SEARCH · Search NASA
Results for “Tagging”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Lab-on-a-Fish: Wireless, Miniaturized, Fully Integrated, Implantable Biotelemetric Tag for Real-Time In Vivo Monitoring of Aquatic Animals
Not Available
PT Symmetry-Enabled Physically Unclonable Functions for Anti-Counterfeiting RF Tags
Not provided.
Gold leaf phantom development for the assessment of proton radiographic sensitivity limits simulating gold nanoparticle tagged tumors
Proton radiography may guide proton therapy cancer treatments with beam’s-eye-view anatomical images and a proton-based estimation of proton stopping power. However, without contrast enhancement, proton radiography will not be able to distinguish tumor from tissue. To provide this contrast, functionalized, high-Z nanoparticles that specifically target a tumor could be injected into a patient before imaging. We conducted this study to understand the ability of gold, as a high-Z, biologically compatible tracer, to differentiate tumors from surrounding tissue. We indicate that AuNP-enhanced proton radiography might be a feasible technology to provide image-guidance to proton therapy, potentially reducing off-target effects and sparing nearby tissue. These data can be used to develop treatment plans and clinical applications can be derived from the simulations.
Deletion Mutants, Archived Transposon Library, and Tagged Protein Constructs of the Model Sulfate-Reducing Bacterium Desulfovibrio vulgaris Hildenborough
ABSTRACT The dissimilatory sulfate-reducing deltaproteobacterium Desulfovibrio vulgaris Hildenborough (ATCC 29579) was chosen by the research collaboration ENIGMA to explore tools and protocols for bringing this anaerobe to model status. Here, we describe a collection of genetic constructs generated by ENIGMA that are available to the research community.
Mobility Pattern Analysis for Power Restoration Activities Using Geo-Tagged Tweets
In this study, we analyzed mobility patterns of at-risk populations affected by an extreme event using geotagged tweets to geo-target power restoration efforts. Unlike other studies that have used tweets to facilitate emergency management activities, we used 1.5 million geotagged tweets generated during Hurricane Sandy (2012) to determine the mobility patterns and geospatial distribution of impacted populations who experienced power outage before, during and after the hurricane. We implemented a three-step analytical framework to: (i) analyze tweet contents with visual methods, including dendrograms, word clouds to identify common keywords pertaining to power outage; (ii) identify target users whose tweets contained information about power outages; and (iii) create a user-tweet locations matrix and an origin-destination matrix to examine clusters of target users and their mobility patterns. Preliminary results indicate that potential clusters were present in and around New York city, Philadelphia, Washington D.C. and Baltimore, which were used as potential evacuation destination cities after hurricane Sandy. The travel pattern and destination information can be used to (i) mobilize restoration efforts by utility companies and (ii) address resource allocation needs both in impacted and destination cities. Future work will focus on analyzing potential destinations for different origins and travel-time to identify evacuation routing patterns.
Exploring unsupervised top tagging using Bayesian inference
Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable \tilde{A}_{3} A ̃ 3 , and in the other we consider the more traditional \tau_{3}/\tau_{2} τ 3 / τ 2 N N -subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC \sim 0.80-0.81 ∼ 0.80 − 0.81 and accuracies of about 69% - − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.
UTS, the universal tagging system for quantitative labeling of biomolecules. Tech Snapshot: Biology
Intended for the Web, the technology snapshot provides a short summary, market, benefits, a contact and a few brief snippets on the technology development.
Study of Heavy Flavor Mesons and Flavor-Tagged Jets with the CMS Detector
The goal of this research program is to implement heavy flavor meson triggers in heavy-ion collisions for the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at CERN, including algorithm design, timing studies, offline validation, and online performance monitoring. The physics analyses which can be achieved by data from these new triggers is to address one of the most important questions in the field: parton flavor dependence of jet-quenching for the understanding of the transport properties of the Quark-Gluon Plasma. This program will allow CMS to collect the highest statistics heavy flavor meson and jet data ever recorded in heavy-ion colliders. The program includes two objectives: (1) Build and maintain the heavy flavor meson and jet triggers for heavy-ion collisions and deploy the trigger algorithms for 2015-2018 PbPb and pPb run at the LHC; (2) Perform heavy flavor meson and jet physics analyses, which can be used to study the parton flavor dependence of jet quenching, to extract the elastic energy loss coefficient of the QGP, and to test whether massive quarks also participate in collective expansion dynamics in heavy-ion collisions. With the heavy flavor physics trigger developed in this project, a competitive heavy flavor physics program in heavy-ion collisions has been established in CMS. This program allows studies of the fully reconstructed and flavor identified charm, beauty, and exotic hadrons that cover the widest transverse momentum range. The novel measurements supported by the award provide new constraints on the size of the flavor dependence of parton energy loss, the value of the in-medium charm quark diffusion coefficient, the mechanism of charm and beauty quark hadronization, and provide new insights to the nature of the X(3872) hadron.
Search for resonance in inclusive and b-tagged dijet mass spectra in proton-proton collision at $\sqrt{s}$ = 13 TeV and constraints on dark matter and other models
Two analyses searching for new heavy resonances and interpreting the result with dark matter are presented in this dissertation. The first analysis searches for resonances decaying into dijet final states using the proton-proton collision data at center mass of $\sqrt{s}=13$ TeV with the CMS detector at Large Hadron Collider in2016, corresponding to a total integrated luminosity of 36 fb -1 . No significant evidence for the production of new particles is observed. A simplified model of interactions between quarks and dark matter is used to interpret the result. The second analysis searches for resonances decaying into dijet final states using the proton-proton collision data at center mass of $\sqrt{s}=13$ TeV with the CMS detector at Large Hadron Collider in 2016, 2017, and 2018, corresponding to a total integrated luminosity of 137.5 fb -1 . A b-tagger named DeepJet is used to examine the events with jets containing B hadrons. No evidence of new heavy resonances is observed, as well. The 95% confidence level cross-section upper limit is set on excited b-quark b* and Coloron models. Limits are also set on a simplified model of interactions between b-quarks and dark matter.
Smart Materials for Highly Complex Optical Tags with Environmental Response.
Abstract not provided.
First Look at Cosmic Ray Tagging (CRT) Track Reconstruction at ICARUS [Poster]
Explore the source record for details and available documents.
Pion and Kaon Tagged DIS at JLab 12-GeV
Explore the source record for details and available documents.
Design and analysis for placement of RFID tags on nuclear material containers in a mockup test environment [Slides]
Abstract not provided.