Nitrogen and potassium nutrition of French basil (Ocimum basilicum Linn.)

Studies were undertaken on red sandy loam soil (Kandiustalf) in a semi-arid tropical climateat Bangalore (Karnataka) to find out the effect of fertilizer application in influencing oilproduction, quality and soil fertility in French basil (Ocimum basilicum). The study showedthat application of nitrogen (up to 100 kg ha-1) increased herb and essential oil yields in themain crop, first ratoon and second crop while potassium application (up to 80 kg ha-1)increased the yields in the second ratoon and second crop suggesting that soil potassiumdepletion occurred with time.  Nitrogen application increased methyl chavicol (by 4.1%) anddecreased linalool (by 14.2%) contents in basil oil. Yield increases were accompanied by higherremoval of nitrogen, phosphorus and potassium from soil (by 247%, 23% and 94%,respectively) by the crop and lower amounts of exchangeable potassium (by 37.8 %) in soil.Due to the depletion of soil potassium, interactions between nitrogen and potassium weresignificant in the second crop of basil. Application of 100 kg nitrogen ha-1 and 80 kg potassiumha-1 gave optimum yield and quality of oil. &nbsp

Energy spectrum and the absolute flux of various celestial X-ray sources

The results on the flux of low energy X-rays in the range 2-18 Kev from Sco-X1, Tau-X1 and Cen-X2 celestial sources observed during two rocket flights, flown from the Thumba Equatorial Rocket Launching Station (TERLS), Trivandrum, India, are presented. The absolute flux and the energy spectrum obtained for these sources are compared with other similar observations. The results indicate a long-term exponential decrease in the energy flux of X-rays from Sco-X1 over the period 1965-1968. The X-ray source Cen-X2, which showed a remarkable outburst of X-rays in April 1967, had ceased to be active after May 1967. We present here the first evidence of the rediscovery of the low energy, X-ray flux from Cen-X2 since May 1967. These short-lived X-ray out-bursts may be attributed to a shock wave from the nova outburst expanding into the circumstellar medium

PAC learning using Nadaraya-Watson estimator based on orthonormal systems

Regression or function classes of Euclidean type with compact support and certain smoothness properties are shown to be PAC learnable by the Nadaraya-Watson estimator based on complete orthonormal systems. While requiring more smoothness properties than typical PAC formulations, this estimator is computationally efficient, easy to implement, and known to perform well in a number of practical applications. The sample sizes necessary for PAC learning of regressions or functions under sup norm cost are derived for a general orthonormal system. The result covers the widely used estimators based on Haar wavelets, trignometric functions, and Daubechies wavelets

Global Chronic Total Occlusion Crossing Algorithm: JACC State-of-the-Art Review

The authors developed a global chronic total occlusion crossing algorithm following 10 steps: 1) dual angiography; 2) careful angiographic review focusing on proximal cap morphology, occlusion segment, distal vessel quality, and collateral circulation; 3) approaching proximal cap ambiguity using intravascular ultrasound, retrograde, and move-the-cap techniques; 4) approaching poor distal vessel quality using the retrograde approach and bifurcation at the distal cap by use of a dual-lumen catheter and intravascular ultrasound; 5) feasibility of retrograde crossing through grafts and septal and epicardial collateral vessels; 6) antegrade wiring strategies; 7) retrograde approach; 8) changing strategy when failing to achieve progress; 9) considering performing an investment procedure if crossing attempts fail; and 10) stopping when reaching high radiation or contrast dose or in case of long procedural time, occurrence of a serious complication, operator and patient fatigue, or lack of expertise or equipment. This algorithm can improve outcomes and expand discussion, research, and collaboration

Asymptotic inference for stochastic processes

This is a survey of some aspects of large-sample inference for stochastic processes. A unified framework is used to study the asymptotic properties of tests and estimators parameters in discrete-time, continuous-time jump-type, and diffusion processes. Two broad families of processes, viz, ergodic and non-ergodic type are introduced and the qualitative differences in the asymptotic results for the two families are discussed and illustrated with several examples. Some results on estimation and testing via Bayesian, nonparametric, and sequential methods are also surveyed briefly.Maximun likelihood estimator likeliohood ratio and score tests ergodic and non-ergodic type processes jump type and diffusion processes asymptotic efficiency of tests and estimators Markov processes density estimation Bayes estimation and tests sequential methods

Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal) and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective