34,681 research outputs found
Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation
Image correlation remote sensing monitoring techniques are becoming key tools for
providing effective qualitative and quantitative information suitable for natural hazard assessments,
specifically for landslide investigation and monitoring. In recent years, these techniques have
been successfully integrated and shown to be complementary and competitive with more standard
remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry.
The objective of this article is to apply the proposed in-depth calibration and validation analysis,
referred to as the Digital Image Correlation technique, to measure landslide displacement.
The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized
by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS
(Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models
and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide
displacement field at values ranging from a few meters (2â3 m in the north-eastern sector of the
landslide) to 20â21 m (local peaks on the central body of the landslide). Furthermore, comprehensive
sensitivity analyses and statistics-based processing approaches are used to identify the role of the
background noise that affects the whole dataset. This noise has a directly proportional relationship to
the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy
of the environmental-instrumental background noise evaluation allowed the actual displacement
measurements to be correctly calibrated and validated, thereby leading to a better definition of
the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability
(ranging from 1/10 to 8/10 pixel) for each processed dataset
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Coronavirus (COVID-19) in the United Kingdom: A personality-based perspective on concerns and intention to self-isolate
Objectives
Public behaviour change is necessary to contain the spread of coronavirus (COVIDâ19). Based on the reinforcement sensitivity theory (RST) framework, this study presents an examination of individual differences in some relevant psychological factors.
Design
Crossâsectional psychometric.
Methods
UK respondents (N = 202) completed a personality questionnaire (RSTâPQ), measures of illness attitudes, concerns about the impact of coronavirus on health services and socioâeconomic infrastructures, personal safety, and likelihood of voluntary selfâisolation.
Results
Respondents most concerned were older, had negative illness attitudes, and scored higher on reward reactivity (RR), indicating the motivation to take positive approach action despite prevailing worry/anxiety. Personal safety concerns were highest in those with negative illness attitudes and higher fightâflightâfreeze system (FFFS, reflecting fear/avoidance) scores. Results suggest people are experiencing psychological conflict: between the urge to stay safe (FFFFârelated) and the desire to maintain a normal, pleasurable (RRârelated) life. Ways of ameliorating conflict may include maladaptive behaviours (panic buying), reflecting rewardârelated displacement activity. Intended selfâisolation related to FFFS, but also low behavioural inhibition system (related to anxiety) scores. Older people reported themselves less likely to selfâisolate.
Conclusions
Interventions need to consider individual differences in psychological factors in behaviour change, and we discuss relevant literature to inform policy makers and communicators
Ocean Eddy Identification and Tracking using Neural Networks
Global climate change plays an essential role in our daily life. Mesoscale
ocean eddies have a significant impact on global warming, since they affect the
ocean dynamics, the energy as well as the mass transports of ocean circulation.
From satellite altimetry we can derive high-resolution, global maps containing
ocean signals with dominating coherent eddy structures. The aim of this study
is the development and evaluation of a deep-learning based approach for the
analysis of eddies. In detail, we develop an eddy identification and tracking
framework with two different approaches that are mainly based on feature
learning with convolutional neural networks. Furthermore, state-of-the-art
image processing tools and object tracking methods are used to support the eddy
tracking. In contrast to previous methods, our framework is able to learn a
representation of the data in which eddies can be detected and tracked in more
objective and robust way. We show the detection and tracking results on sea
level anomalies (SLA) data from the area of Australia and the East Australia
current, and compare our two eddy detection and tracking approaches to identify
the most robust and objective method.Comment: accepted for International Geoscience and Remote Sensing Symposium
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