Structural modifications and fibre processing of hydroxy-functionalised mesogenic polyazomethines

A number of semiflexible homopolyazomethines and copolyazomethines based on hydroxy-functionalised mesogenic cores have been synthesised and characterised. The reported polymers include structural modifications such as an alteration of the coaxiality, shortening of the flexible spacers or copolymerisation to attain materials with reduced melting temperatures and suitable processability. These structural alterations have been introduced by condensing different ,ω-bis-[(4-formyl-3-hydroxyphenyl)oxy]alkanes with 2-methyl-1,4-phenylenediamine and/or 4-methyl-1,3-phenylenediamine. Fibres have been melt extruded from those nematic polyazomethines with the most favourable thermal properties. As-spun and tension-annealed fibres have been investigated by thermal analysis (TGA and DSC), X-ray diffraction and SEM microscopy in an attempt to infer a relationship between microstructure and tensile properties. From the results presented here, copolymerisation incorporating flexible spacers of different lengths seems to be the best strategy to balance ease of processing and tensile properties. Some of the fibres have improved mechanical properties compared with those previously reported for this class of semiflexible polyazomethine. A nematic polyazomethine with a decamethylenic spacer has also been modified with low percentages of several metal ions [Fe(III), Zn(II), V(IV) and Ni(II)] in order to establish a comparison with previously reported Cu(II)-modified fibres.     

ECOC-DRF: Discriminative random fields based on error correcting output codes

We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.     

A Review of Element-Based Galerkin Methods for Numerical Weather Prediction: Finite Elements,Spectral Elements,and Discontinuous Galerkin

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods. Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years.     

A laboratory assessment of the impact of brewery wastewater discharge on sulfide and methane production in a sewer

The impact of brewery wastewater discharge on sulfide and methane production in a sewer was assessed. Experiments were carried out on laboratory scale sewer reactors consisting of both an experimental and a control reactor. The control reactor was intermittently fed with real fresh sewage while the experimental reactor was fed with a mixture of brewery and domestic wastewater at two different proportions (10 and 25% v/v). 10% v/v discharge of brewery wastewater increased the H2S and CH4 production rates in the sewer reactor by 40% and 30%, respectively. When the brewery wastewater fraction was increased to 25% v/v, the H2S production rate of the experimental reactor decreased to the level of the control reactor. In contrast, the CH4 production rate maintained at a level that was 30% higher than that in the control reactor. These results indicate that the discharge of brewery wastewater into sewers can give negative impacts in relation to odour and corrosion management of the systems and will increase the greenhouse gas emissions from sewers. The study also reveals that the impact of trade waste on the biological reactions in sewers is complex, and requires careful experimental assessment in each case.     

Reliable networks with unreliable sensors

Wireless sensor networks (WSNs) deployed in hostile environments suffer from a high rate of node failure. We investigate the effect of such failure rate on network connectivity. We provide a formal analysis that establishes the relationship between node density, network size, failure probability, and network connectivity. We show that large networks can maintain connectivity despite a significantly high probability of node failure. We derive mathematical functions that provide lower bounds on network connectivity in WSNs. We compute these functions for some realistic values of node reliability, area covered by the network, and node density, to show that, for instance, networks with over a million nodes can maintain connectivity with a probability exceeding 95% despite node failure probability exceeding 53%.     

Distance learning for similarity estimation

In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance functions. A new set of distance measures are derived from the harmonic distance, the geometric distance, and their generalized variants according to the Maximum Likelihood theory. These measures can provide a more accurate feature model than the classical Euclidean and Manhattan distances. We also find that the feature elements are often from heterogeneous sources that may have different influence on similarity estimation. Therefore, the assumption of single isotropic distribution model is often inappropriate. To alleviate this problem, we use a boosted distance measure framework that finds multiple distance measures which fit the distribution of selected feature elements best for accurate similarity estimation. The new distance measures for similarity estimation are tested on two applications: stereo matching and motion tracking in video sequences. The performance of boosted distance measure is further evaluated on several benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.