40,248 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
Episodic memory retrieval, parietal cortex, and the default mode network: Functional and topographic analyses
The default mode network (DMN) is often considered a functionally homogeneous system that is broadly associated with internally directed cognition (e.g., episodic memory, theory of mind, self-evaluation). However, few studies have examined how this network interacts with other networks during putative default processes such as episodic memory retrieval. Using functional magnetic resonance imaging, we investigated the topography and response profile of human parietal regions inside and outside the DMN, independently defined using task-evoked deactivations and resting-state functional connectivity, during episodic memory retrieval. Memory retrieval activated posterior nodes of the DMN, particularly the angular gyrus, but also more anterior and dorsal parietal regions that were anatomically separate from the DMN. The two sets of parietal regions showed different resting-state functional connectivity and response profiles. During memory retrieval, responses in DMN regions peaked sooner than non-DMN regions, which in turn showed responses that were sustained until a final memory judgment was reached. Moreover, a parahippocampal region that showed strong resting-state connectivity with parietal DMN regions also exhibited a pattern of task-evoked activity similar to that exhibited by DMN regions. These results suggest that DMN parietal regions directly supported memory retrieval, whereas non-DMN parietal regions were more involved in postretrieval processes such as memory-based decision making. Finally, a robust functional dissociation within the DMN was observed. Whereas angular gyrus and posterior cingulate/precuneus were significantly activated during memory retrieval, an anterior DMN node in medial prefrontal cortex was strongly deactivated. This latter finding demonstrates functional heterogeneity rather than homogeneity within the DMN during episodic memory retrieval
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A high resolution coupled hydrologicâhydraulic model (HiResFlood-UCI) for flash flood modeling
HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The NashâSutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning
Imperfect predictability and mutual fund dynamics. How managers use predictors in changing systematic risk.
Suppose a fund manager uses predictors in changing port-folio allocations over time. How does predictability translate into portfolio decisions? To answer this question we derive a new model within the Bayesian framework, where managers are assumed to modulate the systematic risk in part by observing how the benchmark returns are related to some set of imperfect predictors, and in part on the basis of their own information set. In this portfolio allocation process, managers concern themselves with the potential benefits arising from the market timing generated by benchmark predictors and by private information. In doing this, we impose a structure on fund returns, betas, and bench-mark returns that help to analyse how managers really use predictors in changing investments over time. The main findings of our empirical work are that beta dynamics are significantly affected by economic variables, even though managers do not care about bench-mark sensitivities towards the predictors in choosing their instrument exposure, and that persistence and leverage effects play a key role as well. Conditional market timing is virtually absent, if not negative, over the period 1990-2005. However such anomalous negative timing ability is offset by the leverage effect, which in turn leads to an increase in mutual fund extra performance. JEL Classification: C11, C13, G12, G13Bayesian analysis, conditional asset pricing models, Equity mutual funds, time-varying beta
Cortical Factor Feedback Model for Cellular Locomotion and Cytofission
Eukaryotic cells can move spontaneously without being guided by external
cues. For such spontaneous movements, a variety of different modes have been
observed, including the amoeboid-like locomotion with protrusion of multiple
pseudopods, the keratocyte-like locomotion with a widely spread lamellipodium,
cell division with two daughter cells crawling in opposite directions, and
fragmentations of a cell to multiple pieces. Mutagenesis studies have revealed
that cells exhibit these modes depending on which genes are deficient,
suggesting that seemingly different modes are the manifestation of a common
mechanism to regulate cell motion. In this paper, we propose a hypothesis that
the positive feedback mechanism working through the inhomogeneous distribution
of regulatory proteins underlies this variety of cell locomotion and
cytofission. In this hypothesis, a set of regulatory proteins, which we call
cortical factors, suppress actin polymerization. These suppressing factors are
diluted at the extending front and accumulated at the retracting rear of cell,
which establishes a cellular polarity and enhances the cell motility, leading
to the further accumulation of cortical factors at the rear. Stochastic
simulation of cell movement shows that the positive feedback mechanism of
cortical factors stabilizes or destabilizes modes of movement and determines
the cell migration pattern. The model predicts that the pattern is selected by
changing the rate of formation of the actin-filament network or the threshold
to initiate the network formation
Calibration of Multicurrency LIBOR Market Models
This paper presents a methodf or calibrating a multi currency lognormal LIBOR Market Model to market data of at-the-money caps, swaptions and FX options. By exploiting the fact that multivariate normal distributions are invariant under orthonormal transformations, the calibration problem is decomposed into manageable stages, while maintaining the ability to achieve realistic correlation structures between all modelled market variables.currency options; LIBOR market model; exchange rate risk; interest rate risk
Electrodynamics of an omega-band as deduced from optical and magnetometer data
We investigate an omega-band event that took place above northern Scandinavia
around 02:00â02:30 UT on 9 March 1999. In our analysis we use ground based
magnetometer, optical and riometer measurements together with satellite based
optical images. The optical and riometer data are used to estimate the
ionospheric Hall and Pedersen conductances, while ionospheric equivalent
currents are obtained from the magnetometer measurements. These data sets are
used as input in a local KRM calculation, which gives the ionospheric
potential electric field as output, thus giving us a complete picture of the
ionospheric electrodynamic state during the omega-band event.
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The overall structure of the electric field and field-aligned current (FAC)
provided by the local KRM method are in good agreement with previous studies.
Also the <I><B>E</B></I>×<I><B>B</B></I> drift velocity calculated from the local
KRM solution is in good qualitative agreement with the plasma velocity
measured by the Finnish CUTLASS radar, giving further support for the new
local KRM method. The high-resolution conductance estimates allow us to
discern the detailed structure of the omega-band current system. The highest
Hall and Pedersen conductances, ~50 and ~25 S, respectively, are
found at the edges of the bright auroral tongue. Inside the tongue,
conductances are somewhat smaller, but still significantly higher than
typical background values. The electric field shows a converging pattern
around the tongues, and the field strength drops from ~40 mV/m found at
optically dark regions to ~10 mV/m inside the areas of enhanced
conductivity. Downward FAC flow in the dark regions, while upward currents
flow inside the auroral tongue. Additionally, sharp conductance gradients at
the edge of an auroral tongue are associated with narrow strips of intense
FACs, so that a strip of downward current flows at the eastern (leading) edge
and a similar strip of upward current is present at the western (trailing)
edge. The Joule heating follows the electric field pattern, so that it is
diminished inside the bright auroral tongue
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