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Using the secondary data sources, we identified all new factories, opened by foreign multinational corporations in Russia in 2012-2018. Almost 80% of the 261 factories opened in the last seven years are located in just 20 Russian administrative regions. Moscow (city and oblast), Kaluga oblast, St.Petersburg and Leningrad oblast, The Republic of Tatarstan, Lipetsk oblast, Nizhny Novgorod oblast and Ulyanovsk oblast are the leaders in accommodating foreign industrial investments. The majority of foreign investors preferred special economic zones and industrial parks as territories for installation of new facilities. Proximity to suppliers, availability of the local market, preferred tax regime, guaranteed infrastructure and articulated care of the local authority about the needs of foreign investors are the main factors that determine the choice of the region for industrial investments of foreign corporations.
We explore the meaning of rituals of public plant-opening ceremonies, which are popular in many countries and mandatory in Russia for greenfield investment projects implemented by multinational corporations. The study is based on the analysis of videos from the public opening ceremonies supplemented with the analysis of financial data on the studied industrial projects. We argue that in a country with underdeveloped physical infrastructure and volatile business regulations opening ceremonies for new industrial projects have three implications: 1) such events are festivities which are centered on an exchange of gratitude between foreign investors and the local authorities for their non-opportunistic behavior; and 2) such events serve as “the rite of passage” of a newly-built facility into the local business and social infrastructure; 3) assurances in speeches during opening ceremonies about the long-term nature of industrial projects are taken seriously as obligations and serve as an additional barrier to exit from industrial assets.
in the present paper author explains the results of using Smart TV as a tool for Industry 4.0, in particular for media industry, also measuring of Quality-of-Service and new business development. A Smart TV is a single connected device or intelligent sensor which increases industry performance through the number of services by using the existing network infrastructure. Thanks to special tracking and analyzing information on board Smart TVs help to improve the service for VoD service provider and product quality for Vendor. Results of applying several methods for problem solving will be reviewed at present material
This work is devoted to the investigation of particle acceleration during magnetospheric dipolarizations. A numerical model is presented
taking into account the four scenarios of plasma acceleration that can be realized: (A) total dipolarization with characteristic time scales of
3 min; (B) single peak value of the normal magnetic component Bz occurring on the time scale of less than 1 min; (C) a sequence of rapid
jumps of Bz interpreted as the passage of a chain of multiple dipolarization fronts (DFs); and (D) the simultaneous action of mechanism (C)
followed by the consequent enhancement of electric and magnetic fluctuations with the small characteristic time scale 1 s. In a frame of the
model, we have obtained and analyzed the energy spectra of four plasma populations: electrons e, protons Hþ, helium Heþ, and oxygen Oþ
ions, accelerated by the above-mentioned processes (A)–(D). It is shown that Oþ ions can be accelerated mainly due to the mechanism (A);
Hþ and Heþ ions (and to some extent electrons) can be more effectively accelerated due to the mechanism (C) than the single dipolarization
(B). It is found that high-frequency electric and magnetic fluctuations accompanying multiple DFs (D) can strongly accelerate electrons e
and really weakly influence other populations of plasma. The results of modeling demonstrated clearly the distinguishable spatial and temporal
resonance character of particle acceleration processes. The maximum particle energies depending on the scale of the magnetic acceleration
region and the value of the magnetic field are estimated. The shapes of energy spectra are discussed.
The paper deals with cyclostationarity as a natural extension of stationarity as the key property in designing the widely-used models of random processes. The comparative example of two processes, one is wide-sense stationary and the other is wide-sense cyclostationary, is given in the paper and reveals the lack of the conventional stationary description based on one-dimensional autocorrelation functions. It is shown that two significantly different random processes appear to be characterized by exactly the same autocorrelation function while their two-dimensional autocorrelation functions provide outlook where the difference between processes of two above-mentioned classes becomes much clearer. More concise representation by expanding the two-dimensional autocorrelation function to its Fourier series where the cyclic frequency appears as the transform parameter is illustrated. The closed-form expression for the components of the cyclic autocorrelation function is also given for the random process which is an infinite pulse train made of rectangular pulses with randomly varying amplitudes.
Urban greenery such as trees can effectively reduce air pollution in a natural and eco-friendly way. However, how to spatially locate and arrange greenery in an optimal way remains as a challenging task. We developed an agent-based model of air pollution dynamics to support the optimal allocation and configuration of tree clusters in a city. The Pareto optimal solutions for greenery in the city were computed using the suggested heuristic optimisation algorithm, considering the complex absorptive-diffusive interactions between agent-trees (tree clusters) and air pollutants produced by agent-enterprises (factories) and agent-vehicles (car clusters) located in the city. We applied and tested the model with empirical data in Yerevan, Armenia, and successfully found the optimal strategy under the budget constraint: planting various types of trees around kindergartens and emission sources.
Evolution on changing fitness landscapes (seascapes) is an important problem in evolutionary biology. We
consider the Moran model of finite population evolution with selection in a randomly changing, dynamic
environment. In the model, each individual has one of the two alleles, wild type or mutant. We calculate the
fixation probability by making a proper ansatz for the logarithm of fixation probabilities. This method has been
used previously to solve the analogous problem for the Wright-Fisher model. The fixation probability is related to
the solution of a third-order algebraic equation (for the logarithm of fixation probability).We consider the strong
interference of landscape fluctuations, sampling, and selection when the fixation process cannot be described by
the mean fitness. Such an effect appears if the mutant allele has a higher fitness in one landscape and a lower
fitness in another, compared with the wild type, and the product of effective population size and fitness is large.
We provide a generalization of the Kimura formula for the fixation probability that applies to these cases. When
the mutant allele has a fitness (dis-)advantage in both landscapes, the fixation probability is described by the
The evolution of epidemiological burden in Imperial Russia and, consecutively, the Union of Soviet Socialist Republics (USSR), took place mostly over the duration of the past century. It is very important since dozens of Eastern European and Asian nations, with similarities in life style, regardless of dominant Orthodox Christian, Sunni-Shia Islamic, or Shamanic ethno-religious patterns, share this old statehood tradition. This profound change reflected in gradual movement from communicable, infectious diseases, traumatism, and early childhood and maternal mortality towards chronic non-communicable diseases . To some extent, these changes were accelerated by two world wars and the deep regulatory reforms of social and pension systems, together with health care provision and financing mechanisms imposed, as a result of the Bolshevik, October Revolution in 1917. This long-term evolution, particularly in the post-World War II decades, was ultimately associated with the occurrence of population aging throughout entire Northern Hemisphere . It got worse in the East due to the deep Russian recession reaching the bottom in 1998 and effectively dragging all mutually dependent formerly centrally-planned economies . Compared to their Western European counterparts, Russian and other Eastern European ethnicities mostly remain in a slightly earlier stage of population ageing, which is yet tangible by serious policies . Since the beginning of the 21st century, bold economic recovery and growth took place, ultimately leading to successful fertility policies. The boost in total fertility levels, which was raised from 1.3 to 1.7 children per woman of reproductive age, was the highest net achievement of its kind by any European country during the second decade of the 21st century . All of these complex historical changes following the business cycles  in the capitalist free-market economies had heavily reflected the ability of the Russian Federation and its predecessor states to increase investment in healthcare and provide equitable and affordable medical care to its citizens . Therefore, this paper attempts to look at the inner legislative evolution of health financing in Russia over the last 100 years.
An age-structured bioeconomic model, which is completely continuous in age and time, is developed in order to compare with traditional discrete models. Both types have advantages and disadvantages. The continuous framework complements discrete models as it allows for deeper and more transparent analytical study and leads to analytical results that would be difficult to achieve within a discrete framework. To make the model realistic, a nonlinear recruitment function is introduced and steady state solutions and constant-effort optimal fishing are studied analytically. In addition, the framework has been used for numerical analysis. Simulations are used to investigate how optimal harvesting patterns vary with parameter values.
The current challenges of many mobility solutions are based on an extremely fragmented booking system with complex service layers. A cross-company and user-friendly exchange of information and offers from different mobility providers is often not possible. Against this background, Distributed Ledger Technology (DLT) has the potential to revolutionize the existing mobility sector and enable completely new business models. Thus, we present a distributed mobility platform, which is valuable for a variety of mobility services. In contrast to conventional platform approaches, the data management of our infrastructure is distributed, transparent, and cost-efficient. By prototypically implementing the concept, we can demonstrate its technical feasibility and at the same time demonstrate that the introduction of our distributed mobility concept will benefit both the supply and demand sides of public transportation.
Patient flow modeling in healthcare plays a large role in understanding the operation of the system and its characteristics. Besides, modeling techniques can significantly improve the effectiveness of the medical facilities. The existing level of automation in these facilities enables the accumulation of large amounts of various data. Therefore, the collected data might be considered as the resource of new valuable knowledge. A novel approach to automatically identify the groups of similar clinical pathways based on event hospital data is presented in the paper. More specifically, the approach summarizes the most frequent pathways by implementing hard and soft clustering algorithms in order to describe the behavior patterns. The obtained clusters of clinical pathways serve as a starting point for the development of a personalized approach in modelling the heterogeneous patient flow in urban medical facilities. The results indicate the suitability of multidimensional time series clustering and Additive Regularization of Topic Models (ARTM) for the clinical event data.
This paper introduces the maximum likelihood estimator (MLE) based on artificial neural network (ANN) for a fast computation of the bearing that indicates the direction to the source of the electromagnetic wave received by a passive radar system equipped with an array antenna. Authors propose the cascade scheme for ANN training phase where the network is fed with the pair-wise delays of received stationary or cyclostationary signals and the output of the network goes to the input of the target function being maximized together with the same data. The designed ANN topology has the modified output layer consisting of the custom neuron that implements argument function of a complex number rather than linear or sigmoid-like ones used in the conventional multilayer perceptron topologies. The simulation carried out for the ring array antenna shows that a single estimation obtained via ANN MLE takes 12 times less computational time comparing to the MLE implemented via the numerical optimization technique. The degradation of accuracy measured as the increase of mean-squared error does not exceed 10% of the potential value for the particular signal-to-noise ratio (SNR) and that difference has no tendency to decrease for higher SNR. The estimation error appeared to be independent from the true value in the wide range of bearings.
Information technology (IT) is an indispensable tool for any organization today, so the choice of adequate IT solutions is a critically important skill. In the literature, many methods for selecting IT solutions have been proposed, but often they use vague criteria that are very difficult to quantify and complex methods to compare alternatives. So, the application of these methods outside the theoretical articles is restricted, since practitioners need simpler approaches. We propose a simple method of the evaluation of alternative IT solutions based on five criteria, namely the cost of ownership, the time for the change, security risks, acceptance by users, and confidence in the supplier's ability to implement the solution. In accordance with the theory of probabilistic mental models, a reference class is proposed for each criterion and variables that can be measured quantitatively are chosen on its base. To simplify the decision-making process, a weighted production model is used for the comparison of alternatives.
Mathematical modeling of a stock market functioning is one of the actual and at the same time complex task of the modern theoretical economics. From our point of view, building such mathematical models “ab initio”, by using analogy between the stock market and a certain physical system (in our work, laser), is the most promising approach. This paper proposes a simple econophysical model of stock market as an open nonequilibrium system in form of Lorenz–Haken equation. In this system, variation of ask price, variation of bid price, and instantaneous difference between numbers of agents in active and passive state are intensity of external information flow is a control parameter. This model explains the impossibility of existence of an equilibrium state of the market and shows the presence of deterministic chaos in a stock market.
Auditors use behavioral red flags (BRFs) to examine which individuals are more prone to unwarranted behavior like corruption and asset misappropriation. Using a rich data set from the Association of Certified Fraud Examiners (ACFE), we analyze the impact of BRFs on loss sizes from asset misappropriation. We control for anti-fraud mechanisms established at the company level and other factors both at the individual and the firm level. Performing an exploratory factor analysis yields six factors for BRFs which capture the principal perpetrator’s situation at the private level and the workplace. A general wheeler-dealer attitude and financial distress significantly increase loss sizes. By contrast, we find no evidence that non-monetary private problems lead to higher losses.
Boosting motivation is a challenging task on the way of additional productivity, especially considering new generations Y and Z. In this paper we combine set of methods from process management, pedagogics and psychology to develop an interactive gamification process and test it for generations Y and Z currently studying in the leading Russian university. The efficiency of approach is demonstrated on several models and authors suggest ways how to implement them.
The article is devoted to the analysis of scoring models used in one of the Russian commercial banks. The purpose of the article is to build a comprehensive scoring model that takes into account various groups of additional variables that increase the accuracy of the model and reduce the default percentage of borrowers. To construct such a model, it is proposed to use fuzzy control technologies, as one of the methods of data mining.
This section examines the possibilities of economic growth in Russia from the perspective of organizational capability development, namely through the study of best managerial practices of multinational companies (MNCs) doing business in Russia, and their use by Russian companies. Under the conditions of tightening competition, companies are forced to focus on the development of organizational capabilities. Our large-scale empirical research into the managerial capabilities and management practices of multinational and Russian companies, employs a comprehensive sample of 1,530 companies and 1,245 companies in 2016 and 2017 respectively, covering the 10 main sectors of economic activity in Moscow and the Moscow region. The analysis was performed across five managerial capabilities in communication, leadership, problem solving and decision-making, conflict resolution, and motivation, each subdivided into 5 management practices. Using statistical methods, we identified the major statistically significant differences in and between the managerial practices of multinational and Russian companies operating in the Russian market, and their dynamics in 2016 - 2017. Taking MNCs operating in the Russian market as a benchmark, we discover that Russian companies need to close the gap in 17 out of the 25 managerial practices in order to maintain competitiveness in the Russian market and be able to influence their economic growth in Russia.
The paper is devoted to the assessment of the prospects of implementing clean energy sources in Russia, where the current energy policy goal is to increase the role of renewable and clean energy sources. The research is based on data from the Krasnoyarsk Region as one of the largest territories but also as a representative model of Russia. The aim of the study is to identify where and which renewable energy source (solar, wind, hydro and nuclear) has the highest potential. The novelty of our research lies in its holistic nature: authors consider both geographical and technical potential for renewable energy sources development as well as prospective demand for such resources, while previous research is mostly focused on specific aspects of renewable energy development. We also consider the level of air pollution as an important factor for the development of renewable energy sources. The results of the study show that there is a strong potential for clean energy sources in the Krasnoyarsk Region. The resulting matrix identifies the potential of energy sources across all the municipal entities and also indicates whether the source of energy is primary or supplemental and where several sources may be implemented in cooperation.