The Pan-STARRS Moving Object Processing System

We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves >99.5% efficiency in producing orbits from a synthetic but realistic population of asteroids whose measurements were simulated for a Pan-STARRS4-class telescope. Additionally, using a nonphysical grid population, we demonstrate that MOPS can detect populations of currently unknown objects such as interstellar asteroids. MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope despite differences in expected false detection rates, fill-factor loss, and relatively sparse observing cadence compared to a hypothetical Pan-STARRS4 telescope and survey. MOPS remains highly efficient at detecting objects but drops to 80% efficiency at producing orbits. This loss is primarily due to configurable MOPS processing limits that are not yet tuned for the Pan-STARRS1 mission. The core MOPS software package is the product of more than 15 person-years of software development and incorporates countless additional years of effort in third-party software to perform lower-level functions such as spatial searching or orbit determination. We describe the high-level design of MOPS and essential subcomponents, the suitability of MOPS for other survey programs, and suggest a road map for future MOPS development.

A green perspective on Structured parallel programming

Structured parallel programming, and in particular programming models using the algorithmic skeleton or parallel design pattern concepts, are increasingly considered to be the only viable means of supporting effective development of scalable and efficient parallel programs. Structured parallel programming models have been assessed in a number of works in the context of performance. In this paper we consider how the use of structured parallel programming models allows knowledge of the parallel patterns present to be harnessed to address both performance and energy consumption. We consider different features of structured parallel programming that may be leveraged to impact the performance/energy trade-off and we discuss a preliminary set of experiments validating our claims.

Techno-Economic and Life Cycle Assessment for the Production of Green Composites

Botanically, green composites belong to an economically important seed plant family that includes maize, wheat, rice, and sorghum known as Saccharum offi cinarum. There are so many natural fibers available in the environment such as rice husk, hemp fibers, flax fibers, bamboo fibers, coconut fiber, coconut coir, grawia optiva and many others also. Life Cycle Assessment (LCA) is a process to estimate the environmental feature and potential impacts related to a product, by organizing a directory of pertinent inputs and outputs of a product system, assessing the potential environmental impacts related with the said inputs and outputs, explaining the results of the inventory analysis and impact evaluation phases in connection to the objectives of the study. Particularly Bagasse, an agricultural residue not only becomes a problem from the environmental point of view, but also affects the profitability of the sugarcane industries. This chapter discusses the properties, processing methods and various other aspects including economic and environmental aspects related to green composites.