DECOMPOSITION PRODUCTS OF TRINUCLEAR RUTHENIUM COMPLEXES AS EFFECTIVE CATALYSTS OF WATER OXIDATION

Ruthenium red, a di-mu-oxo-bridged ruthenium complex, and its oxidised form, ruthenium brown, have been studied as possible homogeneous redox catalysts for the oxidation of water to O2 by Ce(IV) ions in H2SO4 and HCIO4. In both media the Ce(IV) ions oxidised the ruthenium red to brown and, with excess of Ce(IV), decomposed the ruthenium brown irreversibly to product(s) with three weak absorption bands at 390, 523 and 593 nm. Only in HCIO4 did the decomposition product(s) appear to act as a stable O2 catalyst. Spectral evidence tentatively suggests that the active catalyst may be a hydrolysed Ru(IV) polymeric species. The rate of catalysis was proportional to the initial concentration of ruthenium red/brown and the activation energy was determined as 36 +/- 1 kJ mol-1 over the temperature range ambient to ca. 50-degrees-C. At temperatures greater than 50-degrees-C the O2 catalyst undergoes an irreversible thermal decomposition reaction.

Exploring Scale Effects of Best/Worst Rank Ordered Choice Data to Estimate Benefits of Tourism in Alpine Grazing Commons

In many environmental valuation applications standard sample sizes for choice modelling surveys are impractical to achieve. One can improve data quality using more in-depth surveys administered to fewer respondents. We report on a study using high quality rank-ordered data elicited with the best-worst approach. The resulting "exploded logit" choice model, estimated on 64 responses per person, was used to study the willingness to pay for external benefits by visitors for policies which maintain the cultural heritage of alpine grazing commons. We find evidence supporting this approach and reasonable estimates of mean WTP, which appear theoretically valid and policy informative. © The Author (2011).

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.

The Rank of Queen's Counsel: Judicial Perspectives

The rank of queen's counsel, granted under the royal prerogative, has been part of the architecture of the legal profession and legal system since 1594 but has undergone many changes in that time, including most recently the adoption of new selection procedures. Recent cases in Northern Ireland have raised the question - what is the legal position of queen's counsel? By examining decided cases in context, this paper aims to explain judicial perspectives on what it means to be a QC.

Epitaxial Magnetic Oxide Nanocrystals Via Phase Decomposition of Bismuth Perovskite Precursors

The phase instability of bismuth perovskite (BiMO₃), where M is a ferromagnetic cation, is exploited to create self-assembled magnetic oxide nanocrystal arrays on oxide supports. Conditions during pulsed laser deposition are tuned so as to induce complete breakdown of the perovskite precursor into bismuth oxide (Bi₂ O₃ ) and metal oxide (M-O_x ) pockets. Subsequent cooling in vacuum volatizes the Bi₂ O₃ leaving behind an array of monodisperse nanocrystals. In situ reflective high energy electron diffraction beam is exploited to monitor the synthesis in real-time. Analysis of the patterns confi rms the phase separation and volatization process. Successful synthesis of M-O_x, where M = Mn, Fe, Co, and Cr, is shown using this template-free facile approach. Detailed magnetic characterization of nanocrystals is carried out to reveal the functionalities such as magnetic anisotropy as well as larger than bulk moments, as expected in these oxide nanostructures.

DRIFTS/MS/Isotopic Labeling Study on the NO-Moderated Decomposition of a Silica-Supported Nickel Nitrate Catalyst Precursor

In the preparation of silica-supported nickel oxide from nickel nitrate impregnation and drying, the replacement of the traditional air calcination step by a thermal treatment in 1% NO/Ar prevents agglomeration, resulting in highly dispersed NiO. The mechanism by which NO prevents agglomeration was investigated by using combined in situ diffuse reflectance infrared fourier transform (DRIFT) spectroscopy and mass spectrometry (MS). After impregnation and drying, a supported nickel hydroxynitrate phase with composition Ni(3)(NO(3))(2)(OH)(4) had been formed. Comparison of the evolution of the decomposition gases during the thermal decomposition of Ni(3)(NO(3))(2)(OH)(4) in labeled and unlabeled NO and O(2) revealed that NO scavenges oxygen radicals, forming NO(2). The DRIFT spectra revealed that the surface speciation evolved differently in the presence of NO as compared with in O(2) or Ar. It is proposed that oxygen scavenging by NO depletes the Ni(3)(NO(3))(2)(OH)(4) phase of nitrate groups, creating nucleation sites for the formation of NiO, which leads to very small (similar to 4 nm) NiO particles and prevents agglomeration.

Chemisorption and decomposition of pyrrole on Pd{111}

XPS, TPD and HREEL results indicate that molecular pyrrole is a fragile adsorbate on clean Pd{111}. At 200 K and for low coverages, the molecule remains intact and adopts an almost flat-lying geometry. With increasing coverage, pyrrole molecules tilt away from the surface and undergo N-H bond cleavage to form strongly tilted pyrrolyl (C4H4N) species. In addition, a weakly bound, strongly tilted form of molecular pyrrole is observed at coverages approaching saturation. Heating pyrrole monolayers results in desorption of similar to 15% of the overlayer as molecular pyrrole and N-a+ C4H4Na recombination with formation of hat-lying pyrrole molecules. This strongly bound species undergoes decomposition to adsorbed CN, CHx and H, leading ultimately to desorption of HCN and H-2. The implications of these results for the production of pyrrole by a heterogeneously catalysed route are discussed.

The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results. © 1964-2012 IEEE.

NFA decomposition and multiprocessing architecture for parallel regular expression processing

This work presents a novel algorithm for decomposing NFA automata into one-state-active modules for parallel execution on Multiprocessor Systems on Chip (MP-SoC). Furthermore, performance related studies based on a 16-PE system for Snort, Bro and Linux-L7 regular expressions are presented. ©2009 IEEE.

Analysis of IL6 Signal Transduction Model using Reduced Rank Regression

This work presents the application of reduced rank regression to the field of systems biology. A computational approach is used to investigate the mechanisms of the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in hepatic cells stimulated by interleukin-6. The results obtained identify the contribution of individual reactions to the dynamics of the model. These findings are compared to previously available results from sensitivity analysis of the model which focused on the parameters involved and their effect. This application of reduced rank regression allows for an understanding of the individual reaction terms involved in the modelled signal transduction pathways and has the benefit of being computationally inexpensive. The obtained results complement existing findings and also confirm the importance of several protein complexes in the MAPK pathway which hints at benefits that can be achieved by further refining the model.

Low-rank representation based action recognition

Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features extracted from videos still remains to be a challenging problem. In this paper, we propose a novel method named low-rank representation based action recognition to recognize human actions. Given a dictionary, low-rank representation aims at finding the lowestrank representation of all data, which can capture the global data structures. According to its characteristics, low-rank representation is robust against noises. Experimental results demonstrate the effectiveness of the proposed approach on several publicly available datasets.