Observation of asymmetric spectrum broadening induced by silver nanoparticles in a heavy-metal oxide glass

We demonstrate experimentally and support by a theoretical analysis an effect of asymmetric spectrum broadening, which results from doping of silver nanoparticles into a heavy-glass matrix, 90(0.5WO3-0.3SbPO4-0.2PbO)-10AgCl. The strong dispersion of the effective nonlinear coefficient of the composite significantly influences the spectral broadening via the self-phase modulation, and leads to a blue upshift of the spectrum. Further extension of the spectrum towards shorter wavelengths is suppressed by a growing loss caused by the plasmon resonance in the silver particles. The red-edge spectral broadening is dominated by the stimulated Raman Scattering.Comment: Accepted for publishing epl13477; EPL Journal 201

Application of multi-agent approach in the electric power control systems within the active energy complex

This paper provides an analytical overview of artificial intelligence technologies used in the tasks of technological management of the electric power industry. The relevance of using multi-agent systems is proved for solving energy problems. The concept of an intelligent energy system is considered with an active adaptive network (IES AAN). A block diagram of the electric power system management system is given within the IES AAN. It is shown that the developed centralized management principles are not fully applicable for small distribution networks - microgrid, with the use of storage devices and renewable sources. The article describes the active energy complex in Russia in the context of an analog of the Microgrid concept. The required functionality of a intelligent controllable connection (ICC) is studied, the main algorithm of the ICC operation is given, and the proposed structure is described for building a distributed system of intelligent multi-agent control of the AEC that implements the required functions. © Published under licence by IOP Publishing Ltd

Некоторые особенности информационного обеспечения судебной экспертизы маркировочных обозначений транспортных средств

The paper examines the structure and peculiarities of developing information support for the forensic analysis of vehicle markings, along with requirements for relevant information resources. Problems related to the organization and functioning of the information management system are investigated, and possible solutions are suggested. In addition, the author describes the capacity of some specific elements of information support systems to address problems facing the vehicle markings examiner.Рассмотрены структура, особенности формирования информационного обеспечения судебной экспертизы маркировочных обозначений транспортных средств и требования, предъявляемые к информационным источникам. Исследованы проблемы организации и функционирования системы информационного обеспечения и предложены возможные пути их решения. Раскрыты возможности некоторых разделов информационного обеспечения для решения задач, стоящих перед судебной экспертизой маркировочных обозначений транспортных средств

Routes to multiphoton double ionization in combined extreme ultraviolet and infrared laser pulses

Xenon multiphoton double ionization pathways are studied in a reaction microscope using a pump-probe arrangement of extreme ultraviolet high harmonic and infrared laser radiation. The momentum of photoelectrons is recorded in coincidence with singly or doubly charged ions. Among all possible routes to multiphoton double ionization, sequential processes using ionic excited states as intermediate steps are clearly identified

Renormalization Group in Non-Relativistic Quantum Statistics

Dynamic behaviour of a boson gas near the condensation transition in the symmetric phase is analyzed with the use of an effective large-scale model derived from time-dependent Green functions at finite temperature. A renormalization-group analysis shows that the scaling exponents of critical dynamics of the effective multi-charge model coincide with those of the standard model A. The departure of this result from the descrip tion of the superfluid transition by either model E or F of the standard phenomenological stochastic models is corroborated by the analysis of a generalization of model F, which takes into account the effect of compressible fluid velocity. It is also shown that, con trary to the single-charge model A, there are several correction exponents in the effective model, which are calculated at the leading order of the = 4 − d expansion

DruGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

© 2017 American Chemical Society. Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anticancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties

3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks

© 2018 American Chemical Society. Convolutional neural networks (CNN) have been successfully used to handle three-dimensional data and are a natural match for data with spatial structure such as 3D molecular structures. However, a direct 3D representation of a molecule with atoms localized at voxels is too sparse, which leads to poor performance of the CNNs. In this work, we present a novel approach where atoms are extended to fill other nearby voxels with a transformation based on the wave transform. Experimenting on 4.5 million molecules from the Zinc database, we show that our proposed representation leads to better performance of CNN-based autoencoders than either the voxel-based representation or the previously used Gaussian blur of atoms and then successfully apply the new representation to classification tasks such as MACCS fingerprint prediction

Reaching the nonlinear regime of Raman amplification of ultrashort laser pulses

The intensity of a subpicosecond laser pulse was amplified by a factor of up to 1000 using the Raman backscatter interaction in a 2 mm long gas jet plasma. The process of Raman amplification reached the nonlinear regime, with the intensity of the amplified pulse exceeding that of the pump pulse by more than an order of magnitude. Features unique to the nonlinear regime such as gain saturation, bandwidth broadening, and pulse shortening were observed. Simulation and theory are in qualitative agreement with the measurements.open695

Functional Analysis of Active Energy Complex Control Systems - Microgrid

The paper proposes an approach for the functional analysis of local energy systems control - active energy complexes. The constructed approach takes into account the specifics of the implementation of the requirements under the legislation of Russia. The proposed approach makes it possible to model.This work is supported by Act 211 Government of the Russian Federation, contract 02.A03.21.0006