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Based on the survey data of hydropower immigrants in Yantan, Guangxi, the combination empowerment method and Topsis model are used to quantify the sustainable livelihoods composed of three abilities, and variance analysis is used to explore the impact of follow-up supports and their specific measures on the sustainable livelihoods for the overall and three types of immigrant households in order to evaluate the effectiveness. The results show that the overall sustainable livelihood level of immigrant households is lower than the medium level, and the sustainable livelihood level of non-poor households, general poverty alleviation households and low-income poverty alleviation households decreases successively. Comprehensive assistance has significantly improved the sustainable livelihoods of overall immigrant households. The involvement of specific measures has different effects on the sustainable livelihoods for different immigrant households, among which education and training have significant effects on the improvement of the sustainable livelihoods of three types of immigrant households. The number of measures involved has a significant impact on the sustainable livelihoods of non-poor households and low-income poverty-stricken households, but has no meaningful impact on ordinary poverty-stricken households. This paper expands the research methods in the field of of evaluating the effectiveness of follow-up support for immigrants, which can provide a basis for accurately and efficiently promoting immigrants support. [ABSTRACT FROM AUTHOR]

Copyright of Sichuan Mental Health is the property of Sichuan Mental Health Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this paper was to introduce the orthogonal design and its quantitative data analysis of variance and the SAS implementation. From the perspective of degrees of freedom， the orthogonal design could be divided into the saturated orthogonal design and the unsaturated orthogonal design. From the perspective of the number of factor levels， the orthogonal design could be divided into the same level orthogonal design and the mixed level orthogonal design. From the perspective of normalization， the orthogonal design could also be divided into the standard orthogonal design and the non-standard orthogonal design. Quantitative data from the standard orthogonal designs could be analyzed by the conventional methods， while quantitative data from the non-standard orthogonal designs needed to be improved. Based on three examples， this paper realized the quantitative data analysis of variance with the standard orthogonal design without repeated experiments and with repeated experiments by means of the SAS software.

The purpose of this paper was to introduce the nested design and its quantitative data analysis of variance and the SAS implementation. If one of the following two characteristics existed in a specific experimental study， a nested design could be considered to arrange the experiment. Firstly， there was a nested relationship between factors in natural attributes. Secondly， with professional knowledge as the basis， the impact of each factor on the quantitative observation results was divided into primary and secondary. The first feature mentioned above meant that the factors related to the subjects had the conditions for grouping and regrouping. The second feature mentioned above meant that the status of each factor was unequal. In the variance analysis of quantitative data， the calculation formulas of variable error mean square was required to use. Based on four examples and with the help of the SAS software， this paper implemented the univariate analysis of variance for the quantitative data of the nested design， and gave the detailed explanations for the output results of SAS software.

The purpose of the paper was to introduce the calculation formulas and the SAS implementation of the analysis of variance for the quantitative data of the crossover design. In the calculation， three test statistics were involved， namely Ftreatment， Fstage and Findividual. They were three test statistics used to evaluate the statistical significance of the effect of the treatment factor， the stage factor， and the individual factor on the quantitative outcome variable， respectively. In general， it was assumed that there was no or negligible interaction among the three factors in a crossover design， so there was no need to evaluate whether the interaction term was statistically significant. With the help of SAS software， this paper conducted the univariate analysis of variance for the quantitative data of crossover designs for three examples of 2×2 crossover design， 3×3 crossover design and three-stage crossover design， and presented the calculation results and drew the statistical and professional conclusions.

The purpose of this paper was to introduce the model， calculation formulas and the SAS implementation of the univariate analysis of variance for the quantitative data with randomized complete block design. In the calculation， two test statistics were involved， namely FA and FB. Among them， the subscript "A" represented the experimental factor， and the subscript "B" represented the block factor （i.e.， the important non-experimental factor）. In general， it was assumed that there was no or negligible interaction between the two factors in a randomized block design， so there was no need to assess whether the interaction term was statistically significant. Therefore， it was not necessary to do repeated experiments under each combination of two factors. With the help of SAS software， this paper conducted the analysis of variance for the quantitative data with randomized complete block design for two instances without and with repeated experiments， gave the calculation results， and made the statistical and professional conclusions.

The purpose of this paper was to introduce the model， calculation formulas and the SAS implementation of the analysis of variance for the quantitative data with balanced incomplete block design. In the calculation， two test statistics were involved， namely FA and FB. Among them， the subscript "A" represented the experimental factor， and the subscript "B" represented the block factor B （i.e.， the important non-experimental factor）. In general， it was assumed that there was no or negligible interaction between the two factors in a balanced incomplete block design， so there was no need to evaluate whether the interaction term was statistically significant. Therefore， it was not necessary to do repeated experiments under each combination of two factors. With the help of SAS software， this paper conducted the analysis of variance for the quantitative data with balanced incomplete block design on two examples， and presented the calculation results and made the statistical and professional conclusions.

The purpose of this paper was to introduce the prerequisites， basic ideas， calculation formulas and the SAS implementation of a single-factor multi-level design quantitative data univariate analysis of variance. The prerequisites included the independence， normality and homogeneity of variance. The core of the basic idea was the decomposition of the sum of squares of the total deviations for the mean. The test statistic F was constructed through the between-group mean square divided by the within-group （or called error） mean square. The result of analysis of variance was a general evaluation of the difference among all means of a factor with the whole levels. When it was found that the difference among all means of the factor was statistically significant， a specific approach needed to be adopted for the multiple comparisons about the multiple means of the factor. With the help of the SAS software， the paper performed the analysis of variances for two examples， and used three approaches to make the multiple comparisons among all means of a factor in one of the examples.

The purpose of the paper was to introduce the test for homoscedasticity and the SAS implementation. The test of homogeneity of variance could be divided into the following three categories， ①analysis of variance directly based on comparison of variances， ②analysis of variance based on mean comparison was adopted for the new data from the variable transformation of the original data， ③the method of the χ2 test was used to analyze the quantitative raw data which followed the normal distribution. In the first category， a test statistic that followed the F distribution was constructed directly based on the variance ratio of the two samples. In the second category， there were a variety of different variable transformation approaches for the original data， and the new data after the transformation， which was viewed as the univariate quantitative data collected from a single-factor with multi-level design， was analyzed by using one-way ANOVA. In the third category， the χ2 test statistic was constructed for quantitative data that followed the normal distribution. The paper was based on SAS software to test the homogeneity of variances of three examples， and explained the output results.

The purpose of the paper was to introduce the multiple comparison method among multiple means and the SAS implementation. The multiple comparison approaches could be subdivided into the pairwise comparisons， the comparisons of all treatment groups with a control group， the comparisons of the mean of each treatment group with the average of all groups， the approximate and simulation-based approach， the multi-stage testing and Bayesian method. Except for the Bayesian approach， the difference between other multiple comparison methods lied in the types of error that were controlled. Error types could be roughly divided into the following three categories， the comparisonwise error rate， the experimentwise error rate and the maximum experimentwise error rate. The multiple comparison methods constructed based on the control of different error rates were not all the same in the strength of inference to draw conclusions. This paper used the SAS software to analyze the examples and explained the output results.

21 jet fuel samples collected from different refineries around different regions in China were took as the research objects. Comprehensive two-dimensional gas chromatography-mass spectrometry was used for qualitative and semi-quantitative analysis. A comprehensive two-dimensional gas chromatography coupled with single quadrupole mass spectrometry ( GC X GC-qMS) equipped with polar-non polar column system can significantly increase the peak capacity, which had obvious advantages in analyzing jet fuel with complex components. Based on percentage response ( the percentage of each peak volume to all peak volumes), the method of F-ratio (the ratio of the average square error between the dataset to the average square error within the dataset) in analysis of variance CANOVA) was used to select feature components of jet fuel samples. The results showed that monocyclic aromatic hydrocarbons, indane series, tetralin series with number of carbon atoms in the range of C10 -C12 varied greatly between different groups of jet fuel samples. Different methods for determining the threshold of F -ratio were compared, and the Z-score method was finally used to select experimental data, in which case a total of 16 feature components with Z-score were greater than 3 based on the percentage response F-ratio of the samples. Ultimately, combined with the extracted ion current chromatogram (EIC) with m/ z 117-118 or m/z 131-132, the traditional one-dimensional gas chromatography-mass spectrometry (GC-MS) and GCX GC- qMS were compared with effectiveness of separation and analysis of the feature components. The detected peaks were obviously more than t he former, and t he extracted ions in this mass-to-charge ratio range had a certain distinguishing effect on jet fuel samples. This paper provided a method for comprehensive analysis of jet fuel components, and also provided a reference for selecting useful data while preserving t he features of t he largest difference between groups, reducing the large amount of experimental data generated in GCXGC-qMS. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on variance theory and coal sampling basic formula, this paper analyzed main theoretical problems on coal sampling and derived their relationships quantitatively. In addition, the paper provides a clear guidance to coal sampling practice, which complements and enriches the coal sampling theory. The problems discussed include: lot precision estimation for sub-lots with the unequal mass, primary increment variance estimation under different mass of lots, coal mass on the influence of the sampling precision, the influence of the number of sub-lots on the sampling precision under the condition of constant number of increments taken from a lot, the relationship of the sampling precision with increment numbers for the same coal, increment numbers on the influence of the sampling precision for duplicate sampling, and the estimation of sampling precision for replicate sampling, etc. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to introduce the fractional factorial design and its quantitative data analysis of variance and the SAS implementation. The fractional factorial designs were very similar to the factorial designs and the orthogonal designs， but they had some differences. The fractional factorial design required significantly fewer combinations of levels than the factorial design of the same size， and even saved sample size than the orthogonal design of the same size. In general， the precision of the results obtained by a fractional factorial design was lower than an orthogonal design and much lower than a factorial design. The fractional factorial design was suitable for the trial tests with many experimental factors， and its main purpose was to explore experimental factors that had a greater impact on the quantitative experimental results. When performing ANOVA and regression analysis on quantitative data with a fractional factorial design， it should be clear which factors or interactions had confounded effects.

The purpose of this paper was to introduce the calculation formulas and the SAS implementation of the analysis of variance of the univariate quantitative data with the Latin square design. The Latin square design could be divided into two categories： the general Latin square design and the Greek Latin square design. The former could be used for the experimental situation with one experimental factor and two block factors， the latter could be used for the experimental situation with two experimental factors and two block factors. In fact， Latin square designs could be further subdivided by whether or not the repeated experiments were performed and whether the block factor was a single individual type. Generally speaking， in addition to satisfying the requirements of "independence， normality and homogeneity of variance"， the interaction between all factors was required to be non-existent or negligible when performing an analysis of variance on the quantitative data with Latin square design. When the quantitative data did not meet the preconditions mentioned above， it was recommended to use a mixed-effects model to build the model and solve it， or to solve the estimated values of the parameters in the ANOVA model based on the generalized estimating equation method.

Aim: The present study was conducted to compare the level of anxiety and depression of satellite project staff and accommodation in companies located in Kharg Island. Method: The present study was a causal-comparative study. The statistical population of the study included all employees of the satellite project and residents of companies located on Kharg Island in 2021. Using the available method, 104 people (52 from the satellite plan and 52 from the island) were selected. Data were collected using the Anxiety-Depression-Stress Questionnaire (DASS-42). Research data were analyzed using descriptive statistics, parametric statistical defaults and multivariate analysis of variance with SPSS-26. Results: The results showed that the mean scores of anxiety (Eta=0.23, P=0.000, F=31.37) and depression (Eta=0.17, P=0.000, F=21.39) were significantly higher in the resident staff group than the satellite plan staff and there was a statistically significant difference between the two groups. Conclusion: The results showed that employees living in companies based on Kharg Island experienced higher levels of anxiety and depression than employees of the satellite project. In this regard, island managers are advised to use psychologists to increase the psychological performance of employees to increase the productivity of the workforce. [ABSTRACT FROM AUTHOR]