Turbulent Pumping of Magnetic Flux Reduces Solar Cycle Memory and thus Impacts Predictability of the Sun's Activity

Prediction of the Sun's magnetic activity is important because of its effect on space environment and climate. However, recent efforts to predict the amplitude of the solar cycle have resulted in diverging forecasts with no consensus. Yeates et al. (2008) have shown that the dynamical memory of the solar dynamo mechanism governs predictability and this memory is different for advection- and diffusion-dominated solar convection zones. By utilizing stochastically forced, kinematic dynamo simulations, we demonstrate that the inclusion of downward turbulent pumping of magnetic flux reduces the memory of both advection- and diffusion-dominated solar dynamos to only one cycle; stronger pumping degrades this memory further. Thus, our results reconcile the diverging dynamo-model-based forecasts for the amplitude of solar cycle 24. We conclude that reliable predictions for the maximum of solar activity can be made only at the preceding minimum--allowing about 5 years of advance planning for space weather. For more accurate predictions, sequential data assimilation would be necessary in forecasting models to account for the Sun's short memory.Comment: 4 figures, 1 tabl

Adequacy of Earnings Replacement in Workers\u27 Compensation Programs: A Report of the Study Panel on Benefit Adequacy of the Workers\u27 Compensation Steering Committee, National Academy of Social Insurance

The Workers’ Compensation Steering Committee of the National Academy of Social Insurance formed the Benefit Adequacy Study Panel to review the literature on benefit adequacy and to develop an approach to document what is currently known—and not known—about benefit adequacy in WC programs. The panel documents the extent to which WC cash benefits replace workers’ lost wages, and assesses the adequacy of that wage replacement.https://research.upjohn.org/up_press/1040/thumbnail.jp

Mission Concept for the Single Aperture Far-Infrared (SAFIR) Observatory

The Single Aperture Far-InfraRed (SAFIR) Observatory's science goals are driven by the fact that the earliest stages of almost all phenomena in the universe are shrouded in absorption by and emission from cool dust and gas that emits strongly in the far-infrared and submillimeter. Over the past several years, there has been an increasing recognition of the critical importance of this spectral region to addressing fundamental astrophysical problems, ranging from cosmological questions to understanding how our own Solar System came into being. The development of large, far-infrared telescopes in space has become more feasible with the combination of developments for the James Webb Space Telescope and of enabling breakthroughs in detector technology. We have developed a preliminary but comprehensive mission concept for SAFIR, as a 10 m-class far-infrared and submillimeter observatory that would begin development later in this decade to meet the needs outlined above. Its operating temperature (<4K) and instrument complement would be optimized to reach the natural sky confusion limit in the far-infrared with diffraction-limited peformance down to at least 40 microns. This would provide a point source sensitivity improvement of several orders of magnitude over that of Spitzer or Herschel, with finer angular resolution, enabling imaging and spectroscopic studies of individual galaxies in the early universe. We have considered many aspects of the SAFIR mission, including the telescope technology, detector needs and technologies, cooling method and required technology developments, attitude and pointing, power systems, launch vehicle, and mission operations. The most challenging requirements for this mission are operating temperature and aperture size of the telescope, and the development of detector arrays.Comment: 36 page

Exact Bayesian curve fitting and signal segmentation.

We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem

Innovative learning at The University of Edinburgh

The activities available to civil engineering students during the University of Edinburgh's innovative learning week in 2012 were examined. The academic staff proposed a wide range of possible activities and student participation was optional. Popular activities were those with a ‘hands-on’ element: making or doing something. The practical activities offered included designing and building trebuchets, relaying railway permanent way on a heritage railway, practical workshops on engineering in international development and learning to juggle. These activities suggested that heuristic learning by trial and error was likely to enhance the visualisation skills that contribute to good engineering design. Further, the linking of achievement to purposeful practice rather than innate talent could inform teaching methods in the future. They also showed that in some cases safety culture messages were still not fully assimilated by students