Phylogenomics of eukaryotes: Impact of missing data on large alignments

Resolving the relationships between Metazoa and other eukaryotic groups as well as between metazoan phyla is central to the understanding of the origin and evolution of animals. The current view is based on limited data sets, either a single gene with many species (e.g., ribosomal RNA) or many genes but with only a few species. Because a reliable phylogenetic inference simultaneously requires numerous genes and numerous species, we assembled a very large data set containing 129 orthologous proteins (similar to30,000 aligned amino acid positions) for 36 eukaryotic species. Included in the alignments are data from the choanoflagellate Monosiga ovata, obtained through the sequencing of about 1,000 cDNAs. We provide conclusive support for choanoflagellates as the closest relative of animals and for fungi as the second closest. The monophyly of Plantae and chromalveolates was recovered but without strong statistical support. Within animals, in contrast to the monophyly of Coelomata observed in several recent large-scale analyses, we recovered a paraphyletic Coelamata, with nematodes and platyhelminths nested within. To include a diverse sample of organisms, data from EST projects were used for several species, resulting in a large amount of missing data in our alignment (about 25%). By using different approaches, we verify that the inferred phylogeny is not sensitive to these missing data. Therefore, this large data set provides a reliable phylogenetic framework for studying eukaryotic and animal evolution and will be easily extendable when large amounts of sequence information become available from a broader taxonomic range.

An investigation of the existing situation and trends in building energy efficiency management in China

According to the Chinese State Council's "Building Energy Efficiency Management Ordinance", a large-scale investigation of energy efficiency (EE) in buildings in contemporary China has been carried out in 22 provincial capitals and major cities in China. The aim of this project is to provide reliable information for drawing up the "Decision on reinforcing building energy efficiency" by the Ministry of Construction of China. The surveyed organizations include government departments, research institutions, property developers, design institutions, construction companies, construction consultancy services companies, facility management departments, financial institutions and those which relate to the business of building energy efficiency. In addition, representatives of the media and residents were also involved. A detailed analysis of the results of the investigation concerning aspects of the cur-rent situation and trends in building energy consumption, energy efficiency strategy and the implementation of energy efficiency measures has been conducted. The investigation supplies essential information to formulate the market entrance policy for new buildings and the refurbishment policy for existing buildings to encourage the development of energy efficient technology.

Carbon reduction in buildings (CaRB) - Understanding the social and technical factors that influence energy use in UK buildings

Attempts to reduce the energy consumed in UK homes have met with limited success. One reason for this is a lack of understanding of how people interact with domestic technology – heating systems, lights, electrical equipment and so forth. Attaining such an understanding is hampered by a chronic shortage of detailed energy use data matched to descriptions of the house, the occupants, the internal conditions and the installed services and appliances. Without such information it is impossible to produce transparent and valid models for understanding and predicting energy use. The Carbon Reduction in Buildings (CaRB) consortium of five UK universities plans to develop socio-technical models of energy use, underpinned by a flow of data from a longitudinal monitoring campaign involving several hundred UK homes. This paper outlines the models proposed, the preliminary monitoring work and the structure of the proposed longitudinal study.

ExtSym: a program to aid space-group determination from powder diffraction data

Once unit-cell dimensions have been determined from a powder diffraction data set and therefore the crystal system is known (e.g. orthorhombic), the method presented by Markvardsen, David, Johnson & Shankland [Acta Cryst. (2001), A57, 47-54] can be used to generate a table ranking the extinction symbols of the given crystal system according to probability. Markvardsen et al. tested a computer program (ExtSym) implementing the method against Pawley refinement outputs generated using the TF12LS program [David, Ibberson & Matthewman (1992). Report RAL-92-032. Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, UK]. Here, it is shown that ExtSym can be used successfully with many well known powder diffraction analysis packages, namely DASH [David, Shankland, van de Streek, Pidcock, Motherwell & Cole (2006). J. Appl. Cryst. 39, 910-915], FullProf [Rodriguez-Carvajal (1993). Physica B, 192, 55-69], GSAS [Larson & Von Dreele (1994). Report LAUR 86-748. Los Alamos National Laboratory, New Mexico, USA], PRODD [Wright (2004). Z. Kristallogr. 219, 1-11] and TOPAS [Coelho (2003). Bruker AXS GmbH, Karlsruhe, Germany]. In addition, a precise description of the optimal input for ExtSym is given to enable other software packages to interface with ExtSym and to allow the improvement/modification of existing interfacing scripts. ExtSym takes as input the powder data in the form of integrated intensities and error estimates for these intensities. The output returned by ExtSym is demonstrated to be strongly dependent on the accuracy of these error estimates and the reason for this is explained. ExtSym is tested against a wide range of data sets, confirming the algorithm to be very successful at ranking the published extinction symbol as the most likely. (C) 2008 International Union of Crystallography Printed in Singapore - all rights reserved.

Impact of transmission errors in enhanced relay-enabled distributed coordination function

This paper analyzes the performance of Enhanced relay-enabled Distributed Coordination Function (ErDCF) for wireless ad hoc networks under transmission errors. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduces energy consumption compared to IEEE 802.11 Distributed Coordination Function (DCF) in an ideal channel environment. However, there is a possibility that this expected gain may decrease in the presence of transmission errors. In this work, we modify the saturation throughput model of ErDCF to accurately reflect the impact of transmission errors under different rate combinations. It turns out that the throughput gain of ErDCF can still be maintained under reasonable link quality and distance.

Performance evaluation of enhanced relay-enabled distributed coordination function

In this paper we evaluate the performance of our earlier proposed enhanced relay-enabled distributed coordination function (ErDCF) for wireless ad hoc networks. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduced energy consumption compared to IEEE 802.11 distributed coordination function (DCF). This is a result of. 1) using relay which helps to increase the throughput and lower overall blocking time of nodes due to faster dual-hop transmission, 2) using dynamic preamble (i.e. using short preamble for the relay transmission) which further increases the throughput and lower overall blocking time and also by 3) reducing unnecessary overhearing (by other nodes not involved in transmission). We evaluate the throughput and energy performance of the ErDCF with different rate combinations. ErDCF (11,11) (ie. R1=R2=11 Mbps) yields a throughput improvement of 92.9% (at the packet length of 1000 bytes) and an energy saving of 72.2% at 50 nodes.

An enhanced relay-enabled medium access control protocol for wireless ad hoc networks

In this paper we propose an enhanced relay-enabled distributed coordination function (rDCF) for wireless ad hoc networks. The idea of rDCF is to use high data rate nodes to work as relays for the low data rate nodes. The relay helps to increase the throughput and lower overall blocking time of nodes due to faster dual-hop transmission. rDCF achieves higher throughput over IEEE 802.11 distributed coordination function (DCF). The protocol is further enhanced for higher throughput and reduced energy. These enhancements result from the use of a dynamic preamble (i.e. using short preamble for the relay transmission) and also by reducing unnecessary overhearing (by other nodes not involved in transmission). We have modeled the energy consumption of rDCF, showing that rDCF provides an energy efficiency of 21.7% at 50 nodes over 802.11 DCF. Compared with the existing rDCF, the enhanced rDCF (ErDCF) scheme proposed in this paper yields a throughput improvement of 16.54% (at the packet length of 1000 bytes) and an energy saving of 53% at 50 nodes.

Analysis of enhanced relay-enabled distributed coordination function under transmission errors

This paper analyzes the performance of enhanced relay-enabled distributed coordination function (ErDCF) for wireless ad hoc networks under transmission errors. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduces energy consumption compared to IEEE 802.11 distributed coordination function (DCF) in an ideal channel environment. However, there is a possibility that this expected gain may decrease in the presence of transmission errors. In this work, we modify the saturation throughput model of ErDCF to accurately reflect the impact of transmission errors under different rate combinations. It turns out that the throughput gain of ErDCF can still be maintained under reasonable link quality and distance.

Radio frequency energy harvesting in wireless sensor networks

As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks (WSNs) has been paid much attention to. This article presents a new approach to making use of electromagnetic energy from useless radio frequency (RF) signals transmitted in WSNs, with a quantitative analysis showing its feasibility. A mechanism to harvest the energy either passively or actively is proposed.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Design, implementation and testing of an intelligent knowledge-based system for the supervisory control of a hot rolling mill

This paper describes the design, implementation and testing of an intelligent knowledge-based supervisory control (IKBSC) system for a hot rolling mill process. A novel architecture is used to integrate an expert system with an existing supervisory control system and a new optimization methodology for scheduling the soaking pits in which the material is heated prior to rolling. The resulting IKBSC system was applied to an aluminium hot rolling mill process to improve the shape quality of low-gauge plate and to optimise the use of the soaking pits to reduce energy consumption. The results from the trials demonstrate the advantages to be gained from the IKBSC system that integrates knowledge contained within data, plant and human resources with existing model-based systems. (c) 2005 Elsevier Ltd. All rights reserved.

CAMCR: Computer-Assisted Mixture model analysis for Capture–Recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

Jpowder: a Java-based program for the display and examination of powder diffraction data

The ability to display and inspect powder diffraction data quickly and efficiently is a central part of the data analysis process. Whilst many computer programs are capable of displaying powder data, their focus is typically on advanced operations such as structure solution or Rietveld refinement. This article describes a lightweight software package, Jpowder, whose focus is fast and convenient visualization and comparison of powder data sets in a variety of formats from computers with network access. Jpowder is written in Java and uses its associated Web Start technology to allow ‘single-click deployment’ from a web page, http://www.jpowder.org. Jpowder is open source, free and available for use by anyone.

Solving surface structures from normal incidence X-ray standing wave data

A program is provided to determine structural parameters of atoms in or adsorbed on surfaces by refinement of atomistic models towards experimentally determined data generated by the normal incidence X-ray standing wave (NIXSW) technique. The method employs a combination of Differential Evolution Genetic Algorithms and Steepest Descent Line Minimisations to provide a fast, reliable and user friendly tool for experimentalists to interpret complex multidimensional NIXSW data sets.