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.

Porous organic-inorganic hybrid aerogels based on Cr3+/Fe3+ and rigid bridging carboxylates

A general method to prepare organic-inorganic hybrid aerogels has been presented. A series of organic-inorganic hybrid aerogels were successfully produced from 3d trivalent transition metals (Cr3+, Fe3+) and bridging carboxylic acids. Gelation of the Cr(III) gels was achieved by heating the precursor solution to temperatures above 80 degrees C, which is in sharp contrast to usual supramolecular gels. Among a range of ligands used, highly porous aerogels could be prepared from rigid carboxylate, e.g. 1,4-benzenedicarboxylate and 1,3,5-benzenetricarboxylate. The porous aerogels can be described as a coherent, rigid spongy network of continuous nanometre-sized particles, which is significantly different from the usual fibrous network of supramolecular gels. The aerogels have tunable porous structures with micro-and mesoporosity depending on their reactant concentrations. Their surface areas, pore volumes, and average pore sizes were analysed by using nitrogen sorption, and the accessibility of the pores to bulky molecules was also evaluated. It represents a strategy to prepare hybrid materials with large porosity utilising structurally simple building blocks as precursors.

Type Synthesis of 3-DOF Multi-Mode Translational/Spherical Parallel Mechanisms with Lockable Joints

A 3-DOF (degrees-of-freedom) multi-mode translational/spherical PM (parallel mechanism) with lockable joints is a novel reconfigurable PM. It has both 3-DOF spatial translational operation mode and 3-DOF spherical operation mode. This paper presents an approach to the type synthesis of translational/spherical PMs with lockable joints. Using the proposed approach, several 3-DOF translational/spherical PMs are obtained. It is found that these translational/spherical PMs do not encounter constraint singular configurations and self-motion of sub-chain of a leg during reconfiguration. The approach can also be used for synthesizing other classes of multi-mode PMs with lockable joints, multi-mode PMs with variable kinematic joints, partially decoupled PMs, and reconfigurable PMs with a reconfigurable platform.

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.

The hydraulics and resulting bed scour within the vicinity of Submerged Single Span Arch Bridges

As a consequence of climate change there is now a more frequent occurrence of extreme rainfall events where, with higher rates of urbanisation, the built environment has become increasingly affected by flooding.. This is of particular importance in relation to the stability of bridge structures that span rivers and canals etc. In November 2009, the UK and Ireland were subjected to extraordinarily severe weather conditions for several days. The rainfall was logged as the highest level of rainfall ever recorded within the UK, and as a direct consequence, unprecedented flooding occurred in Cumbria. This flooding led to the collapse of three road bridges which were generally 19th century masonry arch bridges, with relatively shallow foundations. In the UK, knowledge of the combined effect of bridge scouring and inundation has been not been particularly widely studied. Research carried out by Hamill et al [1] considered the hydraulic analysis of single arch bridges under flood conditions, but no consideration was given towards the likely damage to these structures due to scouring. Prior to this, Bierry and Delleur [2] produced a classic paper in predicting the discharge downstream of an inundated arch, focussing on predicting afflux as opposed to bridge scour. Further work on backwater effects was carried out by Martin-Vide & Prio [3] in semi-circular arch bridges. Both pressurized and free-surface flows at the bridge were investigated. Flows on a mobile bed in clear-water conditions were compared to those with a rigid bed, but no predictive equation for scour under pressurised conditions was considered. This paper will present initial findings from an experimental investigation into the effects of surcharged flow and subsequent scour within the vicinity of single span arch bridges. Velocities profiles will be shown within the vicinity of the arch, in addition to the depth of clear water scour, for a series of flows and model spans. The data will be presented, where results will be correlated to the most recent predictive equations that are proposed.

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.

Increased Exposure to Rigid Routines can Lead to Increased Challenging Behavior Following Changes to Those Routines

Several neurodevelopmental disorders are associated with preference for routine and challenging behavior following changes to routines. We examine individuals with Prader–Willi syndrome, who show elevated levels of this behavior, to better understand how previous experience of a routine can affect challenging behavior elicited by disruption to that routine. Play based challenges exposed 16 participants to routines, which were either adhered to or changed. Temper outburst behaviors, heart rate and movement were measured. As participants were exposed to routines for longer before a change (between 10 and 80 min; within participants), more temper outburst behaviors were elicited by changes. Increased emotional arousal was also elicited, which was indexed by heart rate increases not driven by movement. Further study will be important to understand whether current intervention approaches that limit exposure to changes, may benefit from the structured integration of flexibility to ensure that the opportunity for routine establishment is also limited.

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.