Correlation and path coefficient analysis for phenotypic traits of noble scallop Chlamys nobilis

In April of 2006, a base population of the noble scallop Chlamys nobilis was established by collecting parental breeders from the stocks in Wushi, Zhanjiang. In December of 2006, 200 individuals were randomly sampled from the base population and subjected for correlation and path coefficient analysis. It was found that there were statistically significant phenotypic correlations among the traits （P 〈 0.01）. Total weight was significantly and positively correlated with the shell length （r = 0.934 3）, shell height （r = 0.895 9）, shell width （r = 0.899 1 ）, muscle weight （r = 0.882 0） and shell weight （r = 0.937 9）, respectively. Shell length, shell width, muscle weight, shell height and shell weight had positive and direct effects on the total weight, with values of 0.397 1, 0.321 9, 0.172 1, 0.089 6 and 0.066 9, respectively. Shell length, shell width and muscle weight had higher direct effects on the total weight than shell height and shell weight. A combined evaluation of correlation, direct effects and indirect effects showed that direct selection for shell length, shell width, muscle weight, shell height and shell weight would be effective to improving the total weight. It was concluded that these traits could be regarded as the selection criteria in breeding programs of the species.

Statistical analysis of fracture properties based on particle swarm optimization and Pearson correlation coefficient method

Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.

Correlation Analysis of Experimental Data Applied in the Study of the Extraction Process

The article examines the application of correlation analysis of experimental data in research into the process of extracting bioactive compounds and antioxidant activity in plant extracts from berries and grape pomace. The correlation analysis of the experimental data allowed the establishment of the second order statistical characteristics (autocorrelation function, intercorrelation function and correlation coefficient). Based on the correlation analysis of the experimental data, it was shown that the influencing factor and the measured parameters have zero correlation coefficients for all types of researched extracts. This indicates that they are not independent. Therefore, related mathematical models can be deduced.

On the correlation of nonlinear variables containing secular trend variations： numerical experiments

Due to global warming, the general circulation, underlying surfaces characteristics, and geophysical and meteorological elements all show evident secular trends. This paper points out that when calculating the correlation of two variables containing their own obvious secular trends, the interannual correlation characteristics between the two variables may be distorted （overestimated or underestimated）. Numerical experiments in this paper show that if two variables have opposite secular trends, the correlation coefficient between the two variables is reduced （the positive correlation is underestimated, or the negative correlation is overestimated）; and if the two variables have the same sign of secular trends, the correlation coefficient between the two variables is increased （the positive correlation is overestimated, or the negative correlation is underestimated）. Numerical experiments also suggest that the effect of secular trends on the interannual correlation of the two variables is interchangeable, that is to say, as long as the values of the two trends are not changed, the two variables interchange their positions, and the effect of the secular trends on the interannual correlation coefficient of the two variables remains the same. If the two variables have the same-（opposite-） sign trends, the effect of secular trends on the interannal correlation coefficient is more （less） distinctive. A meteorological example is given.

Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.

Correlation and Path Coefficient and Chi-square Distance Cluster Analysis for Several Characteristics in Tobacco Germplasm Resource

Correlation and path coefficient analyses were conducted for 10 characteristics of 24 pure lines of flue-cured tobacco such as plant height, knot distance, leaf number, the central leaf length and width, ratio of the length to width, stem girth, dates of budding, leaf yield and ratio of the prime-medium tobacco. The leaf number and the central leaf length showed a positive or a strong positive correlation with the yield per plant. And the leaf number and leaf yield per plant showed a strong positive correlation with the ratio of prime-medium tobacco. The results showed that the leaf yield per plant among these characteristics played a major role in determining the ratio of prime-medium tobacco while the others were less related with the ratio. Square sum of deviation method cluster analyses showed that 24 pure lines of flue-cured tobacco were clustered into two groups. Of the pure lines, Line T1706 and Line T1245 had a far relationship with all other lines, and also had a heterosis when crossed with the other lines. Lines Guangdonghuang 1 and R72（3）B-2-1 were closely related.

Study on the Driving Forces of Rocky Desertification in Guizhou Province Based on Variation Coefficient Method

[Objective] The aim was to study the driving forces of rocky desertification in Guizhou Province. [Method] Based on GIS and RS technology, the main driving forces of rocky desertification in Guizhou Province were analyzed by means of correlation analysis and variation coefficient method, and then the distribution of rocky desertification in Guizhou Province was assessed synthetically. [Result] The main driving forces of rocky desertification in Guizhou Province were vegetation cover, rainfall, peasant income and gradient which were obviously correlative with rocky desertification; from theoretical distribution, rocky desertification was the most serious in Bijie, southwest Guizhou, southern Guizhou and northeastern Guizhou, and the theoretical distribution of rocky desertification in Bijie was severer than its current situation, which showed that construction of ecological function area in Bijie restrained the development of rocky desertification effectively; the good consistency between theoretical and current distribution of rocky desertification revealed that the study method was feasible. [Conclusion] The study could provide theoretical references for the practical control of rocky desertification.

Coefficient of Partial Correlation and Its Calculation

This thesis offers the general concept of coefficient of partial correlation.Starting with regres-sion analysis,the paper,by using samples,infers the general formula of expressing coefficient of partial correlation by way of simple correlation coefficient.

METHOD OF PARAMETER STATISTICAL ANALYSIS FOR BIPOLAR INTEGRATED CIRCUIT DESIGN

The method and procedure of realizing parameter statistical correlation analysis ofbipolar analog IC’s are given,and the statistical model of parameter are constructed with doubleparameters(B_F,R_S).Based on the comparison and analysis of the circuit characteristics,it isshown that the method can be used for analysis and design of bipolar IC’s.

Decomposition Method of Genetic Correlation Coefficient Based on NC Ⅱ Mating Design

There are different degrees of correlation between crop traits. The phenotypic correlation is decomposed into genetic and environmental correlation in quantitative genetics. In this paper,according to stochastic model of variance and covariance analysis,we calculate different genetic components,bring up a decomposition method of genetic correlation coefficient based on NC II mating design,and use examples to show analytic steps and interpret results.

Quantitative analysis of urban intelligence and ranking the potential of individual initiatives within a designed smart growth plan

Smart growth has been gaining increasing attention among academia and practitioners as a new technology-based solution to meet the city disease challenges.In the research,we mainly accomplish two tasks.One builds an evaluation system to measure the smart growth of a city.And the other develops a growth plan.Firstly,coordination coefficient(C value) model is applied to measure the smart degree.To begin with,we divide the indicators into four aspects which involve five parameters.Then,entropy method is used to calculate the weight of every parameter.After normalizing data of indicators,we set up a smart growth indicator evaluation system.Aiming to assessing the detailed performances,we rank the eight cities according to the score of C value which corresponds to our normal cognition.Secondly,based on Salvo combat model and dynamic trend analysis model,We draw up a 20-year growth plan with a period of 5 years for the two cities we choose.The Salvo model is adopted to describe the dynamic process.Dynamic trend analysis model is introduced to gain the optimum solution and the optimal point in every stage.In addition,compared with the point of every stage,we can obtain the proportion of investment in different stages.Thirdly,to evaluate the sensitivity of our model with the OFAT Method,we adjust the parameters k_1,k_2 and O_(ij) approximately.It comes out that the change of k_1,k_2 and O_(ij) has an impact on the C value.But the sensitivity of k_1,k_2 is higher.Lastly,we analyze the influence caused by population growth.To a certain extent,it can be concluded that the plan we made can alleviate the negative impact of population growth through the analysis of the chart.

FREE VIBRATION ANALYSIS OF SHEAR WALLS WITH SHORT PIERS

The equations of the lateral deflection curve of the short pier shear wall under a lateral concentrated load at any level are derived by employing a continuous approach. Lateral flexibility matrixes for the dynamic analysis are also obtained by repeatedly calculating the lateral unit load on the wall at each level where a lumped mass located. Dynamic analyses are implemented for short pier shear walls with different parameters, called the integrative coefficient and the pier strength coefficient related to the dimensions of walls. The influences of two coefficients on the dynamic performances of the structure are studied. Results indicate that with the increase of the integrative coefficient, the periods of top two modes apparently decrease but the other periods of higher frequency modes show little variation when the pier strength coefficient remains constant. Similarly, if the integrative coefficient is constant, the top two periods of the free vibration decrease with the increase of the integrative coefficient but the other periods of higher frequency modes show less variation.

Comparative analysis of coal and coal-shale intrinsic factors affecting spontaneous combustion

Coal and coal-shales tend to undergo spontaneous combustion under favourable atmospheric conditions. Spontaneous combustion liability index and intrinsic properties of coals and coal-shales varies between （above and below） coal seams. The spontaneous combustion liability index （obtained from the Wits-Ehac Index） and intrinsic properties （obtained from proximate, ultimate, and petrographic analysis） of fourteen samples representative of in situ coal （bituminous） and fourteen coal-shales obtained in Witbank coalfield, South Africa were experimentally studied. Comparative analysis of the relationships between the spontaneous combustion liability index and intrinsic properties of coals and coalshales were established to evaluate their effects on self-heating potential. The intrinsic properties show linear relationship with spontaneous combustion liability and therefore, identifies the factors affecting spontaneous combustion of these materials. The influence of coal-shales intrinsic properties towards spontaneous combustion liability shows higher correlation coefficients than the coals. Both coals and coal-shales show inertinite maceral as major constituents than the vitrinite and liptinite macerals, hence the reactivity of inertinite macerals may show greater influence on spontaneous combustion liability. A definite positive or negative trends exists between the intrinsic properties and spontaneous combustion liability index. This research is part of a larger project which is considering the influence of intrinsic properties of coals and coal-shales on spontaneous combustion liability.

Data-driven estimation of joint roughness coefficient

Joint roughness is one of the most important issues in the hydromechanical behavior of rock mass.Therefore,the joint roughness coefficient(JRC)estimation is of paramount importance in geomechanics engineering applications.Studies show that the application of statistical parameters alone may not produce a sufficiently reliable estimation of the JRC values.Therefore,alternative data-driven methods are proposed to assess the JRC values.In this study,Gaussian process(GP),K-star,random forest(RF),and extreme gradient boosting(XGBoost)models are employed,and their performance and accuracy are compared with those of benchmark regression formula(i.e.Z2,Rp,and SDi)for the JRC estimation.To analyze the models’performance,112 rock joint profile datasets having eight common statistical parameters(R_(ave),R_(max),SD_(h),iave,SD_(i),Z_(2),R_(p),and SF)and one output variable(JRC)are utilized,of which 89 and 23 datasets are used for training and validation of models,respectively.The interpretability of the developed XGBoost model is presented in terms of feature importance ranking,partial dependence plots(PDPs),feature interaction,and local interpretable model-agnostic explanations(LIME)techniques.Analyses of results show that machine learning models demonstrate higher accuracy and precision for estimating JRC values compared with the benchmark empirical equations,indicating the generalization ability of the data-driven models in better estimation accuracy.

Correlative Analysis between Contents of Nutrients and Secondary Substances in Damaged Pinus massoniana Needles and Population Parameters of Dendrolimus kikuchii(Lepidoptera:Lasiocampidae)

In order to explore the effects of chemical substances changes in damaged masson pine （Pinus massoniana） needles on population dynamics of Dendroli- mus kikuchii, D. kikuchii larvae were reat~ with P. massoniana needles with different damage degrees （mild, moderate and severe）, and its population parame- ters and contents of nutrients and secondary substances in damaged P. massoniana needles were measured, and the integrated correlation coefficient was adopted for data analysis. The results showed that with the damage degree aggravating, flavones in needles increased accordingly, while contents of soluble sugars, polysaeeha- rides and proteins decreased. The average developmental duration and mortality of D. kikuchii larvae increased with the damage degree increasing. No significant correlation was found between the changes in contents of tannins or total phenols and the developmental duration or mortality of each instar larvae. There were signif- icant direct and integrated correlations between contents of nutrients and secondary substances of P. massoniana needles and the developmental duration or mortality of each instar larvae except the 6＇h instar larvae. With the damage degree increasing, all parameters of D. kikuchii population including body weight of the 7~ instar larvae, average feeding capacity of larvae, pupal weight, pupation rate, female ratio and fecundity decreased. No significant correlation was found between the changes in contents of tannins or total phenols and population parameters of D. kikuchii larvae. The results suggest that the contents of nutrients and secondary sub- stances in P. ,mssoniana needles dramatically influenced the population parameters of D. kikuchii, and the importance from high to low successively was soluble sugars 〉 proteins 〉 polysaccharides 〉 flavones. Contents of tannins and total phenols seemed to have little influence.

Prediction of Dengue Fever Outbreak Based on Case Household Locations in Southern Taiwan

Dengue fever is a serious vector-borne infectious viral disease found worldwide. Dengue fever forecasting is in demand in the front line of epidemic prevention and control work. The goal of this study was to evaluate the feasibility of using only notified case home locations to predict new cases and village locations. We took the Tainan City dengue fever outbreak in 2015 as the research subject and divided it into 5 periods according to epidemic temporal change. In each period, the predicted variable was the location of the reported cases in the previous week, the previous 2 weeks, and the previous 3 weeks. In addition, we used 21 preset distances with a radius of 0 to 2000 m at intervals of 100 m to predict the villages where new cases would appear. Accounting for 4 predictors of a confusion matrix at each preset distance, these predictors were used in calculations using the Matthews correlation coefficient (MCC) as the basis for model evaluation. In the lag phase, the optimal predictor was within 1700 m in the 3-week forecast. In the exponential phase, the optimal predictor was within 300 m in the 1-week forecast. In the stationary phase, the optimal predictor was within 100 m in the 3-week forecast and within 200 m in the 2-week forecast. In the early decline phase, the optimal predictor was 0 m in the 1-week forecast. In the late decline phase, the optimal predictor was within 200 m in the 2-week forecast. According to MCC calculations and comparisons among the 5 studied periods, the best MCC score was in the exponential phase, a stage of rapid increase of new cases. The results of this study suggest that the epidemic forecasting model based on the location of notified cases has a high reference value for epidemic control and prevention.

Parameter Sensitivity and Qualitative Analysis of Dynamics of Ovarian Tumor Growth Model with Treatment Strategy

In this paper, we are interested to find the most sensitive parameter, local and global stability of ovarian tumor growth model. For sensitivity analysis, we use Latin Hypercube Sampling (LHS) method to generate sample points and Partial Rank Correlation Coefficient (PRCC) method, uses those sample points to find out which parameters are important for the model. Based on our findings, we suggest some treatment strategies. We investigate the sensitivity of the parameters for tumor volume, y, cell nutrient density, Q and maximum tumor size, ymax. We also use Scatter Plot method using LHS samples to show the consistency of the results obtained by using PRCC. Moreover, we discuss the qualitative analysis of ovarian tumor growth model investigating the local and global stability.

Integrated Expert Analytical System for Assessment of Oil-and-Gas Saturation of Strata

The present research deals with the problem of development of an integrated expert-analytical system for optimum selection of calculated oil-field-geophysical parameters of oil and gas deposits with the purpose of increasing the accuracy of assessment of the reserves of oil and gas deposits. The purpose of the system is to make current adequate decisions on determining of oil-and-gas saturation of strata and future identification of the most significant methods for that, with these methods forming the foundation of knowledge bases for oil-and-gas deposits of the Apsheron peninsula of Azerbaijan. The system architecture allows for expanding the system with its subsequent transformation into a cluster of expert-analytical systems. A logical model of the proposed system is presented. The paper contains a detailed description of the mechanism of operation of the system as a whole and of its individual blocks. Mathematical and formal-logical bases of the intelligent system are explained. The system is equipped with a tool for dynamic statistical analysis of decisions made by it, with representation of the results in real-time mode. The results of the system testing on specific oil-and-gas deposit of the Apsheron peninsula of Azerbaijan in 2013 are given.

Spatial-temporal Variation of Air Temperature for the Recent 40 Years in Lhasa

It was all known that Lhasa went through a high temperature of 30.8℃in late June 2019,which hit record highs.To better understand the reasons,based on observations recorded at automatic weather stations in Lhasa,the characteristics of temperature variation at multiple time scales were studied using the linear trend method,Mann-Kendall mutation test,Morlet wavelet analysis,R/S analysis and so on.The results showed that:(a)the annual mean temperature(AMT)is rising at a rate of 0.5℃/10 a,and the average temperature for different seasons also increased significantly,especially in winter.(b)Although there was an intersection in 1995,it was found that AMT,did not pass the reliability test of significance levelα=0.05,this means there are no abrupt changes for AMT,the values are 7.97 and 9.15℃respectively before and after the intersection point.(c)AMT has a periodic oscillation for 18-25 and 25-32 a based on a mass of data and the wavelet variance diagrams in Lhasa.AMT has a main cycle of 28 a,cyclic patterns of temperature changes in spring,summer and autumn is similar to AMT,but it is relatively complex in winter.(d)The Hurst index of AMT and different seasons demonstrates that the temperature is likely to continue to rise in the future in Lhasa.

Correlation analysis and causality test between Ludong-Huanghai block and South Japan

In this paper, we make a comparative analysis and correlation test for the seismic activities in the South Japan and the Ludong-Huanghai block （a secondary tectonic unit in the North China） and approach the relationship between the energy release processes of these two areas by using co-integration analysis and Granger causality test for the time series of random variables. The results show that the seismic activities in these two areas are correlative and synchronous to a certain extent, and their release series of cumulative strain energy are contemporaneously correlative. Both energy series are first-order difference stationary processes and there is secular and steady co-integration between them. We make a positive analysis on the first-order difference energy series through Granger causality test based on vector error correction （VEC） model and find there is unilateral Granger causality and prominent co-integration between the two energy release processes.