Fiscal capacity of the city: the assessment of the influence on the sustainability of urban environment and the quality of living (the case of «second» cities of the Russian Federation)

The modern financial situation demonstrates tough asymmetry in financial development between the territorial units, which are the capital cities, and the «second» cities of constituent units of the Russian Federation. This is based upon chronic deficit of inner financial sources for covering the budget expenditure items of the latter ones. The instruments of municipal units’ fiscal capacity level equalization, which are being implemented by the national government, lead to adverse effects. These effects of national fiscal practice include the resource dependence on the upper level and lack of interest of the «vice-capitals’» local authorities to broaden the inner income base, disbalance between economic, social, natural-resource components of urban environment’s sustainable development and the fall of the residents’ quality of living. In this connection, the effectiveness research of the current managerial mechanism of fiscal capacity of the «second» cities of constituent units of the Russian Federation in the context of the sustainable development concept is extremely important. The results of the survey on the packaged approach to economical and statistical assessment of the fiscal capacity level as a defining factor of sustainable development of the urban environment and the residents’ quality of living in «vice-capitals» of constituent units of the Russian Federation (the case of Magnitogorsk and Nizhniy Tagil) are presented in this article. Having used the packaged approach, the authors have brought to light the interconnection between the level of the «second» cities’ fiscal capacity and indicator values of ecological and socio-economic well-being of the analysed area. Additionally, they have revealed the character and direction of this connection as well as assessed the competence of management of the financial opportunities generation and usage by means of determining the indicator values of the areas’ fiscal capacity as of the current date and comparing them with optimum values

Economic tomography: the possibility to anticipate and respond to socio-economic crises

The article discusses an approach based on an original hypothesis related to the peculiarities of Russia’s development (on the one hand, its scale, the Russian mentality and a certain closeness of the economy; on the other hand, a significant dominant resource and human potential, and, as a consequence, a genuine role in the global economic community), the diagnosis of which (at the level of the well-being of individuals and inhabited areas) can be used to identify crises, provide an early assessment of threats to socio-economic development of regions as well as help to evaluate the state of the region over a 3 to 5 year period. In other words, in order to ensure that executives have enough time to mount a sufficiently rapid response to the crises and administrative errors and to reduce the impact of emerging threats. The aim of this paper is to present theoretical and methodological tools for the recognition of the early stages of emerging threats, allowing fewer losses to be experienced during the crisis period. Simulation experiments were carried out for the purpose of classifying previously occurring social and economic crises (9 possible variants were reviewed) and mathematically processed trajectories of change in the main indicators for the well-being of individuals and inhabited areas, taking the influence of various factors into account. On the basis of the authors’ proposed approach (referred to as economic tomography) an attempt is made to comprehensively assess the state of sample representative regions of Russia.The research has been supported by the Russian Science Foundation (project № 14–18–00574 'Information-analytical system "Anticrisis:" diagnostics of the regions, threat assessment and scenario forecasting for the preservation and strengthening of economic security and well-being of Russia')

Russia’s Birth Rate Dynamics Forecasting

This article covers contemporary issues of Russia’s population reproduction, their causes and the state policy aimed to overcome the same. The urgency to fulfill the task related to assessment of the most probable future dynamics of Russia’s population birth rate in the context of a low child-woman ratio, and subject to an impact of pronatalist policies implemented by the state, is justified. In order to fulfill the task based on the crude birth rate behavior probability distribution function, a probabilistic assessment of future dynamics of Russia’s population reproduction has been carried out. Based on a modernized method suggested by Hurst, the following two forecasting paths of the crude birth rate dynamics have been built: the first path conforms to the scenario where a value of the crude birth rate is to tend to values between 8–10.5 births/1,000 people (probability is 0.182), in particular, through a negative external impact, the second path is to tend to values between 13–16.5 births/1,000 people (probability — 0.618), in particular, through a positive external impact. Notwithstanding that these scenarios significantly differ from each other, the paths of the crude birth rate dynamics for 2015–2041, corresponding to the reliable prediction time, forecasted according to the abovementioned scenarios, are virtually identical. The analysis of the findings allowed for the conclusion that the state demographic policy is not capable of having a significant impact on the future dynamics of the birth rate, substantially determined by the current situation and conjuncture shifts. These conclusions confirm the view prevailing in academic circles and suggesting that the state regulation of Russia’s demographic situation should be primarily focused on the improvement in health and a rise in the life expectancy of the population.The research has been prepared with the support of the Russian Science Foundation grant (Project No. 14-18-00574 “Information and analytic systems “Anticrisis”: diagnostics of the regions, threat evaluation and scenario forecasting to preserve and reinforce the welfare of Russia”)

Towards the second order adaptation in the next generation remote patient management systems

Remote Patient Management (RPM) systems are expected to be increasingly important for chronic disease management as they facilitate monitoring vital signs of patients at their home, alerting the care givers in case of worsening. They also provide patients with educational content. RPM systems collect a lot of (different types of) data about patients, providing an opportunity for personalizing information services. In our recent work we highlighted the importance of using available information for personalization and presented a possible next generation RPM system that enables personalization of educational content and its delivery to patients. We introduced a generic methodology for personalization and emphasized the role of knowledge discovery (KDD). In this paper we focus on the necessity of the second-order adaptation mechanisms in the RPM systems to address the challenge of continuous on-line (re)learning of actionable patterns from the patient data

Heart failure hospitalization prediction in remote patient management systems

Healthcare systems are shifting from patient care in hospitals to monitored care at home. It is expected to improve the quality of care without exploding the costs. Remote patient management (RPM) systems offer a great potential in monitoring patients with chronic diseases, like heart failure or diabetes. Patient modeling in RPM systems opens opportunities in two broad directions: personalizing information services, and alerting medical personnel about the changing conditions of a patient. In this study we focus on heart failure hospitalization (HFH) prediction, which is a particular problem of patient modeling for alerting. We formulate a short term HFH prediction problem and show how to address it with a data mining approach. We emphasize challenges related to the heterogeneity, different types and periodicity of the data available in RPM systems. We present an experimental study on HFH prediction using, which results lay a foundation for further studies and implementation of alerting and personalization services in RPM systems

Semantics and Pragmatics of Language Units (according to Results of Theoretic and Methodological Seminar of Scientific School of Professor, Doctor of Philology E. P. Ivanyan)

The results of the theoretical-methodological seminar of scientific school of professor, doctor of philology E. P. Ivanyan (Samara) are presented. It is reported that the seminar discussed the topical issue of semantics and pragmatics of language units in modern Russian discourse, the main trends of language development and problems of their study in various research paradigms. The contents of the scientific papers presented during the seminar is highlighted. The main results of the research of the authors of the reports are given

Transmural heterogeneity in the mechanical and electrical properties of cardiomyocytes. Experimental study and modeling

Supported by the Russia Foundation for Basic Research (14-01-00885, 14-01-31134), by Presidium of the Ural Branch of the Russian Academy of Sciences (12-M-14-2009, 12-П-4-1067) by Ural Federal University (Act 211 Government of the Russian Federation #02.A03.21.0006) and by JREX Fellowship for young researchers

Heart failure hospitalization prediction in remote patient management systems

Healthcare systems are shifting from patient care in hospitals to monitored care at home. It is expected to improve the quality of care without exploding the costs. Remote patient management (RPM) systems offer a great potential in monitoring patients with chronic diseases, like heart failure or diabetes. Patient modeling in RPM systems opens opportunities in two broad directions: personalizing information services, and alerting medical personnel about the changing conditions of a patient. In this study we focus on heart failure hospitalization (HFH) prediction, which is a particular problem of patient modeling for alerting. We formulate a short term HFH prediction problem and show how to address it with a data mining approach. We emphasize challenges related to the heterogeneity, different types and periodicity of the data available in RPM systems. We present an experimental study on HFH prediction using, which results lay a foundation for further studies and implementation of alerting and personalization services in RPM systems

Patient condition modeling in remote patient management : hospitalization prediction

In order to maintain and improve the quality of care without exploding costs, healthcare systems are undergoing a paradigm shift from patient care in the hospital to patient care at home. Remote patient management (RPM) systems offer a great potential in reducing hospitalization costs and worsening of symptoms for patients with chronic diseases, e.g., heart failure and diabetes. Different types of data collected by RPM systems provide an opportunity for personalizing information services, and alerting medical personnel about the changing conditions of the patient. In this work we focus on a particular problem of patient modeling that is the hospitalization prediction. We consider the problem definition, our approach to this problem, highlight the results of the experimental study and reflect on their use in decision making