148 research outputs found
Analysis of changes in pharmacotherapy of stable angina over the five-year period at specialized out-patient level of medical care (pharmacoepidemiological study)
Investigate the dynamics of drug prescription rates in patients with stable angina over the five-year period on the example of routine clinical practice of outpatient cardiology institution of Moscow for the purpose of further eliminating the prescribing gap for guideline recommended pharmacological strategies. Our research work was performed as a retrospective pharmacoepidemiological study including two stages with five-year interval using cross-section metho
Forecasting planned electricity consumption for the united power system using machine learning
The paper presents the results of studies of the predictive models development based on retrospective data on planned electricity consumption in the region with a significant share of enterprises in the mineral resource complex. Since the energy intensity of the industry remains quite high, the task of rationalizing the consumption of electricity is relevant. One of the ways to improve control accuracy when planning energy costs is to forecast electrical loads. Despite the large number of scientific papers on the topic of electricity consumption forecasting, this problem remains relevant due to the changing requirements of the wholesale electricity and power market to the accuracy of forecasts. Therefore, the purpose of this study is to support management decisions in the process of planning the volume of electricity consumption. To realize this, it is necessary to create a predictive model and determine the prospective power consumption of the power system. For this purpose, the collection and analysis of initial data, their preprocessing, selection of features, creation of models, and their optimization were carried out. The created models are based on historical data on planned power consumption, power system performance (frequency), as well as meteorological data. The research methods were: ensemble methods of machine learning (random forest, gradient boosting algorithms, such as XGBoost and CatBoost) and a long short-term memory recurrent neural network model (LSTM). The models obtained as a result of the conducted studies allow creating short-term forecasts of power consumption with a fairly high precision
(for a period from one day to a week). The use of models based on gradient boosting algorithms and neural network models made it possible to obtain a forecast with an error of less than 1Β %, which makes it possible to recommend the models described in the paper for use in forecasting the planned electricity power consumption of united power systems
Critical aspects of the management of stable coronary artery disease in primary care practice or how to increase the efficacy of evidence-based pharmacological therapy?
The publication describes a fragment of the pharmacoepidemiologic study conducted to review the quality of management of patients with stable coronary artery disease (SCAD) in primary care over a 12-year period. The aim of the study was to justify the application of standard operating procedures (SOPs). Such determinants of pharmacotherapy as non-pharmacological modification of cardiovascular risk factors (RFs) and medication adherence were analyze
ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π² Π ΠΎΡΡΠΈΠΈ: ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡ ΡΡΠ΅ΠΉΠΊΡ ΠΎΠ»Π΄Π΅ΡΠΎΠ² ΠΈ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ° ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠΎΠ²
Introduction. Entrepreneurial education, as an area of educational practice in higher education, is a relatively new area of activity for Russian universities. In this area, due to the special dynamics of development and transformation, especially in a pandemic, there is the most significant gap between the competencies formed by universities and in demand on the labour market. The rationale for the research stemmed from two major trends in the economy and society: industry demand for workforce with greater enterprise skills, at the same time a new generation, generation Z, seeks more flexible and more fulfilling career path. Therefore, to address these trends, universities have to diversify the skill set included in the academic curriculum. Aim. This study is aimed at studying the problems of interaction between universities and their stakeholders in curricula improvement. Methodology and research methods. Taken into consideration the regulatory nature of the curricula design in Russian Higher Education Institutions (HEIs) a two-step strategy has been adopted for this research. The first step was a concern with meta-analysis of the competencies outlined in Federal State Educational Standard (FSES) in Management through the lens of entrepreneurial competencies. The second step was to investigate inclusion of soft skills in entrepreneurship curricula in across Russian HEIs. To address the objective of research, descriptive statistics and non-parametric Mann-Whitney U-test were applied. Results. The research findings suggest in the environment where the degree programmes have to comply with set Governmental standards, curricula in entrepreneurship struggle to develop essential soft entrepreneurial skills. Most of the analysed curricula are heavily loaded with hard and cognitive skills. Even though the government proclaims a need for innovative development of the nation, creative and innovative thinking is not mentioned either in the FSES nor analysed curricula. The research findings also led to a surprising conclusion that very few core βbusinessβ modules include the development of social or action-oriented skills in their learning outcomes. Scientific novelty. The scientific novelty of this study lies in the fact that for the first time the problems of ensuring the development of soft skills in entrepreneurial education in Russia have been studied. Practical significance. The results of the study will find their application in the design of entrepreneurial curricula to achieve the necessary balance of competencies in them.ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅. ΠΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΎΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ°ΠΊ ΠΎΠ±Π»Π°ΡΡΡ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠΈ Π² Π²ΡΡΡΠ΅ΠΉ ΡΠΊΠΎΠ»Π΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π½ΠΎΠ²ΠΎΠΉ ΡΡΠ΅ΡΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π΄Π»Ρ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
Π²ΡΠ·ΠΎΠ², Π² ΠΊΠΎΡΠΎΡΠΎΠΉ Π² ΡΠΈΠ»Ρ ΠΎΡΠΎΠ±ΠΎΠΉ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΈ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΠΈ, ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ, Π½Π°Π±Π»ΡΠ΄Π°Π΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΡΠ°Π·ΡΡΠ² ΠΌΠ΅ΠΆΠ΄Ρ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΡΠΌΠΈ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ Π²ΡΠ·Π°ΠΌΠΈ ΠΈ Π²ΠΎΡΡΡΠ΅Π±ΠΎΠ²Π°Π½Π½ΡΠΌΠΈ Π½Π° ΡΡΠ½ΠΊΠ΅ ΡΡΡΠ΄Π°. ΠΠ°ΡΡΠΎΡΡΠ΅Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π±Π°Π·ΠΈΡΡΠ΅ΡΡΡ Π½Π° Π΄Π²ΡΡ
ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡΡ
Π² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅ ΠΈ ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅: ΠΎΡΡΠ°ΡΠ»Π΅Π²ΠΎΠΉ ΡΠΏΡΠΎΡ Π½Π° ΡΠ°Π±ΠΎΡΡΡ ΡΠΈΠ»Ρ Ρ Π±ΠΎΠ»Π΅Π΅ Π²ΡΡΠΎΠΊΠΈΠΌΠΈ Π½Π°Π²ΡΠΊΠ°ΠΌΠΈ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΡΠ²Π° ΠΈ Π² ΡΠΎ ΠΆΠ΅ Π²ΡΠ΅ΠΌΡ ΠΏΠΎΠΈΡΠΊ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠ΅ΠΌ Z Π±ΠΎΠ»Π΅Π΅ Π³ΠΈΠ±ΠΊΠΈΡ
ΠΈ Π½Π°ΡΡΡΠ΅Π½Π½ΡΡ
ΠΊΠ°ΡΡΠ΅ΡΠ½ΡΡ
ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ². ΠΠΎΡΡΠΎΠΌΡ Π² ΠΎΡΠ²Π΅Ρ Π½Π° ΡΡΠΈ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΈ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΡ Π΄ΠΎΠ»ΠΆΠ½Ρ ΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·ΠΈΡΡ Π½Π°Π±ΠΎΡ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ, ΡΠΎΡΠΌΠΈΡΡΠ΅ΠΌΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΠΌΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ°ΠΌΠΈ. Π¦Π΅Π»Ρ. ΠΠ°Π½Π½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΎ Π½Π° ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠΎΠ² ΠΈ ΠΈΡ
ΡΡΠ΅ΠΉΠΊΡ
ΠΎΠ»Π΄Π΅ΡΠΎΠ² Π² ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ. ΠΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡ, ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ. Π‘ ΡΡΠ΅ΡΠΎΠΌ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ° ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
Π²ΡΠ·Π°Ρ
Π΄Π»Ρ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»Π° ΠΏΡΠΈΠ½ΡΡΠ° Π΄Π²ΡΡ
ΡΡΠ°ΠΏΠ½Π°Ρ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡ. ΠΠ° ΠΏΠ΅ΡΠ²ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ Π±ΡΠ» ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ ΠΌΠ΅ΡΠ°Π°Π½Π°Π»ΠΈΠ· ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ Π€ΠΠΠ‘ ΠΏΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ Β«ΠΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΒ» ΡΠ΅ΡΠ΅Π· ΠΏΡΠΈΠ·ΠΌΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ. ΠΡΠΎΡΡΠΌ ΡΠ°Π³ΠΎΠΌ Π±ΡΠ»ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΠΊΠ»ΡΡΠ΅Π½ΠΈΡ ΠΌΡΠ³ΠΊΠΈΡ
Π½Π°Π²ΡΠΊΠΎΠ² Π² ΡΡΠ΅Π±Π½ΡΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΏΠΎ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΡΠ²Ρ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
Π²ΡΠ·Π°Ρ
, ΡΠ΅Π°Π»ΠΈΠ·ΡΡΡΠΈΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ. ΠΠ»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ»ΠΈΡΡ ΠΎΠΏΠΈΡΠ°ΡΠ΅Π»ΡΠ½Π°Ρ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ°, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π΅ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ U-ΠΊΡΠΈΡΠ΅ΡΠΈΠΉ ΠΠ°Π½Π½Π° β Π£ΠΈΡΠ½ΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ, ΡΡΠΎ Π² ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ°Ρ
, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
Π€ΠΠΠ‘, ΡΠ΄Π΅Π»ΡΠ΅ΡΡΡ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΡ
ΠΌΡΠ³ΠΊΠΈΡ
ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΡ
Π½Π°Π²ΡΠΊΠΎΠ². ΠΠ΄Π½Π°ΠΊΠΎ Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²ΠΎ ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ ΠΏΠ΅ΡΠ΅Π³ΡΡΠΆΠ΅Π½Ρ Π΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½Π°ΠΌΠΈ, ΡΠΎΡΠΌΠΈΡΡΡΡΠΈΠΌΠΈ ΡΡΡΠΈΠ½Π½ΡΠ΅ ΠΈ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΡΠ΅ Π½Π°Π²ΡΠΊΠΈ. ΠΠ΅ΡΠΌΠΎΡΡΡ Π½Π° ΡΠΎ ΡΡΠΎ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²ΠΎ ΠΏΡΠΎΠ²ΠΎΠ·Π³Π»Π°ΡΠ°Π΅Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΠΈΠ½Π½ΠΎΠ²Π°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π½Π°ΡΠΈΠΈ, ΡΠ²ΠΎΡΡΠ΅ΡΠΊΠΎΠ΅ ΠΈ ΠΈΠ½Π½ΠΎΠ²Π°ΡΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΌΡΡΠ»Π΅Π½ΠΈΠ΅ Π½Π΅ ΡΠΏΠΎΠΌΠΈΠ½Π°Π΅ΡΡΡ Π½ΠΈ Π² ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΡΡ
Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΡΡΠ°Π½Π΄Π°ΡΡΠ°Ρ
(Π€ΠΠΠ‘), Π½ΠΈ Π² Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ°Ρ
. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠΈΠ²Π΅Π»ΠΈ ΠΊ Π½Π΅ΠΎΠΆΠΈΠ΄Π°Π½Π½ΠΎΠΌΡ Π²ΡΠ²ΠΎΠ΄Ρ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ ΠΎΡΠ΅Π½Ρ Π½Π΅ΠΌΠ½ΠΎΠ³ΠΈΠ΅ Β«ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΠ΅Β» ΠΌΠΎΠ΄ΡΠ»ΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ Π²ΠΊΠ»ΡΡΠ°ΡΡ Π² ΡΠ²ΠΎΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΈΠ»ΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π½Π°Π²ΡΠΊΠΎΠ². ΠΠ°ΡΡΠ½Π°Ρ Π½ΠΎΠ²ΠΈΠ·Π½Π°. ΠΠ°ΡΡΠ½Π°Ρ Π½ΠΎΠ²ΠΈΠ·Π½Π° Π½Π°ΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠΎΠΌ, ΡΡΠΎ Π²ΠΏΠ΅ΡΠ²ΡΠ΅ ΠΈΠ·ΡΡΠ΅Π½Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΌΡΠ³ΠΊΠΈΡ
Π½Π°Π²ΡΠΊΠΎΠ² Π² ΠΎΠ±ΡΡΠ΅Π½ΠΈΠΈ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΡΠ²Ρ Π² Π ΠΎΡΡΠΈΠΈ. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π½Π°ΠΉΠ΄ΡΡ ΡΠ²ΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΡ
ΡΡΠ΅Π±Π½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ Π΄Π»Ρ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ Π² Π½ΠΈΡ
Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΠ³ΠΎ Π±Π°Π»Π°Π½ΡΠ° ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ.The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Programme of Development within the Priority-2030 Programme) is gratefully acknowledged.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ ΠΠΈΠ½ΠΈΡΡΠ΅ΡΡΡΠ²Π° Π½Π°ΡΠΊΠΈ ΠΈ Π²ΡΡΡΠ΅Π³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π£ΡΠ°Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ° ΠΈΠΌΠ΅Π½ΠΈ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΠΡΠ΅Π·ΠΈΠ΄Π΅Π½ΡΠ° Π ΠΎΡΡΠΈΠΈ Π. Π. ΠΠ»ΡΡΠΈΠ½Π° Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΎΠΉ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»ΠΈΠ΄Π΅ΡΡΡΠ²Π° Β«ΠΡΠΈΠΎΡΠΈΡΠ΅Ρ-2030Β»
Comparative analysis of oxidative metabolism indicators at acute alcohol and acute surrogate alcohol intoxication
High level of population alcoholization is the cause of many cases of acute alcohol and alcoholic surrogate intoxication. The number of alcohol intoxication cases in Kazakhstan in 2014 amounted to 13891 (80.3 per 100 000 people), the number of fatal intoxication cases amounted to 882 (5.1 per 100 000 people). The problem of alcoholization in Russia remains urgent as well: according to the statistics of 2014,152 551 cases of acute intoxication of chemical etiology were registered, 33.9 % of cases occurred due to alcohol intoxication. Alcoholic beverages in the course of their biotransformation to acetic acid can form oxygen free radicals in particular superoxide anion as a byproduct of acetic aldehyde oxidation reaction. Studies on oxidative metabolism of ethanol intoxication are currently being conducted. At the same time, the state of oxidative metabolism during alcoholic surrogate intoxication was not practically investigated. Evaluation of oxidative metabolism depending on the severity of alcohol or its surrogate intoxication is of special interest. The aim was to compare oxidative metabolism indicators among patients with acute alcohol and alcoholic surrogate intoxication of different severity. The object of the study was blood of 62 people with diagnosed moderate or severe degrees of acute alcohol and alcoholic surrogate intoxication. Indicators of oxidative metabolism in erythrocytes and blood plasma were estimated. Significant differences were found in product concentration of protein oxidation containing bityrosine crosslinks in blood plasma under increase of alcohol intoxication degree
ΠΠ΅ΠΉΡΠΎΠ²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΡΡΠΎΠ΅Π½ΠΈΡ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° Ρ Π΄Π΅ΡΠ΅ΠΉ Ρ Π΄Π΅ΡΡΠΊΠΈΠΌ ΡΠ΅ΡΠ΅Π±ΡΠ°Π»ΡΠ½ΡΠΌ ΠΏΠ°ΡΠ°Π»ΠΈΡΠΎΠΌ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΌΠ°Π³Π½ΠΈΡΠ½ΠΎ-ΡΠ΅Π·ΠΎΠ½Π°Π½ΡΠ½ΠΎΠΉ ΡΡΠ°ΠΊΡΠΎΠ³ΡΠ°ΡΠΈΠΈ
Aim. To perform quantitative evaluation of the degree of white matter tract abnormalities in children with spastic cerebral palsy by magnetic resonance tractography to determine severity of the disease, as well as to carry out a dynamic assessment of treatment effectiveness.Materials and methods. The study included 46 children (32 males, 14 females; average age 5.4 Β± 1.1 years). The participants were divided into two groups. The experimental group consisted of 23 children with spastic cerebral palsy. The control group included 23 children without any neurological disorder. Examination of the brain was performed on the Siemens Essenza 1,5 Π’ system (Siemens, Germany) and included magnetic resonance tractography to reconstruct the major white matter tracts. The number of fibers, average fractional anisotropy value, apparent diffusion coefficient, and coefficient of myelination of major white matter tracts in the brain were calculated and analyzed.Results. We found a significant difference in the above-stated parameters between the groups. The experimental group showed a decrease in the absolute number of fibers at the central and posterior segments of the corpus callosum, corticospinal tracts, and left inferior longitudinal fasciculus. Besides, we detected a decrease in fractional anisotropy at 2β5 segments of the corpus callosum and right lateral corticospinal tract, an increase in the apparent diffusion coefficient at 2, 4, and 5 segments of the corpus callosum and left lateral corticospinal tract, and a decrease in the myelination coefficient in all the examined tracts, except for superior longitudinal fasciculus. We revealed a positive correlation between the intensity of the motor disturbance and the coefficient of myelination at the anterior corpus callosum and inferior longitudinal fasciculus.Conclusion. Magnetic resonance tractography is an informative technique for unbiased evaluation of white matter tract anatomy, as well the level and degree of motor tract damage. The most useful characteristics of white matter tract anatomy are the absolute number of fibers in the tract, fractional anisotropy, and coefficient of myelination. Some of them correlated with the intensity of motor disturbance, so they can be regarded as potential predictors of rehabilitation potential.Β Π¦Π΅Π»Ρ. ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π½Π°ΡΡΡΠ΅Π½ΠΈΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΡΡΠΈΡ
ΠΏΡΡΠ΅ΠΉ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° Ρ Π΄Π΅ΡΠ΅ΠΉ ΡΠΎ ΡΠΏΠ°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠΎΡΠΌΠ°ΠΌΠΈ Π΄Π΅ΡΡΠΊΠΎΠ³ΠΎ ΡΠ΅ΡΠ΅Π±ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΠ°ΡΠ°Π»ΠΈΡΠ° (ΠΠ¦Π) ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΌΠ°Π³Π½ΠΈΡΠ½ΠΎ-ΡΠ΅Π·ΠΎΠ½Π°Π½ΡΠ½ΠΎΠΉ (ΠΠ ) ΡΡΠ°ΠΊΡΠΎΠ³ΡΠ°ΡΠΈΠΈ Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠΆΠ΅ΡΡΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΡΠ΅Π½ΠΊΠ° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π»Π΅ΡΠ΅Π½ΠΈΡ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ 46 Π΄Π΅ΡΠ΅ΠΉ 4β7 Π»Π΅Ρ (ΡΡΠ΅Π΄Π½ΠΈΠΉ Π²ΠΎΠ·ΡΠ°ΡΡ (5,4 Β± 1,1) Π»Π΅Ρ), ΠΈΠ· Π½ΠΈΡ
14 Π΄Π΅Π²ΠΎΡΠ΅ΠΊ (33%) ΠΈ 32 ΠΌΠ°Π»ΡΡΠΈΠΊΠ° (66%). ΠΠ°ΡΠΈΠ΅Π½ΡΡ ΡΠ°Π·Π΄Π΅Π»Π΅Π½Ρ Π½Π° Π΄Π²Π΅ Π³ΡΡΠΏΠΏΡ. ΠΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ Π³ΡΡΠΏΠΏΡ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ 23 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ° ΡΠΎ ΡΠΏΠ°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠΎΡΠΌΠ°ΠΌΠΈ ΠΠ¦Π. Π ΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠ½ΡΡ Π³ΡΡΠΏΠΏΡ Π²ΠΎΡΠ»ΠΈ 23 ΡΠ΅Π±Π΅Π½ΠΊΠ° Π±Π΅Π· Π½Π΅Π²ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΡΠΈΡΠ°. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ Π½Π° ΠΠ -ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠ΅ Siemens Essenza 1,5 Π’ (Siemens, ΠΠ΅ΡΠΌΠ°Π½ΠΈΡ) ΠΈ Π²ΠΊΠ»ΡΡΠ°Π»ΠΎ ΠΌΠ΅ΡΠΎΠ΄ ΠΠ -ΡΡΠ°ΠΊΡΠΎΠ³ΡΠ°ΡΠΈΠΈ. ΠΡΠ»ΠΈ ΡΠ°ΡΡΡΠΈΡΠ°Π½Ρ ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠ°Π½Ρ: ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π²ΠΎΠ»ΠΎΠΊΠΎΠ½, ΡΡΠ΅Π΄Π½ΠΈΠΉ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΡΡΠ°ΠΊΡΠΈΠΎΠ½Π½ΠΎΠΉ Π°Π½ΠΈΠ·ΠΎΡΡΠΎΠΏΠΈΠΈ, ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½Ρ Π΄ΠΈΡΡΡΠ·ΠΈΠΈ, ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½Ρ ΠΌΠΈΠ΅Π»ΠΈΠ½ΠΈΠ·Π°ΡΠΈΠΈ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΏΡΠΎΠ²ΠΎΠ΄ΡΡΠΈΡ
ΠΏΡΡΠ΅ΠΉ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΡΠ²Π»Π΅Π½Π° Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½Π°Ρ ΡΠ°Π·Π½ΠΈΡΠ° ΡΠΊΠ°Π·Π°Π½Π½ΡΡ
Π²ΡΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΠΌΠ΅ΠΆΠ΄Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°ΠΌΠΈ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΠΎΠΉ ΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠ½ΠΎΠΉ Π³ΡΡΠΏΠΏ. Π£ Π΄Π΅ΡΠ΅ΠΉ Ρ ΠΠ¦Π ΠΎΡΠΌΠ΅ΡΠ°Π»ΠΎΡΡ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ Π°Π±ΡΠΎΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° Π²ΠΎΠ»ΠΎΠΊΠΎΠ½ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠ΅Π½ΡΡΠ°Π»ΡΠ½ΡΡ
ΠΈ Π·Π°Π΄Π½Π΅Π³ΠΎ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠΎΠ² ΠΌΠΎΠ·ΠΎΠ»ΠΈΡΡΠΎΠ³ΠΎ ΡΠ΅Π»Π°, ΠΊΠΎΡΡΠΈΠΊΠΎΡΠΏΠΈΠ½Π°Π»ΡΠ½ΡΡ
ΡΡΠ°ΠΊΡΠΎΠ² ΠΈ Π»Π΅Π²ΠΎΠ³ΠΎ Π½ΠΈΠΆΠ½Π΅Π³ΠΎ ΠΏΡΠΎΠ΄ΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡΠΊΠ°. Π’Π°ΠΊΠΆΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΎΡΡ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΡΡΠ°ΠΊΡΠΈΠΎΠ½Π½ΠΎΠΉ Π°Π½ΠΈΠ·ΠΎΡΡΠΎΠΏΠΈΠΈ Π²ΠΎΠ»ΠΎΠΊΠΎΠ½ Π² ΠΎΠ±Π»Π°ΡΡΠΈ 2β5-Π³ΠΎ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠΎΠ² ΠΌΠΎΠ·ΠΎΠ»ΠΈΡΡΠΎΠ³ΠΎ ΡΠ΅Π»Π°, ΠΏΡΠ°Π²ΠΎΠ³ΠΎ ΠΊΠΎΡΡΠΈΠΊΠΎΡΠΏΠΈΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΠΊΡΠ°; ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° Π΄ΠΈΡΡΡΠ·ΠΈΠΈ Π² ΠΎΠ±Π»Π°ΡΡΠΈ 2, 4, 5-Π³ΠΎ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠΎΠ² ΠΈ Π»Π΅Π²ΠΎΠ³ΠΎ ΠΊΠΎΡΡΠΈΠΊΠΎΡΠΏΠΈΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΠΊΡΠ°; ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΠΌΠΈΠ΅Π»ΠΈΠ½ΠΈΠ·Π°ΡΠΈΠΈ Π²ΠΎ Π²ΡΠ΅Ρ
ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ
ΡΡΠ°ΠΊΡΠ°Ρ
, Π·Π° ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ΠΌ Π²Π΅ΡΡ
Π½ΠΈΡ
ΠΏΡΠΎΠ΄ΠΎΠ»ΡΠ½ΡΡ
ΠΏΡΡΠΊΠΎΠ². ΠΡΡΠ²Π»Π΅Π½Π° ΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ Β Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΡΡΡΡ ΠΌΠΎΡΠΎΡΠ½ΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΡΠΈΡΠ° ΠΈ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠΌ ΠΌΠΈΠ΅Π»ΠΈΠ½ΠΈΠ·Π°ΡΠΈΠΈ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΏΠ΅ΡΠ΅Π΄Π½Π΅Π³ΠΎ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ° ΠΌΠΎΠ·ΠΎΠ»ΠΈΡΡΠΎΠ³ΠΎ ΡΠ΅Π»Π° ΠΈ Π½ΠΈΠΆΠ½ΠΈΡ
ΠΏΡΠΎΠ΄ΠΎΠ»ΡΠ½ΡΡ
ΠΏΡΡΠΊΠΎΠ².ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠ -ΡΡΠ°ΠΊΡΠΎΠ³ΡΠ°ΡΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΡΡΠΈΡ
ΠΏΡΡΠ΅ΠΉ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°, ΡΡΠΎΠ²Π½Ρ ΠΈ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΌΠΎΡΠΎΡΠ½ΡΡ
ΡΡΠ°ΠΊΡΠΎΠ². ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΠΌΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΡΡΠΈΡ
ΠΏΡΡΠ΅ΠΉ ΡΠ²Π»ΡΡΡΡΡ Π°Π±ΡΠΎΠ»ΡΡΠ½ΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π²ΠΎΠ»ΠΎΠΊΠΎΠ½ Π² ΡΡΠ°ΠΊΡΠ΅, ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΡΡΠ°ΠΊΡΠΈΠΎΠ½Π½ΠΎΠΉ Π°Π½ΠΈΠ·ΠΎΡΡΠΎΠΏΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ°ΡΡΠ΅ΡΠ½ΡΠΉ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ β ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½Ρ ΠΌΠΈΠ΅Π»ΠΈΠ½ΠΈΠ·Π°ΡΠΈΠΈ. ΠΠ΅ΠΊΠΎΡΠΎΡΡΠ΅ ΠΈΠ· Π²ΡΡΠ²Π»Π΅Π½Π½ΡΡ
ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ ΠΊΠΎΡΡΠ΅Π»ΠΈΡΠΎΠ²Π°Π»ΠΈ Ρ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΡΡΡΡ ΠΌΠΎΡΠΎΡΠ½ΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΡΠΈΡΠ°, ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡ ΠΈΡ
ΠΊΠ°ΠΊ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΎΡΡ ΡΠ΅Π°Π±ΠΈΠ»ΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π°
Landscape science: a Russian geographical tradition
The Russian geographical tradition of landscape science (landshaftovedenie) is analyzed with particular reference to its initiator, Lev Semenovich Berg (1876-1950). The differences between prevailing Russian and Western concepts of landscape in geography are discussed, and their common origins in German geographical thought in the late nineteenth and early twentieth centuries are delineated. It is argued that the principal differences are accounted for by a number of factors, of which Russia's own distinctive tradition in environmental science deriving from the work of V. V. Dokuchaev (1846-1903), the activities of certain key individuals (such as Berg and C. O. Sauer), and the very different social and political circumstances in different parts of the world appear to be the most significant. At the same time it is noted that neither in Russia nor in the West have geographers succeeded in specifying an agreed and unproblematic understanding of landscape, or more broadly in promoting a common geographical conception of human-environment relationships. In light of such uncertainties, the latter part of the article argues for closer international links between the variant landscape traditions in geography as an important contribution to the quest for sustainability
Complex Estimation of Strength Properties of Functional Materials on the Basis of the Analysis of Grain-Phase Structure Parameters
The technique allows analysis using grain-phase structure of the functional material to evaluate its performance, particularly strength properties. The technique is based on the use of linguistic variable in the process of comprehensive evaluation. An example of estimating the strength properties of steel reinforcement, subject to special heat treatment to obtain the desired grain-phase structure
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