Determination of the optical properties of plastic films used as cladding materials in novel energy efficient greenhouses

Greenhouse cladding materials are a major component in the design of energy efficient greenhouses. The optical properties of cladding materials determine a major part of the overall performance of a greenhouse both in terms of the energy balance of the greenhouse and on crop behavior. Various film plastic greenhouse-cladding materials were measured under laboratory conditions using a spectroradiometer equipped with an integrating sphere. Films were measured over a range of angles of incidence and the effect of increasing distance between double films was also measured. PAR transmission remained nearly constant for angles of incidence increased up to 30 degrees but fell rapidly thereafter as the angles of incidence increased up to 90 degrees. Increasing distance between double films did not significantly affect PAR transmission in all films examined. These results are discussed in relation to the design criteria for an energy efficient greenhouse.

Low genetic diversity in a marine nature reserve: re-evaluating diversity criteria in reserve design

Little consideration has been given to the genetic composition of populations associated with marine reserves, as reserve designation is generally to protect specific species, communities or habitats. Nevertheless, it is important to conserve genetic diversity since it provides the raw material for the maintenance of species diversity over longer, evolutionary time-scales and may also confer the basis for adaptation to environmental change. Many current marine reserves are small in size and isolated to some degree (e.g. sea loughs and offshore islands). While such features enable easier management, they may have important implications for the genetic structure of protected populations, the ability of populations to recover from local catastrophes and the potential for marine reserves to act as sources of propagules for surrounding areas. Here, we present a case study demonstrating genetic differentiation, isolation, inbreeding and reduced genetic diversity in populations of the dogwhelk Nucella lapillus in Lough Hyne Marine Nature Reserve (an isolated sea lough in southern Ireland), compared with populations on the local adjacent open coast and populations in England, Wales and France. Our study demonstrates that this sea lough is isolated from open coast populations, and highlights that there may be long-term genetic consequences of selecting reserves on the basis of isolation and ease of protection.

Haem peroxidase activity in Daphnia magna: A biomarker for sub-lethal toxicity assessments of kerosene-contaminated groundwater

A novel biomarker was developed in Daphnia magna to detect organic pollution in groundwater. The haem peroxidase assay, which is an indirect means of measuring oxidase activity, was particularly sensitive to kerosene contamination. Exposure to sub-lethal concentrations of kerosene-contaminated groundwater resulted in a haem peroxidase activity increase by dose with a two-fold activity peak at 25%. Reproduction in D. magna remained unimpaired when exposed to concentrations below 25% for 21 days, and a decline in fecundity was only observed at concentrations above the peak in enzyme activity. The measurement of haem peroxidase activity in D. magna detected sublethal effects of kerosene in just 24 h, whilst offering information on the health status of the organisms. The biomarker may be useful in determining concentrations above which detrimental effects would occur from long-term exposure for fuel hydrocarbons. Moreover, this novel assay detects exposure to chemicals in samples that would normally be classified as non-toxic by acute toxicity tests.

Assessing the PhD: a constructive view of criteria

Research and informed debate reveals that institutional practices in relation to research degree examining vary considerably across the sector. Within a context of accountability and quality assurance/total quality management, the range and specificity of criteria that are used to judge doctoral work is of particular relevance. First, a review of the literature indicates that, although interest in and concern about the process is burgeoning, there is little empirical research published from which practitioners can draw guidance. The second part of the paper reviews that available research, drawing conclusions about issues that seem to pertain at a general level across disciplines and institutions. Lest the variation is an artefact of discipline difference, the third part of the paper focuses on a within discipline study. Criteria expected/predicted by supervisors are compared and contrasted with those anticipated and experienced by candidates and with those implemented and considered important by examiners. The results are disturbing.

Allobaculum stercoricanis gen. nov., sp nov., isolated from canine feces

Morphological, biochemical, and molecular genetic studies were performed on an unknown anaerobic, catalase-negative, nonspore-forming, rod-shaped bacterium isolated from dog feces. The unknown bacterium was tentatively identified as a Eubacterium species, based on cellular morphological and biochemical tests. 16S rRNA gene sequencing studies, however, revealed that it was phylogenetically distant from Eubacterium limosum, the type species of the genus Eubacterium. Phylogenetically, the unknown species forms a hitherto unknown sub-line proximal to the base of a cluster of organisms (designated rRNA cluster XVI), which includes Clostridium innocuum, Streptococcus pleomorphus, and some Eubacterium species. Based on both phenotypic and phylogenetic criteria, it is proposed that the unknown bacterium be classified as a new genus and species, Allobaculum stercoricanis. Using a specific rRNA-targeted probe designed to identify Allobacultan stercoricanis, in situ hybridisation showed this novel species represents a significant organism in canine feces comprising between 0.1% and 3.7% of total cells stained with DAPI (21 dog fecal samples). The type strain of Allobaculum stereoricanis is DSM 13633(T) = CCUG 45212(T). (C) 2004 Elsevier Ltd. All rights reserved.

Cetobacterium somerae sp nov from human feces and emended description of the genus Cetobacterium

Phenotypic and phylogenetic studies were performed on four isolates of an unidentified gram-negative, microaerotolerant, non-spore-forming, rod-shaped bacterium isolated from the feces of children. The unknown organism was bile resistant and produced acetic acid as the major end product of metabolism of peptides and carbohydrates. It possessed a low DNA G + C content of 31 mol %. Comparative 16S rRNA gene sequencing demonstrated that the four isolates were phylogenetically identical (100% 16S rRNA sequence similarity) and represent a hitherto unknown sub-line within the genus Cetobacterium. The novel bacterium displayed approximately 5% sequence divergence with Cetobacterium ceti, and can be readily distinguished from the latter by physiological and biochemical criteria. Based on phylogenetic and phenotypic evidence, it is proposed that the unknown fecal bacterium be classified in the genus Cetobacterium, as Cetobacterium somerae sp. nov. The proposed type strain of Cetobacterium somerae is WAL 14325(T) (ATCC BAA-474(T) = CCUG 46254T).

Stability criteria for the contextual emergence of macrostates in neural networks

More than thirty years ago, Amari and colleagues proposed a statistical framework for identifying structurally stable macrostates of neural networks from observations of their microstates. We compare their stochastic stability criterion with a deterministic stability criterion based on the ergodic theory of dynamical systems, recently proposed for the scheme of contextual emergence and applied to particular inter-level relations in neuroscience. Stochastic and deterministic stability criteria for macrostates rely on macro-level contexts, which make them sensitive to differences between different macro-levels.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Reach and grasp therapy: effects of the Gentle/G System assessing sub-acute stroke whole-arm rehabilitation

This paper reports preliminary results of a reach and grasp study of robot mediated neurorehabilitation. These results are presented on a case-by-case basis and give a good indication of a positive effect of robot mediated therapy. The study investigated both reach and grasp assistance and although it is not possible to attribute the response to the benefits of providing assistance of both modalities the study is a good indicator that this strategy should be pursued. The paper also reports on the benefits of motivational queues such as exercise scores and on subject attitudes to the robot mediated therapy.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

Minimum stable convergence criteria for stochastic diffusion search

An analysis of Stochastic Diffusion Search (SDS), a novel and efficient optimisation and search algorithm, is presented, resulting in a derivation of the minimum acceptable match resulting in a stable convergence within a noisy search space. The applicability of SDS can therefore be assessed for a given problem.

Application of auto regressive models of wavelet sub-bands for classifying Terahertz pulse measurements

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) models on the wavelet transforms of measured T-ray pulse data is presented. Two example applications are examined - the classi. cation of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of six different powder samples. A variety of model types and orders are used to generate descriptive features for subsequent classification. Wavelet-based de-noising with soft threshold shrinkage is applied to the measured T-ray signals prior to modeling. For classi. cation, a simple Mahalanobis distance classi. er is used. After feature extraction, classi. cation accuracy for cancerous and normal cell types is 93%, whereas for powders, it is 98%.