Using 448.0 × 10 6 ψ(3686) events collected with the BESIII detector, an amplitude analysis is performed for ψ(3686) → γχc1, χc1 → ηπ + π − decays. The most dominant two-body structure observed is a0 (980) ± π ∓ ; a0(980) ± → ηπ ± . The a0(980) line shape is modeled using a dispersion relation, and a significant non-zero a0(980) coupling to the η ′ π channel is measured. We observe χc1 → a2(1700)π production for the first time, with a significance larger than 17σ. The production of mesons with exotic quantum numbers, J P C = 1 −+ , is investigated, and upper limits for the branching fractions χc1 → π1(1400) ± π ∓ , χc1 → π1(1600) ± π ∓ , and χc1 → π1(2015) ± π ∓ , with subsequent π1(X) ± → ηπ ± decay, are determined.

SUMMARYHadoop MapReduce has become a major computing technology in support of big data analytics. The Hadoop framework has over 190 configuration parameters, and some of them can have a significant effect on the performance of a Hadoop job. Manually tuning the optimum or near optimum values of these parameters is a challenging task and also a time consuming process. This paper optimizes the performance of Hadoop by automatically tuning its configuration parameter settings. The proposed work first employs gene expression programming technique to build an objective function based on historical job running records, which represents a correlation among the Hadoop configuration parameters. It then employs particle swarm optimization technique, which makes use of the objective function to search for optimal or near optimal parameter settings. Experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings. Moreover, it outperforms both rule-of-thumb settings and the Starfish model in Hadoop performance optimization.