scholarly journals Unravelling the scientific potential of high resolution fishery data

2018 ◽  
Vol 31 ◽  
pp. 24 ◽  
Author(s):  
Kristian Schreiber Plet-Hansen ◽  
Erling Larsen ◽  
Lars Olof Mortensen ◽  
J. Rasmus Nielsen ◽  
Clara Ulrich
Keyword(s):  
High Resolution ◽  
Marine Species ◽  
Scientific Data ◽  
Detailed Knowledge ◽  
Data Types ◽  
Fisheries Science ◽  
Fishing Vessels ◽  
Spatio Temporal ◽  
Management Advice ◽  
The Individual

Fisheries science and fisheries management advice rely on both scientific and commercial data to estimate the distribution and abundance of marine species. These two data types differ, with scientific data having a broader geographical coverage but less intensity and time coverage compared to commercial data. Here we present a new type of commercial data with high resolution and coverage. To our knowledge, the dataset presented in this study has never been used for scientific purposes. While commercial datasets usually include the total weight by species on per haul basis, the new data also include the commercial size class for the species landed, recorded directly on a haul-by-haul basis. Thus, this dataset has the potential to provide knowledge on landed fish with as high spatio-temporal resolution as when coupling logbooks and sales slips but with the addition of detailed knowledge on the size distribution. Such information may otherwise be obtained through on-board observer programmes but unlike the observers’ data, the dataset presented here is routinely collected on most of the trips of the vessels involved, which means that the coverage of the data for the individual vessel is larger than observers’ data. Furthermore, the risk of changes in fishing behaviour due to the presence of an observer on-board is avoided. This paper describes the coverage and completeness of the dataset, and explores the reliability of the data available. We conclude that the main limitation is the small number of fishing vessels covered by the program, but that the data from those vessels are accurate, detailed, and relatively reliable.

Standardized Doppler lidar processing for operational use in a future network

2021 ◽  
Author(s):  
Markus Kayser ◽  
Eileen Päschke ◽  
Carola Detring ◽  
Volker Lehmann ◽  
Frank Beyrich ◽  
...  
Keyword(s):  
Scientific Data ◽  
Quality Level ◽  
Meteorological Observatory ◽  
Mean Wind Speed ◽  
Remote Sensing Systems ◽  
Thin Cloud ◽  
Spatio Temporal ◽  
Short Term Forecasting ◽  
Scanning Strategies ◽  
The Individual

<p>Fibre-optic based Doppler wind lidars (DL) are widely used for both meteorological research and in the wind energy sector. These compact systems are able to retrieve vertical profiles of kinematic quantities, such as mean wind, from the atmospheric boundary layer as well as from optically thin cloud layers in the free troposphere with high spatio-temporal resolution. It is therefore likely that especially short-term forecasting would benefit from assimilating these data. However, their potential is currently not yet employed operationally.</p> <p>As part of DWD's effort to evaluate ground-based remote sensing systems for their operational readiness, called "Pilotstation", we developed a software client (DL-client) that standardizes the processing of mean wind based on the Velocity Azimuth Display method. Results of a long-term assessment of DLs at the Meteorological Observatory Lindenberg, starting in 2012, show that the DL-client assures a high quality Level-2 product, which is compatible with the EUMETNET's E-PROFILE observation program. We verified the retrieved mean wind speed and direction with the help of independent reference data from a 482 MHz radar wind profiler and 6-hourly radiosonde ascents. Hence, the DL-client not only facilitates processing and archiving of the DL data, but also forms a basis for operational network deployment and data assimilation. Furthermore, through speeding up and standardizing the data processing, the individual users can concentrate on more advanced scientific data analyses.</p> <p>Finally, the software is freely accessible and will be continuously improved to account for different scanning strategies. Its modular build-up of processing steps offers the possibility to extend the list of products with additional retrievals, e.g. for turbulent kinetic energy and wind gusts, which are currently under development at Lindenberg.</p>

Integrating multiple geotechnical data types with machine learning to construct high-resolution 3D geological models

2020 ◽  
Author(s):  
Willem Dabekaussen ◽  
Renée de Bruijn ◽  
Romée H. Kars ◽  
Bart M.L. Meijninger ◽  
Jan Stafleu
Keyword(s):  
High Resolution ◽  
The Netherlands ◽  
Detailed Knowledge ◽  
Geological Model ◽  
Data Types ◽  
Voxel Size ◽  
High Data ◽  
Geological Modelling ◽  
Data Density ◽  
Geological Models

<p>In the central and western parts of the Netherlands, the low-lying areas are vulnerable to flooding by rivers. During times of peak runoff, dikes are essential to keep the land dry and the people safe. Rigorous safety standards are in place to ensure dikes are capable of withstanding extreme water level conditions. Key components for the strength and stability of a dike are the internal structure and composition of the dike and the geology in the subsurface: a sandy aquifer may lead to piping and undercutting of the dike while weak or layered strata under certain hydraulic pressures could potentially lead to collapse and catastrophic failure of the dike.</p><p>For the dike reinforcement project ‘Sterke Lekdijk’, the regional water authority ‘Hoogheemraadschap de Stichtse Rijnlanden’ is investigating a 55 km long section of the dike along the right bank of the river Lek. Detailed knowledge about the subsurface is crucial when quantifying the conditions of dikes. Given the very heterogeneous build-up of the Holocene sediments this is not an easy task. For the shallow subsurface (down to 50 m below surface level) TNO – Geological Survey of the Netherlands builds and maintains a nation-wide stochastic 3D geological model called GeoTOP. With a 100x100x0.5 m voxel size this model gives a sense of the overall geology, but lacks the very detailed information below the dikes that is needed for the task at hand.</p><p>Construction of a high-resolution geological model requires a high data density. Traditionally, shallow geological models are based on borehole information. However, in the built environment another data source is available in the form of cone penetration tests (CPTs), which are routinely obtained to measure the strength of subsurface sediments for geotechnical purposes. Although classification charts are available to translate CPT measurements into lithological classes, these charts require adjustments for local use and resulting performance remains variable. To enable the use of CPTs for geological modelling an artificial neural network (ANN) was trained to translate CPT measurements to lithological classes. Training of the ANN was done on neighboring borehole-CPT pairs (spaced at max. 10 meters). The ANN produces realistic results, with cross-validation statistics showing a vast increase in performance of the ANN results compared to traditional classification charts.</p><p>The disclosure of CPTs for geological modelling greatly increases the data density along man-made structures such as dikes. A local high-resolution version of the GeoTOP model was constructed, with a voxel size of 25x25x0.25 m. This detailed information includes the lithostratigraphical unit the voxel belongs to, the most probable lithological class of the voxel as well as the probability of occurrence of particular lithological classes. The high-resolution model enables the local water authority to better estimate dike stability, better target additional measurements in areas of high uncertainty, and take more location specific reinforcement measures.</p>

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

1995 ◽  
Vol 53 ◽  
pp. 650-651
Author(s):  
D. E. Becker
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Laser Scanning ◽  
Cell Counting ◽  
Wide Area ◽  
Data Set ◽  
Computational Synthesis ◽  
Automated Cell Counting ◽  
Partial View ◽  
The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

High Resolution Transmission Electron Microscopy of an automobile catalytic converter

1990 ◽  
Vol 48 (4) ◽  
pp. 242-243
Author(s):  
Jan-Olle Malm ◽  
Jan-Olov Bovin
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Catalytic Converter ◽  
Active Material ◽  
Image Intensifier ◽  
High Resolution Electron Microscopy ◽  
Detailed Knowledge ◽  
Transmission Electron ◽  
Resolution Electron ◽  
Atomic Surface

Understanding of catalytic processes requires detailed knowledge of the catalyst. As heterogeneous catalysis is a surface phenomena the understanding of the atomic surface structure of both the active material and the support material is of utmost importance. This work is a high resolution electron microscopy (HREM) study of different phases found in a used automobile catalytic converter.The high resolution micrographs were obtained with a JEM-4000EX working with a structural resolution better than 0.17 nm and equipped with a Gatan 622 TV-camera with an image intensifier. Some work (e.g. EDS-analysis and diffraction) was done with a JEM-2000FX equipped with a Link AN10000 EDX spectrometer. The catalytic converter in this study has been used under normal driving conditions for several years and has also been poisoned by using leaded fuel. To prepare the sample, parts of the monolith were crushed, dispersed in methanol and a drop of the dispersion was placed on the holey carbon grid.

Geolocation estimation of marine species using a high resolution light tag

OCEANS 2009 ◽  
2009 ◽  
Author(s):  
Prajas John ◽  
Jaison Peter ◽  
Adrine Antony Correya ◽  
M. H. Supriya ◽  
P. R. Saseendran Pillai
Keyword(s):  
High Resolution ◽  
Marine Species

scholarly journals Spatio-temporal distribution patterns of Plutella xylostella (Lepidoptera: Plutellidae) in a fine-scale agricultural landscape based on geostatistical analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian-Yu Li ◽  
Yan-Ting Chen ◽  
Meng-Zhu Shi ◽  
Jian-Wei Li ◽  
Rui-Bin Xu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Plutella Xylostella ◽  
Agricultural Landscape ◽  
Seasonal Pattern ◽  
Distribution Patterns ◽  
Geostatistical Analysis ◽  
Distribution Model ◽  
Detailed Knowledge ◽  
Fine Scale ◽  
Spatio Temporal

AbstractA detailed knowledge on the spatial distribution of pests is crucial for predicting population outbreaks or developing control strategies and sustainable management plans. The diamondback moth, Plutella xylostella, is one of the most destructive pests of cruciferous crops worldwide. Despite the abundant research on the species’s ecology, little is known about the spatio-temporal pattern of P. xylostella in an agricultural landscape. Therefore, in this study, the spatial distribution of P. xylostella was characterized to assess the effect of landscape elements in a fine-scale agricultural landscape by geostatistical analysis. The P. xylostella adults captured by pheromone-baited traps showed a seasonal pattern of population fluctuation from October 2015 to September 2017, with a marked peak in spring, suggesting that mild temperatures, 15–25 °C, are favorable for P. xylostella. Geostatistics (GS) correlograms fitted with spherical and Gaussian models showed an aggregated distribution in 21 of the 47 cases interpolation contour maps. This result highlighted that spatial distribution of P. xylostella was not limited to the Brassica vegetable field, but presence was the highest there. Nevertheless, population aggregations also showed a seasonal variation associated with the growing stage of host plants. GS model analysis showed higher abundances in cruciferous fields than in any other patches of the landscape, indicating a strong host plant dependency. We demonstrate that Brassica vegetables distribution and growth stage, have dominant impacts on the spatial distribution of P. xylostella in a fine-scale landscape. This work clarified the spatio-temporal dynamic and distribution patterns of P. xylostella in an agricultural landscape, and the distribution model developed by geostatistical analysis can provide a scientific basis for precise targeting and localized control of P. xylostella.

scholarly journals POS1391 MEASURING SUBCLINICAL INFLAMMATION IN HAND AND FOREFOOT IN PATIENTS WITH ARTHRALGIA USING 1.5T OR 3.0T MRI: DOES FIELD STRENGTH MATTER?

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 978.1-978
Author(s):  
D. Krijbolder ◽  
M. Verstappen ◽  
F. Wouters ◽  
L. R. Lard ◽  
P. D. De Buck ◽  
...  
Keyword(s):  
Field Strength ◽  
Scientific Evidence ◽  
Correlation Coefficients ◽  
Daily Practice ◽  
Scientific Data ◽  
Imaging Features ◽  
Subclinical Inflammation ◽  
The Individual ◽  
3.0T Mri ◽  
Inflammation Score

Background:Magnetic resonance imaging (MRI) of small joints sensitively detects inflammation. MRI-detected subclinical inflammation, and tenosynovitis in particular, has been shown predictive for RA development in patients with arthralgia. These scientific data are mostly acquired on 1.0T-1.5T MRI scanners. However, 3.0T MRI is nowadays increasingly used in practice. Evidence on the comparability of these field strengths is scarce and it has never been studied in arthralgia where subclinical inflammation is subtle. Moreover, comparisons never included tenosynovitis, which is, of all imaging features, the strongest predictor for progression to RA.Objectives:To determine if there is a difference between 1.5T and 3.0T MRI in detecting subclinical inflammation in arthralgia patients.Methods:2968 locations (joints, bones or tendon sheaths) in hands and forefeet of 28 arthralgia patients were imaged on both 1.5T and 3.0T MRI. Two independent readers scored for erosions, osteitis, synovitis (according to RAMRIS) and tenosynovitis (as described by Haavaardsholm et al.). Scores were also summed as total inflammation (osteitis, synovitis and tenosynovitis) and total RAMRIS (erosions, osteitis, synovitis and tenosynovitis) scores. Interreader reliability (comparing both readers) and field strength agreement (comparing 1.5T and 3.0T) was assessed with interclass correlation coefficients (ICCs). Next, field strength agreement was assessed after dichotomization into presence or absence of inflammation. Analyses were performed on patient- and location-level.Results:ICCs between readers were excellent (>0.90). Comparing 1.5 and 3.0T revealed excellent ICCs of 0.90 (95% confidence interval 0.78-0.95) for the total inflammation score and 0.90 (0.78-0.95) for the total RAMRIS score. ICCs for individual inflammation features were: tenosynovitis: 0.87 (0.74-0.94), synovitis 0.65 (0.24-0.84) and osteitis 0.96 (0.91-0.98). The field strength agreement on dichotomized scores was 83% for the total inflammation score and 89% for the total RAMRIS score. Of the individual features, agreement for tenosynovitis was the highest (89%). Analyses on location- level showed similar results.Conclusion:Agreement of subclinical inflammation scores on 1.5T and 3.0T were good to excellent, in particular for tenosynovitis. This suggests that scientific evidence on predictive power of MRI in arthralgia patients, obtained on 1.5T, can be generalized to 3.0T when this field strength would be used for diagnostic purposes in daily practice.Disclosure of Interests:None declared

scholarly journals Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1218
Author(s):  
Laura Tuşa ◽  
Mahdi Khodadadzadeh ◽  
Cecilia Contreras ◽  
Kasra Rafiezadeh Shahi ◽  
Margret Fuchs ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Ore Deposits ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Drill Core ◽  
Data Types ◽  
Mineralogical Characterization ◽  
Core Samples

Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association.

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