Aging of a giant: a stochastic population forecast for China, 2001-2050

This paper presents a stochastic population forecast for China with a special emphasis on population aging. Stochastic forecasting methods have the advantage of producing a projection of the future population including a probabilistic prediction interval. The socalled scaled model for error was used to quantify the uncertainty attached to the population predictions in this study. Data scarcity was a major problem in the specification of the expected error of the population forecast. Therefore, the error structures estimated for European countries were employed with some modifications taking the large size and heterogeneity of the Chinese population into account. The stochastic forecast confirms the expectation of extremely rapid population aging during the first half of the 21st century in China. The old age dependency ratio (OADR) will increase with certainty. By mid-century, with 80% probability, the OADR will lie between 0.41 and 0.56, with the median of the predictive distribution being 0.48, nearly five-fold its current value of 0.1. In particular, the oldest-old population will grow faster than any other age group. This development has major implications for China: to smoothly adjust current birth control policies to less restrictive ones, strengthen the family support system, and improve the social security system for the elderly.China

Tracer Measurements in Growing Sea Ice Support Convective Gravity Drainage Parameterizations

Gravity drainage is the dominant process redistributing solutes in growing sea ice. Modeling gravity drainage is therefore necessary to predict physical and biogeochemical variables in sea ice. We evaluate seven gravity drainage parameterizations, spanning the range of approaches in the literature, using tracer measurements in a sea ice growth experiment. Artificial sea ice is grown to around 17 cm thickness in a new experimental facility, the Roland von Glasow air‐sea‐ice chamber. We use NaCl (present in the water initially) and rhodamine (injected into the water after 10 cm of sea ice growth) as independent tracers of brine dynamics. We measure vertical profiles of bulk salinity in situ, as well as bulk salinity and rhodamine in discrete samples taken at the end of the experiment. Convective parameterizations that diagnose gravity drainage using Rayleigh numbers outperform a simpler convective parameterization and diffusive parameterizations when compared to observations. This study is the first to numerically model solutes decoupled from salinity using convective gravity drainage parameterizations. Our results show that (1) convective, Rayleigh number‐based parameterizations are our most accurate and precise tool for predicting sea ice bulk salinity; and (2) these parameterizations can be generalized to brine dynamics parameterizations, and hence can predict the dynamics of any solute in growing sea ic

Classification of Pixel Tracks to Improve Track Reconstruction from Proton-Proton Collisions

In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN (Conseil européen pour la recherche nucléaire) uses a massive underground particle collider, called the Large Hadron Collider or LHC, to produce particle collisions at extremely high speeds. There are several layers of detectors in the collider that track the pathways of particles as they collide. The data produced from collisions contains an extraneous amount of background noise, i.e., decays from known particle collisions produce fake signal. Particularly, in the first layer of the detector, the pixel tracker, there is an overwhelming amount of background noise that hinders analysts from seeing true track reconstruction. This paper aims to find and optimize methods that are instrumental in figuring out how the true particle track can be decoupled from the background noise produced at the pixel tracker level of the detector. The results of this study include successful implementation of machine learning techniques to classify signal and background from particle collision data. From these results, it was concluded that neural networks are a successful resource for analyzing and processing particle collision data to reconstruct particle pathways

NASA aeronautics research and technology

The technical accomplishments and research highlights of 1986 are featured, along with information on possible areas of future research. These include hypersonic, supersonic, high performance, subsonic, and rotorcraft vehicle technology. Fundamental disciplinary research areas discussed include aerodynamics, propulsion, materials and structures, information sciences and human factors, and flight systems/safety. A description of the NASA organization and facilities is given

Aeronautical Engineering: A Continuing Bibliography with Indexes (supplement 194)

This bibliography lists 369 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1985

Using high-performance networks to enable computational aerosciences applications

One component of the U.S. Federal High Performance Computing and Communications Program (HPCCP) is the establishment of a gigabit network to provide a communications infrastructure for researchers across the nation. This gigabit network will provide new services and capabilities, in addition to increased bandwidth, to enable future applications. An understanding of these applications is necessary to guide the development of the gigabit network and other high-performance networks of the future. In this paper we focus on computational aerosciences applications run remotely using the Numerical Aerodynamic Simulation (NAS) facility located at NASA Ames Research Center. We characterize these applications in terms of network-related parameters and relate user experiences that reveal limitations imposed by the current wide-area networking infrastructure. Then we investigate how the development of a nationwide gigabit network would enable users of the NAS facility to work in new, more productive ways