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.

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.

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.

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）.

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.

Multi-factor analysis of initial poor graft function after orthotopic liver transplantation

BACKGROUND: In the early period of orthotopic liver transplantation (OLT), initial poor graft function (IPGF) is one of the complications which leads to primary graft non-function (PGNF) in serious cases. This study set out to establish the clinical risk factors resulting in IPGF after OLT. METHODS: Eighty cases of OLT were analyzed. The IPGF group consisted of patients with alanine aminotransferase (ALT) and/or aspartate aminotransferase (AST) above 1500 IU/L within 72 hours after OLT, while those in the non-IPGF group had values below 1500 IU/L. Recipient-associated factors before OLT analyzed were age, sex, primary liver disease and Child-Pugh classification; factors analyzed within the peri-operative period were non-heart beating time (NHBT), cold ischemia time (CIT), rewarming ischemic time (RWIT), liver biopsy at the end of cold ischemia; and factors analyzed within 72 hours after OLT were ALT and/or AST values. A logistic regression model was applied to filter the possible factors resulting in IPGF. RESULTS: Donor NHBT, CIT and RWIT were significantly longer in the IPGF group than in the non-IPGF group; in the logistic regression model, NHBT was the risk factor leading to IPGF (P < 0.05), while CIT and RWIT were possible risk factors. In one case in the IPGF group, PGNF appeared with moderate hepatic steatosis. CONCLUSIONS: Longer NHBT is an important risk factor leading to IPGF, while serious steatosis in the donor liver, CIT and RWIT are potential risk factors.

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.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

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.

Multi-factor sensitivity analysis of shallow unsaturated clay slope stability

An unsaturated clay slope, with various sloping angles and a thickness of 14 m, consists of backfill, slope soil and residual soil. Slide interfaces were determined by geophysical approaches and the original slope was reconstructed. Sub-slope masses were classified based on the varieties of sloping angle. A force recursive principle was proposed to calculate the stability coefficient of the sub-slope masses. The influencing factors such as sloping angle, water content, hydrostatic pressure, seismic force as well as train load were analyzed. The range and correlation of the above-mentioned factors were discussed and coupled wave equations were established to reflect the relationships between unit weight, cohesion, internal frictional angle, and water content, as well as between internal frictional angle and cohesion. The sensitivity analysis of slope stability was carried out and susceptive factors were determined when the factors were taken as independent and dependent variables respectively. The results show that sloping angle, water content and earthquake are the principal susceptive factors influencing slope stability. The impact of hydrostatic pressure on slope stability is similar to the seismic force in quantity. Train load plays a small role in slope stability and its influencing only reaches the roadbed and its neighboring slope segment. If the factors are taken as independent variables, the influencing extent of water content and cohesion on slope stability can be weakened and train load can be magnified.

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.

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。

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.