53,539 research outputs found
Recommended from our members
Stacking-based visualization of trajectory attribute data
Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well
Optimizing surveillance for livestock disease spreading through animal movements
The spatial propagation of many livestock infectious diseases critically
depends on the animal movements among premises; so the knowledge of movement
data may help us to detect, manage and control an outbreak. The identification
of robust spreading features of the system is however hampered by the temporal
dimension characterizing population interactions through movements. Traditional
centrality measures do not provide relevant information as results strongly
fluctuate in time and outbreak properties heavily depend on geotemporal initial
conditions. By focusing on the case study of cattle displacements in Italy, we
aim at characterizing livestock epidemics in terms of robust features useful
for planning and control, to deal with temporal fluctuations, sensitivity to
initial conditions and missing information during an outbreak. Through spatial
disease simulations, we detect spreading paths that are stable across different
initial conditions, allowing the clustering of the seeds and reducing the
epidemic variability. Paths also allow us to identify premises, called
sentinels, having a large probability of being infected and providing critical
information on the outbreak origin, as encoded in the clusters. This novel
procedure provides a general framework that can be applied to specific
diseases, for aiding risk assessment analysis and informing the design of
optimal surveillance systems.Comment: Supplementary Information at
https://sites.google.com/site/paolobajardi/Home/archive/optimizing_surveillance_ESM_l.pdf?attredirects=
Multilayer Complex Network Descriptors for Color-Texture Characterization
A new method based on complex networks is proposed for color-texture
analysis. The proposal consists on modeling the image as a multilayer complex
network where each color channel is a layer, and each pixel (in each color
channel) is represented as a network vertex. The network dynamic evolution is
accessed using a set of modeling parameters (radii and thresholds), and new
characterization techniques are introduced to capt information regarding within
and between color channel spatial interaction. An automatic and adaptive
approach for threshold selection is also proposed. We conduct classification
experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT.
Results among various literature methods are compared, including deep
convolutional neural networks with pre-trained architectures. The proposed
method presented the highest overall performance over the 5 datasets, with 97.7
of mean accuracy against 97.0 achieved by the ResNet convolutional neural
network with 50 layers.Comment: 20 pages, 7 figures and 4 table
Preferential Paths of Air-water Two-phase Flow in Porous Structures with Special Consideration of Channel Thickness Effects.
Accurate understanding and predicting the flow paths of immiscible two-phase flow in rocky porous structures are of critical importance for the evaluation of oil or gas recovery and prediction of rock slides caused by gas-liquid flow. A 2D phase field model was established for compressible air-water two-phase flow in heterogenous porous structures. The dynamic characteristics of air-water two-phase interface and preferential paths in porous structures were simulated. The factors affecting the path selection of two-phase flow in porous structures were analyzed. Transparent physical models of complex porous structures were prepared using 3D printing technology. Tracer dye was used to visually observe the flow characteristics and path selection in air-water two-phase displacement experiments. The experimental observations agree with the numerical results used to validate the accuracy of phase field model. The effects of channel thickness on the air-water two-phase flow behavior and paths in porous structures were also analyzed. The results indicate that thick channels can induce secondary air flow paths due to the increase in flow resistance; consequently, the flow distribution is different from that in narrow channels. This study provides a new reference for quantitatively analyzing multi-phase flow and predicting the preferential paths of immiscible fluids in porous structures
Fitting EXAFS data using molecular dynamics outputs and a histogram approach
The estimation of metal nanoparticle diameter by analysis of extended x-ray absorption fine structure (EXAFS) data from coordination numbers is nontrivial, particularly for particles <5 nm in diameter, for which the undercoordination of surface atoms becomes an increasingly significant contribution to the average coordination number. These undercoordinated atoms have increased degrees of freedom over those within the core of the particle, which results in an increase in the degree of structural disorder with decreasing particle size. This increase in disorder, however, is not accounted for by the standard means of EXAFS analysis, where each coordination shell is fitted with a single bond length and disorder term. In addition, the surface atoms of nanoparticles have been observed to undergo a greater contraction than those in the core, further increasing the range of bond distances. Failure to account for this structural change results in an increased disorder being measured, and therefore, a lower apparent coordination number and corresponding particle size are found. Here, we employ molecular dynamics (MD) simulations for a range of nanoparticle sizes to determine each of the nearest neighbor bond lengths, which were then binned into a histogram to construct a radial distribution function (RDF). Each bin from the histogram was considered to be a single scattering path and subsequently used in fitting the EXAFS data obtained for a series of carbon-supported platinum nanoparticles. These MD-based fits are compared with those obtained using a standard fitting model using Artemis and the standard model with the inclusion of higher cumulants, which has previously been used to account for the non-Gaussian distribution of neighboring atoms around the absorber. The results from all three fitting methods were converted to particle sizes and compared with those obtained from transmission electron microscopy (TEM) and x-ray diffraction (XRD) measurements. We find that the use of molecular dynamics simulations resulted in an improved fit over both the standard and cumulant models, in terms of both quality of fit and correlation with the known average particle size
Characterization of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction
Several experiments evidence that specialized brain regions functionally
interact and reveal that the brain processes and integrates information in a
specific and structured manner. Networks can be used to model brain functional
activities constituting a way to characterize and quantify this structured form
of organization. Reports state that different physiological states or even
diseases that affect the central nervous system may be associated to
alterations on those networks, that might reflect in graphs of different
architectures. However, the relation of their structure to different states or
conditions of the organism is not well comprehended. Thus, experiments that
involve the estimation of functional neural networks of subjects exposed to
different controlled conditions are of great relevance. Within this context,
this research has sought to model large scale functional brain networks during
an anesthetic induction process. The experiment was based on intra-cranial
recordings of neural activities of an old world macaque of the species Macaca
fuscata. Neural activity was recorded during a Ketamine-Medetomidine anesthetic
induction process. Networks were serially estimated in time intervals of five
seconds. Changes were observed in various networks properties within about one
and a half minutes after the administration of the anesthetics. These changes
reveal the occurrence of a transition on the networks architecture. During
general anesthesia a reduction in the functional connectivity and network
integration capabilities were verified in both local and global levels. It was
also observed that the brain shifted to a highly specific and dynamic state.
The results bring empirical evidence and report the relation of the induced
state of anesthesia to properties of functional networks, thus, they contribute
for the elucidation of some new aspects of neural correlates of consciousness.Comment: 28 pages , 9 figures, 7 tables; - English errors were corrected;
Figures 1,3,4,5,6,8 and 9 were replaced by (exact the same)figures of higher
resolution; Three(3) references were added on the introduction sectio
- …