Free-space quantum links under diverse weather conditions

Free-space optical communication links are promising channels for establishing secure quantum communication. Here we study the transmission of nonclassical light through a turbulent atmospheric link under diverse weather conditions, including rain or haze. To include these effects, the theory of light transmission through atmospheric links in the elliptic-beam approximation presented by Vasylyev et al. [D. Vasylyev et al., Phys. Rev. Lett. 117, 090501 (2016); arXiv:1604.01373] is further generalized.It is demonstrated, with good agreement between theory and experiment, that low-intensity rain merely contributes additional deterministic losses, whereas haze also introduces additional beam deformations of the transmitted light. Based on these results, we study theoretically the transmission of quadrature squeezing and Gaussian entanglement under these weather conditions.Comment: 14 pages, 8 figure

Исследование возможности проведения изотопного обмена в диоксиде углерода в каскаде газовых центрифуг

Проведено исследование применения реакций изотопного обмена при получении высокообогащенных изотопов углерода в каскаде газовых центрифуг, работающих на диоксиде углерода, и испытание опытного реактора изотопного обмена с никелевым катализатором. Показана принципиальная возможность применения реакций и реактора изотопного обмена для получения высокообогащенных изотопов углерода в каскаде газовых центрифуг

«Разработка пенного пожаротушения в туннеле земле-приготовительного участка литейно-формовочного цеха №10 ЮМЗ»

Реферат Выпускная квалификационная работа составляет из 80 страниц, 4 рисунка, 50 источников, 6 таблиц. Ключевые слова: ПОЖАРНАЯ БЕЗОПАСНОСТЬ, ПРОТИВОДЫМНАЯ ЗАЩИТА, ДЫМОУДАЛЕНИЕ,ЭВАКУАЦИЯ, ДЫМ, ПРОДУКТЫ ГОРЕНИЯ. Объектом исследования являются туннель землеприготовительного участка литейно-формовочного цеха №10 ООО «Юргинский машиностроительный завод» Цель работы: разработать систему противопожарной защиты направленную на обеспечение подавления пожара в туннеле землеприготовительного участка литейно-формовочного цеха №10 ООО «Юргинский машиностроительный завод" В процессе работы проводились расчёты тушения пожара на объекте анализ возможных вариантов развития и последствий пожара, а также определения причин и вероятности его возникновения. Оценка наиболее опасной пожарной ситуации и её последствия. В результате были выявлены не доработки пожарной защиты и её ликвидацииAbstract Final qualifying work is 80 pages, 4 figures, 50 sources, 6 tables. Keywords: fire safety, smoke protection, smoke evacuation, smoke, PRODUCTS OF BURNING. The object of the study are zemleprigotovitelnogo tunnel area Casting and molding shop №10 LLC "Yurga Machine Building Plant" Objective: To develop a fire protection system aimed at providing suppressing fire in the tunnel area zemleprigotovitelnogo Casting and molding shop №10 LLC "Yurga Machine Building Plant" In operation, the calculations were carried out on-site firefighting analysis of possible development options and the consequences of a fire, as well as determine the causes and the probability of its occurrence. Evaluation of the most dangerous fire situation and its consequences. As a result, it was found not fire protection improvements and its liquidatio

Ultrasonic particle sizing in aqueous suspensions of solid particles of unknown density

Estimates of particle size distributions (PSDs) in solid-in-liquid suspensions can be made on the basis of measurements of ultrasonic wave attenuation combined with a mathematical propagation model, which typically requires seven physical parameters to describe each phase of the mixture. The estimation process is insensitive to all of these except the density of the solid particles, which may not be known or difficult to measure. This paper proposes that an unknown density value is incorporated into the sizing computation as a free variable. It is shown that this leads to an accurate estimate of PSD, as well as the unknown density

Algorithms and literate programs for weighted low-rank approximation with missing data

Linear models identification from data with missing values is posed as a weighted low-rank approximation problem with weights related to the missing values equal to zero. Alternating projections and variable projections methods for solving the resulting problem are outlined and implemented in a literate programming style, using Matlab/Octave's scripting language. The methods are evaluated on synthetic data and real data from the MovieLens data sets

Stem Cell Therapies for Cervical Spinal Cord Injury

Cervical-level injuries account for the majority of presented spinal cord injuries (SCIs), yet there are few therapies that successfully improve the overall quality of life for patients. Regenerative therapies aimed at ameliorating deficits in respiratory and motor function are urgently needed. Cellular transplantation strategies are a promising therapeutic avenue. These strategies seek to overcome the inhibitory environment of the injury site, increase native regenerative capacities, provide scaffolding to bridge the lesion, or replace injury-lost neurons and glia

Hybrid-Entanglement in Continuous Variable Systems

Entanglement is one of the most fascinating features arising from quantum-mechanics and of great importance for quantum information science. Of particular interest are so-called hybrid-entangled states which have the intriguing property that they contain entanglement between different degrees of freedom (DOFs). However, most of the current continuous variable systems only exploit one DOF and therefore do not involve such highly complex states. We break this barrier and demonstrate that one can exploit squeezed cylindrically polarized optical modes to generate continuous variable states exhibiting entanglement between the spatial and polarization DOF. We show an experimental realization of these novel kind of states by quantum squeezing an azimuthally polarized mode with the help of a specially tailored photonic crystal fiber

Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.Comment: 5th Geotechnical Earthquake Engineering and Soil Dynamics Conference, Austin, TX, USA, June 10-13. (2018

The statistical mechanics of complex signaling networks : nerve growth factor signaling

It is becoming increasingly appreciated that the signal transduction systems used by eukaryotic cells to achieve a variety of essential responses represent highly complex networks rather than simple linear pathways. While significant effort is being made to experimentally measure the rate constants for individual steps in these signaling networks, many of the parameters required to describe the behavior of these systems remain unknown, or at best, estimates. With these goals and caveats in mind, we use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. To establish the usefulness of our approach, we have applied our methods towards modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. Using our approach, we are able to extract predictions that are highly specific and accurate, thereby enabling us to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. We show that extracting biologically relevant predictions from complex signaling models appears to be possible even in the absence of measurements of all the individual rate constants. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems wherein particular ''soft'' combinations of parameters can be varied over wide ranges without impacting the final output and demonstrating that a few ''stiff'' parameter combinations center around the paramount regulatory steps of the network. We refer to this property -- which is distinct from robustness -- as ''sloppiness.''Comment: 24 pages, 10 EPS figures, 1 GIF (makes 5 multi-panel figs + caption for GIF), IOP style; supp. info/figs. included as brown_supp.pd