Entropic risk analysis by a high level decision support system (e-FDSS) for construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.

Delivering high-level food industry skills for future food security through Advanced Training Partnerships

The UK Biotechnology and Biological Sciences Research Council’s Advanced Training Partnerships initiative represents a significant investment in the provision of high-level skills for the UK food industry sector to address global food security from farm to fork. This paper summarises the background, aims and scope of the Advanced Training Partnerships, their development so far, and offers a view on future directions and evaluation of impact.

Once upon a time, there was a fabulous funambulist: What children learn about the “high-level” vocabulary they encounter while listening to stories

Previous research has shown that listening to stories supports vocabulary growth in preschool and school-aged children and that lexical entries for even very difficult or rare words can be established if these are defined when they are first introduced. However, little is known about the nature of the lexical representations children form for the words they encounter while listening to stories, or whether these are sufficiently robust to support the child’s own use of such ‘high-level’ vocabulary. This study explored these questions by administering multiple assessments of children’s knowledge about a set of newly-acquired vocabulary. Four- and 6-year-old children were introduced to nine difficult new words (including nouns, verbs and adjectives) through three exposures to a story read by their class teacher. The story included a definition of each new word at its first encounter. Learning of the target vocabulary was assessed by means of two tests of semantic understanding – a forced choice picture-selection task and a definition production task – and a grammaticality judgment task, which asked children to choose between a syntactically-appropriate and syntactically-inappropriate usage of the word. Children in both age groups selected the correct pictorial representation and provided an appropriate definition for the target words in all three word classes significantly more often than they did for a matched set of non-exposed control words. However, only the older group was able to identify the syntactically-appropriate sentence frames in the grammaticality judgment task. Further analyses elucidate some of the components of the lexical representations children lay down when they hear difficult new vocabulary in stories and how different tests of word knowledge might overlap in their assessment of these components.

High-level face adaptation without awareness

When a visual stimulus is suppressed from awareness, processing of the suppressed image is necessarily reduced. Although adaptation to simple image properties such as orientation still occurs, adaptation to high-level properties such as face identity is eliminated. Here we show that emotional facial expression continues to be processed even under complete suppression, as indexed by substantial facial expression aftereffects.

Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.

GODIVA2: interactive visualization of environmental data on the Web

GODIVA2 is a dynamic website that provides visual access to several terabytes of physically distributed, four-dimensional environmental data. It allows users to explore large datasets interactively without the need to install new software or download and understand complex data. Through the use of open international standards, GODIVA2 maintains a high level of interoperability with third-party systems, allowing diverse datasets to be mutually compared. Scientists can use the system to search for features in large datasets and to diagnose the output from numerical simulations and data processing algorithms. Data providers around Europe have adopted GODIVA2 as an INSPIRE-compliant dynamic quick-view system for providing visual access to their data.

Uses of airborne scanning laser altimetry for river flood modelling and coastal geomorphology

Two ongoing projects at ESSC that involve the development of new techniques for extracting information from airborne LiDAR data and combining this information with environmental models will be discussed. The first project in conjunction with Bristol University is aiming to improve 2-D river flood flow models by using remote sensing to provide distributed data for model calibration and validation. Airborne LiDAR can provide such models with a dense and accurate floodplain topography together with vegetation heights for parameterisation of model friction. The vegetation height data can be used to specify a friction factor at each node of a model’s finite element mesh. A LiDAR range image segmenter has been developed which converts a LiDAR image into separate raster maps of surface topography and vegetation height for use in the model. Satellite and airborne SAR data have been used to measure flood extent remotely in order to validate the modelled flood extent. Methods have also been developed for improving the models by decomposing the model’s finite element mesh to reflect floodplain features such as hedges and trees having different frictional properties to their surroundings. Originally developed for rural floodplains, the segmenter is currently being extended to provide DEMs and friction parameter maps for urban floods, by fusing the LiDAR data with digital map data. The second project is concerned with the extraction of tidal channel networks from LiDAR. These networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt-marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. A semi-automatic technique has been developed to extract networks from LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low level algorithms first extract channel fragments based mainly on image properties then a high level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism.

Mechanisms and function of flower and inflorescence reversion

Flower and inflorescence reversion involve a switch from floral development back to vegetative development, thus rendering flowering a phase in an ongoing growth pattern rather than a terminal act of the meristem. Although it can be considered an unusual event, reversion raises questions about the nature and function of flowering. It is linked to environmental conditions and is most often a response to conditions opposite to those that induce flowering. Research on molecular genetic mechanisms underlying plant development over the last 15 years has pinpointed some of the key genes involved in the transition to flowering and flower development. Such investigations have also uncovered mutations which reduce floral maintenance or alter the balance between vegetative and floral features of the plant. How this information contributes to an understanding of floral reversion is assessed here. One issue that arises is whether floral commitment (defined as the ability to continue flowering when inductive conditions no longer exist) is a developmental switch affecting the whole plant or is a mechanism which assigns autonomy to individual meristems. A related question is whether floral or vegetative development is the underlying default pathway of the plant. This review begins by considering how studies of flowering in Arabidopsis thaliana have aided understanding of mechanisms of floral maintenance. Arabidopsis has not been found to revert to leaf production in any of the conditions or genetic backgrounds analysed to date. A clear-cut reversion to leaf production has, however, been described in Impatiens balsamina. It is proposed that a single gene controls whether Impatiens reverts or can maintain flowering when inductive conditions are removed, and it is inferred that this gene functions to control the synthesis or transport of a leaf-generated signal. But it is also argued that the susceptibility of Impatiens to reversion is a consequence of the meristem-based mechanisms controlling development of the flower in this species. Thus, in Impatiens, a leaf-derived signal is critical for completion of flowering and can be considered to be the basis of a plant-wide floral commitment that is achieved without accompanying meristem autonomy. The evidence, derived from in vitro and other studies, that similar mechanisms operate in other species is assessed. It is concluded that most species (including Arabidopsis) are less prone to reversion because signals from the leaf are less ephemeral, and the pathways driving flower development have a high level of redundancy that generates meristem autonomy even when leaf-derived signals are weak. This gives stability to the flowering process, even where its initiation is dependent on environmental cues. On this interpretation, Impatiens reversion appears as an anomaly resulting from an unusual combination of leaf signalling and meristem regulation. Nevertheless, it is shown that the ability to revert can serve a function in the life history strategy (perenniality) or reproductive habit (pseudovivipary) of many plants. In these instances reversion has been assimilated into regular plant development and plays a crucial role there.

Inter-simple sequence repeat and aggressiveness analyses revealed high genetic diversity, recombination and long-range dispersal in Fusarium culmorum

Inter-simple sequence repeat (ISSR) analysis and aggressiveness assays were used to investigate genetic variability within a global collection of Fusarium culmorum isolates. A set of four ISSR primers were tested, of which three primers amplified a total of 37 bands out of which 30 (81%) were polymorphic. The intraspecific diversity was high, ranging from four to 28 different ISSR genotypes for F. culmorum depending on the primer. The combined analysis of ISSR data revealed 59 different genotypes clustered into seven distinct clades amongst 75 isolates of F. culmorum examined. All the isolates were assayed to test their aggressiveness on a winter wheat cv. 'Armada'. A significant quantitative variation for aggressiveness was found among the isolates. The ISSR and aggressiveness variation existed on a macro- as well as micro-geographical scale. The data suggested a long-range dispersal of F. culmorum and indicated that this fungus may have been introduced into Canada from Europe. In addition to the high level of intraspecific diversity observed in F. culmorum, the index of multilocus association calculated using ISSR data indicated that reproduction in F. culmorum cannot be exclusively clonal and recombination is likely to occur.

Random number generation as an index of controlled processing

Random number generation (RNG) is a functionally complex process that is highly controlled and therefore dependent on Baddeley's central executive. This study addresses this issue by investigating whether key predictions from this framework are compatible with empirical data. In Experiment 1, the effect of increasing task demands by increasing the rate of the paced generation was comprehensively examined. As expected, faster rates affected performance negatively because central resources were increasingly depleted. Next, the effects of participants' exposure were manipulated in Experiment 2 by providing increasing amounts of practice on the task. There was no improvement over 10 practice trials, suggesting that the high level of strategic control required by the task was constant and not amenable to any automatization gain with repeated exposure. Together, the results demonstrate that RNG performance is a highly controlled and demanding process sensitive to additional demands on central resources (Experiment 1) and is unaffected by repeated performance or practice (Experiment 2). These features render the easily administered RNG task an ideal and robust index of executive function that is highly suitable for repeated clinical use.

Increasing detection rate of user-to-root attacks using genetic algorithms

An extensive set of machine learning and pattern classification techniques trained and tested on KDD dataset failed in detecting most of the user-to-root attacks. This paper aims to provide an approach for mitigating negative aspects of the mentioned dataset, which led to low detection rates. Genetic algorithm is employed to implement rules for detecting various types of attacks. Rules are formed of the features of the dataset identified as the most important ones for each attack type. In this way we introduce high level of generality and thus achieve high detection rates, but also gain high reduction of the system training time. Thenceforth we re-check the decision of the user-to- root rules with the rules that detect other types of attacks. In this way we decrease the false-positive rate. The model was verified on KDD 99, demonstrating higher detection rates than those reported by the state- of-the-art while maintaining low false-positive rate.

Collaterally cued labelling framework underpinning semantic-level visual content descriptor

In this paper, we introduce a novel high-level visual content descriptor devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt for bridging the so called "semantic gap". The proposed image feature vector model is fundamentally underpinned by an automatic image labelling framework, called Collaterally Cued Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts accompanying the images with the state-of-the-art low-level visual feature extraction techniques for automatically assigning textual keywords to image regions. A subset of the Corel image collection was used for evaluating the proposed method. The experimental results indicate that our semantic-level visual content descriptors outperform both conventional visual and textual image feature models.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

Self-assembled molecular complexes and coordination polymers of CdII, hexamine, and monocarboxylates: structural analysis and theoretical studies of supramolecular interactions

Four new cadmium(II) complexes [Cd-2(bz)(4)(H2O)(4)(mu 2-hmt)]center dot Hbz center dot H2O (1), [Cd-3(bz)(6)(H2O)(6)(mu 2-hmt)(2)]center dot 6H(2)O (2), [Cd(pa)(2)(H2O)(mu(2)-hmt)](n) (3), and {[Cd-3(ac)(6)(H2O)(3)(mu(3)-hmt)(2)]center dot 6H(2)O}(n) (4) with hexamine (hmt) and monocarboxylate ions, benzoate (bz), phenylacetate (pa), or acetate (ac) have been synthesized and characterized structurally. Structure determinations reveal that 1 is dinuclear, 2 is trinuclear, 3 is a one-dimensional (1D) infinite chain, and 4 is a two-dimensional (2D) polymer with fused hexagonal rings consisting of Cd-II and hmt. All the Cd-II atoms in the four complexes (except one CdII in 2) possess seven-coordinate pentagonal bipyramidal geometry with the various chelating bidentate carboxylate groups in equatorial sites. One of the CdII ions in 2, a complex that contains two monodentate carboxylates is in a distorted octahedral environment. The bridging mode of hmt is mu 2- in complexes 1-3 but is mu 3- in complex 4. In all complexes, there are significant numbers of H-bonds, C-H/pi, and pi-pi interactions which play crucial roles in forming the supramolecular networks. The importance of the noncovalent interactions in terms of energies and geometries has been analyzed using high level ab initio calculations. The effect of the cadmium coordinated to hmt on the energetic features of the C-H/pi interaction is analyzed. Finally, the interplay between C-H/pi and pi-pi interactions observed in the crystal structure of 3 is also studied.

Methodology To model the energy and greenhouse gas emissions of electronic software distributions

A new electronic software distribution (ESD) life cycle analysis (LCA)methodology and model structure were constructed to calculate energy consumption and greenhouse gas (GHG) emissions. In order to counteract the use of high level, top-down modeling efforts, and to increase result accuracy, a focus upon device details and data routes was taken. In order to compare ESD to a relevant physical distribution alternative,physical model boundaries and variables were described. The methodology was compiled from the analysis and operational data of a major online store which provides ESD and physical distribution options. The ESD method included the calculation of power consumption of data center server and networking devices. An in-depth method to calculate server efficiency and utilization was also included to account for virtualization and server efficiency features. Internet transfer power consumption was analyzed taking into account the number of data hops and networking devices used. The power consumed by online browsing and downloading was also factored into the model. The embedded CO2e of server and networking devices was proportioned to each ESD process. Three U.K.-based ESD scenarios were analyzed using the model which revealed potential CO2e savings of 83% when ESD was used over physical distribution. Results also highlighted the importance of server efficiency and utilization methods.