The Curtailment of Critical Commentary in Australian Economics
Coelho, De Worken-Eley and McClure (2005) showed that, 1963 through 2004, critical commentary declined significantly in the American Economic Review, Economic Journal, Journal of Political Economy, Quarterly Journal of Economics and the Review of Economics and Statistics. Using the same method, we investigated the same question for all of the economic journals produced in Australia, and then published the results in Australian Economic Papers (Dollery, Byrnes and Akimova 2007). We found the same trend in Australia for the period 1962 to 2005. This suggests that the downward trend may apply across economics journals generally. This short note simply relates the basic finding of our Australian Economic Papers article.
Why Has Critical Commentary Been Curtailed at Top Economics Journals? A Reply to Robert Whaples
IN OUR ARTICLE WITH FREDERICK DE WORKEN-ELEY III IN THE April 2005 issue of this Journal, we documented the decline in critical commentary (i.e., comments, replies, rejoinders) that occurred between 1963 and 2004 in the top general interest journals in economics. Explaining the decline was not our focus, although we lamented the decline because it makes the journals less valuable as forums for discussion.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.