Emerging economy entrepreneurs and open data: Decision-making for natural disaster resilience

The aim of this study is to examine the role of Open Data in entrepreneurial decision-making in a destination threatened by natural disasters and located in an emerging economy. The region of La Araucanía, in Chile, was chosen because it constantly faces the threat of devastating natural disasters and is also the poorest region of Chile. Primary data was collected through semi-structured interviews and awareness-building workshops, with a convenience sample of 32 entrepreneurs out of 150 registered for Federation of Tourism Businesses (FEDETUR’s CET) program. The study found that local entrepreneurs are reasonably aware of the advantages and disadvantages of platforms that link supply and demand for tourism services. However, they express little interest or trust in publicly available information, and use terms like ‘internet data’ and ‘technology’ interchangeably with ‘information’ and ‘platforms’. We conclude that in order for entrepreneurs in emerging economies to strengthen their businesses’ resilience to natural disasters in the digital economy era, adjustments in their decision-making processes need to be made. Tourism-dependent places situated in emerging economies rely heavily on micro and small businesses. Greater awareness of how future economies are both ‘atom-enabled’ (landscape and other tourism resources) and ‘bit-dependent’ (digitalization of tourism) would benefit tourism entrepreneurs facing natural disaster-induced business disruptions by enabling timely and more appropriate responses. The study opens the academic debate on the role that open data could come to play in entrepreneurial decision-making within emerging economies when tourism businesses are disrupted by natural disasters

Modeling the mechanical response of polycrystals deforming by climb and glide

This paper presents a crystallographically-based constitutive model of a single crystal deforming by climb and glide. The proposed constitutive law is an extension of the rate-sensitivity approach for single crystal plasticity by dislocation glide. Based on this description at single crystal level, a homogenization-based polycrystal model for aggregates deforming in a climb-controlled thermal creep regime is developed. To illustrate the capabilities of the proposed model, we present calculations of effective behavior of olivine and texture evolution of aluminum at warm temperature and low strain rate. In both cases, the addition of climb as a complementary single-crystal deformation mechanism improves the polycrystal model predictions

Visual Odometry Based on Structural Matching of Local Invariant Features Using Stereo Camera Sensor

This paper describes a novel sensor system to estimate the motion of a stereo camera. Local invariant image features are matched between pairs of frames and linked into image trajectories at video rate, providing the so-called visual odometry, i.e., motion estimates from visual input alone. Our proposal conducts two matching sessions: the first one between sets of features associated to the images of the stereo pairs and the second one between sets of features associated to consecutive frames. With respect to previously proposed approaches, the main novelty of this proposal is that both matching algorithms are conducted by means of a fast matching algorithm which combines absolute and relative feature constraints. Finding the largest-valued set of mutually consistent matches is equivalent to finding the maximum-weighted clique on a graph. The stereo matching allows to represent the scene view as a graph which emerge from the features of the accepted clique. On the other hand, the frame-to-frame matching defines a graph whose vertices are features in 3D space. The efficiency of the approach is increased by minimizing the geometric and algebraic errors to estimate the final displacement of the stereo camera between consecutive acquired frames. The proposed approach has been tested for mobile robotics navigation purposes in real environments and using different features. Experimental results demonstrate the performance of the proposal, which could be applied in both industrial and service robot fields

Exacerbated leishmaniasis caused by a viral endosymbiont can be prevented by immunization with Its viral capsid

Recent studies have shown that a cytoplasmic virus called Leishmaniavirus (LRV) is present in some Leishmania species and acts as a potent innate immunogen, aggravating lesional inflammation and development in mice. In humans, the presence of LRV in Leishmania guyanensis and in L. braziliensis was significantly correlated with poor treatment response and symptomatic relapse. So far, no clinical effort has used LRV for prophylactic purposes. In this context, we designed an original vaccine strategy that targeted LRV nested in Leishmania parasites to prevent virus-related complications. To this end, C57BL/6 mice were immunized with a recombinant LRV1 Leishmania guyanensis viral capsid polypeptide formulated with a T helper 1-polarizing adjuvant. LRV1-vaccinated mice had significant reduction in lesion size and parasite load when subsequently challenged with LRV1+ Leishmania guyanensis parasites. The protection conferred by this immunization could be reproduced in naïve mice via T-cell transfer from vaccinated mice but not by serum transfer. The induction of LRV1 specific T cells secreting IFN-γ was confirmed in vaccinated mice and provided strong evidence that LRV1-specific protection arose via a cell mediated immune response against the LRV1 capsid. Our studies suggest that immunization with LRV1 capsid could be of a preventive benefit in mitigating the elevated pathology associated with LRV1 bearing Leishmania infections and possibly avoiding symptomatic relapses after an initial treatment. This novel anti-endosymbiotic vaccine strategy could be exploited to control other infectious diseases, as similar viral infections are largely prevalent across pathogenic pathogens and could consequently open new vaccine opportunities

High-velocity outflows in massive post-starburst galaxies at z > 1

We investigate the prevalence of galactic-scale outflows in post-starburst (PSB) galaxies at high redshift (1 1010M⊙⁠) PSBs at z > 1, there is clear evidence for a strong blue-shifted component to the Mg ii absorption feature, indicative of high-velocity outflows (⁠vout∼1150±160kms−1⁠) in the interstellar medium. We conclude that such outflows are typical in massive PSBs at this epoch, and potentially represent the residual signature of a feedback process that quenched these galaxies. Using full spectral fitting, we also obtain a typical stellar velocity dispersion σ* for these PSBs of ∼200kms−1⁠, which confirms they are intrinsically massive in nature (dynamical mass Md∼1011M⊙⁠). Given that these high-z PSBs are also exceptionally compact (re ∼ 1–2kpc⁠) and spheroidal (Sérsic index n ∼ 3), we propose that the outflowing winds may have been launched during a recent compaction event (e.g. major merger or disc collapse) that triggered either a centralized starburst or active galactic nuclei (AGN) activity. Finally, we find no evidence for AGN signatures in the optical spectra of these PSBs, suggesting they were either quenched by stellar feedback from the starburst itself, or that if AGN feedback is responsible, the AGN episode that triggered quenching does not linger into the post-starburst phase.Publisher PDFPeer reviewe

Galaxies Going Bananas: Inferring the 3D Geometry of High-Redshift Galaxies with JWST-CEERS

The 3D geometry of high-redshift galaxies remains poorly understood. We build a differentiable Bayesian model and use Hamiltonian Monte Carlo to efficiently and robustly infer the 3D shapes of star-forming galaxies in JWST-CEERS observations with $\log M_*/M_{\odot}=9.0-10.5$ at $z=0.5-8.0$. We reproduce previous results from HST-CANDELS in a fraction of the computing time and constrain the mean ellipticity, triaxiality, size and covariances with samples as small as $\sim50$ galaxies. We find high 3D ellipticities for all mass-redshift bins suggesting oblate (disky) or prolate (elongated) geometries. We break that degeneracy by constraining the mean triaxiality to be $\sim1$ for $\log M_*/M_{\odot}=9.0-9.5$ dwarfs at $z>1$ (favoring the prolate scenario), with significantly lower triaxialities for higher masses and lower redshifts indicating the emergence of disks. The prolate population traces out a ``banana'' in the projected $b/a-\log a$ diagram with an excess of low $b/a$, large $\log a$ galaxies. The dwarf prolate fraction rises from $\sim25\%$ at $z=0.5-1.0$ to $\sim50-80\%$ at $z=3-8$. If these are disks, they cannot be axisymmetric but instead must be unusually oval (triaxial) unlike local circular disks. We simultaneously constrain the 3D size-mass relation and its dependence on 3D geometry. High-probability prolate and oblate candidates show remarkably similar S\'ersic indices ($n\sim1$), non-parametric morphological properties and specific star formation rates. Both tend to be visually classified as disks or irregular but edge-on oblate candidates show more dust attenuation. We discuss selection effects, follow-up prospects and theoretical implications.Comment: Submitted to ApJ, main body is 35 pages of which ~half are full-page figures, comments welcom

Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing

We present photometric redshift estimates for galaxies used in the weak lensing analysis of the Dark Energy Survey Science Verification (DES SV) data. Four model- or machine learning-based photometric redshift methods—ANNZ2, BPZ calibrated against BCC-Ufig simulations, SKYNET, and TPZ—are analyzed. For training, calibration, and testing of these methods, we construct a catalogue of spectroscopically confirmed galaxies matched against DES SV data. The performance of the methods is evaluated against the matched spectroscopic catalogue, focusing on metrics relevant for weak lensing analyses, with additional validation against COSMOS photo-z’s. From the galaxies in the DES SV shear catalogue, which have mean redshift 0.72 0.01 over the range 0.3 < z < 1.3, we construct three tomographic bins with means of z ¼ f0.45; 0.67; 1.00g. These bins each have systematic uncertainties δz ≲ 0.05 in the mean of the fiducial SKYNET photo-z nðzÞ. We propagate the errors in the redshift distributions through to their impact on cosmological parameters estimated with cosmic shear, and find that they cause shifts in the value of σ8 of approximately 3%. This shift is within the one sigma statistical errors on σ8 for the DES SV shear catalogue. We further study the potential impact of systematic differences on the critical surface density, Σcrit, finding levels of bias safely less than the statistical power of DES SV data. We recommend a final Gaussian prior for the photo-z bias in the mean of nðzÞ of width 0.05 for each of the three tomographic bins, and show that this is a sufficient bias model for the corresponding cosmology analysis