LAWYER OCCUPATION IN DIGITALIZATION AGE

The relevance of the research is due to the need to consider the future of the lawyer occupation in Russia in the context of digitalization, as well as to study the process of transformation of the lawyer occupation in new conditions. The purpose of the article is to understand the main directions of digitization of the lawyer occupation, new requirements for the level of training and education of a lawyer, the prospects of the lawyer occupation in Russia and the world. The leading approach to the study of this problem is the methodology developed by legal anthropology, which is supplemented by a system-based method that allows us to comprehensively consider the features of the lawyer occupation in the era of digitalization. The article proves that the further introduction of digital technologies will lead to a complete transformation of the lawyer occupation; the main directions of changes in the lawyer occupation would be to replace the man-lawyer who performs routine, standard legal operation with a robot-lawyer; a key feature of training of lawyers will be the ability to use LegalTech; transformation of the lawyer occupation will lead to the transformation of legal education, which will be expressed in the need for relevant IT training; the specifics of digitization of the lawyer occupation in Russia will largely be determined by its unique characteristics: the vast territory and demographic characteristics of the population; the most important result of digitization of the lawyer occupation will be the division of lawyers into digital and traditional. The materials of the article can be useful for legal scholars who study the problems of digitalization of the lawyer occupation, law students and legal practitioners who are interested in the future of their profession and the main directions of its transformation in the first half of the XXI century

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Performance and Operation of the CMS Electromagnetic Calorimeter

The operation and general performance of the CMS electromagnetic calorimeter using cosmic-ray muons are described. These muons were recorded after the closure of the CMS detector in late 2008. The calorimeter is made of lead tungstate crystals and the overall status of the 75848 channels corresponding to the barrel and endcap detectors is reported. The stability of crucial operational parameters, such as high voltage, temperature and electronic noise, is summarised and the performance of the light monitoring system is presented

To the Question of the Use of Mobile Educational Game Apps as a Simulator in Russian Language Teaching

В статье рассматривается перспектива использования учебно-игровых мобильных приложений в обучении русскому языку как средства закрепления и контроля усвоения языкового материала в общеобразовательной школе.The urgency of this study can be attributed to the high pace of development of modern distance learning practice and the need for analysis and theoretical understanding of new training formats in line with computer linguodidactics. In this regard, the purpose of the study is to analyze the prospects for using mobile training and gaming applications as a means of fixing and diagnosing language mistakes and controlling theacquisition. The object of research includes the methods of application of mobile learning game apps in teaching

Разработка интеллектуальной системы оценки состояния беспилотного летательного аппарата на основе нейросетевых технологий

Nowadays, unmanned aerial vehicles (UAV) become more applicable right along. The growing demand for their use is stipulated by economic considerations, and also by a capability for fulfilling the high-risk tasks. One of the most important tasks arising, when developing the unmanned equipment, is to detect dangerous situations because of possible failure of the on-board systems. Presently, this problem is solved mostly by multiple redundancies. Through computer technology development, along with traditional approaches, data mining tools, in particular artificial neural networks become more commonly used. The use of neural network tools to analyse multi-dimensional data can reduce the redundancy level, as well as to solve a wide range of tasks in real time. The paper suggests a new approach, which uses the multidimensional data analysis based on the neural network models, to develop an integrated system for assessing the UAV state. This system is designed to solve a wide range of tasks, such as troubleshooting the on-board equipment based on the complex analysis of measurements, redundancy of faulty sensors, assessment of the aerial vehicle state, and hazard prediction and prevention. Also, this system allows troubleshooting in the control system and enables the capability to complete a maneuver by assuming the control. Another important task is to keep logging of measurements and assess the aerial vehicle state using the neural network forecasting models. An equally important task is to verify the reliability of the UAV model comparing with real flight data. This system allows us not only to determine a divergence between the model and the object, but also localise the error and produce recommendations for correction. The paper presents a solution to these problems based both on the simulation results and on the real flight data.В настоящее время сфера применения беспилотных летательных аппаратов (БЛА) непрерывно расширяется. Растущий спрос на них обусловлен экономическими соображениями, а также связан с возможностью выполнять задачи повышенного риска. Одной из важнейших задач, возникающих при создании беспилотной техники, является детектирование нештатных ситуаций, причиной которых может служить выход из строя бортовых систем. В настоящий момент времени данная проблема решается преимущественно многократным резервированием. Благодаря развитию вычислительной техники, наряду с традиционными подходами все большее применение находят средства интеллектуального анализа данных, в частности, – искусственные нейронные сети. Применение нейросетевых средств анализа многомерных данных может позволить сократить уровень резервирования, а также решать широкий круг разноплановых задач в режиме реального времени. В работе предложен новый подход к разработке комплексной системы оценки состояния БЛА, основанный на анализе многомерных данных с помощью нейросетевых моделей. Данная система предназначена для решения широкого круга задач, таких как детектирование неисправностей бортовой аппаратуры на основе комплексного анализа измерений, резервирование неисправных датчиков, оценка состояния летательного аппарата, прогнозирование и предотвращение опасных ситуаций. Также данная система позволяет определять неисправность системы управления и обеспечивает возможность завершить маневр, взяв управления на себя. Еще одной важной задачей является ведение журнала измерений и оценка состояния летательного аппарата с использованием нейросетевого прогнозирования. Не менее важной задачей является определение достоверности модели беспилотного летательного аппарата самолетного типа на основе его полетных данных. Данная система позволяет не только определять ее расхождение с объектом, но также локализовывать ошибку и вырабатывать рекомендации по корректировке. В работе предоставляется решение этих задач как на основе результатов моделирования, так и реальных полетных данных

Identification and Filtering of Uncharacteristic Noise in the CMS Hadron Calorimeter

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Performance of CMS hadron calorimeter timing and synchronization using test beam, cosmic ray, and LHC beam data

This paper discusses the design and performance of the time measurement technique and of the synchronization systems of the CMS hadron calorimeter. Time measurement performance results are presented from test beam data taken in the years 2004 and 2006. For hadronic showers of energy greater than 100 GeV, the timing resolution is measured to be about 1.2 ns. Time synchronization and out-of-time background rejection results are presented from the Cosmic Run At Four Tesla and LHC beam runs taken in the Autumn of 2008. The inter-channel synchronization is measured to be within ±2 ns

Thomson scattering diagnostics at the Globus M2 tokamak

The paper is devoted to the Thomson scattering (TS) diagnostics recently developed for the Globus-M2 spherical tokamak and prototyping the ITER divertor TS diagnostics. The distinctive features of the system are the use of spectrometers, acquisition system and lasers that meet the base requirements for ITER TS diagnostics. The paper describes the diagnostic system that allows precise measurements of TS signals, as well as the results of the first measurements of electron temperature and density in both central region of the plasma column and scrape-off layer. The system provides measurements of electron temperature $T_{e}$ in the range of 5 eV to 5 keV and density $n_{e}$ in the range of $5{\cdot}10^{17}{\div}3.25{\cdot}10^{20} m^{-3}$. The use of two ITER-grade probing lasers of different wavelengths (Nd:YAG 1064.5 nm and Nd:YLF 1047.3 nm) allows reliable measurement of $T_{e}$ in multi-colour mode, i.e., assuming that spectral calibration is unknown