Temat: multidimensional statistical method - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Badanie zmienności fenotypowej genotypów lnu oleistego (Linum usitatissimum L.) za pomocą statystycznych metod wielowymiarowych
Investigation of phenotypic distance of genotypes of oilseed flax (Linum usitatissimum L.) using multivariate statistical methods
Autorzy:: Bocianowski, J.
Silska, G.
Praczyk, M.
Powiązania:: https://bibliotekanauki.pl/articles/832680.pdf
Data publikacji:: 2013
Wydawca:: Instytut Hodowli i Aklimatyzacji Roślin
Tematy:: len oleisty
Linum usitatissimum
genotyp
zmiennosc fenotypowa
metody statystyczne
metody statystyczne wielowymiarowe
wielowymiarowa analiza wariancji
odleglosc Mahalanobisa
linseed
genotype
phenotypic variability
statistical method
multidimensional statistical method
variance analysis
Mahalanobis distance
Opis:: Celem badań była wielocechowa charakterystyka zmienności dziesięciu cech osiemnastu geno-typów lnu uprawnego (Linum usitatissimum L.). Zastosowano metodę analizy zmiennych kanonicznych opartą na modelu wielowymiarowej analizy wariancji dla obserwowanych cech. Materiałem do badań było: dwanaście rodów hodowlanych i sześć odmian lnu uprawnego. Doświadczenia założono w układzie całkowicie losowym w trzech powtórzeniach, w dwóch sezonach wegetacji (2008, 2009) w Zakładzie Doświadczalnym IWNiRZ w Wojciechowie. Rody i odmiany oceniono pod względem dziesięciu cech ilościowych. Przeprowadzona wielowymiarowa analiza wariancji wykazała istotne zróżnicowanie genotypów, jak również zróżnicowanie ekspresji genotypów w latach. Zastosowana metoda zmiennych kanonicznych wykazała różne zachowanie genotypów w poszczególnych latach. Stwierdzono istotną statystycznie korelację odległości Mahalanobisa otrzymanych w obu latach: r = 0,5942, P < 0,001. Genotypy charakteryzujące się największym zróżnicowaniem genetycznym mogą posłużyć do tworzenia odrębnych pul genetycznych lnu uprawnego.
The aim of this study was the multivariate characteristics of the variability of ten traits of eighteen flax objects. The multivariate analysis of variance and canonical variate analysis were used. The plant material consisted of 12 breeding lines and 6 registered varieties of flax The experiments were carried out using completely randomized design in three replications during two crop seasons of 2008 and 2009 in an experimental station of Institute of Natural Fibres & Medicinal Plants in Wojciechów. Tested genotypes of flax were evaluated in respect to ten quantitative traits. Multivariate analysis of variance showed significant differences of the investigated genotypes and expression of genotypes in the years of study. The results of canonical variate analysis revealed differences between all the experimental objects in the years of study. Statistically significant correlation of Mahalanobis distance obtained in both years, r = 0.5942, P < 0.001, was found.. Genotypes having the greatest genetic diversity can be used to create a gene pool of flax.
Źródło:: Rośliny Oleiste - Oilseed Crops; 2013, 34, 2
1233-8273
Pojawia się w:: Rośliny Oleiste - Oilseed Crops
Dostawca treści:: Biblioteka Nauki

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Tytuł:: Application of the observational tunnels method to select a set of features sufficient to identify a type of coal
Autorzy:: Jamroz, D.
Niedoba, T.
Powiązania:: https://bibliotekanauki.pl/articles/109317.pdf
Data publikacji:: 2014
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:: multidimensional statistical analysis
observational tunnels method
coal
image visualization
energetic materials
Opis:: Coal is a material which has many features deciding about its quality. Among them, the decisive ones are mainly ash contents, sulfur contents and combustion heat. The paper presents the investigation of coal characteristics of three selected coal types in the context of their energetic value. For this purpose samples were collected from three different Polish mines: coal types 31, 34.2 and 35 (Polish classification of coals). Each of these materials was separated into particle size fractions (9 fractions) and then into 8 density fractions by separation in heavy liquids. For each size-density fractions obtained in this way, chemical analyses were performed which allowed for determination of such features as combustion heat, sulfur contents, ash contents, volatile parts contents and analytical moisture. Altogether, seven dimensions of grained material characteristics were obtained. The data prepared in this way was subsequently analyzed for correlation with the purpose of determining significant relations between investigated features. It was stated that the most correlated coal features are density, combustion heat, ash contents and volatile parts contents. For multidimensional analysis and identification of coal type, the modern image visualization technique, the Observational Tunnels Method, was applied. After performing seven-dimensional analysis aimed at the proper recognition of coal type, it was decided to determine the minimum amount of random variables, which describe a particular material in order to identify its type. It was stated that the crucial coal identification parameter is “analytical moisture”. Due to existing correlation between individual features, three of them were selected for testing: analytical moisture, sulfur contents and volatile parts contents. On the basis of the obtained images, it was stated that it was possible to obtain a view with the data concerning each type of coal being located in other part of the space. Subsequently, it was checked if a similar result is possible when the parameter “volatile parts contents” is replaced with highly correlated parameters “combustion heat” and “ash contents”. In both cases the exchange of these variables did not produce good enough results. This can be explained by a different scale of empirical data making it impossible to obtain a clear multidimensional image for which all three types of coal would be located in other parts of space. However, it was proved that the modern graphical and computer methods can be successfully applied to identify the types of particulate materials.
Źródło:: Physicochemical Problems of Mineral Processing; 2014, 50, 1; 185-202
1643-1049
2084-4735
Pojawia się w:: Physicochemical Problems of Mineral Processing
Dostawca treści:: Biblioteka Nauki

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