Study of Λb→Λ(ϕ,η(′))\Lambda_b\to \Lambda (\phi,\eta^{(\prime)}) and Λb→ΛK+K−\Lambda_b\to \Lambda K^+K^- decays

We study the charmless two-body $\Lambda_b\to \Lambda (\phi,\eta^{(\prime)})$ and three-body $\Lambda_b\to \Lambda K^+K^-$ decays. We obtain ${\cal B}(\Lambda_b\to \Lambda\phi)=(3.53\pm 0.24)\times 10^{-6}$ to agree with the recent LHCb measurement. However, we find that ${\cal B}(\Lambda_b\to \Lambda(\phi\to)K^+ K^-)=(1.71\pm 0.12)\times 10^{-6}$ is unable to explain the LHCb observation of ${\cal B}(\Lambda_b\to\Lambda K^+ K^-)=(15.9\pm 1.2\pm 1.2\pm 2.0)\times 10^{-6}$, which implies the possibility for other contributions, such as that from the resonant $\Lambda_b\to K^- N^*,\,N^*\to\Lambda K^+$ decay with $N^*$ as a higher-wave baryon state. For $\Lambda_b\to \Lambda \eta^{(\prime)}$, we show that ${\cal B}(\Lambda_b\to \Lambda\eta,\,\Lambda\eta^\prime)= (1.47\pm 0.35,1.83\pm 0.58)\times 10^{-6}$, which are consistent with the current data of $(9.3^{+7.3}_{-5.3},<3.1)\times 10^{-6}$, respectively. Our results also support the relation of ${\cal B}(\Lambda_b\to \Lambda\eta) \simeq {\cal B}(\Lambda_b\to\Lambda\eta^\prime)$, given by the previous study.Comment: 8 pages, 1 figure, revised version accepted by EPJ

Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive realworld trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher