Analysis of nematodes and soil-borne fungi fromAmmophila arenaria (Marram grass) in Dutch coastal foredunes by multivariate techniques

1995 ◽  
Vol 101 (2) ◽  
pp. 149-162 ◽  
Author(s):  
P. C. E. M. de Rooij-van der Goes ◽  
W. H. van der Putten ◽  
C. van Dijk
Keyword(s):  
Multivariate Techniques ◽  
Marram Grass ◽  
Coastal Foredunes ◽  
Soil Borne Fungi

Vegetation development in coastal foredunes in relation to methods of establishing marram grass (Ammophila arenaria)

1997 ◽  
Vol 3 (2) ◽  
pp. 179-190 ◽  
Author(s):  
D. van der Laan ◽  
O. F. R. van Tongeren ◽  
W. H. van der Putten ◽  
G. Veenbaas
Keyword(s):  
Ammophila Arenaria ◽  
Vegetation Development ◽  
Marram Grass ◽  
Coastal Foredunes

Effects of sand movement by wind on nematodes and soil-borne fungi in coastal foredunes

1997 ◽  
Vol 3 (1) ◽  
pp. 133-142
Author(s):  
P. C. E. M. Goes ◽  
C. Dijk ◽  
W. H. Putten ◽  
P. D. Jungerius
Keyword(s):  
Sand Movement ◽  
Coastal Foredunes ◽  
Soil Borne Fungi

Effects of sand movement by wind on nematodes and soil-borne fungi in coastal foredunes

1997 ◽  
Vol 3 (2) ◽  
pp. 133-142 ◽  
Author(s):  
P. C. E. M. de Rooij-van der Goes ◽  
C. van Dijk ◽  
W. H. van der Putten ◽  
P. D. Jungerius
Keyword(s):  
Sand Movement ◽  
Coastal Foredunes ◽  
Soil Borne Fungi

A Short Review on Structural Equation Modeling: Applications and Future Research Directions

2015 ◽  
Vol 4 (3) ◽  
Author(s):  
Surajit Bag
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
First Generation ◽  
Analytical Techniques ◽  
Short Review ◽  
Equation Modeling ◽  
Future Research ◽  
Multivariate Techniques ◽  
Statistical Efficiency ◽  
Future Research Directions

The application of multivariate techniques is mainly to expand the researchers explanatory ability and statistical efficiency. The first generation analytical techniques share a common limitation i.e. each technique can examine only a single relationship at a time. Structural Equation Modeling, an extension of several multivariate techniques is the technique popularly used today can examine a series of dependence relationships simultaneously. The purpose of this study is to provide a short review on Structural Equation Modeling (SEM) being used in social sciences research. A comprehensive literature review of article appearing in top journals is conducted in order to identify how often SEM theory is used. Also the key SEM steps have been provided offering potential researchers with a theoretical supported systematic approach that simplify the multiple options with performing SEM.

scholarly journals Visualizing Profiles of Large Datasets of Weighted and Mixed Data

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 891
Author(s):  
Aurea Grané ◽  
Alpha A. Sow-Barry
Keyword(s):  
Multidimensional Scaling ◽  
Random Sample ◽  
Simulation Study ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Interpolation Formula ◽  
Large Datasets ◽  
Mixed Data ◽  
Multivariate Techniques ◽  
High Computational Cost

This work provides a procedure with which to construct and visualize profiles, i.e., groups of individuals with similar characteristics, for weighted and mixed data by combining two classical multivariate techniques, multidimensional scaling (MDS) and the k-prototypes clustering algorithm. The well-known drawback of classical MDS in large datasets is circumvented by selecting a small random sample of the dataset, whose individuals are clustered by means of an adapted version of the k-prototypes algorithm and mapped via classical MDS. Gower’s interpolation formula is used to project remaining individuals onto the previous configuration. In all the process, Gower’s distance is used to measure the proximity between individuals. The methodology is illustrated on a real dataset, obtained from the Survey of Health, Ageing and Retirement in Europe (SHARE), which was carried out in 19 countries and represents over 124 million aged individuals in Europe. The performance of the method was evaluated through a simulation study, whose results point out that the new proposal solves the high computational cost of the classical MDS with low error.

scholarly journals Parasites of the head of Scomber colias (Osteichthyes: Scombridae) from the western Mediterranean Sea

2014 ◽  
Vol 59 (1) ◽  
Author(s):  
Salvatore Mele ◽  
Maria Pennino ◽  
Maria Piras ◽  
José Bellido ◽  
Giovanni Garippa ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Eastern Mediterranean ◽  
Western Mediterranean ◽  
Parasite Fauna ◽  
Point Of View ◽  
Published Data ◽  
Multivariate Techniques ◽  
Western Mediterranean Sea ◽  
Scomber Colias ◽  
Non Parametric

AbstractThe metazoan parasite assemblage of the head of 30 specimens of the Atlantic chub mackerel (Scomber colias) from the western Mediterranean Sea was analysed. Eight species of parasites were found, four mazocraeid monogeneans: Grubea cochlear (prevalence = 10%), Kuhnia scombercolias (59%), K. scombri (52%), Pseudokuhnia minor (86%); three didymozoid trematodes: Nematobothrium cf. faciale (21%), N. filiforme (41%), N. scombri (7%); and one laerneopodid copepod: Clavelissa scombri (7%). Results were compared with previously published data from 14 localities of the eastern Mediterranean Sea and the Atlantic Ocean, using non-parametric univariate and multivariate analyses, and the whole parasite fauna of S. colias was compared with that of the congeners (S. australasicus, S. japonicus and S. scombrus). Parasites showed to reflect the biogeographical and phylogenetic history of host. From a methodological point of view, the use of both non-parametric univariate and multivariate techniques proved to be effective tools to detect dissimilarities between parasite assemblages.

scholarly journals Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2547 ◽  
Author(s):  
Tuo Gao ◽  
Yongchen Wang ◽  
Chengwu Zhang ◽  
Zachariah A. Pittman ◽  
Alexandra M. Oliveira ◽  
...  
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Single Type ◽  
Multivariate Techniques ◽  
Integrated Sensor ◽  
Resistance Change ◽  
Linear Discriminant ◽  
Lift Off

Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (ΔR/R0), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products.

scholarly journals Soil-borne fungi influence seed germination and mortality, with implications for coexistence of desert winter annual plants

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0224417 ◽  
Author(s):  
Yue M. Li ◽  
Justin P. Shaffer ◽  
Brenna Hall ◽  
Hongseok Ko
Keyword(s):  
Seed Germination ◽  
Annual Plants ◽  
Winter Annual ◽  
Soil Borne Fungi
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