In this paper, we suggest two machine learning methods for seismic hazard forecast. The first method is used for spatial forecasting of maximum possible earthquake magnitudes ( M m a x ), whereas the second is used for spatio-temporal forecasting of strong earthquakes. The first method, the method of approximation of interval expert estimates, is based on a regression approach in which values of M m a x at the points of the training sample are estimated by experts. The method allows one to formalize the knowledge of experts, to find the dependence of M m a x on the properties of the geological environment, and to construct a map of the spatial forecast. The second method, the method of minimum area of alarm, uses retrospective data to identify the alarm area in which the epicenters of strong (target) earthquakes are expected at a certain time interval. This method is the basis of an automatic web-based platform that systematically forecasts target earthquakes. The results of testing the approach to earthquake prediction in the Mediterranean and Californian regions are presented. For the tests, well known parameters of earthquake catalogs were used. The method showed a satisfactory forecast quality.

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Контакты: Татьяна Евгеньевна Скачкова adora.wh@gmail.com В сыворотке крови 336 первичных больных раком предстательной железы (РПЖ) с исходным уровнем общего простатического специфического антигена (общПСА) < 30,0 нг/мл исследованы показатели свободного ПСА (свПСА), [-2]проПСА, определены %свПСА, %[-2]проПСА, индекс здоровья предстательной железы (ИЗП) и новый показатель ВИЗГ, рассчитанный на базе лабораторных анализов с учетом возраста, стадии Т и индекса Глисона по результатам биопсии. Полученные данные сопоставлены со стадией опухолевого процесса (pTNM) и степенью злокачественности опухоли по шкале Глисона в соответствии с окончательным гистологическим заключением после проведения простатэктомии. Показано, что ВИЗГ имеет статистически достоверное преимущество перед ПСА-ассоциированными маркерами в дифференцировке клинически значимых подгрупп РПЖ: pT2c/pT3a/pT3b; локализованный индолентный РПЖ/локализованный агрессивный/местно-распространенный/РПЖ с регионарными метастазами; сумма баллов по шкале Глисона 5-6/7(3 + 4)/7(4 + 3). Ключевые слова: индолентный и агрессивный рак предстательной железы, простатический специфический антиген, общПСА, свПСА, [-2]проПСА, индекс здоровья предстательной железыSerum of 336 patients with primary prostate cancer (PC) with baseline total prostate-specific antigen level (totPSA) < 30.0 ng/ml was tested for free PSA (freePSA) and [-2]proPSA; %freePSA, %[-2]proPSA, prostate health index (phi), and a new index APHIG calculated using lab tests and taking into account age, T stage and Gleason score from biopsy were evaluated. Obtained data was compared to tumor stage (pTNM) and malignancy grade according to the Gleason score based on the final histological report after prostatectomy. APHIG has statistically significant benefits compared to PSA-associated markers for differentiation of clinically significant subgroups of PC: pT2c/pT3a/pT3b; local indolent PC/local aggressive/locally advanced/PC with regional metastases; total Gleason score 5-6/7(3 + 4)/7(4 + 3).

This paper describes a spatial-dynamic, stochastic optimization model that ta1ces account of the complexities and dependencies of catastrophic risks. Following a description of the general model, the paper briefly discusses a case study of earthquake risk in the Irkutsk region of Russia. For this purpose the risk management model is customized to explicitly incorporate the geological characteristics of the region, as well as the seismic hazards and the vulnerability of the built environment. In its general form, the model can analyze the interplay between investment in mitigation and risk-sharing measures. In the application described in this paper, the model generates insurance strategies that are less vulnerable to insolvency.

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