High-Pressure Synthesis of β-Ir4B5 and Determination of the Compressibility of Various Iridium Borides

"A new iridium boride, beta-Ir4B5, was synthesized under high-pressure/high-temperature conditions of 10.5 GPa and 1500 degrees C in a multianvil press with a Walker-type module. The new modification beta-Ir4B5 crystallizes in a new structure type in the orthorhombic space group Pnma (no. 62) with the lattice parameters a = 10.772(2) angstrom, b = 2.844(1) angstrom, and c = 6.052(2) angstrom with R1 = 0.0286, wR2 = 0.0642 (all data), and Z = 2. The structure was determined by single-crystal X-ray and neutron powder diffraction on samples enriched in B-11. The compound is built up by an alternating stacking of boron and iridium layers with the sequence ABA'B'. Additionally, microcalorimetry, hardness, and compressibility measurements of the binary iridium borides alpha-Ir4B5, beta-Ir4B5, Ir5B4, hexagonal Ir4B3-x and orthorhombic Ir4B3-x were carried out and theoretical investigations based on density function theory (DFT) were employed to complement a comprehensive evaluation of structure-property relations. The incorporation of boron into the structures does not enhance the compressibility but leads to a significant reduction of the bulk moduli and elastic constants in comparison to elemental iridium.

Towards an interoperable web generalisation services framework – current work in progress

Web Generalisation Services are now receiving considerable attention from the research community. In this context, interoperability has been identified as a crucial beneficiary of the Web Service concept. This has inspired the formation of a working group to establish consensus for Web Generalisation Services and to specify further technical requirements. The intent of this working group is to enable a higher degree of interoperability and thereby increase the possibilities for exchanging and sharing generalisation functionality over the Web. OGC’s WPS interface specification has been taken as a basis, since it provides a standardized means within the geo-spatial domain to establish processes such as generalisation. This paper reports the current status of the working group and the forthcoming work items

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments.

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >107-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >107 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated

Utilising urban context recognition and machine learning to improve the generalisation of buildings

The introduction of automated generalisation procedures in map production systems requires that generalisation systems are capable of processing large amounts of map data in acceptable time and that cartographic quality is similar to traditional map products. With respect to these requirements, we examine two complementary approaches that should improve generalisation systems currently in use by national topographic mapping agencies. Our focus is particularly on self-evaluating systems, taking as an example those systems that build on the multi-agent paradigm. The first approach aims to improve the cartographic quality by utilising cartographic expert knowledge relating to spatial context. More specifically, we introduce expert rules for the selection of generalisation operations based on a classification of buildings into five urban structure types, including inner city, urban, suburban, rural, and industrial and commercial areas. The second approach aims to utilise machine learning techniques to extract heuristics that allow us to reduce the search space and hence the time in which a good cartographical solution is reached. Both approaches are tested individually and in combination for the generalisation of buildings from map scale 1:5000 to the target map scale of 1:25 000. Our experiments show improvements in terms of efficiency and effectiveness. We provide evidence that both approaches complement each other and that a combination of expert and machine learnt rules give better results than the individual approaches. Both approaches are sufficiently general to be applicable to other forms of self-evaluating, constraint-based systems than multi-agent systems, and to other feature classes than buildings. Problems have been identified resulting from difficulties to formalise cartographic quality by means of constraints for the control of the generalisation process

Web service approaches for providing enriched data structures to generalisation operators

Web service technologies can be used to establish an interoperable framework between different generalisation systems. In a previous article three categories of generalisation web services were identified, including support services, operator services and processing services. This paper focuses on the category of support services. In a service-based generalisation system, the purpose of support services is to assist the generalisation process by providing auxiliary measures, procedures and data structures that allow the representation of structural cartographic knowledge. The structural knowledge of the spatial and semantic context and the modelling of structural and spatial relationships is critical for the understanding of the role of cartographic features and thus for automated generalisation. Support services should extract and model this knowledge from the raw data and make it available to other generalisation operators. On the one hand the structural knowledge can be expressed by enriching map features with additional geometries or attributes. On the other hand, there exist various hierarchical and nonhierarchical relationships between map features, many of which can be represented by graph data structures. After a brief introduction to the interoperable web service framework, this paper proposes a taxonomy of generalisation support services and discusses its elements. It is then shown how the complex output of such services can be represented for use with web services and stored in a reusable fashion. Finally, the utilisation of support services is illustrated on four implementation examples of support services that also highlight the interactions with the generalisation operators that use these auxiliary services