Two-stage photo-biological production of hydrogen by marine green alga Platymonas subcordiformis

A marine green alga, Platymonas subcordiformis, was demonstrated to photobiologically evolve hydrogen (H-2) after the first stage of photosynthesis, when subjected to a two-phase incubation protocol in a second stage of H2 production: anaerobic incubation in the dark followed by the exposure to light illumination. The anaerobic incubation induced hydrogenase activity to catalyse H? evolution in the following phase of light illumination. H,) evolution strongly depended upon the duration of anaerobic incubation, deprivation of sulphur (S) from the medium and the medium pH. An optimal anaerobic incubation period of 32 h gave the maximum H2 evolution in the second phase in the absence of sulphur. Evolution of H,) was greatly enhanced by 13 times when S was deprived from the medium. This result suggests that S plays a critical role in the mediation of H-2 evolution from R subcordiformis. A 14-fold increase in H-2 production was obtained when the medium pH increased from 5 to 8; with a sharp decline at pH above eight. H-2 evolution was enhanced by 30-50% when supplementing the optimal concentrations of 25 mM acetate and 37.5 mM glucose. (C) 2003 Elsevier B.V. All rights reserved.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

Red and green upconversion luminescence of Gd2O3 : Er3+, Yb3+ nanoparticles

Gd2O3:Er3+, Yb3+ nanoparticles have been synthesized by a homogeneous precipitation method with EDTA 2Na of two different concentrations. Upconversion luminescence spectra of the samples have been studied under 980 nm laser excitation. The results of XRD show that obtained Gd2O3:Er3+, Yb3+ nanoparticles are of a cubic structure. The average crystallite sizes could be calculated as 22 and 29 nm, respectively. The strong green and red upconversion emission were observed, and attributed to the H-2(11/2), S-4(3/2) -> I-4(15/2) and F-4(19/2) -> I-4(15/2) transitions of Er3+ ion, respectively.