Betatron modulation of microwave instability in small isochronous ring

A novel coupling of the transverse betatron motion to the longitudinal microwave instability is studied. Besides the radial coherent dipole mode space charge field, simulation and theoretical studies in this paper show that the longitudinal coherent dipole mode space charge field due to centroid wiggles also plays an important role in the isochronous regime, it induces betatron oscillation frequencies in temporal evolutions of spectra of longitudinal charge densities, radial centroid offsets and coherent energy deviations of local centroids.Comment: 13 page

Identifying Factors Influencing Continuance Intention and Actual Behavior of Online Computer Games In Chongqing, China

Purpose: This research aims to identify the factors influencing students’ continuance intention and actual behavior of online computer games in Chongqing, China. Seven variables were used to construct a conceptual framework of this study including attitudes, utilitarian outcome expectations, hedonic outcome expectations, subjective norms, time constraint, continuance intention and actual behavior. Research design, data and methods: The data were collected from 500 participants. Nonprobability sampling were accounted, including judgmental sampling, quota sampling and convenience sampling. The index of item-objective congruence (IOC) and Cronbach's Alpha were assessed to approve validity and reliability before the data collection. Structural equation model (SEM) and confirmatory factor analysis (CFA) were applied in the statistical analysis, including goodness of fit indices, reliability and validity. Results: Attitude, utilitarian outcome expectation, hedonic outcome expectation, subjective norms, time constraints significantly influence continuance intention. Furthermore, the continuance intention has the strongest influence on the actual behavior of online computer games among students. Conclusions: Game developers and marketers are recommended to design and promote the features of online computer games to enhance users’ continuance intention and actual behavior

Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning

Mesoscale eddies, including surface-intensified eddies (SEs) and subsurface-intensified eddies (SSEs), significantly influence phytoplankton distribution in the ocean. Nevertheless, due to the sparse in situ data, understanding of the characteristics of SSEs and their influence on chlorophyll a (Chl a) concentration is still unclear. Consequently, the study utilized a deep learning model to extract SEs and SSEs in the northern Indian Ocean (NIO) from 2000 to 2015, using satellite-derived sea surface height (SSH) and sea surface temperature (SST) data. The analysis revealed that SSEs accounted for 39 % of the total eddies in the NIO, and their SST signatures exhibited opposite behaviour compared to SEs. Furthermore, by integrating ocean colour remote-sensing data, the study investigated the contrasting impacts of SEs and SSEs on Chl a concentration in two basins of the NIO, the Arabian Sea (AS) and the Bay of Bengal (BoB), known for their disparate biological productivity. In the AS, SEs induced Chl a anomalies that were 2 to 3 times higher than those caused by SSEs. Notably, there were no significant differences in Chl a anomalies induced by the same type of eddies between summer and winter. In contrast, the BoB exhibited distinct seasonal variations, where SEs induced slightly higher Chl a anomalies than SSEs during the summer, while substantial differences were observed during the winter. Specifically, subsurface-intensified anticyclonic eddies (SSAEs) led to positive Chl a anomalies, contrasting the negative anomalies induced by surface-intensified anticyclonic eddies (SAEs) with comparable magnitudes. Moreover, while both subsurface-intensified cyclonic eddies (SSCEs) and surface-intensified cyclonic eddies (SCEs) resulted in positive Chl a anomalies during winter in the BoB, the magnitude of SSCEs was only one-third of that induced by SCEs. Besides, subsurface Chl a induced by SSAEs (SSCEs) is ∼0.1 mg m−3 greater (less) than that caused by SAEs (SCEs) in the upper 30 (50) m using Biogeochemical Argo profiles. The distinct Chl a between SEs and SSEs can be attributed to their contrasting subsurface structures revealed by Argo profiles. Compared to SAEs (SCEs), SSAEs (SSCEs) enhance (decrease) production via the convex (concave) of the isopycnals that occur around the mixed layer. The study provides a valuable approach to investigating subsurface eddies and contributes to a comprehensive understanding of their influence on chlorophyll concentration.</p

K-nearest Neighbor Search by Random Projection Forests

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.Comment: 15 pages, 4 figures, 2018 IEEE Big Data Conferenc