Анализ эффективности эксплуатации системы регенерации высокого давления на Ростовской атомной станции

Объектом исследования является система регенерации высокого давления энергоблока АЭС с реактором ВВЭР-1000. Цель работы – проанализировать эффективность работы системы регенерации высокого давления Ростовской АЭС. В процессе расчетов проведен тепло-гидравлический и конструкторский расчеты подогревателя высокого давления. В результате расчета определены оптимальные геометрические размеры поверхности теплообмена подогревателя. Основные конструктивные, технологические и технико-эксплуатационные характеристики: теплоноситель - влажный пар с давлением 2.785 МПа и степенью сухости 0.919, рабочее тело - питательная во-да с давлением 8.922 МПа, температурой на входе 200.4 на выходе 225 .The object of research is the system of high-pressure regeneration NPP with VVER-1000 reactor. Objective - To analyze the efficiency of regeneration system of Rostov NPP high pressure. In the process of calculation performed heat-hydraulic calculations and design of high pressure heater. As a result of calculating the optimal geometric dimensions heater heat exchange surface. The basic constructive, technological and technical and operational characteristics: coolant - wet steam with pressures-tion 2.785 MPa and the degree of dryness of 0.919, working body - the food first, but with the pressure of 8,922 MPa, inlet temperature of 200.4 at the output 225

Learning in Experimental 2 X 2 Games

In this paper, we introduce two new learning models: impulse-matching learning and action-sampling learning. These two models together with the models of self-tuning EWA and reinforcement learning are applied to 12 different 2 x 2 games and their results are compared with the results from experimental data. We test whether the models are capable of replicating the aggregate distribution of behavior, as well as correctly predicting individuals' round-by-round behavior. Our results are two-fold: while the simulations with impulse-matching and action-sampling learning successfully replicate the experimental data on the aggregate level, individual behavior is best described by self-tuning EWA. Nevertheless, impulse-matching learning has the second highest score for the individual data. In addition, only self-tuning EWA and impulse-matching learning lead to better round-by-round predictions than the aggregate frequencies, which means they adjust their predictions correctly over time

DNA methylation-based classification of central nervous system tumours

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challengingwith substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology