Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware.
Compressed air energy storage (CAES) is an economic, large-scale energy storage technology, but its further applications are limited by thermodynamic inefficiency. Although high-exergy destruction components can be highlighted through exergy analysis, the interactions among components and the true potential for the improvement of CAES are not obvious. In this study, an advanced exergy analysis was applied to the CAES system. The exergy destruction within each system component was split into four parts, namely, endogenous, exogenous, avoidable, and unavoidable. The thermodynamic properties of CAES were discussed in detail by combining the four parts. Results indicate that the unavoidable part of exergy destruction within the components of the system is larger than the avoidable part. The most important components based on the avoidable exergy destruction are combustion chambers, intercoolers, and aftercoolers. Exergy destruction can be significantly reduced by improving the main component efficiencies. More than half of the avoidable exergy destruction is exogenous, which indicates that interactions among components have a considerable impact on the CAES performance.
A new early-warning system for international currency crises is developed in this paper. The existing crisis indicators in the literature are essentially static. We examine the relationship between the dynamics of foreign reserves and currency crises. It is shown that rapid reserve depletion is a prominent feature before the collapse of the exchange rate system. The results from our threshold autoregressive model suggest that when the Reserves-to-Short-Term External Debt falls by more than 29.1%, or if the Reserves-to-M2 ratio drops by more than 24.3% within six months, the likelihood of a crisis increases. Our model provides clear warning signals for policy makers to take actions before the reserves have reached a critical value that heralds the arrival of a full-blown crisis. * We would like to thank Charles Engel, Andrew Rose, Yin-Wong Cheung, Julan Du and Kang Shi for helpful comments. We also thank Michael Lu, Tingting Zhu, Mansfield Wong, Carrella Ernesto and Lumpkin Mcspadden for able research assistance. All errors are ours.
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