Identification of Seawater Quality by Multivariate Statistical Analysis in Xisha Islands, South China Sea

Xisha waters are considered to be in pristine condition, while facing the fast increasing stress under anthropogenic activities. Water quality around Yongxing Island (YX) has been measured in May, 2012. The results show that the water quality is of the first class standards as compared to the water quality of China, with insignificant difference among the monitoring stations. Robust principal component analysis (PCA) was used to identify the spatial pattern of water quality. YX is characterized by high DO, salinity, and Chl-a with low nutrients, indicating phytoplankton photosynthesis is stronger in YX island waters than the rest of the study areas. Beidao (BD) is characterized by high NH4-N and COD, and low pH, implying that these areas may have higher organic matter decomposition than rest of the areas. The water quality monitoring stations should cover spatially and temporally around Xisha waters for protecting the marine environment

Reliability assessment for distributed systems via communication abstraction and refinement

Distributed systems like cloud-based services are ever more popular. Assessing the reliability of distributed systems is highly non-trivial. Particularly, the order of executions among distributed components adds a dimension of non-determinism, which invalidates existing reliability assessment methods based on Markov chains. Probabilistic model checking based on models like Markov decision processes is designed to deal with scenarios involving both probabilistic behavior (e.g., reliabilities of system components) and non-determinism. However, its application is currently limited by state space explosion, which makes reliability assessment of distributed system particularly difficult. In this work, we improve the probabilistic model checking through a method of abstraction and reduction, which controls the communications among system components and actively reduces the size of each component. We prove the soundness and completeness of the proposed approach. Through an implementation in a software toolkit and evaluations with several systems, we show that our approach often reduces the size of the state space by several orders of magnitude, while still producing sound and accurate assessment.No Full Tex