies, especially for the diseases with a late age at onset. The traditional parametric methods for sib pair data need to take the categorical nature of a disease phenotype into account and to explicitly model the non-linear relationship between the discrete phenotype and genetic determinants, or to force their relationship to be linear. The first approach is desirable theoretically, but explicitly modeling the sophisticated genetic architecture of a complex disease can be prohibitively complex and computational demand is high. The second approach, typically a linear regression, relies on a large sample theory and is not appropriate. In this paper, we propose to apply Analysis of Variance (ANOVA) to sib pair linkage analysis of complex human diseases. This approach has avoided building the complicated relationship between the phenotype (the affection group or status of a sib pair) and the underlying genetic determinant (identical-by-decent (IBD) values etc). We have explored its statistical efficiency and properties in sibpair linkage analysis of ordinal complex human diseases via simulation. The simulation suggests that it is a powerful approach for locating genes that presumably control phenotypic expression of complex human diseases.

Comparison of performance between rescaled range analysis and rescaled variance analysis in detecting abrupt dynamic change

In the present paper, a comparison of the performance between moving cutting data-rescaled range analysis （MC- R/S） and moving cutting data-rescaled variance analysis （MC-V/S） is made. The results clearly indicate that the operating efficiency of the MC-R/S algorithm is higher than that of the MC-V/S algorithm. In our numerical test, the computer time consumed by MC-V/S is approximately 25 times that by MC-R/S for an identical window size in artificial data. Except for the difference in operating efficiency, there are no significant differences in performance between MC-R/S and MC-V/S for the abrupt dynamic change detection. Mc-R/s and MC-V/S both display some degree of anti-noise ability. However, it is important to consider the influences of strong noise on the detection results of MC-R/S and MC-V/S in practical application

ONE-WAY MULTIVARIATE REPEATED MEASUREMENTS ANALYSIS OF VARIANCE MODEL

The one-way multivariate repeated measurements analysis of variance (1-way MRM ANOVA) model for complete data and the sphericity test are studied.

Did the Revision of the Japanese Medical Payment System Work Properly?—An Analysis of Averages and Variances of Length of Hospital Stay for Type 2 Diabetes Patients by Individual Hospital

We evaluated the effects of the 2010 revision of the medical payment system on the length of stay (LOS). In this analysis, we assessed not only the average length of stay (ALOS), but also variance of LOS at individual hospitals. We used a dataset of 18,641 type 2 diabetes patients collected from 51 general hospitals. The variables found to affect LOS were age, comorbidities, complications, acute hospitalization, introduced by other hospitals, winter, one-week hospitalization, specific hospitalization period, and principal diseases coded E11.5, E11.6 and E11.7. Although the effect was marginal, the 2010 revision did reduce ALOS, and the reduction was larger as ALOS became longer. On the other hand, we did not find that the variance of LOS within hospitals became smaller. The results of the study suggest that new incentives and assistance to hospitals to help them make efficient use of medical information are needed.

Analysis on Chinese Regional Differences in International Tourism Foreign Exchange Earnings with Method of One-way Variance Analysis

Based on data over 31 provinces and cities in China from2006 to 2013,this thesis first divides those 31 provinces and cities into four economic regions including northeastern region,central region,eastern region and western region.Based on data over 31 provinces and cities in China from 2006 to 2013,those 31 provinces and cities were devided into four economic regions in this thesis,including northeastern region,central region,eastern region and western region.Then,it takes international tourism foreign exchange earnings as the dependent variable,the four economic regions as the factor to measure the difference of international tourism foreign exchange earnings in different regions,and finds out the main reasons of it.Through the one-way variance analysis on international tourism foreign exchange earnings,we can know that international tourism foreign exchange earnings have differences in different regions apparently.Besides,significant differences can be found between northeastern and central regions as well as eastern and western regions,while it is not the same case between the central and western regions.

Variance analysis for passive neutron multiplicity counting

Passive neutron multiplicity counting is widely used as a nondestructive assay technique to quantify mass of plutonium material. One goal of this technique is to achieve good precision in a short measurement time. In this paper, we describe a procedure to derive mass assay variance for multiplicity counting based on the threeparameter model, and analytical equations are established using the measured neutron multiplicity distribution.Monte Carlo simulations are performed to evaluate precision versus plutonium mass under a fixed measurement time with the equations. Experimental data of seven weapons-grade plutonium samples are presented to test the expected performance. This variance analysis has been used for the counter design and optimal gate-width setting at Institute of Nuclear Physics and Chemistry.

Application of Improved Analysis of Variance to Ionospheric TEC Short-Term Forecast

An improved superposition analysis of periodical wave variance is used for short-term forecast of the ionosphere TEC in this study. Using the ionospheric TEC data provided by IGS as the real value, the forecasting precision of this me-thod at different locations in China with 40 days data is evaluated. The result shows that the improved method has a better forecasting precision which could reach 1.1 TECU. But the forecasting precision still relates to geographical position, it is proportional to longitude and inversely proportional to latitude. Compared with the current-used methods, the improved method has many advantages as higher precision, using fewer parameters and easier to calculate. So, it applied to ionosphere short-term prediction in China very well.

Measuring Global Supply Chain Vulnerabilities Using Trade Network Analysis Method

With the trade network analysis method and bilateral country-product level trade data of 2017-2020,this paper reveals the overall characteristics and intrinsic vulnerabilities of China’s global supply chains.Our research finds that first,most global supply-chain-vulnerable products are from technology-intensive sectors.For advanced economies,their supply chain vulnerabilities are primarily exposed to political and economic alliances.In comparison,developing economies are more dependent on regional communities.Second,China has a significant export advantage with over 80%of highly vulnerable intermediate inputs relying on imports of high-end electrical,mechanical and chemical products from advanced economies or their multinational companies.China also relies on developing economies for the import of some resource products.Third,during the trade frictions from 2018 to 2019 and the subsequent COVID-19 pandemic,there was a significant reduction in the supply chain vulnerabilities of China and the US for critical products compared with other products,which reflects a shift in the layout of critical product supply chains to ensure not just efficiency but security.China should address supply chain vulnerabilities by bolstering supply-side weaknesses,diversifying import sources,and promoting international coordination and cooperation.

New Configurations of the Fuzzy Fractional Differential Boussinesq Model with Application in Ocean Engineering and Their Analysis in Statistical Theory

The fractional-order Boussinesq equations(FBSQe)are investigated in this work to see if they can effectively improve the situation where the shallow water equation cannot directly handle the dispersion wave.The fuzzy forms of analytical FBSQe solutions are first derived using the Adomian decomposition method.It also occurs on the sea floor as opposed to at the functionality.A set of dynamical partial differential equations(PDEs)in this article exemplify an unconfined aquifer flow implication.Thismethodology can accurately simulate climatological intrinsic waves,so the ripples are spread across a large demographic zone.The Aboodh transform merged with the mechanism of Adomian decomposition is implemented to obtain the fuzzified FBSQe in R,R^(n) and(2nth)-order involving generalized Hukuhara differentiability.According to the system parameter,we classify the qualitative features of the Aboodh transform in the fuzzified Caputo and Atangana-Baleanu-Caputo fractional derivative formulations,which are addressed in detail.The illustrations depict a comparison analysis between the both fractional operators under gH-differentiability,as well as the appropriate attributes for the fractional-order and unpredictability factorsσ∈[0,1].A statistical experiment is conducted between the findings of both fractional derivatives to prevent changing the hypothesis after the results are known.Based on the suggested analyses,hydrodynamic technicians,as irrigation or aquifer quality experts,may be capable of obtaining an appropriate storage intensity amount,including an unpredictability threshold.

Dealing with Multicollinearity in Factor Analysis: The Problem, Detections, and Solutions

Multicollinearity in factor analysis has negative effects, including unreliable factor structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and difficulties in interpreting factors. It also leads to reduced stability, hindered factor replication, misinterpretation of factor importance, increased parameter estimation instability, reduced power to detect the true factor structure, compromised model fit indices, and biased factor loadings. Multicollinearity introduces uncertainty, complexity, and limited generalizability, hampering factor analysis. To address multicollinearity, researchers can examine the correlation matrix to identify variables with high correlation coefficients. The Variance Inflation Factor (VIF) measures the inflation of regression coefficients due to multicollinearity. Tolerance, the reciprocal of VIF, indicates the proportion of variance in a predictor variable not shared with others. Eigenvalues help assess multicollinearity, with values greater than 1 suggesting the retention of factors. Principal Component Analysis (PCA) reduces dimensionality and identifies highly correlated variables. Other diagnostic measures include the condition number and Cook’s distance. Researchers can center or standardize data, perform variable filtering, use PCA instead of factor analysis, employ factor scores, merge correlated variables, or apply clustering techniques for the solution of the multicollinearity problem. Further research is needed to explore different types of multicollinearity, assess method effectiveness, and investigate the relationship with other factor analysis issues.

CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.

Dependence of epoch-wise two-way nested ANOVA estimates of variances of unmodeled effects present in relative GPS positioning on satellite elevation cutoff angle and PDOP mask

Impact of satellite elevation cutoff angle and position dilution of precision(PDOP)mask change on epoch-wise variance components of unmodeled effects that accompany relative Global Positioning System(GPS)positioning is presented herein.Data used for this study refer to the winter and summer periods of the years with minimal(2008)and maximal(2013)solar activity.These data were collected every 30 s in static mode,at two permanent GPS stations located in Montenegro,establishing a mediumdistance(116-km-long)baseline with a height difference of approximately 760 m between its endpoints.The study showed that changing satellite elevation cutoff angle,with a fixed PDOP mask,affects epochwise two-way nested ANOVA estimates of variances related to the‘far-field’multipath(considered as the nested factor herein)and the combined unmodeled effect of tropospheric and ionospheric refraction(considered as the nesting factor herein).However,changing of PDOP mask,with a fixed satellite elevation cutoff angle,doesn’t affect epoch-wise two-way nested ANOVA estimate of variance of the combined unmodeled effect of tropospheric and ionospheric refraction,but,generally,affects the estimate of variance of the‘far-field’multipath(possibly mixed with a part of a‘shorter-term’ionospheric refraction),which is especially pronounced for the summer period.It should also be noted that there is a significant influence of satellite elevation cutoff angle change on both epoch-wise horizontal and vertical position accuracy,only for the summer period,especially in the presence of maximal solar activity,while there is no significant impact of PDOP mask change on epoch-wise positional accuracy.

基于K-Means-NCS-ANOVA的八大民窑颜色聚类与应用偏好研究

中华陶瓷文化源远流长,颜色作为陶瓷的一种装饰,可以表达不同的设计理念、情感意识、地域特征和服务对象。为了更好地传承陶瓷艺术文化,本研究提出了一种基于K-means-NCS-ANOVA的颜色分类研究方法。首先,采用K-means颜色聚类、NCS颜色体系提取宋代八大民窑的颜色特征,分析八大瓷窑的区域特征及设计理念;基于上述分析结果制作问卷调查;并利用描述性分析、单因素方差分析(One-Way ANOVA),探究现代设计师对八大民窑颜色的应用偏好。由实验结果可知,设计师性别、专业、工龄也会对八大民窑颜色的应用偏好产生影响;景德镇窑的颜色最受现代设计师欢迎,而耀州窑、磁州窑、定窑的颜色已经不太符合现代审美。本研究采用现代人工智能技术识别八大民窑特征,调研现代设计师对八大民窑颜色的使用偏好,为文物识别、陶瓷文化传承及人才培养提供理论和技术支持。

应用Open Meta-Analysis软件实现诊断准确性试验的Meta分析

Open Meta-Analysis软件是用于二分类数据、连续型数据以及诊断数据Meta分析的开放软件,该软件提供了四种模型来执行诊断准确性试验的Meta分析,即诊断随机效应模型、倒方差混合效应模型、双变量模型和分层综合受试者工作特征曲线法,其中前两者为单变量模型,只能执行单个指标合并,而后两者为双变量模型,能够对灵敏度和特异度之间的负相关性进行综合分析。本文以实例就Open Meta-Analysis软件实现诊断准确性试验Meta分析做相关简述。

Spatial analysis increases efficiency of progeny testing of Chinese fir

We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir （Cunninghamia lanceolata （Lamb.） Hook） on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height （DBH） and tree height data had significant spatial auto-correlations among rows and columns. Adding a firstorder separable autoregressive term more effectively modelled the spatial variation than did the incomplete block （IB） model used for the experimental design. The spatial model also accounted for effects of experimental design factors and greatly reduced residual variances. The spatial analysis rel- ative to the IB analysis improved estimation of genetic parameters with the residual variance reduced 13 and 19% for DBH and tree height, respectively; heritability increased 35 and 51% for DBH and tree height, respectively; and genetic gain improved 3-5%. Fitting global trend and postblocking did not improve the analyses under IB model. The use of a spatial model or combined with a design model is recommended for forest genetic trials, particularly with global trend and local spatial variation of hilly sites.

GENETIC MODELS AND ANALYSIS METHODS FOR SEX-LINKED AND MATERNAL GENE EFFECTS

Genetic models are proposed for analyzing sex-linked and maternal effects as well as autosomal gene effects.For the model with no genotype×environment interaction,the total genetic effect is partitioned into direct additive （A）,direct dominance （D）,sexlinked （L）,maternal additive （Am） and maternal dominance （Dm） genetic components.For the model including genotype×environment interaction （GE）,GE can also be partitioned into components of direct additive by environment interaction （AE）,direct dominance by environment interaction （DE）,sex-linked by environment interaction （LE）,maternal additive by environment interaction （AmE ）,and maternal dominance by environment interaction （DmE）.Linear functions of genetic components are listed for parent,F1,and F2.A set of parents,their reciprocal F1’s and F2’s is applicable for efficient analysis.Variance and covariance components can be well mated by MINQUE（O/l） with the jackknife procedure.The t-test conducted by the jackknife procedure is applicable for detecting significance of variation.Adjusted Unbiased Prediction （AUP） method is suggested for predicting genetic effects.

Optimizing SUS 304 wire drawing process by grey relational analysis utilizing Taguchi method

In the stainless steel 304 （SUS 304） wire drawing process, optimizing the die life and wire tensile strength, which are the larger-the-better quality characteristics （QCH） types, is of main interest. Three control factors, involving reduction ratio, lubricant temperature, and drawing speed, were investigated utilizing L9（34） orthogonal array （OA）. The grey relational analysis was conducted for the normalized signal-to-noise （S/N） ratios. The ordinal value of the grey grade was then used to decide optimal factor levels. The anticipated improvements in die life and wire tensile strength were estimated 25.31 h and 22.50 kg/mm2, respectively. To decide the significant factor which had effect on each QCH and predict the average value of each QCH, analysis of variance （ANOVA） was performed for S/N ratio and QCH. Confirmation experiments were then conducted, where a good overlap was noticed between the predicted and confirnation intervals for each QCH. The Hotelling T2 and the sample generalized variance control charts were finally utilized in controlling and monitoring future production. In conclusion, the grey relational analysis utilizing Taguchi method is an effective approach for optimizing the die life and wire tensile strength for SUS wire drawing process. 2008 University of Science and Technology Beijing. All rights reserved.

基于ANOVA和BO-SVM的变压器故障诊断方法

为了提高变压器故障诊断模型的诊断精度,提出了一种基于方差分析(analysis of variance, ANOVA)和黑猩猩算法优化支持向量机(bonobo optimizer-support vector machine, BO-SVM)的变压器故障诊断方法。首先,采用方差分析对模型输入量,即变压器油中溶解气体特征进行筛选降维。其次,用黑猩猩算法对影响支持向量机诊断性能的2个参数(核参数与惩罚因子)进行寻优。最后,利用该文所提方法对变压器进行故障诊断,实例仿真结果表明:与IEC、Rogers法相比,采用ANOVA对模型输入量进行筛选降维能更好地提升模型性能;BO算法优化后的支持向量机对比网格搜索算法优化后的支持向量机,在训练速度与诊断精度两方面都有所提升;与粒子群优化支持向量机(particle swarm optimization-support vector machine,PSO-SVM)和遗传算法优化支持向量机(genetic algorithm-support vector machine, GA-SVM)相比,所提方法收敛更快,且故障诊断精度更高、更稳定,BO-SVM、PSO-SVM和GA-SVM的平均诊断正确率分别为91.69%、83.29%、81.34%,验证了所提方法的优越性。