Демографічні фактори розвитку соціального капіталу

У статті проаналізовано тенденції змін основних демографічних показників в Україні й Донецькому регіоні зокрема, показано вплив цих факторів на розвиток соціального капіталу. Акцентовано, що дослідження демографічних факторів формування соціального капіталу на державному і регіональному рівнях дозволяє відповідним державним органам управління отримувати повну інформацію стосовно будь-яких змін демографічного розвитку та вживати заходів з оптимізації параметрів трудового та інтелектуального потенціалу населення.In the article the tendencies of changes of basic demographic indicators are analysed in Ukraine and Donetsk region in particular, influence of these factors is rotined on development of social capital. It is accented, that research of demographic factors of forming of social capital on state and regional levels allows the proper public organs of management to get complete information on any changes of demographic development and take measures from optimization of parameters of labour and intellectual potential of population

Semi-parametric Expected Shortfall Forecasting

Intra-day sources of data have proven effective for dynamic volatility and tail risk estimation. Expected shortfall is a tail risk measure, that is now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to indirectly model expected shortfall, is generalised to incorporate information on the intra-day range. An asymmetric Gaussian density model error formulation allows a likelihood to be developed that leads to semiparametric estimation and forecasts of expectiles, and subsequently of expected shortfall. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation, while their performance is assessed via a simulation study. The proposed models compare favourably with a large range of competitors in an empirical study forecasting seven financial return series over a ten year period

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Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge.

Small molecule distribution coefficients between immiscible nonaqueuous and aqueous phases-such as cyclohexane and water-measure the degree to which small molecules prefer one phase over another at a given pH. As distribution coefficients capture both thermodynamic effects (the free energy of transfer between phases) and chemical effects (protonation state and tautomer effects in aqueous solution), they provide an exacting test of the thermodynamic and chemical accuracy of physical models without the long correlation times inherent to the prediction of more complex properties of relevance to drug discovery, such as protein-ligand binding affinities. For the SAMPL5 challenge, we carried out a blind prediction exercise in which participants were tasked with the prediction of distribution coefficients to assess its potential as a new route for the evaluation and systematic improvement of predictive physical models. These measurements are typically performed for octanol-water, but we opted to utilize cyclohexane for the nonpolar phase. Cyclohexane was suggested to avoid issues with the high water content and persistent heterogeneous structure of water-saturated octanol phases, since it has greatly reduced water content and a homogeneous liquid structure. Using a modified shake-flask LC-MS/MS protocol, we collected cyclohexane/water distribution coefficients for a set of 53 druglike compounds at pH 7.4. These measurements were used as the basis for the SAMPL5 Distribution Coefficient Challenge, where 18 research groups predicted these measurements before the experimental values reported here were released. In this work, we describe the experimental protocol we utilized for measurement of cyclohexane-water distribution coefficients, report the measured data, propose a new bootstrap-based data analysis procedure to incorporate multiple sources of experimental error, and provide insights to help guide future iterations of this valuable exercise in predictive modeling