scholarly journals The application of statistical methods to indicators of triticale photosynthesis under chloride stress

2020 ◽  
Vol 61 (3) ◽  
pp. 105-111
Author(s):  
Viktor V. Ivanishchev ◽  
Keyword(s):  
Hydrogen Peroxide ◽  
Lipid Peroxidation ◽  
Cluster Analysis ◽  
Electron Transport ◽  
Carbonic Anhydrase ◽  
Correlation Coefficients ◽  
Analysis Method ◽  
Experimental Conditions ◽  
Close Relationship ◽  
Cluster Analysis Method

The paper presents the results of a study of the relationship between photosynthesis indicators of triticale shoots under conditions of short-term NaCl stress. For this, the principal component analysis (PCA) and cluster analysis were used. It was established that the characteristics of photosynthesis form three groups: (1) carbonic anhydrase and electron transport, (2) chlorophyll and carotenoids, (3) photosynthesis intensity. The correlation coefficients between the indicators are 0.85 and 0.97 for the first two groups, respectively. Using the cluster analysis method has led to similar results. Further analysis of the data with PCA for the characteristics of photosynthesis and oxidative stress showed that the indicators can also be combined into three groups: (1) stomatal conductivity, chlorine and sodium, (2) chlorophyll and carotenoids, (3) photosynthesis intensity and Rubisko activity. For physiological and biochemical parameters, the highest correlation coefficients were as follows: electron transport – lipid peroxidation (0.98), chlorophyll – carotenoids (0.97), carbonic anhydrase – LPO (0.86), carbonic anhydrase – electron transport (0.85), electron transport – hydrogen peroxide (0.63), LPO – hydrogen peroxide (0.62). The cluster analysis method suggests a close relationship between electron transport and the accumulation of hydrogen peroxide, carbonic anhydrase and lipid peroxidation. Based on the results obtained, conclusions are drawn about the close relationship between the activity of carbonic anhydrase and electron transport, and the magnitude of lipid peroxidation under experimental conditions, which, however, requires direct experimental evidence of such connections.

scholarly journals Detection of drought events in Greece using daily precipitation

2010 ◽  
Vol 41 (2) ◽  
pp. 126-133 ◽  
Author(s):  
N. Kalamaras ◽  
H. Michalopoulou ◽  
H. R. Byun
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Daily Precipitation ◽  
Hierarchical Cluster ◽  
Seasonal Distribution ◽  
Drought Severity ◽  
Severe Drought ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
Insight Into

In this study a method proposed by Byun & Wilhite, which estimates drought severity and duration using daily precipitation values, is applied to data from stations at different locations in Greece. Subsequently, a series of indices is calculated to facilitate the detection of drought events at these sites. The results provide insight into the trend of drought severity in the region. In addition, the seasonal distribution of days with moderate and severe drought is examined. Finally, the Hierarchical Cluster Analysis method is used to identify sites with similar drought features.

Cluster Analysis Using N-gram Statistics for Daihinmin Programs and Performance Evaluations

2016 ◽  
Vol 4 (2) ◽  
pp. 33-57 ◽  
Author(s):  
Seiya Okubo ◽  
Takaaki Ayabe ◽  
Tetsuro Nishino
Keyword(s):  
Cluster Analysis ◽  
Imperfect Information ◽  
Computer Game ◽  
Computer Experiments ◽  
Performance Evaluations ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
N Gram ◽  
And Performance ◽  
Feature Values

In this paper, the authors elucidate the characteristics of the computer game Daihinmin, a popular Japanese card game that uses imperfect information. They first propose a method to extract feature values using n-gram statistics and a cluster analysis method that employs feature values. By representing the program card hands as several symbols, and the order of hands as simplified symbol strings, they obtain data that is suitable for feature extraction. The authors then evaluate the effectiveness of the proposed method through computer experiments. In these experiments, they apply their method to ten programs that were used in the UEC Computer Daihinmin Convention. In addition, the authors evaluate the robustness of the proposed method and apply it to recent programs. Finally, they show that their proposed method can successfully cluster Daihinmin programs with high probability.

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