A statistical classification of Mediterranean species based on their flammability components

2001 ◽  
Vol 10 (2) ◽  
pp. 113 ◽  
Author(s):  
A. P. Dimitrakopoulos
Keyword(s):  
Particle Density ◽  
Heat Content ◽  
Volume Ratio ◽  
Hierarchical Cluster ◽  
Individual Species ◽  
Statistical Classification ◽  
Mediterranean Species ◽  
Multivariate Statistical ◽  
Good Agreement

Eight dominant Mediterranean species were classified into similar groups according to their expected flammability, by applying multivariate statistical methods (Hierarchical Cluster Analysis and Canonical Discriminant Analysis) on the values of their most significant pyric properties (heat content, total and mineral ash content, surface area-to-volume ratio, particle density). Based on the statistical classification, meaningful explanations of the flammability differences among individual species were deduced. The results were in good agreement with similar rankings based on laboratory tests. Further validation may render the method widely applicable for the assessment of species potential flammability without laboratory flammability tests.

scholarly journals Geochemical classification of groundwater using multivariate statistical analysis in Latvia

2016 ◽  
Vol 47 (4) ◽  
pp. 799-813 ◽  
Author(s):  
Inga Retike ◽  
Andis Kalvans ◽  
Konrads Popovs ◽  
Janis Bikse ◽  
Alise Babre ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Methods ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Ion Chemistry ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Gypsum Dissolution ◽  
Major Ion

Multivariate statistical methods – principal component analysis (PCA) and hierarchical cluster analysis (HCA) – are applied to identify geochemically distinct groundwater groups in the territory of Latvia. The main processes observed to be responsible for groundwater chemical composition are carbonate and gypsum dissolution, fresh and saltwater mixing and ion exchange. On the basis of major ion concentrations, eight clusters (C1–C8) are identified. C6 is interpreted as recharge water not in equilibrium with most sediment forming minerals. Water table aquifers affected by diffuse agricultural influences are found in C3. Groundwater in C4 reflects brine or seawater admixture and gypsum dissolution in C5. C7 and C2 belong to typical bicarbonate groundwater resulting from calcite and dolomite weathering. Extremely low Cl− and SO42− are observed in C8 and described as pre-industrial groundwater or a solely carbonate weathering result. Finally, C1 seems to be a poorly defined subgroup resulting from mixing between other groups. This research demonstrates the validity of applying multivariate statistical methods (PCA and HCA) on major ion chemistry to distribute characteristic trace elements in each cluster even when incomplete records of trace elements are present.

Pyric properties of some dominant Mediterranean vegetation species

2001 ◽  
Vol 10 (1) ◽  
pp. 23 ◽  
Author(s):  
A. P. Dimitrakopoulos ◽  
P.I. Panov
Keyword(s):  
Surface Area ◽  
Mediterranean Basin ◽  
Particle Density ◽  
Heat Content ◽  
Volume Ratio ◽  
Ash Content ◽  
Mediterranean Vegetation ◽  
The Mediterranean ◽  
Vegetation Species ◽  
The Mediterranean Basin

The chemical and physical pyric properties of several species, dominant in the Mediterranean Basin, are quantified and compared with each other. Heat content and total and mineral (silica-free) ash content are measured and analysed for 13 species, while surface area-to-volume ratio and particle density are measured for 8 species.

scholarly journals Peripheral areas and their distinctive characteristics: The case of Hungary

2021 ◽  
Vol 29 (3) ◽  
pp. 217-230
Author(s):  
János Pénzes ◽  
Gábor Demeter
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Development Policies ◽  
Relevant Variables ◽  
Peripheral Areas

Abstract The delimitation and classification of peripheral settlements using multivariate statistical methods is presented in this article, with a case study of Hungary. A combination of four different methods provided the basis for the delimitation of settlements defined as peripheral. As significant overlapping was detected between the results of the different methods, peripheries – more than one-fifth of the Hungarian settlements – were identified in a common set of the results. The independence of the results from the applied methods points to the fact that peripherisation is multi-faceted, and the peripheries of Hungary are stable and well-discernible from other regions. After the identification of peripheral areas, we classified these settlements into groups based on their specific features. Multiple steps specifying the relevant variables resulted in selecting the most appropriate 10 indicators and these served as the basis for a hierarchical cluster analysis, through which 7 clusters (types of peripheries) were identified. Five of them comprised enough cases to detect the most important dimensions and specific features of the backwardness of these groups. These clusters demonstrated a spatial pattern and their socioeconomic and infrastructural features highlighted considerable disparities. These differences should be taken into consideration when development policies are applied at regional levels or below.

Differentiation between Cistus L. (Sub-) Species (Cistaceae) Using NMR Metabolic Fingerprinting

Planta Medica ◽  
2020 ◽  
Vol 86 (15) ◽  
pp. 1148-1155
Author(s):  
Simon Moosmang ◽  
Sonja Sturm ◽  
Johannes Novak ◽  
Brigitte Lukas ◽  
Hermann Stuppner
Keyword(s):  
Influencing Factors ◽  
Biological Effects ◽  
Individual Species ◽  
Multivariate Statistical ◽  
Robust Model ◽  
Morphological Criteria ◽  
Broad Array ◽  
The Individual ◽  
Specific Species

AbstractThe genus Cistus is taxonomically complex, as taxonomic classification of individual species based on morphological criteria is often difficult and ambiguous. However, specific species contain valuable natural products, especially terpenoids and polyphenols, which exert various biological effects and might therefore be used for treatment of a broad array of disorders. Hence, a fast and reliable method for clear identification of different Cistus (sub-) species is required. Approaches for analysis of secondary metabolite profiles, e.g., with NMR, might remedy the challenging classification of Cistus (sub-) species and help to identify specific markers for differentiation between them. In the present study, 678 samples from wild-growing Cistus populations, including 7 species and 6 subspecies/varieties thereof, were collected in 3 years from populations in 11 countries all over the Mediterranean basin. Samples were extracted with buffered aqueous methanol and analysed with NMR. From the resulting 1D-1H-NOESY and J-Res profile spectra, marker signals or spectral regions for the individual (sub-) species were identified with multivariate statistical tools. By examining the NMR profiles of these extracts, we were able to identify discriminators and specific markers for the investigated Cistus (sub-) species. Various influencing factors, like (sub-) species, wild harvestings of different populations from several countries, numerous collection sites, different years, and cultivation in greenhouses have been considered in this work. As the here identified markers are independent from these influencing factors, the results can be considered a robust model and might be used for future differentiation between Cistus (sub-) species.

Differential activation and classification of cutaneous afferents in the rat

1993 ◽  
Vol 70 (6) ◽  
pp. 2411-2424 ◽  
Author(s):  
J. W. Leem ◽  
W. D. Willis ◽  
S. C. Weller ◽  
J. M. Chung
Keyword(s):  
Hierarchical Cluster ◽  
Cluster Solution ◽  
Response Patterns ◽  
Sensory Receptors ◽  
Multivariate Statistical ◽  
Stimulus Set ◽  
Somatosensory Neurons ◽  
C 30 ◽  
Afferent Unit

1. A total of 312 cutaneous afferent units identified in the rat foot as belonging to one of nine major types of sensory receptors were included in the present study. A natural stimulus set was defined to differentiate optimally among those receptor types according to the distinguishing response patterns that it produced. It included air puffs, 30- and 300-Hz sinusoids, 200-mN force indentation of the skin, 1.2- and 6-N compressions of a skin fold, cooling the skin by 5 and 20 degrees C, warming by 5 degrees C, and heating by 15 degrees C. 2. The responses to predefined stimuli of 188 units were subjected to multivariate statistical analyses. The responses of an individual unit were measured as the number of impulses evoked by 10 stimuli, each lasting 10 s. Additionally, the number of impulses occurring for 5 s after withdrawal of a 200-mN indentation (1 of the 10 stimuli) was counted. 3. In discriminant analysis, the 11 stimulus variables predicted fairly correctly the grouping of afferent units into nine predetermined receptor categories (175 of 188, 93.1%), indicating a powerful ability to discriminate among different receptor types. Using hierarchical cluster analysis, afferent unit data described by 11 variables were divided into clusters that well represented prior receptor categories (170 of 188, 90.4%), suggesting the reliable application of this procedure to the classification of newly recorded cutaneous sensory receptors. 4. Eleven variables were then reduced to 7 on the basis of the results of factor analysis (95% of variance accounted for). The seven variables corresponded to 1.2-N compression, heating the skin by 15 degrees C, cooling the skin by 20 degrees C, 30- and 300-Hz sinusoids, withdrawal of a 200-mN indentation, and air puffs. 5. The seven selected variables correctly assigned afferent units into five modality-based categories in the discriminant solution (177 of 188, 94.1%). In the cluster solution, afferent units described by the seven selected variables were divided into clusters, most of whose members were modality specific (176 of 188, 93.6%). 6. The results indicate that cutaneous receptors can be divided into modality-specific groups according to similarities in their responses to seven stimulus variables. It is proposed that the stimulus set developed here and multivariate statistical methods can be used as powerful tools for the functional classification of central somatosensory neurons.

Dioxin, dioxin-like PCBS and organotin compounds in the river Elbe and the Hamburg harbour: identification of sources

1998 ◽  
Vol 37 (6-7) ◽  
pp. 207-215 ◽  
Author(s):  
Rainer Götz ◽  
Bernd Steiner ◽  
Susanne Sievers ◽  
Peter Friesel ◽  
Klaus Roch ◽  
...  
Keyword(s):  
Surface Waters ◽  
Hierarchical Cluster ◽  
Chemical Production ◽  
Multivariate Statistical ◽  
Multivariate Statistical Method ◽  
Flood Plains ◽  
River Elbe ◽  
Thermal Origin ◽  
Dioxin Contamination

Using neural networks (in this case the Kohonen network) and a multivariate statistical method - the hierarchical cluster analysis -, a classification of dioxin data has been carried out. A principal conclusion, which can be drawn, is that a significant source of dioxin in the river Elbe, Hamburg harbour, the soils of the flood plains of the river Elbe and in soils originating from dredging materials, has been shown to originate from the dioxin contaminated region of Bitterfeld. The results indicate that the dioxin contamination in the Bitterfeld region was caused partly by metallurgy processes, not just by chemical production. Furthermore, the results show that a main dioxin source responsible for the contamination of Hamburg surface waters, not influenced by the river Elbe, is of “thermal origin”. The river Elbe shows a characteristic butyltin pattern. The cause is probably a plant in Bitterfeld. The precise sources of the dioxin-like PCB are still unknown.

Multivariate statistical classification of soil spectra

1996 ◽  
Vol 57 (2) ◽  
pp. 108-118 ◽  
Author(s):  
Alicia Palacios-Orueta ◽  
Susan L. Ustin
Keyword(s):  
Statistical Classification ◽  
Multivariate Statistical
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