scholarly journals Supplemental Material: A sub-centennial-scale optically stimulated luminescence chronostratigraphy and late Holocene flood history from a temperate river confluence

2020 ◽  
Author(s):  
Ben Pears
Keyword(s):  
Statistical Analysis ◽  
Accumulation Rate ◽  
Sediment Accumulation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Sediment Accumulation Rate ◽  
Data Sets ◽  
Raw Data ◽  
Summary Sheet ◽  
Agglomerative Hierarchical Cluster Analysis

S1–S16; Figures S1 (sediment accumulation rate modeled by OxCal and Bacon) and S2 (relative moisture values between OSL and LOI analytical methods); Table S1 (OSL procedure from the Rivers Severn-Teme confluence at Powick, UK); and Data Sets S1 (raw data for the modeled calendric dates, sediment accumulation rate, and sedimentological analyses), S2 (raw and log normalized data for ITRAX XRF analysis and key elements Zr, Rb, Fe, Mn, and heavy metals illustrated in Fig. 2), S3 (individual raw data sets for each 5 cm pOSL run alongside a background sediment sample and a summary sheet of all data and replicates), S4 (raw data, log normalized data, and statistical analysis used in the agglomerative hierarchical cluster analysis illustrated in Fig, 2), S5 (calculated log data of sedimentary analyses by 50 yr period and the statistical analysis used in the principal component analysis illustrated in Fig. 3), and S6 (20 yr grouping for the sediment deposition models for the Severn-Teme confluence at Powick, Broadwas, and Buildwas and climatic datasets illustrated in Fig. 4)<br>

Supplemental Material: A sub-centennial-scale optically stimulated luminescence chronostratigraphy and late Holocene flood history from a temperate river confluence

2020 ◽  
Author(s):  
Ben Pears
Keyword(s):  
Statistical Analysis ◽  
Accumulation Rate ◽  
Sediment Accumulation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Sediment Accumulation Rate ◽  
Data Sets ◽  
Raw Data ◽  
Summary Sheet ◽  
Agglomerative Hierarchical Cluster Analysis

S1–S16; Figures S1 (sediment accumulation rate modeled by OxCal and Bacon) and S2 (relative moisture values between OSL and LOI analytical methods); Table S1 (OSL procedure from the Rivers Severn-Teme confluence at Powick, UK); and Data Sets S1 (raw data for the modeled calendric dates, sediment accumulation rate, and sedimentological analyses), S2 (raw and log normalized data for ITRAX XRF analysis and key elements Zr, Rb, Fe, Mn, and heavy metals illustrated in Fig. 2), S3 (individual raw data sets for each 5 cm pOSL run alongside a background sediment sample and a summary sheet of all data and replicates), S4 (raw data, log normalized data, and statistical analysis used in the agglomerative hierarchical cluster analysis illustrated in Fig, 2), S5 (calculated log data of sedimentary analyses by 50 yr period and the statistical analysis used in the principal component analysis illustrated in Fig. 3), and S6 (20 yr grouping for the sediment deposition models for the Severn-Teme confluence at Powick, Broadwas, and Buildwas and climatic datasets illustrated in Fig. 4)<br>

scholarly journals Supplemental Material: A sub-centennial-scale optically stimulated luminescence chronostratigraphy and late Holocene flood history from a temperate river confluence

2020 ◽  
Author(s):  
Ben Pears
Keyword(s):  
Statistical Analysis ◽  
Accumulation Rate ◽  
Sediment Accumulation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Sediment Accumulation Rate ◽  
Data Sets ◽  
Raw Data ◽  
Summary Sheet ◽  
Agglomerative Hierarchical Cluster Analysis

S1–S16; Figures S1 (sediment accumulation rate modeled by OxCal and Bacon) and S2 (relative moisture values between OSL and LOI analytical methods); Table S1 (OSL procedure from the Rivers Severn-Teme confluence at Powick, UK); and Data Sets S1 (raw data for the modeled calendric dates, sediment accumulation rate, and sedimentological analyses), S2 (raw and log normalized data for ITRAX XRF analysis and key elements Zr, Rb, Fe, Mn, and heavy metals illustrated in Fig. 2), S3 (individual raw data sets for each 5 cm pOSL run alongside a background sediment sample and a summary sheet of all data and replicates), S4 (raw data, log normalized data, and statistical analysis used in the agglomerative hierarchical cluster analysis illustrated in Fig, 2), S5 (calculated log data of sedimentary analyses by 50 yr period and the statistical analysis used in the principal component analysis illustrated in Fig. 3), and S6 (20 yr grouping for the sediment deposition models for the Severn-Teme confluence at Powick, Broadwas, and Buildwas and climatic datasets illustrated in Fig. 4)<br>

scholarly journals Impurity profile analysis of drug products containing acetylsalicylic acid: a chemometric approach

2013 ◽  
Vol 11 (7) ◽  
pp. 1091-1100 ◽  
Author(s):  
Joanna Ronowicz ◽  
Bogumiła Kupcewicz ◽  
Joanna Mydłowska ◽  
Elżbieta Budzisz
Keyword(s):  
Acetylsalicylic Acid ◽  
Profile Analysis ◽  
Principal Component ◽  
Analytical Techniques ◽  
Hierarchical Cluster ◽  
Data Sets ◽  
Impurity Profile ◽  
Hierarchical Segmentation ◽  
Drug Products ◽  
Impurity Profiles

AbstractIn this work attention is focused on impurity profile analysis in combination with infrared spectroscopy and chemometric methods. This approach is considered as an alternative to generally complex and time-consuming classic analytical techniques such as liquid chromatography. Various strategies for constructing descriptive models able to identify relations among drug impurity profiles hidden in multivariate chromatographic data sets are also presented and discussed. The hierarchical (cluster analysis) and non-hierarchical segmentation algorithms (k-means method) and principal component analysis are applied to gain an overview of the similarities and dissimilarities among impurity profiles of acetylsalicylic acid formulations. A tree regression algorithm based on infrared spectra is used to predict the relative content of impurities in the drug products investigated. Satisfactory predictive abilities of the models derived indicate the possibility of implementing them in the quality control of drug products.

DIVA-GIS based insight into geographical distribution and diversity spectrum of Indian sunnhemp (Crotalaria juncea L.) accessions suitable for diversified applications

2019 ◽  
Author(s):  
R. T. Maruthi ◽  
A. A. Kumar ◽  
S. B. Choudhary ◽  
H. K. Sharma ◽  
Jiban Mitra
Keyword(s):  
Geographical Distribution ◽  
Seed Yield ◽  
Diversity Index ◽  
Principal Component ◽  
Morphological Diversity ◽  
Hierarchical Cluster ◽  
Morphological Characters ◽  
Mean Values ◽  
North West ◽  
Agglomerative Hierarchical Cluster Analysis

Commercial prospects of sunnhemp inspired present study to understand geographical distribution pattern(s) and to scale agro-morphological diversity spectrum of forty-four sunnhemp accessions naturalized across diverse habitats of India. Field experiment revealed broad spectrum diversity for all the 11 agro-morphological traits. Wider range of plant height (110.50 to 173.17 cm), number of pods per plant (35.33 to 143.00), seeds per pod (6.33-15.17g) and seed yield per plant (8.27-29.43g) highlighted the adequacy of present genetic resources to improve sunnhemp for diversified applications. Principal component analysis of the agro-morphological characters identified the first PC with 1109.6 eigen value explaining 61.70% of total variation followed by PC-II (22.9%) and PC-III (11.1%). In PC-I significant contribution was made by traits like NLP, NPP and PH. Agglomerative hierarchical cluster analysis grouped all accessions into four distinct seed producing clusters irrespective of their origin. Cluster wise mean values suggested that cluster-II is the best with outstanding trait values for majority of traits. DIVA-GIS based analysis identified accessions from Rajasthan, Western Gujarat and Jharkhand with high diversity index for number of leaves/plant. But, accessions from North West Jharkhand and Maharashtra with highest diversity index for seed yield/plant.

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