Implementation and testing of Lanczos-based algorithms for Random-Phase Approximation eigenproblems

The treatment of the Random-Phase Approximation Hamiltonians, encountered in different frameworks, like time-dependent density functional theory or Bethe-Salpeter equation, is complicated by their non-Hermicity. Compared to their Hermitian Hamiltonian counterparts, computational methods for the treatment of non-Hermitian Hamiltonians are often less efficient and less stable, sometimes leading to the breakdown of the method. Recently [Gruning et al. Nano Lett. 8 (2009) 28201, we have identified that such Hamiltonians are usually pseudo-Hermitian. Exploiting this property, we have implemented an algorithm of the Lanczos type for Random-Phase Approximation Hamiltonians that benefits from the same stability and computational load as its Hermitian counterpart, and applied it to the study of the optical response of carbon nanotubes. We present here the related theoretical grounds and technical details, and study the performance of the algorithm for the calculation of the optical absorption of a molecule within the Bethe-Salpeter equation framework. (C) 2011 Elsevier B.V. All rights reserved.

Temperature and water potential relations of tropical Trametes and other wood-decay fungi from the indigenous forests of Zimbabwe

Three isolates each, of nine different Trametes and five other wood inhabiting basidiomycetes, were collected from the indigenous forests of Zimbabwe, and the impact of temperature (20-60 degrees C), osmotic and matric potential (-0.5 to - 8.0 MPa), and their interactions on in vitro growth compared. Generally, there was no significant difference between growth of isolates of the same species in relation to temperature. Temperature relationships of the species studied correlated well with their geographic distributions. Species occurring in hot, dry regions tolerated a wide temperature range, with some showing unusually high thermotolerance (55 degrees, T. socotrana, T. cingulata and T. cervina). There were significant intra-strain differences for individual species in relation to solute potential on glycerol-modified media. Generally, growth of ail species was better on glycerol- and KCl-modified osmotic media than on a metrically-modified medium (PEG 8000) at 25, 30 and 37 degrees. The limits for growth on the osmotic media were significantly wider than matric medium, being - 4.5 to - 5.0 and - 2.5 to - 4.5 MPa, respectively. An Irpex sp. grew at lower water potentials than all other species, with good growth at - 7.0 MPa. This study suggests that the capacity of these fungi for effective growth over a range of temperatures, osmotic and matric potentials contributes to their rapid wood decay capacities in tropical climates.

Wood degradation, and cellulase and ligninase production, by Trametes and other wood-inhabiting basidiomycetes from indigenous forests of Zimbabwe

Nine species of Trametes and five other wood inhabiting basidiomycetes, were collected from the indigenous forests of Zimbabwe and analysed for cellulases, ligninases, extracellular phenolases and wood degrading ability for the first time. Cellulase enzyme activities varied widely among the species. After 15 d growth exo-glucanase activity had increased in the majority of species whilst Biter paper activity showed the opposite trend, being greatly reduced in all species on day 15 compared to day IO. Endo-glucanase activity was relatively uniform at both sampling times. The fungi were more active against water soluble cellulose derivatives than filter paper cellulase. In all the fungi tested, cellulose activity on filter paper was significantly less than endo- and exo-glucanase activities. The highest cellulase activity was expressed by Cerrena meyenii (683 U mg(-1)) Phaeotrametes decipiens, Trametes modesta, and T. pocas also expressed relatively high cellulase activity on all types of cellulose tested. All Trametes species tested positive for extracellular phenol oxidases whilst Fomotopsis spragueii and Irpex stereoides tested negative. Ail but one of the Trametes species in the study were able to degrade two different lignin preparations in tests for lignin degradation. T. menziesii was unable to degrade both lignin preparations although it had tested positive for production of extracellular oxidase. The species in this study degraded hardwood to a greater extent than softwood. Eight of them caused more than 80% dry weight loss of wood blocks during 70 d incubation. Those fungi that expressed high cellulase activity also caused high weight loss on wood.

Monte Carlo simulation of complex cohesive fracture in random heterogeneous quasi-brittle materials

A numerical method is developed to simulate complex two-dimensional crack propagation in quasi-brittle materials considering random heterogeneous fracture properties. Potential cracks are represented by pre-inserted cohesive elements with tension and shear softening constitutive laws modelled by spatially varying Weibull random fields. Monte Carlo simulations of a concrete specimen under uni-axial tension were carried out with extensive investigation of the effects of important numerical algorithms and material properties on numerical efficiency and stability, crack propagation processes and load-carrying capacities. It was found that the homogeneous model led to incorrect crack patterns and load–displacement curves with strong mesh-dependence, whereas the heterogeneous model predicted realistic, complicated fracture processes and load-carrying capacity of little mesh-dependence. Increasing the variance of the tensile strength random fields with increased heterogeneity led to reduction in the mean peak load and increase in the standard deviation. The developed method provides a simple but effective tool for assessment of structural reliability and calculation of characteristic material strength for structural design.

An investigation of Mode I and Mode II fracture toughness enhancement using aligned carbon nanotubes forests at the crack interface

A novel approach for introducing aligned multi-walled carbon nanotubes (MWCNTs) in a carbon-fibre composite pre-impregnated (prepreg) laminate, to improve the through-thickness fracture toughness, is presented. Carbon nanotube (CNT) 'forests' were grown on a silicon substrate with a thermal oxide layer, using a chemical vapour deposition (CVD) process. The forests were then transferred to a pre-cured laminate interface, using a combination of pressure and heat, while maintaining through-thickness CNT alignment. Standard Mode I and four-point bend end-notched flexure Mode II tests were undertaken on a set of specimens and compared with pristine specimens. Mode I fracture toughness for T700/M21 laminates was improved by an average of 31% while for T700/SE84LV specimens, an improvement of 61% was observed. Only T700/M21 specimens were tested in Mode II which yielded an average fracture toughness improvement of 161%. Scanning Electron Microscopy (SEM) showed good wetting of the CNT forest as well as evidence of penetration of the forest into the adjacent plies. © 2013 Elsevier Ltd.

Threats and knowledge gaps for ecosystem services provided by kelp forests: a northeast Atlantic perspective.

Kelp forests along temperate and polar coastlines represent some of most diverse and productive habitats on the Earth. Here, we synthesize information from >60 years of research on the structure and functioning of kelp forest habitats in European waters, with particular emphasis on the coasts of UK and Ireland, which represents an important biogeographic transition zone that is subjected to multiple threats and stressors. We collated existing data on kelp distribution and abundance and reanalyzed these data to describe the structure of kelp forests along a spatial gradient spanning more than 10° of latitude. We then examined ecological goods and services provided by kelp forests, including elevated secondary production, nutrient cycling, energy capture and flow, coastal defense, direct applications, and biodiversity repositories, before discussing current and future threats posed to kelp forests and identifying key knowledge gaps. Recent evidence unequivocally demonstrates that the structure of kelp forests in the NE Atlantic is changing in response to climate- and non-climate-related stressors, which will have major implications for the structure and functioning of coastal ecosystems. However, kelp-dominated habitats along much of the NE Atlantic coastline have been chronically understudied over recent decades in comparison with other regions such as Australasia and North America. The paucity of field-based research currently impedes our ability to conserve and manage these important ecosystems. Targeted observational and experimental research conducted over large spatial and temporal scales is urgently needed to address these knowledge gaps.

Catalyst distribution and carbon nanotube morphology in multilayer forests by mixed CVD processes

Carbon nanotubes can be grown as forests of aligned fibers on a substrate with a catalyst coated prior to or added during synthesis. A major process interruption can initiate the growth of second and successive layers of forest on top or at the base of the existing layers which are thereby lifted up. We report on the generation of multilayer CNT forests where the first forest is generated either by catalyst coinjection (CCI) of ferrocene with hydrocarbon (xylene) or by catalyst predeposition (CPD) of iron followed with hydrocarbon (acetylene). Subsequent layers are then produced by CCI alone to give uniform (all CCI) or mixed (CPD and CCI) structures to study the distribution of the iron catalyst and CNT morphology and to determine whether the CPD forest templates or otherwise influences the growth of subsequent CCI forests. The bottom-up base growth of second and subsequent CCI forests is reaction rate controlled. CCI multilayer forests accumulate catalyst (iron) in a variety of distinct locations. A pre-existing CPD forest modifies subsequent CCI forest initiation, morphology, and catalyst distribution but does not itself accumulate catalyst or change appearance. © 2009 American Chemical Society.

Understanding the synthesis of directly spinnable carbon nanotube forests

Carbon nanotube forests that can be spun directly from the growth substrate into pure, highly aligned webs, ribbons or yarn promise novel applications that capture the strength and other characteristics of this material. The precise conditions for high spinnability over a maximum proportion of the reactor space are extremely sensitive. The roles of catalyst, substrate, temperature, gas flow rates, reaction time with acetylene etc. were studied to identify and understand the key parameters and develop a robust, scalable process. Using a 44 mm (id) reactor, the optimum values for these variables were determined as comprising a 2.3 nm thick iron catalyst layer on a silicon substrate with 50 nm of thermal oxide; 670 °C running temperature; 650 sccm helium and 34 sccm acetylene for 20 min. The effects of deviating from these optima were explored and the role of amorphous carbon deposition clarified. Crown Copyright © 2009.

Making heads or tails of probability:an experiment with random generators

The equiprobability bias is a tendency for individuals to think of probabilistic events as 'equiprobable' by nature, and to judge outcomes that occur with different probabilities as equally likely. The equiprobability bias has been repeatedly found to be related to formal education in statistics, and it is claimed to be based on a misunderstanding of the concept of randomness.

Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.