Constraint and gauge shocks in one-dimensional numerical relativity

We study how different types of blowups can occur in systems of hyperbolic evolution equations of the type found in general relativity. In particular, we discuss two independent criteria that can be used to determine when such blowups can be expected. One criteria is related to the so-called geometric blowup leading to gradient catastrophes, while the other is based upon the ODE-mechanism leading to blowups within finite time. We show how both mechanisms work in the case of a simple one-dimensional wave equation with a dynamic wave speed and sources, and later explore how those blowups can appear in one-dimensional numerical relativity. In the latter case we recover the well known "gauge shocks" associated with Bona-Massó–type slicing conditions. However, a crucial result of this study has been the identification of a second family of blowups associated with the way in which the constraints have been used to construct a hyperbolic formulation. We call these blowups "constraint shocks" and show that they are formulation specific, and that choices can be made to eliminate them or at least make them less severe

Graph Based Learning for Building Prediction in Smart Cities

Anticipating pedestrians’ activity is a necessary task for providing a safe and energy efficient environment in an urban area. By locating strategically sensors throughout the city useful information could be obtained. By knowing the average activity of those throughout different days of the week we could identify the typology of the buildings neighboring those sensors. For these type of purposes, clustering methods show great capability forming groups of items that have great similarity intra clusters and dissimilarity inter cluster. Different approaches are made to classify sensors depending on the typology of buildings surrounding them and the mean pedestrians’ counts for different time intervals. By this way, sensors could be classified in different groups according to their activation patterns and the environment in which they are located through clustering processes and using graph convolutional networks. This study reveals that there is a close relationship between the activity pattern of the pedestrians’ and the type of environment sensors that collect pedestrians’ data are located. By this way, institutions could alleviate a great amount of effort needed to ensure safe and energy efficient urban areas, only knowing the typology of buildings of an urban zone

Gauge and constraint shocks in one-dimensional numerical relativity

We study how different types of blow-ups can occur in systems of hyperbolic evolution equations of the type found in general relativity. In particular, we discuss two independent criteria that can be used to determine when such blow-ups can be expected. One criteria is related with the so-called geometric blow-up leading to gradient catastrophes, while the other is based upon the ODE-mechanism leading to blow-ups within finite time. We show how both mechanisms work in the case of a simple one-dimensional wave equation with a dynamic wave speed and sources, and later explore how those blow-ups can appear in one-dimensional numerical relativity. In the latter case we recover the well known ``gauge shocks'' associated with Bona-Masso type slicing conditions. However, a crucial result of this study has been the identification of a second family of blow-ups associated with the way in which the constraints have been used to construct a hyperbolic formulation. We call these blow-ups ``constraint shocks'' and show that they are formulation specific, and that choices can be made to eliminate them or at least make them less severe

Polycrystalline Ni nanotubes under compression: a molecular dynamics study

Mechanical properties of nanomaterials, such as nanowires and nanotubes, are an important feature for the design of novel electromechanical nano-architectures. Since grain boundary structures and surface modifications can be used as a route to modify nanostructured materials, it is of interest to understand how they affect material strength and plasticity. We report large-scale atomistic simulations to determine the mechanical response of nickel nanowires and nanotubes subject to uniaxial compression. Our results suggest that the incorporation of nanocrystalline structure allows completely flexible deformation, in sharp contrast with single crystals. While crystalline structures at high compression are dominated by dislocation pinning and the multiplication of highly localized shear regions, in nanocrystalline systems the dislocation distribution is significantly more homogeneous. Therefore, for large compressions (large strains) coiling instead of bulging is the dominant deformation mode. Additionally, it is observed that nanotubes with only 70% of the nanowire mass but of the same diameter, exhibit similar mechanical behavior up to 0.3 strain. Our results are useful for the design of new flexible and light-weight metamaterials, when highly deformable struts are required.Fil: Rojas Nunez, J.. Universidad de Santiago de Chile; ChileFil: Baltazar, S. E.. Universidad de Santiago de Chile; ChileFil: Gonzalez, R. I.. Universidad Mayor; ChileFil: Bringa, Eduardo Marcial. Universidad Mayor; Chile. Universidad de Mendoza. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Allende, S.. Universidad de Santiago de Chile; ChileFil: Kiwi, M.. Universidad de Santiago de Chile; ChileFil: Valencia, F. J.. Universidad de Santiago de Chile; Chile. Universidad Mayor; Chil

Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estima-tion for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmen-tations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL-and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray mat-ter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.Radiolog

Polystyrene Nanopillars with Inbuilt Carbon Nanotubes Enable Synaptic Modulation and Stimulation in Interfaced Neuronal Networks

The use of nanostructured materials and nanosized-topographies has the potential to impact the performance of implantable biodevices, including neural interfaces, enhancing their sensitivity and selectivity, while reducing tissue reactivity. As a result, current trends in biosensor technology require the effective ability to improve devices with controlled nanostructures. Nanoimprint lithography to pattern surfaces with high-density and high aspect ratio nanopillars (NPs) made of polystyrene (PS-NP, insulating), or of a polystyrene/carbon-nanotube nanocomposite (PS-CNT-NP, electrically conductive) are exploited. Both substrates are challenged with cultured primary neurons. They are demonstrated to support the development of suspended synaptic networks at the NPs’ interfaces characterized by a reduction in proliferating neuroglia, and a boost in neuronal emergent electrical activity when compared to flat controls. The authors successfully exploit their conductive PS-CNT-NPs to stimulate cultured cells electrically. The ability of both nanostructured surfaces to interface tissue explants isolated from the mouse spinal cord is then tested. The integration of the neuronal circuits with the NP topology, the suspended nature of the cultured networks, the reduced neuroglia formation, and the higher network activity together with the ability to deliver electrical stimuli via PS-CNT-NP reveal such platforms as promising designs to implement on neuro-prosthetic or neurostimulation devices

The Large Aperture GRB Observatory

The Large Aperture GRB Observatory (LAGO) is aiming at the detection of the high energy (around 100 GeV) component of Gamma Ray Bursts, using the single particle technique in arrays of Water Cherenkov Detectors (WCD) in high mountain sites (Chacaltaya, Bolivia, 5300 m a.s.l., Pico Espejo, Venezuela, 4750 m a.s.l., Sierra Negra, Mexico, 4650 m a.s.l). WCD at high altitude offer a unique possibility of detecting low gamma fluxes in the 10 GeV - 1 TeV range. The status of the Observatory and data collected from 2007 to date will be presented.Comment: 4 pages, proceeding of 31st ICRC 200