A Web application to estimate the climate change effects on agriculture in Thailand

A Web application was developed to estimate the effects of climate change on agriculture. We chose the northeast and north of Thailand as the target areas of this application, although most countries in the Asian monsoon region are agricultural areas. Northeastern Thailand is a rainfed farming area where rice and drought-tolerant crops are cultivated. Northern Thailand is a mountainous area where several types of vegetables are cultivated. The application identifies and recommends crops that can be grown based on the simulation results of a crop model DSSAT. The DSSAT was executed 365 times; the start date was moved forward one day each time. The effects of climate change were estimated by inputting meteorological data that reflects the influence of climate change. The most favorable start date and the yield are summarized and displayed as output. Application of this system to grid meteorological data required the shortening of execution time and handling a huge volume of data. These problems were solved using multithreading, StAX and Hadoop. The Web application with witch output data were presented was designed based on the assumption that it would be used by a farmer in the field with a 7-inch tablet.</jats:p

The 3-D image recognition based on fuzzy neural network technology

Three dimensional stereoscopic image recognition system based on fuzzy-neural network technology was developed. The system consists of three parts; preprocessing part, feature extraction part, and matching part. Two CCD color camera image are fed to the preprocessing part, where several operations including RGB-HSV transformation are done. A multi-layer perception is used for the line detection in the feature extraction part. Then fuzzy matching technique is introduced in the matching part. The system is realized on SUN spark station and special image input hardware system. An experimental result on bottle images is also presented

Disk wind feedback from high-mass protostars

We perform a sequence of 3D magnetohydrodynamic (MHD) simulations of the outflow-core interaction for a massive protostar forming via collapse of an initial cloud core of $60~{M_\odot}$. This allows us to characterize the properties of disk wind driven outflows from massive protostars, which can allow testing of different massive star formation theories. It also enables us to assess quantitatively the impact of outflow feedback on protostellar core morphology and overall star formation efficiency. We find that the opening angle of the flow increases with increasing protostellar mass, in agreement with a simple semi-analytic model. Once the protostar reaches $\sim24~{M_\odot}$ the outflow's opening angle is so wide that it has blown away most of the envelope, thereby nearly ending its own accretion. We thus find an overall star formation efficiency of $\sim50\%$, similar to that expected from low-mass protostellar cores. Our simulation results therefore indicate that the MHD disk wind outflow is the dominant feedback mechanism for helping to shape the stellar initial mass function from a given prestellar core mass function.Comment: Accepted for publication in Ap