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The importance of severe accident management is being re-examined because severe accident management failed during the Fukushima nuclear accident. Having an accurate estimation of a severe accident is important for coping properly with unfavorable conditions. In this study, the extended group method of data handling (GMDH) with a fuzzy concept is proposed as a means of predicting the major severe accident events that represent the severe accident management guideline (SAMG) entry time, including the time at which the reactor vessel (RV) water level decreases, the time when the core exit temperature (CET) reaches 450 degrees-centigrade, the time when the hydrogen concentration in the containment is over 4%, and the time when the reactor coolant system (RCS) pressure is over 2.86 MPa under loss of coolant accidents (LOCAs) in pressurized water reactors (PWRs). To train the extended GMDH model, it was necessary to acquire the data needed from a number of numerical simulations due to the lack of actual LOCA data. The data was obtained by carrying out simulations using the MAAP5 code. To optimize the developed model, the optimal input selection processes were performed using the clustering analysis including self-organizing feature map (SOFM), hierarchical and non-hierarchical clustering methods. The prediction accuracy of the three types of initiating accident, small break (SB), medium break (MB), and large break (LB) LOCAs, was high enough to predict the SAMG entry time. When compared with other artificial intelligence (AI) methods, the extended GMDH was found to be superior to the original GMDH and support vector regression (SVR). Therefore, it is expected that the extended GMDH model will help operators to properly mitigate severe accidents in PWRs. [ABSTRACT FROM AUTHOR]

We study, from the perspective of optimal stopping theory, entry-exit decision problems of a project in the context that the log-price process follows a Lévy process with exponential jumps. A closed-form solution to the problems is obtained. To be specific, we show explicitly an optimal entry time, an optimal exit time and an expression of the maximal expected present value of the project. Moreover, it is also anatomized how the jumping of the Lévy process affects optimal entry and exit times. While the negative effect of jumping on prices grows, the optimal exit time gets earlier, and the optimal entry time, however, first moves up and then moves down. In addition, the optimal exit time decreases with the frequency increasing of jumps when the negative effect dominates the positive effect, and increases when the opposite situation holds; the optimal entry time first increases and then decreases as the frequency becomes higher if the negative effect dominates the positive effect, and increases if the contrary condition is satisfied. [ABSTRACT FROM AUTHOR]

For large-scale traffic assignment applications, mesoscopic simulation has more advantages than microscopic and macroscopic simulation because it has a well-designed tradeoff between the level of details and the running efficiency. Currently, the time complexity of time-stepped mesoscopic supply framework is linear to the number of vehicles on the lanes. To simulate an entire city the computation cost is often practically unacceptable. To tackle this problem, we propose a novel approach named 'Entry Time based Supply Framework (ETSF)' to improve the performance of mesoscopic applications. The main idea is that ETSF updates the 'entry_time_to_pass' and 'entry_time_to_queue' parameters of each lane to compute the moving/queuing parts of link/lane vehicles, which means reducing the time complexity of the number of vehicles passing the lanes. To validate this innovation of ETSF, a series of experiments were carried out on a sub-network of Singapore and the outcomes show that ETSF is less sensitive with the demand level (or the total number of vehicles) and greatly improves the performance, especially when simulating congested traffic scenarios. In the global research framework of mesoscopic traffic simulations, ETSF offers an innovative and time-efficient way to model traffic dynamics on links. [ABSTRACT FROM AUTHOR]

Termites are a group of eusocial insects. Mate choice is the most important step which affects the subsequent new colony foundation and development in termites. This study investigated the effects of the four factors on mate choice in the subterranean termite Reticulitermes chinensis Snyder, including colony origin, entry time, physical damage of antennae, and fresh body weights of male dealates. The results showed that the pairing number of dealates from the different colonies was significantly higher than that from the same colonies. The male dealates with early entry time achieved a significantly higher pairing number than the male dealates with late entry time. The male dealates with complete antennae achieved a the significantly higher pairing number than the male dealates lacking the six terminal segments of antennae. Moreover, the heavier male dealates achieved a significantly higher pairing number than the lighter male dealates. However, there were no significant differences in the pairing time for all the experimental treatments. These results suggested that the male dealates, which are heterogenous, healthy, heavy, and more familiar with the environment in R. chinensis, have the obvious advantages in the mate choice. This rule of mate choice is helpful for termites to avoid inbreeding and to maintain the genetic diversity of offspring, which is very important for the environmental adaptability and development of termite colonies. [ABSTRACT FROM AUTHOR]