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.

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。

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.

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。

Mold Wear During Die Forging Based on Variance Analysis and Prediction of Die Life

A process parameter optimization method for mold wear during die forging process is proposed and a mold life prediction method based on polynomial fitting is presented,by combining the variance analysis method in the orthogonal test with the finite element simulation test in the forging process.The process parameters with the greatest influence on the mold wear during the die forging process and the optimal solution of the process parameters to minimize the wear depth of the mold are derived.The hot die forging process is taken as an example,and a mold wear correction model for hot forging processes is derived based on the Archard wear model.Finite element simulation analysis of die wear process in hot die forging based on deform software is performed to study the relationship between the wear depth of the mold working surface and the die forging process parameters during hot forging process.The optimized process parameters suitable for hot forging are derived by orthogonal experimental design and analysis of variance.The average wear amount of the mold during the die forging process is derived by calculating the wear depth of a plurality of key nodes on the mold surface.Mold life for the entire production process is predicted based on average mold wear depth and polynomial fitting.

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.

Using Contrasts in One-Way Analysis of Variance with Control Groups and an Application

In this study, it was shown that, same comparisons can be made by using contrast coefficients instead of Dunnett＇s test in the experiments with control groups. It was also shown that, in situations with an ordinal scale and equal spacing quantitative grouping, a trend investigation could be done by contrast coefficients. For this purpose, a small part of the data from a TUBITAK project in the Soil Science Department, Agriculture Faculty, Kahramanmaras Sutcu Imam University, was used with permission. The soils were absorbed to natural zeolite in concentration of 0, 2.5, 5 and 10 mg/kg, and after two years, the available Zinc （Zn） amounts in the soil were analyzed. As a result, in both Dunnett＇s test and contrast methods, the Zn amounts in control and 2.5 mg/kg concentration groups were found similar （P 〉 0.01）; but were different （P 〈 0.01） between control and 5 mg/kg concentration groups, and control and 10 mg/kg concentration groups. Furthermore, when orthogonal polynomial contrast coefficients were investigated, linear effects were found significant （P 〈 0.01） and cubic effects were obtained significant （P 〈 0.05）, but quadratic effect was obtained insignificant （P 〉 0.05）.

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.

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.

Authorized variance analysis with consumption rate

On the base of budget or plan information, comparing with actual results, making variance analysis, finding real reasons behind variance, this is important way of control and also important function of budget. However, without consideration of changes of environment, there are some limitations for static variance analysis with benchmark of plan data. Adjusting for benchmark according to actual condition, then doing variance analysis, these will improve utilization of variance analysis. Adjusted benchmark is often known as authorized cost/profit. For different understanding for authority concept, with RCA＇s （the abbreviation for Resource Consumption Accounting） view and numerical examples, this paper brings out the concept of consumption rate variance to promoting deeper understanding and analyzing reasons behind variance.

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.

黄淮海11个夏大豆品种(系)产量稳定性和适应性分析

为比较不同大豆品种(系)产量的稳定性和适应性,筛选优良大豆品种(系),对2020—2021年国家黄淮海夏大豆南组区域试验数据进行多因素方差分析和GGE双标图分析。结果表明:除年份效应外,各因素及互作效应对大豆产量影响都达到极显著水平(P<0.01),其中地点(55.31%)的贡献率最大,品种(5.97%)和年份(0.02%)贡献率较小。12个试点中平均产量最高的是山东济宁,比产量最低的安徽阜阳高33.63%,差异显著(P<0.05);11个参试品种中平均产量最高的是邯豆13,比对照中黄13(CK A)和中黄13(CK B)分别增产10.93%和9.91%,差异显著。GGE双标图分析结果显示,江苏灌云和徐州,山东临沂和济宁相似度较高,试点有重复设置的可能。河南周口对参试品种的鉴别力和代表性最强,是理想试点。12个试点被分为两组,徐9416-8在第一组试点产量最高,柳豆108在第二组试点产量最高。丰产性和稳产性分析结果表明,邯豆13、圣育6号和南农60的丰产性和稳产性较好。本研究筛选得到稳定性和适应性较强的大豆品种(系),并为优异种质资源的推广应用提供参考。

我国城镇污水处理厂污泥产率系数现状及影响因素分析

以我国城镇污水处理厂生态环境统计和监测数据为基础,分析了我国城镇污水处理厂经验污泥产率系数(YQ)和以去除COD导致的污泥增殖来计算的污泥产率系数(YCOD),研究了进水COD、设计处理规模、处理工艺、区域分布等单一因素以及多因素与污泥产率系数的响应关系。结果表明,我国城镇污水处理厂YQ平均值为1.33×10-4 t/m3,YCOD平均值为0.81 kg/kg,YQ、YCOD总体呈正偏态分布;YQ平均值与进水COD、设计处理规模正相关,YCOD平均值与进水COD、设计处理规模负相关;YQ、YCOD平均值对应的COD区间范围为150~250 mg/L,设计处理规模为1×104~1×105 m3/d的污水处理厂YQ、YCOD与平均值最为接近,相对偏差分别为2.8%、-4.3%;A2/O工艺、氧化沟类、普通活性污泥法、A/O工艺的YQ、YCOD与平均值接近,相对偏差范围分别为-1.8%~1.9%、-2.3%~5.8%;华东和华中地区的YQ,华东、西南地区YCOD与平均值基本持平;多因素方差分析显示进水COD为YQ和YCOD的显著影响因素。

基于DeST的超低能耗建筑负荷特性研究

为实现节能减排,进一步降低建筑的能耗,通过DeST软件对严寒地区SJZU超低能耗示范中心建筑的负荷进行模拟计算,对比超低能耗建筑和不同节能水平的建筑负荷特性,分析新风量和内扰对建筑负荷的影响规律,利用数学分析方法研究内扰因素对建筑负荷的显著性。结果表明:典型超低能耗建筑的全年最大热负荷为14.74 kW,全年最大冷负荷为13.23 kW;全年累计负荷较不同节能水平下的节能建筑显著降低;人均新风量增加5 m^(3)/(h·人),全年累计热负荷平均增幅为4.73%,而全年累计冷负荷几乎不变;超低能耗建筑全年累计负荷的影响最显著的内扰因素是人员密度。

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.