The borders of the exclave Kaliningrad region have a more pronounced barrier function than those of other Russian territories. That is why the local socio-economic system is more self-contained. The economic, social, demographic, ekistic, and other processes are easy to model there, and regional theories are convenient to test. The aim of the article is to identify the patterns of polarization of settlement in the Kaliningrad region and the factors that have the most significant impact on the migration movement of the population in the region and the development of rural areas. This article analyses statistical data on changes in settlement patterns to reveal elements of the centre-periphery concept. We use economic-statistical, graphical, and cartographic methods to track the polarisation of settlement patterns and the economy. A distinction is drawn between the centre and the periphery, which differ qualitatively. The rapidly developing Kaliningrad agglomeration in the west of the region constitutes the centre, whilst the less urbanised underachieving east makes up the periphery. In the west, the rural population is often involved in non-agricultural activities and works in the city. Proximity to Kaliningrad and regional towns allows the rural population to benefit from the urban infrastructure, which improves their quality of life. Both urban and rural areas have a positive net migration rate accounted for by the inflow from the east of the region, Russia’s eastern and northern territories, and neighbouring countries. People are leaving eastern municipalities because of the modernisation of agriculture, a reduction in employment in the industry, the slow emergence of new jobs, and poorer living conditions.

This paper presents a comparative analysis of the gross regional products (GRPs) of 85 Russian regions. Statistical methods were used to analyze datasets on regional GRP, its sectoral composition and growth rates. Many datasets have been computed for the first time, including those of gross value added per capita and per employee. The work reviews a range of evidence on the contribution of the production of goods and market and non-market services to regional GRPs. The production of goods was further analyzed by economic sectors for each region. The data used in computations were provided by the Russian Federal State Statistics Service (ROSSTAT). The authors built a typology of Russian regions based on GRP figures, GRP sectoral composition and growth rates. The data obtained make it possible to determine a set of indicators of the geographical distribution of Russia's economy and its growth rates. The findings are presented in the form of tables, maps and graphic materials (three tables, three charts and 11 cartographic maps), displaying quantitative measures of the economic performance of Russian regions .

Humanitarian workers operate in complex environments with various challenges and demanding working conditions. These challenges put aid workers in a range of risks and under the pressure. However, human resources are crucial for success of humanitarian operations in general. At the same time, each humanitarian operation is reliant on logistics and logistics activities are always connected with logistic staff. Understanding what motivates logisticians to join the humanitarian sector is essential information for humanitarian organizations and for recruiters within. Also, knowing which factors influence motivation and job satisfaction of humanitarian logisticians could help the organizations to struggle with the extremely turnover they have to face. Up to this moment, needed skills and the performance of humanitarian logisticians were examined. Also, the motivators of humanitarian workers are covered in previous research. Therefore, the additional aim of this research is to extend the knowledge about the human resources in humanitarian sector as well.

Аннотация. Эпикардиальный жир (ЭЖ), или эпикардиальная жировая ткань (ЭЖТ), представляет собой депозит висцерального жира вокруг сердца. Была получена ассоциация ЭЖТ с частотой смертельных и несмертельных сердечно-сосудистых событий. Цель-оценить толщину эпикардиального жира (ТЭЖ) у пациентов с семейной гиперхолестеринемией (СГХС) по сравнению с пациентами с нормальными показателями липидного спектра. Материалы и методы. Проведен сравнительный анализ ТЭЖ у 47 нормотензивных пациентов с СГХС, без ИБС (не получавших ранее гиполипидемической терапии), и у 41 нормотензивного пациента с нормальными показателями липидного спектра. Критериями исключения были: наличие у пациента в анамнезе гиполипидемической терапии, ИБС. ТЭЖ определялась во время эхокардиоскопии. Результаты. Вне зависимости от уровня индекса массы тела у пациентов с СГХС ТЭЖ была выше (4.4±0.24 мм), чем у пациентов с нормальным липидным спектром (3.7±0.23 мм) (p=0.03). У лиц с избыточной массой тела показатель ТЭЖ при СГХС выше и составляет 4.6±0.22 мм по сравнению с 3.69±0.27 мм у лиц из группы контроля (р=0.009). Достоверных гендерных различий по уровню ТЭЖ ни в группе СГХС, ни в группе пациентов с нормальными показателями липидного спектра выявлено не было. Максимальные различия между пациентами с СГХС и лицами с нормальным уровнем липидного спектра касались женщин, составив, соответственно, 4.31±0.22 мм и 3.52±0.19 мм (р=0.004). Выводы: У пациентов с СГХС выявлен достоверно более высокий уровень ТЭЖ вне зависимости от наличия избыточной массы тела. Ключевые слова: семейная гиперхолестеринемия, эпикардиальный жир.

The nonparametric method of dynamic data envelopment analysis (DDEA) has become increasingly popular for conducting comparative efficiency evaluations. In recent years, dynamic data envelopment analysis (DDEA), a variant of this method, has gained significant attention. This article applies dynamic analysis to evaluate the efficiency of the research sector in Russian regions. Traditional input variables such as the number of research staff and R&D expenditure are considered, while publication and patent metrics serve as output indicators. The analysis covers a substantial time period, spanning from 2009 to 2020. Notably, the proposed evaluation method incorporates publication quality measures as a carry-over variable, in addition to accumulated R&D expenditure. The study employs dynamic data envelopment analysis to compare the obtained results with previous evaluations of the research and technology sector in Russian regions. The findings demonstrate that the proposed method serves as a valuable ranking technique, enhancing existing evaluations of regions’ research and technology potential in terms of efficiency. The article concludes by discussing the prospects and limitations of the method in evaluating and forecasting research and technology profiles of regions.