40,603 research outputs found
Pilot workload and fatigue: A critical survey of concepts and assessment techniques
The principal unresolved issues in conceptualizing and measuring pilot workload and fatigue are discussed. These issues are seen as limiting the development of more useful working concepts and techniques and their application to systems engineering and management activities. A conceptual analysis of pilot workload and fatigue, an overview and critique of approaches to the assessment of these phenomena, and a discussion of current trends in the management of unwanted workload and fatigue effects are presented. Refinements and innovations in assessment methods are recommended for enhancing the practical significance of workload and fatigue studies
Solar flare gamma-ray line spectroscopy
The techniques and the results of solar elemental abundance determinations using observations of gamma ray lines from the April 27 1981 olar flare were outlined. The techniques are elaborated on and observed and the best-fitting theoretical spectra are presented. Numerical values for the photon fluences and the total number of protons involved in the thick-target production of these gamma rays are derived
Abundances from solar-flare gamma-ray line spectroscopy
Elemental abundances of the ambient gas at the site of gamma ray line production inthe solar atmosphere are deduced using gamma ray line observations from a solar flare. The resultant abundances are different from local galactic abundances which are thought to be similar to photospheric abundances
Rotational CARS application to simultaneous and multiple-point temperature and concentration determination in a turbulent flow
Coherent anti-Stokes Raman scattering (CARS) from the pure rotational Raman lines of N2 is employed to measure the instantaneous (approximately 10 ns) rotational temperature of N2 gas at room temperature and below with good spatial resolution (0.2 x 0.2 x 3.0 cu mm). A broad bandwidth dye laser is used to obtain the entire rotational spectrum from a single laser pulse; the CARS signal is then dispersed by a spectrograph and recorded on an optical multichannel analyzer. A best fit temperature is found in several seconds with the aid of a computer for each experimental spectrum by a least squares comparison with calculated spectra. The model used to calculate the theoretical spectra incorporates the temperature and pressure dependence of the pressure-broadened rotational Raman lines, includes the nonresonant background susceptibility, and assumes that the pump laser has a finite linewidth. Temperatures are fit to experimental spectra recorded over the temperature range of 135 to 296 K, and over the pressure range of .13 to 15.3 atm
Application of plasmonic nanomaterials in nanomedicine
Plasmonic nanoparticles are being researched as a noninvasive tool for ultrasensitive
diagnostic, spectroscopic and, recently, therapeutic technologies. With particular
antibody coatings on nanoparticles, they attach to the abnormal cells of interest (cancer
or otherwise). Once attached, nanoparticles can be activated/heated with UV/visible/IR,
RF or X-ray pulses, damaging the surrounding area of the abnormal cell to the point of
death. Here, we describe an integrated approach to improved plasmonic therapy composed
of nanomaterial optimization and the development of a theory for selective radiation
nanophotothermolysis of abnormal biological cells with gold nanoparticles and selfassembled
nanoclusters. The theory takes into account radiation-induced linear and
nonlinear synergistic effects in biological cells containing nanostructures, with focus on
optical, thermal, bubble formation and nanoparticle explosion phenomena. On the basis
of the developed models, we discuss new ideas and new dynamic modes for cancer
treatment by radiation activated nanoheaters, which involve nanocluster aggregation in
living cells, microbubbles overlapping around laser-heated intracellular nanoparticles/
clusters, and laser thermal explosion mode of single nanoparticles (‘nanobombs’)
delivered to the cells.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2058
Selected reliability studies for the NERVA program
An investigation was made into certain methods of reliability analysis that are particularly suitable for complex mechanisms or systems in which there are many interactions. The methods developed were intended to assist in the design of such mechanisms, especially for analysis of failure sensitivity to parameter variations and for estimating reliability where extensive and meaningful life testing is not feasible. The system is modeled by a network of interconnected nodes. Each node is a state or mode of operation, or is an input or output node, and the branches are interactions. The network, with its probabilistic and time-dependent paths is also analyzed for reliability and failure modes by a Monte Carlo, computerized simulation of system performance
PassGAN: A Deep Learning Approach for Password Guessing
State-of-the-art password guessing tools, such as HashCat and John the
Ripper, enable users to check billions of passwords per second against password
hashes. In addition to performing straightforward dictionary attacks, these
tools can expand password dictionaries using password generation rules, such as
concatenation of words (e.g., "password123456") and leet speak (e.g.,
"password" becomes "p4s5w0rd"). Although these rules work well in practice,
expanding them to model further passwords is a laborious task that requires
specialized expertise. To address this issue, in this paper we introduce
PassGAN, a novel approach that replaces human-generated password rules with
theory-grounded machine learning algorithms. Instead of relying on manual
password analysis, PassGAN uses a Generative Adversarial Network (GAN) to
autonomously learn the distribution of real passwords from actual password
leaks, and to generate high-quality password guesses. Our experiments show that
this approach is very promising. When we evaluated PassGAN on two large
password datasets, we were able to surpass rule-based and state-of-the-art
machine learning password guessing tools. However, in contrast with the other
tools, PassGAN achieved this result without any a-priori knowledge on passwords
or common password structures. Additionally, when we combined the output of
PassGAN with the output of HashCat, we were able to match 51%-73% more
passwords than with HashCat alone. This is remarkable, because it shows that
PassGAN can autonomously extract a considerable number of password properties
that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS
2018 Workshop on Security in Machine Learning (SecML'18), see
https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd
The Li-7 and Be-7 deexcitation lines: Probes for accelerated particle transport models in solar flares
The photon energy spectrum of a spectral feature composed of the 429 and 478 keV gamma-ray lines from Li-7 and Be-7 (produced by interactions of flare-accelerated alpha particles with ambient He in the solar atmosphere) depends on the angular distribution of the interacting accelerated particles. This spectrum is calculated for limb and disc-centered flares using a loop model for the transport of the ions. In this model, the flux tube magnetic field is constant in the corona and converges in the chromosphere to the photosphere. Magnetic mirroring and MHD pitch-angle scattering are both taken into account. Comparison of these results with data from other experiments is presented
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