Tuning mesoporous molecular sieve SBA-15 for the immobilization of α-amylase

The present work describes the immobilization of α-amylase over well ordered mesoporous molecular sieve SBA-15 with different pore diameters synthesized by post synthesis treatment (PST) hydrothermally after reaction at 40°C. The materials were characterized by N 2 adsorption–desorption studies, small angle X-ray diffraction, scanning electron microscopy and high resolution transmission electron microscopy. Since α-amylase obtained from Bacillus subtilis has dimensions of 35 × 40 × 70 Å it is expected that the protein have access to the pore of SBA-15 (PST-120°C) with diameter 74 Å. The pore dimension is appropriate to prevent considerable leaching. The rate of adsorption of the enzyme on silica of various pore sizes revealed the influence of morphology, pore diameter, pore volume and pH.

Studies on Soft Bycatch Reduction Devices for Selective Trawling

Trawling, though an efficient method of fishing, is known to be one of the most non-selective methods of fish capture. The bulk of the wild caught penaeid shrimps landed in India are caught by trawling.In addition to shrimps, the trawler fleet also catches considerable amount of non-shrimp resources. The term bycatch means that portion of the catch other than target species caught while fishing, which are either retained or discarded. Bycatch discards is a serious problem leading to the depletion of the resources and negative impacts on biodiversity. In order to minimize this problem, trawling has to be made more selective by incorporating Bycatch Reduction Devices (BRDs). There are several advantages in using BRDs in shrimp trawling. BRDs reduce the negative impacts of shrimp trawling on marine community. Fishers could benefit economically from higher catch value due to improved catch quality, shorter sorting time, lower fuel costs, and longer tow duration. Adoption of BRDs by fishers would forestall criticism by conservation groups against trawling.

Computationally Efficient Bootstrap Prediction Intervals for Returns and Volatilities in ARCH and GARCH Processes

We propose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box–Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the nonlinear GARCH framework. Our simulation studies indicate that the new re-sampling method provides sharp and well calibrated prediction intervals for both returns and volatilities while reducing computational costs by up to 100 times, compared to other available re-sampling techniques for ARCH/GARCH models. The proposed procedure is illustrated by an application to Yen/U.S. dollar daily exchange rate data.