Reed canary grass yield and fuel quality in Estonian farmers’ fields

Reed canary grass (Phalaris arundinacea) is one of the possible raw materials for bioenergy production in northern Europe. Its cultivation is favoured because its high productivity and local origin. However, problems with the biomass quality for combustion have been reported. Usually delayed harvest in spring is suggested to improve the quality and decrease the moisture content of biomass. On the other hand, the feasibility of spring harvest depends on local climatic conditions and may cause yield losses. In current paper we studied reed canary grass fields in Estonia locating on different soil types and cultivated with various varieties. The influence of several fertilisation schemes on biomass yield was analysed. Our results indicated that production was higher on mineral soils than on the organic soil of abandoned peat extraction sites. Even different types of fertilisation did not increase the production on organic soils to the level comparable to those on mineral soils. Among studied varieties ‘Venture’ had the highest production. The highest yield per area was obtained late in the autumn (12.7 t d.w. per ha and 7.2 t d.w. per ha on mineral and organic soils, respectively). By spring the amount of biomass had decreased in all studied sites. Due to wet soil some of the fields remained unharvested, the others had high yield losses during practical harvesting. The chemical analyses did not reveal significant differences in the composition of biomass between late autumn and spring. Therefore we conclude that late autumn harvest should be preferred in local climatic conditions.     

Highly adaptable and sensitive protease assay based on fluorescence resonance energy transfer

Proteases are widely used in analytical sciences and play a central role in several widespread diseases. Thus, there is an immense need for highly adaptable and sensitive assays for the detection and monitoring of various proteolytic enzymes. We established a simple protease fluorescence resonance energy transfer (pro-FRET) assay for the determination of protease activities, which could in principle be adapted for the detection of all proteases. As proof of principle, we demonstrated the potential of our method using trypsin and enteropeptidase in complex biological mixtures. Briefly, the assay is based on the cleavage of a FRET peptide substrate, which results in a dramatic increase of the donor fluorescence. The assay was highly sensitive and fast for both proteases. The detection limits for trypsin and enteropeptidase in Escherichia coli lysate were 100 and 10 amol, respectively. The improved sensitivity for enteropeptidase was due to the application of an enzyme cascade, which leads to signal amplification. The pro-FRET assay is highly specific as even high concentrations of other proteases did not result in significant background signals. In conclusion, this sensitive and simple assay can be performed in complex biological mixtures and can be easily adapted to act as a versatile tool for the sensitive detection of proteases.     

Microstructure Effects in Multidipped Tin Oxide Films

Porous ultrafine tin oxide films (particle size 70–90 Å) have been prepared from tin alkoxide by dip coating. The influence of the number of coating applications on the thickness, microstructure, and electrical properties of multidipped tin oxide films was investigated. With an increase in the number of coating applications, the porosity of the multidipped films decreased from 60% to 12%, but the particle size of the films increased. The resistivity of the films decreased from 5.5 × 10^–2 to 5.7 × 10^–3Ω·cm with increasing the number of coating applications from 1 to 10. This tendency of the resistivity to decrease is due to the microstructural change of the films with the number of coating applications. The microstructure, the surface structure, and the composition of the multidipped films were investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and Auger electron spectroscopy (AES).     

Cognitive networks: adaptation and learning to achieve end-to-end performance objectives

In this article we advance the idea of a cognitive network, capable of perceiving current network conditions and then planning, learning, and acting according to end-to-end goals. Cognitive networks are motivated by the complexity, heterogeneity, and reliability requirements of tomorrow's networks, which are increasingly expected to self-organize to meet user and application objectives. We compare and contrast cognitive networks with related research on cognitive radios and cross-layer design. By defining cognitive networks, examining their relationship to other technologies, discussing critical design issues, and providing a framework for implementation, we aim to establish a foundation for further research and discussion     

Reprocessing of thermally oxidized aluminum arsenide (AlAs) in epitaxial multilayers without delamination

Annealing or processing of AlAs that has been subjected to a wet thermal oxidation process can result in severe delamination of material at the oxidation front. This paper reports a procedure for preventing this delamination and presents a possible cause for the delamination.     

Effect of electric fields on solid-state reactions between oxides

The solid-state reaction between CaO and AI₂O₃ in intimately mixed powders and pressed polycrystalline pellets to form a series of calcium aluminates is assisted by the application of external d.c. electric fields. The formation of all the aluminate phases is particularly enhanced in the vicinity of the positive electrode. The major effect of the electric field is probably to assist the diffusion of Ca into AI₂O₃, and the results can be qualitatively explained in terms of a simple diffusional model which takes account of the orientation of the reactant grains relative to each other and to the electric field vector. Possible effects of field-induced sintering and the anion packing density of the various product phases are also discussed.     

CDUL: Class directed unsupervised learning

A novel neural network called Class Directed Unsupervised Learning (CDUL) is introduced. The architecture, based on a Kohonen self-organising network, uses additional input nodes to feed class knowledge to the network during training, in order to optimise the final positioning of Kohonen nodes in feature space. The structure and training of CDUL networks is detailed, showing that (a) networks cannot suffer from the problem of single Kohonen nodes being trained by vectors of more than one class, (b) the number of Kohonen nodes necessary to represent the classes is found during training, and (c) the number of training set passes CDUL requires is low in comparison to similar networks. CDUL is subsequently applied to the classification of chemical excipients from Near Infrared (NIR) reflectance spectra, and its performance compared with three other unsupervised paradigms. The results thereby obtained demonstrate a superior performance which remains relatively constant through a wide range of network parameters.