Investigation of Optical Properties and Radiation Stability of TiO2 Powders before and after Modification by Nanopowders of Various Oxides

The titanium dioxide powders are widely used as a pigment for coatings and paints, the important characteristics of which are reflectivity and stability to irradiation. The results of investigations of the optical properties and radiation stability of titanium dioxide powders before and after high-temperature modification with nanopowders are presented in this chapter. The diffuse reflection spectra of various titanium dioxide powders in the UV, visible, and near-IR ranges, and their change during irradiation by electrons with 30 keV energy and a different fluence in vacuum in situ were investigated: (1) TiO2 powders with particle size in the range 60–240 nm; (2) Microsized TiO2 powder (240 nm) modified by Al2O3, ZrO2, SiO2, TiO2, ZnO, MgO nanoparticles with grain size from 30 up to 60 nm; (3) Microsized TiO2 powder (260 nm) modified by SiO2 with the grain size of 12–14 nm at the temperature of 150, 400, and 800°C. The reduction in reflectivity in entire spectrum with decrease in grain sizes of TiO2 nanopowders was established. Nanopowder with the grain size of 80 nm possesses the highest stability to irradiation. It was shown that the average grain size and specific surface of introduced nanoparticles effect noticeably on the radiation stability increase of titanium dioxide powders modified with nanoparticles of various oxides. The micro-sized TiO2 powder heating at temperature of 800оС is the factor which positively influences on the radiation stability

The Curriculum Project on Professional and Pedagogical Teachers’ Communication Culture Formation

The changes in the socio-economic and spiritual spheres of modern society, trends in the renewal of the educational process put forward new demands to the level of modern teachers professional and pedagogical communication culture formation. The solution to this problem objectively requires the development of more flexible curricula of professional growth, aimed at the efficient formation of their professional and pedagogical communication culture. In this regard, this article presents the project of the curriculum "Fundamentals of professional and pedagogical communication culture formation", which allowed reveal its effectiveness for teachers who has two years of experience of professional activity. The materials of the articles are of theoretical and practical value for teachers of secondary schools, and for teachers - beginners of high schools and colleges of vocational education. DOI: 10.5901/mjss.2015.v6n2s3p20

On Propagation of Excitation Waves in Moving Media: The FitzHugh-Nagumo Model

BACKGROUND: Existence of flows and convection is an essential and integral feature of many excitable media with wave propagation modes, such as blood coagulation or bioreactors. METHODS/RESULTS: Here, propagation of two-dimensional waves is studied in parabolic channel flow of excitable medium of the FitzHugh-Nagumo type. Even if the stream velocity is hundreds of times higher that the wave velocity in motionless medium (), steady propagation of an excitation wave is eventually established. At high stream velocities, the wave does not span the channel from wall to wall, forming isolated excited regions, which we called "restrictons". They are especially easy to observe when the model parameters are close to critical ones, at which waves disappear in still medium. In the subcritical region of parameters, a sufficiently fast stream can result in the survival of excitation moving, as a rule, in the form of "restrictons". For downstream excitation waves, the axial portion of the channel is the most important one in determining their behavior. For upstream waves, the most important region of the channel is the near-wall boundary layers. The roles of transversal diffusion, and of approximate similarity with respect to stream velocity are discussed. CONCLUSIONS: These findings clarify mechanisms of wave propagation and survival in flow

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License