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 biochemical, physiological, and metabolic effects of Apollo nominal mission and contingency diets on human subjects while on a simulated Apollo mission Final report, Feb. - Jun. 1966

Biochemical, physiological, and metabolic effects of simulated Apollo mission with space diet on human