Decrease of RMI Application Coupling Degree Based on Dynamic Proxy and Interceptor

Based on Java RMI framework, a new design is introduced to plug common services into RMI application without modifying the remote interface. This design improves the mechanism of RMI, which is based on dynamic proxy and interceptor technology. The implementation of class loader and stub is custom. The design ensures both the integrity of RMI mechanism and the plug-ins of some common services that are based on component. The design improves the modularization of application and decreases the coupling degree among different modules; by this means these middle-ware's modules gain more scalability and flexibility.

Production optimization under waterflooding with long short-term memory and metaheuristic algorithm

In petroleum domain,optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of the petroleum companies,but also fulfills the increasing global demand of energy.However,applying numerical reservoir simulation(NRS)to optimize production can induce high computational footprint.Proxy models are suggested to alleviate this challenge because they are computationally less demanding and able to yield reasonably accurate results.In this paper,we demonstrated how a machine learning technique,namely long short-term memory(LSTM),was applied to develop proxies of a 3D reservoir model.Sampling techniques were employed to create numerous simulation cases which served as the training database to establish the proxies.Upon blind validating the trained proxies,we coupled these proxies with particle swarm optimization to conduct production optimization.Both training and blind validation results illustrated that the proxies had been excellently developed with coefficient of determination,R2 of 0.99.We also compared the optimization results produced by NRS and the proxies.The comparison recorded a good level of accuracy that was within 3%error.The proxies were also computationally 3 times faster than NRS.Hence,the proxies have served their practical purposes in this study.

The Tibetan Ozone Low and Its Long-Term Variation During 1979-2010

A Tibetan ozone low was found in the 1990s after the Antarctic ozone hole. Whether this ozone low has been recovering from the beginning of the 2000s following the global ozone recovery is an intriguing topic. With the most recent merged TOMS/SBUV （Total Ozone Mapping Spectrometer/Solar Backscatter Ultra Violet） ozone data, the Tibetan ozone low and its long-term variation during 1979-2010 are analyzed using a statistical regression model that includes the seasonal cycle, solar cycle, quasi-biennial oscillation （QBO）, ENSO signal, and trends. The results show that the Tibetan ozone low maintains and may become more severe on average during 1979-2010, compared with its mean state in the periods before 2000, possibly caused by the stronger downward trend of total ozone concentration over the Tibet. Compared with the ozone variation over the non-Tibetan region along the same latitudes, the Tibetan ozone has a larger downward trend during 1979-2010, with a maximum value of-0.40±0.10 DU yr^-1 in January, which suggests the strengthening of the Tibetan ozone low in contrast to the recovery of global ozone. Regression analyses show that the QBO signal plays an important role in determining the total ozone variation over the Tibet. In addition, the long-term ozone variation over the Tibetan region is largely affected by the thermal-dynamical proxies such as the lower stratospheric temperature, with its contribution reaching around 10% of the total ozone change, which is greatly different from that over the non-Tibetan region.