We present a constraint on the tensor-to-scalar ratio, r, derived from measurements of cosmic microwave background (CMB) polarization B-modes with “delensing,” whereby the uncertainty on r contributed by the sample ...
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We present a constraint on the tensor-to-scalar ratio, r, derived from measurements of cosmic microwave background (CMB) polarization B-modes with “delensing,” whereby the uncertainty on r contributed by the sample variance of the gravitational lensing B-modes is reduced by cross-correlating against a lensing B-mode template. This template is constructed by combining an estimate of the polarized CMB with a tracer of the projected large-scale structure. The large-scale-structure tracer used is a map of the cosmic infrared background derived from Planck satellite data, while the polarized CMB map comes from a combination of South Pole Telescope, bicep/Keck, and Planck data. We expand the bicep/Keck likelihood analysis framework to accept a lensing template and apply it to the bicep/Keck dataset collected through 2014 using the same parametric foreground modeling as in the previous analysis. From simulations, we find that the uncertainty on r is reduced by ∼10%, from σ(r)=0.024 to 0.022, which can be compared with a ∼26% reduction obtained when using a perfect lensing template or if there were zero lensing B-modes. Applying the technique to the real data, the constraint on r is improved from r0.05<0.090 to r0.05<0.082 (95% C.L.). This is the first demonstration of improvement in an r constraint through delensing.
The jet cross section and jet-substructure observables in p+p collisions at s=200 GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-parti...
The jet cross section and jet-substructure observables in p+p collisions at s=200 GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-kt algorithm with a jet radius of R=0.3 for jets with transverse momentum within 8.0
A critical assessment of computational hit-finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three pa...
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A critical assessment of computational hit-finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening paired with advanced computational and machine learning methods to each predict up to 100 inhibitory ligands. Across all teams, 1957 compounds were predicted and were subsequently procured from commercial catalogs for biophysical assays. Of these compounds, 0.7% were confirmed to bind to Nsp13 in a surface plasmon resonance assay. The six best-performing computational workflows used fragment growing, active learning, or conventional virtual screening with and without complementary deep-learning scoring functions. Follow-up functional assays resulted in identification of two compound scaffolds that bound Nsp13 with a below 10 μM and inhibited helicase activity. Overall, CACHE #2 participants were successful in identifying hit compound scaffolds targeting Nsp13, a central component of the coronavirus replication-transcription complex. Computational design strategies recurrently successful across the first two CACHE challenges include linking or growing docked or crystallized fragments and docking small and diverse libraries to train ultrafast machine-learning models. The CACHE #2 competition reveals how crowd-sourcing ligand prediction efforts using a distinct array of approaches followed with critical biophysical assays can result in novel lead compounds to advance drug discovery efforts.
This research paper examined the connectedness of STEM faculty to others both within and across academic departments who might be potential resources for diffusion of Learner-centered practices, and the impact of part...
Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of ...
Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided
This paper presents the application program of fingerprint detection using wavelet transform for authentication. Fingerprints are obtained from the site of crime, old documents and excavated things. This paper propose...
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This is an evidence-based paper based on research that has shown faculty beliefs influence their classroom practices and reformed teaching methods like engagement teaching improve student performance and retention in ...
This is an evidence-based paper based on research that has shown faculty beliefs influence their classroom practices and reformed teaching methods like engagement teaching improve student performance and retention in science, technology, engineering, and mathematics (STEM) fields. To better understand the relationships between faculty beliefs and practice and student outcomes such as performance and attitudes, three tools were utilized. The first tool is a 24 question guided interview to gauge general beliefs towards teaching;the second is the Approaches to Teaching Inventory (ATI) that measures faculty beliefs towards instructor-centered knowledge transmission and instructor-centered strategies versus student-centered conceptual change intention and strategies. Lastly, the third tool is the Reformed Teaching Observational Protocol (RTOP) which is an observational protocol that quantitatively measures degree of student-centered classroom behaviors. By combining ATI and RTOP scores with emergent theme (ET) analysis on relevant interview questions, faculty characteristics influencing student outcomes can be determined. This work addressees the research questions, "What is the relationship between faculty beliefs and practice?" and "What is the relationship between faculty practice and student outcomes?" 30 faculty members who teach freshman or sophomore level science, math, or engineering courses at a large, southwestern university were interviewed about their teaching beliefs, were surveyed using the ATI, and were observed using the RTOP. Interview questions were analyzed using emergent theme analysis and related to their ATI responses and RTOP scores. The interview question responses were coded numerically as either teacher-centered (-1), student centered (+1), or mixed/neither (0) using the dimensions of the ATI as a basis. The total RTOP scores, the ATI dimension scores, and the sum of the interview ET analyses for every faculty member were then ranked in ascendin
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot...
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A thermochemical library, Thermochimica, utilizes new algorithms and solvers designed for calculating equilibria of multicomponent and multiphase systems and has been coupled to the nuclear fuel performance code BISON...
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ISBN:
(纸本)9780894487309
A thermochemical library, Thermochimica, utilizes new algorithms and solvers designed for calculating equilibria of multicomponent and multiphase systems and has been coupled to the nuclear fuel performance code BISON. Thermochimica utilizes a generalized thermochemical database for urania-based fuels that has been under development for a number of years and continues to be expanded. One of the key motivations for use of thermodynamic models is that they can better represent the physics of oxygen migration. The coupling of the BISON code with Thermochimica and examples of its use in representing fuel phenomena are described. In the current implementation in BISON, fluxes are driven by the elemental concentration gradient. However, equilibrium is characterized by the absence of spatial variations of chemical potentials of the system species/components. The driving force to equilibrium (species flux) is proportional to the deviation from equilibrium, i.e., the gradient in chemical potential. It is therefore necessary to integrate chemical potential driven diffusion in the BISON representation of oxygen transport. The diffusion kernels based on thermodynamic models have been developed to simulate multicomponent transport in light water reactor (LWR) materials. The Thermochimica library was used to calculate various thermodynamics properties needed for transport calculations. The library also can calculate properties that are specific to various transport mechanisms for different fuel materials. For example, it can calculate defect site fractions among the sublattices in oxide fuels as a function of temperature, burnup, and stoichiometry. The transport and thermodynamic models are being integrated with other simulation efforts such as calculation of mobility functions that couple fluxes and chemical gradients.
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