How Well Do We Know the Beta-Decay of 16N and Oxygen Formation in Helium Burning

We review the status of the 12C(a,g)16O reaction rate, of importance for stellar processes in a progenitor star prior to a super-nova collapse. Several attempts to constrain the p-wave S-factor of the 12C(a,g)16O reaction at Helium burning temperatures (200 MK) using the beta-delayed alpha-particle emission of 16N have been made, and it is claimed that this S-factor is known, as quoted by the TRIUMF collaboration. In contrast reanalyses (by G.M. hale) of all thus far available data (including the 16N data) does not rule out a small S-factor solution. Furthermore, we improved our previous Yale-UConn study of the beta- delayed alpha-particle emission of \n16 by improving our statistical sample (by more than a factor of 5), improving the energy resolution of the experiment (by 20%), and in understanding our line shape, deduced from measured quantities. Our newly measured spectrum of the beta-delayed alpha-particle emission of 16N is not consistent with the TRIUMF('94) data, but is consistent with the Seattle('95) data, as well as the earlier (unaltered !) data of Mainz('71). The implication of this discrepancies for the extracted astrophysical p-wave s-factor is briefly discussed.Comment: 6 pages, 4 figures, Invited Talk, Physics With Radioactive Beams, Puri, India, Jan. 12-17, 1998, Work Supported by USDOE Grant No. DE-FG02-94ER4087

Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation

Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters