Synthesis and cation-receptor properties of macrocyclic imines of anthraquinone

At the present study a series of crown-containing imines of 1-hydroxy-9,10-anthraquinone with donor and acceptor substituents at the anthraquinone nucleus were synthesized. Compounds were prepared photochemically from the corresponding photoactive 1-phenoxyanthraquinones and 4-aminobenzo-15-crown-5 ether. It was established spectrophotometrically that for crown-containing anthraquinone imines that are characterized by "imine-enamine" prototropic tautomerism, the insertion of acceptor substituents shifts the equilibrium to the "enamine" form. This shift leads to essential spectral changes in complexing chlor- and nitrocontaining macrocyclic imines of anthraquinone with alkali and alkaline-earth metal cations

A fast and effective multiple kernel clustering method on incomplete data

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The AMKC method first clusters the initialized incomplete data. Then, it constructs a new multiple-kernel-based data space, referred to as K-space, from multiple sources to learn kernel combination coefficients. Finally, it seamlessly integrates an incomplete-kernel-imputation objective, a multiple-kernel-learning objective, and a kernel-clustering objective in order to achieve absent multiple kernel clustering. The three stages in this process are carried out simultaneously until the convergence condition is met. Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is significantly better than state-of-the-art competitors. Meanwhile, the proposed method gains fast convergence speed

Современные тенденции деятельности страховых компаний РФ по автострахованию (на примере ООО СБ «Коместра-Страхование»)

Объект исследования в данной работе рынок автострахования России. Целью работы было: изучить рынок автострахования России на примере компании "Коместра страхование".Основными задачами было:изучить сущность и структуру страхового рынка,дать характеристику участников страхового рынка,проанализировать состояние российского страхового рынка в части его динамики, масштабов, уровня оказываемых страховых услуг. Данная тема актуальна, так как множество различных компаний открываются и стараются конкурировать с уже устоявшимися компаниями. В данной работе предложен ряд решений которые могут увеличить уровень конкурентоспособности компании в целом.The object of research in this work is the car insurance market of Russia. The aim of the work was: to study the Russian auto insurance market using the example of the company "Comestra Insurance". The main tasks were: to study the nature and structure of the insurance market, to characterize the participants of the insurance market, to analyze the state of the Russian insurance market in terms of its dynamics, scale, level of insurance services provided. This topic is relevant, as many different companies open up and try to compete with already established companies. In this work, a number of solutions are proposed that can increase the level of competitiveness of the company as a whole

Measurement of the Spectral Shape of the beta-decay of 137Xe to the Ground State of 137Cs in EXO-200 and Comparison with Theory

We report on a comparison between the theoretically predicted and experimentally measured spectra of the first-forbidden non-unique $\beta$-decay transition ^{137}\textrm{Xe}(7/2^-)\to\,^{137}\textrm{Cs}(7/2^+). The experimental data were acquired by the EXO-200 experiment during a deployment of an AmBe neutron source. The ultra-low background environment of EXO-200, together with dedicated source deployment and analysis procedures, allowed for collection of a pure sample of the decays, with an estimated signal-to-background ratio of more than 99-to-1 in the energy range from 1075 to 4175 keV. In addition to providing a rare and accurate measurement of the first-forbidden non-unique $\beta$-decay shape, this work constitutes a novel test of the calculated electron spectral shapes in the context of the reactor antineutrino anomaly and spectral bump.Comment: Version as accepted by PR

Deep Neural Networks for Energy and Position Reconstruction in EXO-200

We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For the first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo.Comment: Accepted version. 33 pages, 28 figure