R13 - U.S.-Mexico Taskforce to Support the Health Supply Chain Systems for Infrastructure and Workforce Threatened by the COVID19 Pandemic

The project's milestones include the integration of a triple-helix Binational Taskforce, production of spatio-temporal near real-time analytics following a risk systems approach, and publication of a monthly U.S.-Mexico COVID-19 Risk bulletin

Actionable Information - Research Briefs - Vaccination

Includes both static PDF version and the dynamic web version.Five research problems to identify evidence sources and provide initial validation of the risk framework model and data lake have been identified. We focused on COVID-19 Vaccination in the United States and Mexico as the predominant mitigating action. In addition to reliable sources of information, we've identified preordered vaccine supply, its main supply chain elements, and the critical facilities involved in the manufacturing and distribution of millions of doses. In this document we present an initial assessment and findings for this mitigating action

COVID-19 Vaccination Supply Chains

The ongoing COVID-19 pandemic has severely disrupted supply chains in the United States; the objective of this poster is to provide relevant information to key stakeholders in academia, government, and the general public by providing evidence-based predictive models and risk analytics on the causes and effects posed by COVID-19, on the U.S. trade supply chain infrastructure. The identification and characterization of evidence depicting the dynamics of infrastructure interactions of U.S. domestic and international trade supply chains, from procurement, manufacturing, warehousing, to transportation processes, are expected to derive inferences from public sources of information, and databases following a common risk framework developed by our research group at Texas A&M University

U.S.-Mexico Risk Taskforce to Support the Health Supply Chain Systems for Infrastructure and Workforce Threatened by the COViD19 Pandemic

The purpose of this project was to integrate a binational Taskforce from Mexico and the U.S. that could serve as an advisory group to guide the identification and characterization of key variables and processes addressing the impact of COVID-19 in the state of risk of supply chains of critical value for trade between Mexico and the U.S. This project aimed as well as providing the best technology integrator to assimilate evidence that could facilitate the production of risk-based analytics to better inform stakeholders about the likely implementation of risk mitigating strategies that secure the optimal operation of supply chain systems involving trade between both countries. It aimed as well to produce a risk information system that would produce periodical information about the state of risk of Mexico and U.S. supply chain

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

CBTS-SGL Webinar - Breaking Silos. The power of collaboration and abstraction - Catalina Herrera (Dataiku)

Recorded and organized by Araceli Lopez AcostaAs part of the CBTS's Distinguished Speaker Series, CBTS and SGL presented the webinar "Breaking Silos: the power of collaboration and abstraction", by Dataiku's Senior Engineer Catalina Herrera. This webinar introduced examples of a community working together to deliver a common end result using (and reusing data) to maximize community outcomes. It also presented how to leverage Dataikus capabilities to a wide spectrum of applications, including Data4Good, wind turbines with public data, Co2 emissions, among others, to help drive understanding of how to deliver and consume data and insights from many diverse data sources, including observations, model predictions, and experts knowledge (i.e. evidence). The presentation showed how once you break down silos, its important to enhance data products through collaboration, and leverage Machine Learning / Artificial Intelligence to deliver applied data science as transparent consumables.DHS Award Number 18STCBT00001-04-00 and CFDA Number 97.06

CBTS-SGL Webinar - The U.S. Census Bureau's Community Resilience Estimates- Dr. Bethany DeSalvo

On March, 2021 the CBTS COE, and the Stochastic Geomechanics Laboratory (SGL) organized a webinar with Dr. Bethany DeSalvo from the U.S. Census Bureau as a guest speaker to present the methodology behind the Community Resilience Estimates (CRE). The CRE provide an easily understood metric for how at-risk every neighborhood in the United States is to the impacts of COVID-19. This metric uses granular data to measure the individual and community's ability to respond to the effects of the pandemic. Information from the CRE could be easily utilized by policy makers to inform vaccine distribution, where to provide education on public health standards, and pinpoint areas that are at a greater risk of inequitable incomes