Corrections.
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC5691334 | biostudies-literature | 2013 Nov
REPOSITORIES: biostudies-literature
Publications
Corrections.
The American journal of pathology 20131022 5
Similar Datasets
Project description:IntroductionWhile smoking rates are 3-4 times higher among criminal justice populations than in the general population, no studies have previously examined smoking characteristics in a community corrections population.MethodsThe current study involved descriptive analyses of self-reported survey data from 217 criminal justice supervisees reporting for urine drug screens during a 5-day period at a community corrections facility in the southeastern United States.ResultsMost participants were current smokers (72.3%), males (65.9%), and Black (50.2%) who reported smoking three fourths of a pack of cigarettes per day and had been smoking for about 15 years. More than half of smokers reported that they would be interested in receiving cessation assistance if free help were available and of these, 60% were interested in pharmacotherapy. White smokers used more cigarettes per day, were more likely to have already tried medication to help them quit smoking, and were also more interested in pharmacotherapies and less interested in behavioral therapies compared with Black smokers. Female smokers did not differ from male smokers on key smoking characteristics, but male smokers were more likely to have tried or regularly used other tobacco products, such as cigars. Female smokers were significantly more likely to report interest in using a pharmacotherapy agent for future cessation, while male smokers reported more interest in nonpharmacotherapy approaches to quit smoking.DiscussionResults from this study highlight important differences among smoking groups and may indicate the need to test tailored smoking interventions.
| S-EPMC2902910 | biostudies-literature
Project description:Neutrino oscillation experiments at accelerator energies aim to establish charge-parity violation in the neutrino sector by measuring the energy-dependent rate of νe appearance and νμ disappearance in a νμ beam. These experiments can precisely measure νμ cross sections at near detectors, but νe cross sections are poorly constrained and require theoretical inputs. In particular, quantum electrodynamics radiative corrections are different for electrons and muons. These corrections are proportional to the small quantum electrodynamics coupling α ≈ 1/137; however, the large separation of scales between the neutrino energy and the proton mass (~GeV), and the electron mass and soft-photon detection thresholds (~MeV) introduces large logarithms in the perturbative expansion. The resulting flavor differences exceed the percent-level experimental precision and depend on nonperturbative hadronic structure. We establish a factorization theorem for exclusive charged-current (anti)neutrino scattering cross sections representing them as a product of two factors. The first factor is flavor universal; it depends on hadronic and nuclear structure and can be constrained by high-statistics νμ data. The second factor is non-universal and contains logarithmic enhancements, but can be calculated exactly in perturbation theory. For charged-current elastic scattering, we demonstrate the cancellation of uncertainties in the predicted ratio of νe and νμ cross sections. We point out the potential impact of non-collinear energetic photons and the distortion of the visible lepton spectra, and provide precise predictions for inclusive observables.
| S-EPMC9458660 | biostudies-literature
Project description:Theoretical models capture very precisely the behaviour of magnetic materials at the microscopic level. This makes computer simulations of magnetic materials, such as spin dynamics simulations, accurately mimic experimental results. New approaches to efficient spin dynamics simulations are limited by integration time step barrier to solving the equations-of-motions of many-body problems. Using a short time step leads to an accurate but inefficient simulation regime whereas using a large time step leads to accumulation of numerical errors that render the whole simulation useless. In this paper, we use a Deep Learning method to compute the numerical errors of each large time step and use these computed errors to make corrections to achieve higher accuracy in our spin dynamics. We validate our method on the 3D Ferromagnetic Heisenberg cubic lattice over a range of temperatures. Here we show that the Deep Learning method can accelerate the simulation speed by 10 times while maintaining simulation accuracy and overcome the limitations of requiring small time steps in spin dynamic simulations.
| S-EPMC7426868 | biostudies-literature