The Alpha Magnetic Spectrometer (AMS) on the international space station: Part II — Results from the first seven years

The Alpha Magnetic Spectrometer (AMS) is a precision particle physics detector on the International Space Station (ISS) conducting a unique, long-duration mission of fundamental physics research in space. The physics objectives include the precise studies of the origin of dark matter, antimatter, and cosmic rays as well as the exploration of new phenomena. Following a 16-year period of construction and testing, and a precursor flight on the Space Shuttle, AMS was installed on the ISS on May 19, 2011. In this report we present results based on 120 billion charged cosmic ray events up to multi-TeV energies. This includes the fluxes of positrons, electrons, antiprotons, protons, and nuclei. These results provide unexpected information, which cannot be explained by the current theoretical models. The accuracy and characteristics of the data, simultaneously from many different types of cosmic rays, provide unique input to the understanding of origins, acceleration, and propagation of cosmic rays.</p

Cost analysis in choosing group size when group testing for Potato virus Y in the presence of classification errors

In many areas of the world, Potato virus Y (PVY) is one of the most economically important disease problems in seed potatoes. In Taiwan, generation 2 (G2) class certified seed potatoes are required by law to be free of detectable levels of PVY. To meet this standard, it is necessary to perform accurate tests at a reasonable cost. We used a two-stage testing design involving group testing which was performed in Taiwan's Seed Improvement and Propagation Station to identify plants infected with PVY. At the first stage of this two-stage testing design, plants are tested in groups. The second stage involves no retesting for negative test groups and exhaustive testing of all constituent individual samples from positive test groups. In order to minimise costs while meeting government standards, it is imperative to estimate optimal group size. However, because of limited test accuracy, classification errors for diagnostic tests are inevitable; to get a more accurate estimate, it is necessary to adjust for these errors. Therefore, this paper describes an analysis of diagnostic test data in which specimens are grouped for batched testing to offset costs. The optimal batch size is determined by various cost parameters as well as test sensitivity, specificity and disease prevalence. Here, the Bayesian method is employed to deal with uncertainty in these parameters. Moreover, we developed a computer program to determine optimal group size for PVY tests such that the expected cost is minimised even when using imperfect diagnostic tests of pooled samples. Results from this research show that, compared with error free testing, when the presence of diagnostic testing errors is taken into account, the optimal group size becomes smaller. Higher diagnostic testing costs, lower costs of false negatives or smaller prevalence can all lead to a larger optimal group size. Regarding the effects of sensitivity and specificity, optimal group size increases as sensitivity increases; however, specificity has little effect on determining optimal group size. From our simulated study, it is apparent that the Bayesian method can truly update the prior information to more closely approximate the intrinsic characteristics of the parameters of interest. We believe that the results of this study will be useful in the implementation of seed potato certification programmes, particularly those which require zero tolerance for quarantine diseases in certified tubers