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.

Azimuthal asymmetry in collective electron dynamics in relativistically transparent laser-foil interactions

Asymmetry in the collective dynamics of ponderomotively-driven electrons in the interaction of an ultraintense laser pulse with a relativistically transparent target is demonstrated experimentally. The 2D profile of the beam of accelerated electrons is shown to change from an ellipse aligned along the laser polarization direction in the case of limited transparency, to a double-lobe structure aligned perpendicular to it when a significant fraction of the laser pulse co-propagates with the electrons. The temporally-resolved dynamics of the interaction are investigated via particle-in-cell simulations. The results provide new insight into the collective response of charged particles to intense laser fields over an extended interaction volume, which is important for a wide range of applications, and in particular for the development of promising new ultraintense laser-driven ion acceleration mechanisms involving ultrathin target foils.

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.

Quantum yield measurements for the photocatalytic oxidation of Acid Orange 7 (AO7) and reduction of 2,6-dichloroindophenol (DCIP) on transparent TiO2 films of various thickness

This work comprises the photoactivity assessment of transparent sol–gel TiO2 coatings of various thickness using two test systems. The initial rates of both photocatalytic reactions, namely the oxidative bleaching of Acid Orange 7 (AO7) and the reductive bleaching of 2,6-dichlorindophenol (DCIP) increase linearly with increasing titania film thickness as well as with increasing absorbed light flux. The latter work revealed quantum yields (QY) of 0.19% and 92% for the AO7 and DCIP test system, respectively. The low QY for the AO7 oxidation is due to the combination of a slow irreversible reduction of oxygen and also for the oxidation of AO7, thus favouring the high efficiency for electron–hole recombination that is typical for aqueous organic pollutants. In contrast, the very high QY for the photocatalysed reduction of DCIP is due to the presence of a vast excess of glycerol which traps the photogenerated holes efficiently and so allow time for the slower reduction of dye to take place. Furthermore, the oxidation of glycerol results in the generation of highly reducing R-hydroxyalkyl radicals that are able to also reduce DCIP. As a consequence of this ‘current doubling’ effect, the observed QY (92%) is much higher than the apparent theoretical value of 50%.

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.

Semi-Qualitative Probabilistic Networks in Computer Vision Problems

This paper explores the application of semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information to computer vision problems. Our version of SQPN allows qualitative influences and imprecise probability measures using intervals. We describe an Imprecise Dirichlet model for parameter learning and an iterative algorithm for evaluating posterior probabilities, maximum a posteriori and most probable explanations. Experiments on facial expression recognition and image segmentation problems are performed using real data.

Belief Updating and Learning in Semi-Qualitative Probabilistic Networks

This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.

Systemic oxidative-nitrosative-inflammatory stress during acute exercise in hypoxia; implications for microvascular oxygenation and aerobic capacity

New Findings

What is the central question of this study?Exercise performance is limited during hypoxia by a critical reduction in cerebral and skeletal tissue oxygenation. To what extent an elevation in systemic free radical accumulation contributes to microvascular deoxygenation and the corresponding reduction in maximal aerobic capacity remains unknown.What is the main finding and its importance?We show that altered free radical metabolism is not a limiting factor for exercise performance in hypoxia, providing important insight into the fundamental mechanisms involved in the control of vascular oxygen transport.

Exercise performance in hypoxia may be limited by a critical reduction in cerebral and skeletal tissue oxygenation, although the underlying mechanisms remain unclear. We examined whether increased systemic free radical accumulation during hypoxia would be associated with elevated microvascular deoxygenation and reduced maximal aerobic capacity (). Eleven healthy men were randomly assigned single-blind to an incremental semi-recumbent cycling test to determine  in both normoxia (21% O_{2) and hypoxia (12% O2) separated by a week. Continuous-wave near-infrared spectroscopy was employed to monitor concentration changes in oxy- and deoxyhaemoglobin in the left vastus lateralis muscle and frontal cerebral cortex. Antecubital venous blood samples were obtained at rest and at  to determine oxidative (ascorbate radical by electron paramagnetic resonance spectroscopy), nitrosative (nitric oxide metabolites by ozone-based chemiluminescence and 3-nitrotyrosine by enzyme-linked immunosorbent assay) and inflammatory stress biomarkers (soluble intercellular/vascular cell adhesion 1 molecules by enzyme-linked immunosorbent assay). Hypoxia was associated with increased cerebral and muscle tissue deoxygenation and lower  (P < 0.05 versus normoxia). Despite an exercise-induced increase in oxidative–nitrosative–inflammatory stress, hypoxia per se did not have an additive effect (P > 0.05 versus normoxia). Consequently, we failed to observe correlations between any metabolic, haemodynamic and cardiorespiratory parameters (P > 0.05). Collectively, these findings suggest that altered free radical metabolism cannot explain the elevated microvascular deoxygenation and corresponding lower  in hypoxia. Further research is required to determine whether free radicals when present in excess do indeed contribute to the premature termination of exercise in hypoxia.}