scholarly journals Socio-Ecological Systems (SESs)—Identification and Spatial Mapping in the Central Himalaya

2021 ◽  
Vol 13 (14) ◽  
pp. 7525
Author(s):  
Praveen Kumar ◽  
Christine Fürst ◽  
P. K. Joshi
Keyword(s):  
Agricultural Practices ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Well Being ◽  
Ecological Systems ◽  
Central Himalaya ◽  
Ecological Data ◽  
Natural Systems ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Diverse Application

The Himalaya is a mosaic of complex socio-ecological systems (SESs) characterized by a wide diversity of altitude, climate, landform, biodiversity, ethnicity, culture, and agriculture systems, among other things. Identifying the distribution of SESs is crucial for integrating and formulating effective programs and policies to ensure human well-being while protecting and conserving natural systems. This work aims to identify and spatially map the boundaries of SESs to address the questions of how SESs can be delineated and what the characteristics of these systems are. The study was carried out for the state of Uttarakhand, India, a part of the Central Himalaya. The presented approach for mapping and delineation of SESs merges socio-economic and ecological data. It also includes validation of delineated system boundaries. We used 32 variables to form socio-economic units and 14 biophysical variables for ecological units. Principal component analysis followed by sequential agglomerative hierarchical cluster analysis was used to delineate the units. The geospatial statistical analysis identified 6 socio-economic and 3 ecological units, together resulting in 18 SESs for the entire state. The major characteristics for SESs were identified as forest types and agricultural practices, indicating the influence and dependency of SESs on these two features. The database would facilitate diverse application studies in vulnerability assessment, climate change adaptation and mitigation, and other socio-ecological studies. Such a detailed database addresses particularly site-specific characteristics to reduce risks and impacts. Overall, the identified SESs will help in recognizing local needs and gaps in existing policies and institutional arrangements, and the given methodological framework can be applied for the entire Himalayan region and for other mountain systems across the world.

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.

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 Motor Capacities in Boys with High Functioning Autism: Which Evaluations to Choose?

2019 ◽  
Vol 8 (10) ◽  
pp. 1521
Author(s):  
Véronique-Aurélie BRICOUT ◽  
Marion PACE ◽  
Léa DUMORTIER ◽  
Sahal MIGANEH ◽  
Yohan MAHISTRE ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Autism Spectrum ◽  
Small Sample ◽  
Ensemble Classification ◽  
Control Group ◽  
Children With Asd ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Motor Tests

The difficulties with motor skills in children with autism spectrum disorders (ASD) has become a major focus of interest. Our objectives were to provide an overall profile of motor capacities in children with ASD compared to neurotypically developed children through specific tests, and to identify which motor tests best discriminate children with or without ASD. Twenty-two male children with ASD (ASD—10.7 ± 1.3 years) and twenty controls (CONT—10.0 ± 1.6 years) completed an evaluation with 42 motor tests from European Physical Fitness Test Battery (EUROFIT), the Physical and Neurological Exam for Subtle Signs (PANESS) and the Movement Assessment Battery for Children ( M-ABC). However, it was challenging to design a single global classifier to integrate all these features for effective classification due to the issue of small sample size. To this end, we proposed a hierarchical ensemble classification method to combine multilevel classifiers by gradually integrating a large number of features from different motor assessments. In the ASD group, flexibility, explosive power and strength scores (p < 0.01) were significantly lower compared to the control group. Our results also showed significant difficulties in children with ASD for dexterity and ball skills (p < 0.001). The principal component analysis and agglomerative hierarchical cluster analysis allowed for the classification of children based on motor tests, correctly distinguishing clusters between children with and without motor impairments.

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>

Food webs with humans: In name only?

2017 ◽  
Author(s):  
Emma C. Fuller
Keyword(s):  
Human Behavior ◽  
Well Being ◽  
Ecological Systems ◽  
Natural Systems ◽  
Fishing Vessels ◽  
The Social ◽  
The Us ◽  
Dungeness Crabs ◽  
Us West

This chapter highlights the importance of considering people as integral to foodwebs. Despite extensive recent research on coupled human-natural systems, lacking are models that incorporate human behavior in a way that yields pragmatic insights into the management of multispecies fisheries. Using the US West Coast commercial fisheries system as a case study, this chapter develops a novel network approach of linking the social system (i.e., fishing communities) to the ecological system (the fish). The analysis reveals that fisheries that seem unconnected biologically, such as benthic Dungeness crabs and pelagic tuna, can in fact be strongly linked by fishing vessels that are active in both fisheries. Understanding how human behavior connects seemingly disparate ecological systems has important implications for fisheries managers seeking to balance human well-being with sustainable populations of fish.

scholarly journals Comparison of Multivariate Analysis Methodologies in a Palliative Care Setting

2012 ◽  
Vol 49 (1) ◽  
pp. 67-79
Author(s):  
Isabel Pinto Doria ◽  
Ana Sousa Ferreira ◽  
Otília Dias ◽  
Helena Bacelar-Nicolau ◽  
Georges Le Calvé
Keyword(s):  
Palliative Care ◽  
Care Setting ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Comprehensive Analysis ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Palliative Care Setting ◽  
Analysis Models ◽  
Symbolic Approach

Summary This study is focused on measuring the quality and the satisfaction with the palliative care provided to oncology patients in domicile. The SERVQUAL methodology adapted for the Portuguese context was used to evaluate the quality of palliative care and patient satisfaction. The Portuguese SERVQUAL questionnaire is composed of five perception scales and two questionnaires, one about the patient and another about the caregiver. The data analysis presented is the analysis of the answers to the five perception scales, composed of partial ordered variables, evaluating different aspects of quality and satisfaction.The data was analysed comparing metric and symbolic approaches, using Principal Component Analysis Methods and Agglomerative Hierarchical Cluster Analysis Models. The results suggest that a symbolic approach provides a more comprehensive analysis for this kind of data.

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 On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1318 ◽  
Author(s):  
Mohamed El Sayed Said ◽  
Abdelraouf. M. Ali ◽  
Maurizio Borin ◽  
Sameh Kotb Abd-Elmabod ◽  
Ali A. Aldosari ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Agricultural Development ◽  
Agricultural Sector ◽  
Soil Depth ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Land Evaluation ◽  
Land Capability ◽  
Soil Suitability ◽  
Agglomerative Hierarchical Cluster Analysis

The development of the agricultural sector is considered the backbone of sustainable development in Egypt. While the developing countries of the world face many challenges regarding food security due to rapid population growth and limited agricultural resources, this study aimed to assess the soils of Sidi Barrani and Salloum using multivariate analysis to determine the land capability and crop suitability for potential alternative crop uses, based on using principal component analysis (PCA), agglomerative hierarchical cluster analysis (AHC) and the Almagra model of MicroLEIS. In total, 24 soil profiles were dug, to represent the geomorphic units of the study area, and the soil physicochemical parameters were analyzed in laboratory. The land capability assessment was classified into five significant classes (C1 to C5) based on AHC and PCA analyses. The class C1 represents the highest capable class while C5 is assigned to lowest class. The results indicated that about 7% of the total area was classified as highly capable land (C1), which is area characterized by high concentrations of macronutrients (N, P, K) and low soil salinity value. However, about 52% of the total area was assigned to moderately high class (C2), and 29% was allocated in moderate class (C3), whilst the remaining area (12%) was classified as the low (C4) and not capable (C5) classes, due to soil limitations such as shallow soil depth, high salinity, and increased erosion susceptibility. Moreover, the results of the Almagra soil suitability model for ten crops were described into four suitability classes, while about 37% of the study area was allocated in the highly suitable class (S2) for wheat, olive, alfalfa, sugar beet and fig. Furthermore, 13% of the area was categorized as highly suitable soil (S2) for citrus and peach. On the other hand, about 50% of the total area was assigned to the marginal class (S4) for most of the selected crops. Hence, the use of multivariate analysis, mapping land capability and modeling the soil suitability for diverse crops help the decision makers with regard to potential agricultural development.

scholarly journals Spatial Patterns and Temporal Variability of Drought in Beijing-Tianjin-Hebei Metropolitan Areas in China

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Wanyuan Cai ◽  
Yuhu Zhang ◽  
Qiuhua Chen ◽  
Yunjun Yao
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Metropolitan Areas ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Drought Severity ◽  
Monthly Precipitation ◽  
Resources Management ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
North And South

Drought identification and assessment are essential for regional water resources management. In this paper, the spatiotemporal characteristics of drought were evaluated based on monthly precipitation data from 33 synoptic stations during the period of 1960–2010. The percent of normal precipitation was applied to illustrate the driest years in Beijing-Tianjin-Hebei metropolitan areas (BTHMA) (1965, 1997, and 2002). The modified Reconnaissance Drought Index (RDI) was applied to capture the drought patterns and to estimate the drought severity at 33 meteorological stations. Agglomerative hierarchical cluster analysis (AHCA) and principal component analysis (PCA) were used to identify three different drought subregions R1, R2, and R3 based on the monthly precipitation values in BTHMA, which is located in southeast, north, and south of BTHMA, respectively. The year 1965 was the driest and 1964 was the wettest during the observed period. The characteristics of drought were analyzed in terms of the temporal evolution of the RDI-12 values and the frequency of drought for the three identified regions. The percentage of years characterized by drought was 13.73% for R1, 16.50% for R2, and 15.53% for R3. 66.91% of drought belongs to the near normal drought category. The obtained results can aid to improve water resources management in the area.

scholarly journals Multi-Block Data Integration Analysis for Identifying and Validating Targeted N-Glycans as Biomarkers for Type II Diabetes Mellitus

2022 ◽  
Author(s):  
Eric Adua ◽  
Ebenezer Afrifa Yamoah ◽  
Emmanuel Peprah-Yamoah ◽  
Enoch Odame Anto ◽  
Emmanuel Acheampong ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type Ii Diabetes ◽  
High Performance ◽  
Univariate Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Type Ii Diabetes Mellitus ◽  
Healthy Controls ◽  
Type Ii ◽  
Agglomerative Hierarchical Cluster Analysis

Abstract Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. Principal component analysis (PCA), discriminatory analysis and agglomerative hierarchical cluster analysis (HCA) were performed. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis P. Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.

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