Automatic decomposition and efficient semi-structured meshing of complex solids

In this paper, a novel approach to automatically sub-divide a complex geometry and apply an efficient mesh is presented. Following the identification and removal of thin-sheet regions from an arbitrary solid using the thick/thin decomposition approach developed by Robinson et al. [1], the technique here employs shape metrics generated using local sizing measures to identify long-slender regions within the thick body. A series of algorithms automatically partition the thick region into a non-manifold assembly of long-slender and complex sub-regions. A structured anisotropic mesh is applied to the thin-sheet and long-slender bodies, and the remaining complex bodies are filled with unstructured isotropic tetrahedra. The resulting semi-structured mesh possesses significantly fewer degrees of freedom than the equivalent unstructured mesh, demonstrating the effectiveness of the approach. The accuracy of the efficient meshes generated for a complex geometry is verified via a study that compares the results of a modal analysis with the results of an equivalent analysis on a dense tetrahedral mesh.

Quantitative structure-toxicity relationships for chlorophenols to bioluminescent lux-marked bacteria using atom-based semi-empirical molecular-orbital descriptors

Literature data on the toxicity of chlorophenols for three luminescent bacteria (Vibrio fischeri, and the lux-marked Pseudomonas fluorescens 10586s pUCD607 and Burkholderia spp. RASC c2 (Tn4431)) have been analyzed in relation to a set of computed molecular physico-chemical properties. The quantitative structure-toxicity relationships of the compounds in each species showed marked differences when based upon semi-empirical molecular-orbital molecular and atom based properties. For mono-, di- and tri-chlorophenols multiple linear regression analysis of V. fischeri toxicity showed a good correlation with the solvent accessible surface area and the charge on the oxygen atom. This correlation successfully predicted the toxicity of the heavily chlorinated phenols, suggesting in V. fischeri only one overall mechanism is present for all chlorophenols. Good correlations were also found for RASC c2 with molecular properties, such as the surface area and the nucleophilic super-delocalizability of the oxygen. In contrast the best QSTR for P. fluorescens contained the 2nd order connectivity index and ELUMO suggesting a different, more reactive mechanism. Cross-species correlations were examined, and between V. fischeri and RASC c2 the inclusion of the minimum value of the nucleophilic susceptibility on the ring carbons produced good results. Poorer correlations were found with P. fluorescens highlighting the relative similarity of V. fischeri and RASC c2, in contrast to that of P. fluorescens.

Influence of the type of coarse lightweight aggregate on properties of Semi-Lightweight Self-Consolidating Concrete

This paper presents studies on the properties of fresh and hardened semilightweight self-consolidating concrete (SLWSCC) mixtures, produced with two types of manufactured coarse lightweight aggregates (LWA) and normal weight sand. The first type, a sintered pulverized fuel ash, was made from an industrial by-product, fly ash, whereas the second one, an expanded clay, was produced from a naturally sourced clay. For all mixtures, normal weight sand was used as a fine fraction of aggregates, and the portland cement was partially replaced with a limestone powder. The SLWSCC was produced with different water presaturation regimes of the LWAs. The desired initial slump-flow spread was set between 700 and 800 mm. The effect of three superplasticizers was evaluated by testing properties of SLWSCC, normal weight SCC, and paste mixtures. Three SCC fresh properties were measured: the slump-flow, the V-funnel flow time, and the J-ring blocking step. Moreover, the slump-flow loss was evaluated. The degree of segregation was assessed in both fresh and hardened states. Additionally, the hardened density and the compressive strengths were tested. All SLWSCC mixtures were produced with a desired range of slump-flow spread and with satisfactory passing ability assessed with the J-ring test. SLWSCCs prepared with the expanded clay LWA were less sensitive to the variation of water presaturation levels and showed lower viscosity than those made with the sintered pulverized fuel ash LWA. Only mixtures containing SP-3 superplasticizer showed acceptable workability loss resistance. The saturated surface-dry density of all of the mixtures varied in a range of 2,025–2,125??kg/m 3 . Mixtures containing 29% of coarse LWAs and 71% of sand (by mass) had 24-h and 28-day compressive strengths above 20 and 40 MPa, respectively, but the mixtures made with the expanded clay were slightly weaker.

Management in distributed systems: A semi-formal approach

The reverse engineering of a skeleton based programming environment and redesign to distribute management activities of the system and thereby remove a potential single point of failure is considered. The Ore notation is used to facilitate abstraction of the design and analysis of its properties. It is argued that Ore is particularly suited to this role as this type of management is essentially an orchestration activity. The Ore specification of the original version of the system is modified via a series of semi-formally justified derivation steps to obtain a specification of the decentralized management version which is then used as a basis for its implementation. Analysis of the two specifications allows qualitative prediction of the expected performance of the derived version with respect to the original, and this prediction is borne out in practice.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Determinants of root-associated fungal communities within Asteraceae in a semi-arid grassland

Summary

1.While plant–fungal interactions are important determinants of plant community assembly and ecosystem functioning, the processes underlying fungal community composition are poorly understood.
2.Here, we studied for the first time the root-associated eumycotan communities in a set of co-occurring plant species of varying relatedness in a species-rich, semi-arid grassland in Germany. The study system provides an opportunity to evaluate the importance of host plants and gradients in soil type and landscape structure as drivers of fungal community structure on a relevant spatial scale. We used 454 pyrosequencing of the fungal internal transcribed spacer region to analyse root-associated eumycotan communities of 25 species within the Asteraceae, which were sampled at different locations within a soil type gradient. We partitioned the variance accounted for by three predictors (host plant phylogeny, spatial distribution and soil type) to quantify their relative roles in determining fungal community composition and used null model analyses to determine whether community composition was influenced by biotic interactions among the fungi.
3.We found a high fungal diversity (156 816 sequences clustered in 1100 operational taxonomic units (OTUs)). Most OTUs belonged to the phylum Ascomycota (35.8%); the most abundant phylotype best-matched Phialophora mustea. Basidiomycota were represented by 18.3%, with Sebacina as most abundant genus. The three predictors explained 30% of variation in the community structure of root-associated fungi, with host plant phylogeny being the most important variance component. Null model analysis suggested that many fungal taxa co-occurred less often than expected by chance, which demonstrates spatial segregation and indicates that negative interactions may prevail in the assembly of fungal communities.
4.Synthesis. The results show that the phylogenetic relationship of host plants is the most important predictor of root-associated fungal community assembly, indicating that fungal colonization of host plants might be facilitated by certain plant traits that may be shared among closely related plant species.

Uncovering the challenges of managing cachexia in advanced cancer: Preliminary results from semi-structured interviews with healthcare professionals in a regional cancer centre

Background: Cancer cachexia is a complex metabolic syndrome characterised by severe and progressive weight loss which is predominantly muscle mass. It is a devastating and distressing complication of advanced cancer with profound bio-psycho-social implications for patients and their families. At present there is no curative treatment for cachexiain advanced cancer therefore the most important healthcare response entails the minimisation of the psycho-social distress associated with it. However the literature suggests healthcare professionals’are missing opportunities to intervene and respond to the multi-dimensional needs of this population.

Objective:The objective of this study was to explore healthcare professionals’ response to cachexia in advanced cancer.

Methods: An interpretative qualitative approach was adopted in this study. A purposive sample of doctors, nurses, specialist nurses and dieticians were recruited from a regional cancer centre between November 2009 and November 2010. Data was collection was twofold: two multi-professional focus groups were conducted first to uncover the main themes and issues in cachexia management. This data then informed the interview schedule for the following 25 individual semi-structured interviews.

Results: Preliminary data analysis of the semi-structured interviews revealed distinct differences between disciplines in their perceptions of cancer cachexia which influenced their response to it in clinical practice. The commonality between disciplines, with the exception of palliative care, was a reliance on the biomedical approach to cancer cachexia management.

Discussion and Conclusions: Cancer cachexia is a complex and challenging syndrome which needs to be addressed from a holistic model of care to reflect the multi-dimensional needs of this patient group. The perspectives of those involved in care delivery is required in order to inform the development of interventions aimed at minimising the distress associated with this devastating syndrome.

Complexity of Inferences in Polytree-shaped Semi-Qualitative Probabilistic Networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the Bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that inferences can be performed in linear time if there is a single observed node, which is a relevant practical case. Because our proof is constructive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynomial-time algorithm for SQPNs. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.