scholarly journals Paleoenvironmental analysis of the Hautawa Shellbed, Whanganui Basin

2021 ◽  
Author(s):  
◽  
Joanna Eveline Grace Elliott
Keyword(s):  
Principal Component ◽  
Data Set ◽  
Sequence Stratigraphic ◽  
Fine Grained ◽  
Temporal And Spatial ◽  
On Farm ◽  
Subunit B ◽  
Lateral Variations ◽  
Paleoenvironmental Analysis

<p>The Hautawa Shellbed, Whanganui Basin is described in detail to uncover lateral variations in depositional paleoenvironment. This was achieved through the in situ documentation of the macrofaunal assemblage and its taphonomic attributes at three localities. The sites from west to east are: Ridge Road, Old Hautawa Road, and the type section on West Road. They are all exposures on farm tracks and cover a 20-km range across the central Whanganui Basin. The descriptions were collected at 15-cm intervals and analysed using k-means clustering and Principal Component Analysis (PCA) to uncover trends within the data set. Combining the assemblage data with the taphonomic has allowed six major biofacies to be recognised. In turn, the arrangement of the biofacies in the sections suggest three subunits: A, B, and C. Subunits A and C are laterally continuous between all of the sections and always relate to the lowermost and upper-most portions of the Hautawa Shellbed. In contrast, subunit B is only observed to occur at West Road overlying subunit A. These subunits have can also be equated to sequence stratigraphic terminology. Subunits A and B form an onlap shellbed and subunit C a backlap shellbed. Hence, the Hautawa Shellbed represents deposition during the transgressive systems tract of a single cyclothem. This study is unique compared to other Whanganui Basin stratigraphic research in its statistically robust approach for comparing data gathered at various sites along outcrop strike to better understand the preserved paleoenvironment. To support the macro-faunal investigation, census counts of foraminifera were conducted for samples collected from the fine-grained sediments encompassing the Hautawa Shellbed at each of the three sites. Together, the macrofaunal and foraminiferal studies reveal temporal and spatial paleoenvironmental changes within the Hautawa Shellbed. The presence of biostratigraphically important fauna within the Hautawa Shellbed has been used to link the unit to other similar formations in both the Whanganui and East Coast Basins. This key assemblage which highlights the Nukumaruan-Mangapanian Stage boundary at 2.40 Ma includes: Zygochlamys delicatula, Crassostrea ingens, Phialopecten thomsoni, Phialopecten triphooki, and Mesopeplum convexum. The paleoenvironmental variations observed and presented here for the Hautawa Shellbed have been combined with published work on other parallel formations to produce a paleogeographic map of the Whanganui Basin for 2.40 Ma.</p>

scholarly journals Paleoenvironmental analysis of the Hautawa Shellbed, Whanganui Basin

2021 ◽  
Author(s):  
◽  
Joanna Eveline Grace Elliott
Keyword(s):  
Principal Component ◽  
Data Set ◽  
Sequence Stratigraphic ◽  
Fine Grained ◽  
Temporal And Spatial ◽  
On Farm ◽  
Subunit B ◽  
Lateral Variations ◽  
Paleoenvironmental Analysis

<p>The Hautawa Shellbed, Whanganui Basin is described in detail to uncover lateral variations in depositional paleoenvironment. This was achieved through the in situ documentation of the macrofaunal assemblage and its taphonomic attributes at three localities. The sites from west to east are: Ridge Road, Old Hautawa Road, and the type section on West Road. They are all exposures on farm tracks and cover a 20-km range across the central Whanganui Basin. The descriptions were collected at 15-cm intervals and analysed using k-means clustering and Principal Component Analysis (PCA) to uncover trends within the data set. Combining the assemblage data with the taphonomic has allowed six major biofacies to be recognised. In turn, the arrangement of the biofacies in the sections suggest three subunits: A, B, and C. Subunits A and C are laterally continuous between all of the sections and always relate to the lowermost and upper-most portions of the Hautawa Shellbed. In contrast, subunit B is only observed to occur at West Road overlying subunit A. These subunits have can also be equated to sequence stratigraphic terminology. Subunits A and B form an onlap shellbed and subunit C a backlap shellbed. Hence, the Hautawa Shellbed represents deposition during the transgressive systems tract of a single cyclothem. This study is unique compared to other Whanganui Basin stratigraphic research in its statistically robust approach for comparing data gathered at various sites along outcrop strike to better understand the preserved paleoenvironment. To support the macro-faunal investigation, census counts of foraminifera were conducted for samples collected from the fine-grained sediments encompassing the Hautawa Shellbed at each of the three sites. Together, the macrofaunal and foraminiferal studies reveal temporal and spatial paleoenvironmental changes within the Hautawa Shellbed. The presence of biostratigraphically important fauna within the Hautawa Shellbed has been used to link the unit to other similar formations in both the Whanganui and East Coast Basins. This key assemblage which highlights the Nukumaruan-Mangapanian Stage boundary at 2.40 Ma includes: Zygochlamys delicatula, Crassostrea ingens, Phialopecten thomsoni, Phialopecten triphooki, and Mesopeplum convexum. The paleoenvironmental variations observed and presented here for the Hautawa Shellbed have been combined with published work on other parallel formations to produce a paleogeographic map of the Whanganui Basin for 2.40 Ma.</p>

scholarly journals The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture

2021 ◽  
Vol 13 (4) ◽  
pp. 825
Author(s):  
David Mengen ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Anke Fluhrer ◽  
Cosimo Brogi ◽  
...  
Keyword(s):  
Test Site ◽  
Satellite Mission ◽  
Data Set ◽  
Root Crops ◽  
Crop Type ◽  
Infrared Measurements ◽  
L Band ◽  
Temporal And Spatial ◽  
Plant Sampling

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-frequency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

Advanced monitoring of water systems using in situ measurement stations: data validation and fault detection

2013 ◽  
Vol 68 (5) ◽  
pp. 1022-1030 ◽  
Author(s):  
Janelcy Alferes ◽  
Sovanna Tik ◽  
John Copp ◽  
Peter A. Vanrolleghem
Keyword(s):  
Water Quality ◽  
Treatment Plant ◽  
Principal Component ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Data Quality Assessment ◽  
Multiple Sensor ◽  
Pertinent Information

In situ continuous monitoring at high frequency is used to collect water quality information about water bodies. However, it is crucial that the collected data be evaluated and validated for the appropriate interpretation of the data so as to ensure that the monitoring programme is effective. Software tools for data quality assessment with a practical orientation are proposed. As water quality data often contain redundant information, multivariate methods can be used to detect correlations, pertinent information among variables and to identify multiple sensor faults. While principal component analysis can be used to reduce the dimensionality of the original variable data set, monitoring of some statistical metrics and their violation of confidence limits can be used to detect faulty or abnormal data and can help the user apply corrective action(s). The developed algorithms are illustrated with automated monitoring systems installed in an urban river and at the inlet of a wastewater treatment plant.

The variation of angles between anterior and posterior carinae of tyrannosaurid teeth

2012 ◽  
Vol 49 (3) ◽  
pp. 477-491 ◽  
Author(s):  
Miriam Reichel
Keyword(s):  
Strong Correlation ◽  
Multivariate Analyses ◽  
Principal Component ◽  
Tooth Size ◽  
Data Set ◽  
In Situ Data ◽  
Principal Component Analyses ◽  
Canonical Variates ◽  
Discriminant Analyses

Tyrannosaurid tooth measurements have been shown to be a powerful tool for systematic analyses, as well as for studies on function and evolution of theropod dentition. In this analysis, a variable not previously addressed in depth is added to the tyrannosaurid data set. The angle between the anterior and posterior carinae can be difficult to measure consistently and a method is hereby proposed through the use of a digitizer. Five tyrannosaurid genera were analyzed: Tyrannosaurus , Tarbosaurus , Albertosaurus , Daspletosaurus , and Gorgosaurus . Only in situ data were used, and therefore some of the taxa had a limited amount of information available for this analysis. The measurements were analyzed through multivariate analyses using Paleontological Statistics (PAST), version 2.06. The analyses included principal component analyses (PCAs), discriminant analyses (DAs), and canonical variates analyses (CVAs). The results of these analyses revealed that the angle between carinae contributes significantly to the variation in the tyrannosaurid tooth data set. Additionally, this variable showed a strong correlation to tooth function (and, consequently, to tooth families), rather than tooth size. The variation observed between taxa at this stage seems insufficient for systematic purposes, however additional in situ data would help improve the effectiveness of this tool.

scholarly journals Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning

2019 ◽  
Vol 11 (22) ◽  
pp. 2690 ◽  
Author(s):  
Yushi Chen ◽  
Lingbo Huang ◽  
Lin Zhu ◽  
Naoto Yokoya ◽  
Xiuping Jia
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Conditional Random Field ◽  
Hyperspectral Imagery ◽  
Principal Component ◽  
Processing Technique ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Fine Grained

Hyperspectral remote sensing obtains abundant spectral and spatial information of the observed object simultaneously. It is an opportunity to classify hyperspectral imagery (HSI) with a fine-grained manner. In this study, the fine-grained classification of HSI, which contains a large number of classes, is investigated. On one hand, traditional classification methods cannot handle fine-grained classification of HSI well; on the other hand, deep learning methods have shown their powerfulness in fine-grained classification. So, in this paper, deep learning is explored for HSI supervised and semi-supervised fine-grained classification. For supervised HSI fine-grained classification, densely connected convolutional neural network (DenseNet) is explored for accurate classification. Moreover, DenseNet is combined with pre-processing technique (i.e., principal component analysis or auto-encoder) or post-processing technique (i.e., conditional random field) to further improve classification performance. For semi-supervised HSI fine-grained classification, a generative adversarial network (GAN), which includes a discriminative CNN and a generative CNN, is carefully designed. The GAN fully uses the labeled and unlabeled samples to improve classification accuracy. The proposed methods were tested on the Indian Pines data set, which contains 33,3951 samples with 52 classes. The experimental results show that the deep learning-based methods provide great improvements compared with other traditional methods, which demonstrate that deep models have huge potential for HSI fine-grained classification.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

scholarly journals Development of the Statistical Model for Monitoring Salinization in the Mekong Delta of Vietnam Using Remote Sensing Data and In-Situ Measurements

Proceedings ◽  
2018 ◽  
Vol 2 (10) ◽  
pp. 565
Author(s):  
Nguyen Nguyen Vu ◽  
Le Van Trung ◽  
Tran Thi Van
Keyword(s):  
Statistical Model ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Measured Data ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Component Band

This article presents the methodology for developing a statistical model for monitoring salinity intrusion in the Mekong Delta based on the integration of satellite imagery and in-situ measurements. We used Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat- 8 OLI and TIRS) satellite data to establish the relationship between the planetary reflectance and the ground measured data in the dry season during 2014. The three spectral bands (blue, green, red) and the principal component band were used to obtain the most suitable models. The selected model showed a good correlation with the exponential function of the principal component band and the ground measured data (R2 > 0.8). Simulation of the salinity distribution along the river shows the intrusion of a 4 g/L salt boundary from the estuary to the inner field of more than 50 km. The developed model will be an active contribution, providing managers with adaptation and response solutions suitable for intrusion in the estuary as well as the inner field of the Mekong Delta.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

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