Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D – C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D –1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Further we run the algorithms on a mini-Grid and investigate their efficiency depending on the granularity. Corresponding experimental results are presented.

A sparse parallel hybrid Monte Carlo algorithm for matrix computations

In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on hybrid Monte Carlo algorithms for matrix inversion and solving systems of linear algebraic equations. This algorithm consists of two parts, approximate inversion by Monte Carlo and iterative refinement using a deterministic method. Here we present a parallel hybrid Monte Carlo algorithm, which uses Monte Carlo to generate an approximate inverse and that improves the accuracy of the inverse with an iterative refinement. The new algorithm is applied efficiently to sparse non-singular matrices. When we are solving a system of linear algebraic equations, Bx = b, the inverse matrix is used to compute the solution vector x = B(-1)b. We present results that show the efficiency of the parallel hybrid Monte Carlo algorithm in the case of sparse matrices.

Application of neural network to hybrid systems with binary inputs

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.

Emerging hybrid practices in construction design work: role of mixed media

Information technologies are used across all stages of the construction process, and are crucial in the delivery of large projects. Drawing on detailed research on a construction megaproject, we take a practice-based approach to examining the practical and theoretical tensions between existing ways of working and the introduction of new coordination tools in this paper. We analyze the new hybrid practices that emerge, using insights from actor-network theory to articulate the delegation of actions to material and digital objects within ecologies of practice. The three vignettes that we discuss highlight this delegation of actions, the “plugging” and “patching” of ecologies occurring across media and the continual iterations of working practices between different types of media. By shifting the focus from tools to these wider ecologies of practice, the approach has important managerial mplications for the stabilization of new technologies and practices and for managing technological change on large construction projects. We conclude with a discussion of new directions for research, oriented to further elaborating on the importance of the material in understanding change.

Synthesis, x-ray structure and anion binding properties of a cryptand-like hybrid calixpyrrole

The novel cryptand in/out-3, containing two tripyrrolemethane units briged by three 1,3- diisopropylidenbenzene arms was readily synthesized by a convergent three-step synthesis. It binds fluoride by inclusion with excellent selectivity with respect to a number of other tested anions. The structure of the free receptor and that of its fluoride complex were investigated in solution by NMR spectroscopy. The solid state X-ray structure of the free cryptand 3 was also determined.

In vitro fermentation of rice bran combined with Lactobacillus acidophilus 14 150B or Bifidobacterium longum 05 by the canine faecal microbiota

The fermentability of rice bran (RB), alone or in combination with one of two probiotics, by canine faecal microbiota was evaluated in stirred, pH-controlled, anaerobic batch cultures. RB enhanced the levels of bacteria detected by probes Bif164 (bifidobacteria) and Lab158 (lactic acid bacteria); however, addition of the probiotics did not have a significant effect on the predominant microbial counts compared with RB alone. RB sustained levels of Bifidobacterium longum 05 throughout the fermentation; in contrast, Lactobacillus acidophilus 14 150B levels decreased significantly after 5-h fermentation. RB fermentation induced changes in the short-chain fatty acid (SCFA) profile. However, RB combined with probiotics did not alter the SCFA levels compared with RB alone. Denaturing gradient gel electrophoresis analysis of samples obtained at 24 h showed a treatment effect with RB, which was not observed in the RB plus probiotic systems. Overall, the negative controls displayed lower species richness than the treatment systems and their banding profiles were distinct. This study illustrates the ability of a common ingredient found in pet food to modulate the canine faecal microbiota and highlights that RB may be an economical alternative to prebiotics for use in dog food.

Physiological and proteomic approaches to address heat tolerance during anthesis in rice (Oryza sativa L.)

Episodes of high temperature at anthesis, which in rice is the most sensitive stage to temperature, are expected to occur more frequently in future climates. The morphology of the reproductive organs and pollen number, and changes in anther protein expression, were studied in response to high temperature at anthesis in three rice (Oryza sativa L.) genotypes. Plants were exposed to 6 h of high (38 °C) and control (29 °C) temperature at anthesis and spikelets collected for morphological and proteomic analysis. Moroberekan was the most heat-sensitive genotype (18% spikelet fertility at 38 °C), while IR64 (48%) and N22 (71%) were moderately and highly heat tolerant, respectively. There were significant differences among the genotypes in anther length and width, apical and basal pore lengths, apical pore area, and stigma and pistil length. Temperature also affected some of these traits, increasing anther pore size and reducing stigma length. Nonetheless, variation in the number of pollen on the stigma could not be related to measured morphological traits. Variation in spikelet fertility was highly correlated (r=0.97, n=6) with the proportion of spikelets with ≥20 germinated pollen grains on the stigma. A 2D-gel electrophoresis showed 46 protein spots changing in abundance, of which 13 differentially expressed protein spots were analysed by MS/MALDI-TOF. A cold and a heat shock protein were found significantly up-regulated in N22, and this may have contributed to the greater heat tolerance of N22. The role of differentially expressed proteins and morphology during anther dehiscence and pollination in shaping heat tolerance and susceptibility is discussed.

Genetic analysis of heat tolerance at anthesis in rice

Genetic analysis of heat tolerance will help breeders produce rice (Oryza sativa L.) varieties adapted to future climates. An F6 population of 181 recombinant inbred lines of Bala (tolerant) × Azucena (susceptible) was screened for heat tolerance at anthesis by measuring spikelet fertility at 30°C (control) and 38°C (high temperature) in experiments conducted in the Philippines and the United Kingdom. The parents varied significantly for absolute spikelet fertility under control (79–87%) and at high temperature (2.9–47.1%), and for relative spikelet fertility (high temperature/control) at high temperature (3.7–54.9%). There was no correlation between spikelet fertility in control and high-temperature conditions and no common quantitative trait loci (QTLs) were identified. Two QTLs for spikelet fertility under control conditions were identified on chromosomes 2 and 4. Eight QTLs for spikelet fertility under high-temperature conditions were identified on chromosomes 1, 2, 3, 8, 10, and 11. The most significant heat-responsive QTL, contributed by Bala and explaining up to 18% of the phenotypic variation, was identified on chromosome 1 (38.35 mega base pairs on the rice physical genome map). This QTL was also found to influence plant height, explaining 36.6% of the phenotypic variation. A comparison with other studies of abiotic (drought, cold, salinity) stresses showed QTLs at similar positions on chromosomes 1, 3, 8, and 10, suggesting common underlying stress-responsive regions of the genome.

A hybrid data assimilation scheme for model parameter estimation: application to morphodynamic modelling

We present a novel algorithm for joint state-parameter estimation using sequential three dimensional variational data assimilation (3D Var) and demonstrate its application in the context of morphodynamic modelling using an idealised two parameter 1D sediment transport model. The new scheme combines a static representation of the state background error covariances with a flow dependent approximation of the state-parameter cross-covariances. For the case presented here, this involves calculating a local finite difference approximation of the gradient of the model with respect to the parameters. The new method is easy to implement and computationally inexpensive to run. Experimental results are positive with the scheme able to recover the model parameters to a high level of accuracy. We expect that there is potential for successful application of this new methodology to larger, more realistic models with more complex parameterisations.