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.

An innovative classification system for ranking the biological effects of marine aromatic hydrocarbons based on fish embryotoxicity

Petroleum hydrocarbon pollution is a global concern,particularly in coastal environments.Polycyclic aromatic hydrocarbons(PAHs) are regarded as the most toxic components of petroleum hydrocarbons.In this study,the biomonitoring and ranking effects of petroleum hydrocarbons and PAHs on the marine fish model Oryzias melastigma embryos were determined in the Jiulong River Estuary(JRE) and its adjacent waters in China.The results showed that the levels of petroleum hydrocarbons from almost all sites met the primary standard for marine seawater quality,and the concentrations of the 16 priority PAHs in the surface seawater were lower compared with those in other coastal areas worldwide.A new fish expert system based on the embryotoxicity of O.melastigma(OME-FES) was developed and applied in the field to evaluate the biological effects of petroleum hydrocarbons and PAHs.The selected physiological index and molecular indicators in OME-FES were appropriate biomarkers for indicating the harmful effects of petroleum hydrocarbons and PAHs.The outcome of OME-FES revealed that the biological effect levels of the sampling sites ranged from level Ⅰ(no stress) to level Ⅲ(medium stress),which is further corroborated by the findings of nested analysis of variance(ANOVA) models.Our results suggest that the OME-FES is an effective tool for evaluating and ranking the biological effects of marine petroleum hydrocarbons and PAHs.This method may also be applied to evaluate other marine pollutants based on its framework.

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.

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.

Spatio-temporal variation and relationship between agricultural efficiency and irrigation intensity in a semi-arid region of India

A surging population in Karnataka State,a semi-arid region in India,poses a threat to both food security and livelihood sustainability,necessitating a concentrated effort to bolster agricultural efficiency and achieve United Naton’s Sustainable Development Goal 2(zero hunger).Therefore,in order to address the pressing issue of food scarcity in Karnataka,this study meticulously examined the spatio-temporal variation of agricultural efficiency and irrigation intensity in Karnataka,uncovering its significant dependence of agricultural efficiency on irrigation intensity.Specifically,this study used a one-way analysis of variance(ANOVA)to ascertain significant differences in the means of agricultural efficiency and irrigation intensity during 2004-2005 and 2018-2019.This study showed that the maximum improvement in agricultural efficiency index was recorded in Belgaum(40.24),Gulbarga(24.77),and Yadgir districts(22.92)between 2004-2005 and 2018-2019,which indicated the progressing trend and better scope for agriculture extension.On the contrary,some districts expressed threat(a decline of above 20.00 of agricultural efficiency index)and needed special care for the improvement of agricultural efficiency in four northern districts(Bagalkot,Bidar,Raichur,and Bijapur),three southern districts(Chitradurga,Chikballapur and Hassan),and two southern districts(Koppal and Gadag)in Karnataka.During 2004-2005,irrigation intensity varied from 3.19%to 56.39%,with the lowest irrigation intensity in Kodagu District and the highest irrigation intensity in Shimoga District.During 2018-2019,irrigation intensity changed from 0.77%to 72.77%,with the lowest irrigation intensity in Kodagu District and the highest in Dakshin Kannad District.Moreover,the research scrutinized the complex relationship between agricultural efficiency and irrigation intensity,with the correlation coefficient increased from 0.162 during 2004-2005 to 0.255 during 2018-2019.It implies that in both periods,a low positive correlation existed between these two variables.Over time,several factors(high-yield seeds and chemical fertilizers)other than irrigation intensity gradually became essential for agricultural efficiency.This research offers a wealth of valuable insights for regional planners and policy-makers contending with comparable challenges in various regions of India and other developing countries.

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.

Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package

Generalized Additive Models(GAMs)are widely employed in ecological research,serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors.Nevertheless,evaluating the relative importance of predictors with concurvity(analogous to collinearity)on response variables in GAMs remains a challenge.To address this challenge,we developed an R package named gam.hp.gam.hp calculates individual R^(2) values for predictors,based on the concept of'average shared variance',a method previously introduced for multiple regression and canonical analyses.Through these individual R^(2)s,which add up to the overall R^(2),researchers can evaluate the relative importance of each predictor within GAMs.We illustrate the utility of the gam.hp package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London,UK.We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.

The Toxic Effects of Strong Chlorin Disinfectant on Mangroves and Emission Thresholds

In this study, based on the simulated discharge results of chemical disinfectants, hypocotyl germination concentration gradient pre-test and concentration gradient determination experiment were set up respectively. Laboratory cultivation was conducted to compare and analyze the root germination and germination indexes, three mangrove hypocotyls of Kandelia candel (Linn.) Druce, Ceriopstagal C.B. Rob. and Bruguiera sexangula var. Rhynchopetalas’ efficiency of cumulative root germination, cumulative germination and the cumulative expansion of the second pair of leaves, one-way analysis of variance was used to obtain the tolerance threshold of three mangrove hypocotyls to strong chlorin disinfectant. The study determined that the by-products of strong chlorin disinfectant, the toxic threshold concentrations of Kandelia candel (Linn.) Druce, Ceriopstagal C.B. Rob. and Bruguiera sexangula var. rhynchopetala are close to 0.55 mg/L, 0.55 mg/L and 0.25 mg/L, respectively. This concentration range is lower than the average concentration of 1.183 mg/L of active chlorine emitted from strong chlorine concentrate during pond clearing in high-level shrimp ponds, indicating that transient emissions of strong chlorine concentrate during pond clearing can have a toxic effect on mangrove plants. The strength of tolerance of the embryonic axes of the three mangrove species to effective chlorine contamination was, Ceriopstagal C.B. Rob. stronger than Bruguiera sexangula var. rhynchopetala, and Kandelia candel (Linn.) Druce is the weakest.

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.

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。

Detailed Statistic Analysis Method of Split-split Plot Design in Agricultural Research

Based on a practical research test, the statistic analysis method for the experimental data in the split-split plot design was introduced in detail, especially in- troduced the significant test method of three-factor interaction and the calculation of test value, which solved the problem of how to make statistical analysis on the data in split-split plot design.

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.

Analysis of Heat Transfer Performance of Oscillating Heat Pipes Based on a Central Composite Design

Oscillating heat pipes (OHPs) are very promising cooling devices. Their heat transfer performance is af- fected by many factors, and the form of the relationship between the performance and the factors is complex and non-linear. In this paper, the effects of charging ratio, inclination angle, and heat input and their interaction effects on heat transfer performance of a looped copper-water OHP are analyzed. First, suppose that the relationship between the response and the variables approximates a second-order model. And use the central composite design to arrange the ex- periment. Then, the method of least squares is used to estimate the parameters in the second-order model. Finally, multi- variate variance analysis is used to analyze the model. The results show that the assumption is right, that is to say, the re- lationship is well modeled by a second-order function. Among the three main effect variables, the effect of inclination angle is the most significant, but their interaction effects are not significant. In the range of the considered factors, both the optimum charging ratio and the optimum inclination angle increase as the heating water flow rate increases.

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。

Regression analysis of major parameters affecting the intensity of coal and gas outbursts in laboratory

Estimating the intensity of outbursts of coal and gas is important as the intensity and frequency of outbursts of coal and gas tend to increase in deep mining. Fully understanding the major factors contributing to coal and gas outbursts is significant in the evaluation of the intensity of the outburst. In this paper, we discuss the correlation between these major factors and the intensity of the outburst using Analysis of Variance(ANOVA) and Contingency Table Analysis(CTA). Regression analysis is used to evaluate the impact of these major factors on the intensity of outbursts based on physical experiments. Based on the evaluation, two simple models in terms of multiple linear and nonlinear regression were constructed for the prediction of the intensity of the outburst. The results show that the gas pressure and initial moisture in the coal mass could be the most significant factors compared to the weakest factor-porosity. The P values from Fisher's exact test in CTA are: moisture(0.019), geostress(0.290), porosity(0.650), and gas pressure(0.031). P values from ANOVA are moisture(0.094), geostress(0.077), porosity(0.420), and gas pressure(0.051). Furthermore, the multiple nonlinear regression model(RMSE: 3.870) is more accurate than the linear regression model(RMSE: 4.091).