Towards a choreography for IRS-III

In this paper we describe our ongoing work in developing a choreog-raphy for IRS-III. IRS-III is a framework and platform for developing WSMO based semantic web services. Our choreography framework is based on the KADS system-user co-operation model and distinguishes between the direction of messages within a conversation and which actor has the initiative. The im-plementation of the framework is based on message pattern handlers which are triggered whenever an incoming message satisfies pre-defined constraints. Our framework is explained through an extensive example

A trust based methodology for web service selection

In this paper, we propose a methodology for addressing trust in Semantic Web Services (SWS) - based applications. The aim is to enhance the capability-driven selection provided by current SWS frameworks with the introduction of trust-based selection criteria. We present an ontology - Web Services Trust Ontology (WSTO) - that models the context of a trust-based interaction and enables the participants to describe semantically their trust requirements and guarantees. WSTO makes use of WSMO as reference ontology for representing Web Services and embodies the problem of finding the most "trusted" Web service as a classification problem. To test our methodology, we implemented a specific module within IRS-III - a WSMO-based SWS broker - and deployed a prototype application based on a use case scenario

Formation of high entropy metal diborides using arc-melting and combinatorial approach to study quinary and quaternary solid solutions

High entropy metal diborides (HEBs) represent a radically new approach to extend the chemical composition window of ultra-high temperature ceramics (UHTCs). In this work, arc-melting was used to produce dense HEBs starting from UHTC powders. In order to understand the influence of each individual diboride within the quinary system (HfB2, ZrB2, TiB2, TaB2 and CrB2), we investigated five quaternary equimolar solid solutions e.g. Hf-Zr-Ti-Ta, Hf-Zr-Ti-Cr, Hf-Zr-Ta-Cr, Hf-Ti-Ta-Cr, Zr-Ti-Ta-Cr and the overall quinary equimolar combination. Arc-melting allowed a rapid screening of favorable and unfavorable combinations. The produced HEBs were free from undesired oxides and characterized by linear variation of lattice parameters typical of diborides and binary solid solutions. Because of evaporation during arc melting, CrB2 was hardly found in the solid solution, suggesting that vapor pressure should be taken into account when designing HEB compositions especially for operating temperatures exceeding 2000 °C. Finally, Vickers microhardness ranged between the typical values of starting diborides

Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation

Insects have a remarkable ability to identify and track odour sources in multi-odour backgrounds. Recent behavioural experiments show that this ability relies on detecting millisecond stimulus asynchronies between odourants that originate from different sources. Honeybees, Apis mellifera , are able to distinguish mixtures where both odourants arrive at the same time (synchronous mixtures) from those where odourant onsets are staggered (asynchronous mixtures) down to an onset delay of only 6 ms. In this paper we explore this surprising ability in a model of the insects' primary olfactory brain area, the antennal lobe. We hypothesize that a winner-take-all inhibitory network of local neurons in the antennal lobe has a symmetry-breaking effect, such that the response pattern in projection neurons to an asynchronous mixture is different from the response pattern to the corresponding synchronous mixture for an extended period of time beyond the initial odourant onset where the two mixture conditions actually differ. The prolonged difference between response patterns to synchronous and asynchronous mixtures could facilitate odour segregation in downstream circuits of the olfactory pathway. We present a detailed data-driven model of the bee antennal lobe that reproduces a large data set of experimentally observed physiological odour responses, successfully implements the hypothesised symmetry-breaking mechanism and so demonstrates that this mechanism is consistent with our current knowledge of the olfactory circuits in the bee brain

Toughening effect of non-periodic fiber distribution on crack propagation energy of UHTC composites

Different configurations of continuous carbon fiber-reinforced ultrahigh temperature ceramics (UHTCs), by combining coatings and matrix, were produced via electrophoretic deposition (EPD) and slurry infiltration. The toughening of non-periodic fiber distribution induced by the EPD process was investigated through work of fracture analysis. The results show that a non-periodic fiber distribution results in toughness increase from 8 MPa√m to 11 MPa√m with respect to a periodic fiber distribution. This toughness improvement does not strongly affect the flexural strength, which is mainly related to the fiber volumetric amount. It is shown that the assembling of carbon fibers into bundles (i.e. by dispersing the fibers with a non-periodic distribution) increases the crack propagation energy dissipated on the crack-wake from 0.5 kJ/m2 to 1 kJ/m2, which can be mainly ascribed to the fiber/bundle pull-out. On the other hand, the energy dissipated on the crack-tip (as fiber/matrix debonding) is fiber distribution-independent and increases from 0.3 kJ/m2 to 0.4 kJ/m2 with increasing the fiber amount from 33 vol% to 40 vol%. Finally, WoF analysis is proposed as test to evaluate pull-out toughening instead of push-in and push-out tests

Effect of PAN-based and pitch-based carbon fibres on microstructure and properties of continuous Cf/ZrB2-SiC UHTCMCs

In this paper the microstructure and mechanical properties of two different Cf/ZrB2-SiC composites reinforced with continuous PyC coated PAN-derived fibres or uncoated pitch-derived fibres were compared. Pitch-derived carbon fibres showed a lower degree of reaction with the matrix phase during sintering compared to PyC/PAN-derived fibres. The reason lies in the different microstructure of the carbon. The presence of a coating for PAN-derived fibres was found to be essential to limit the reaction at the fibre/matrix interface during SPS. However, coated bundles were more difficult to infiltrate, resulting in a less homogeneous microstructure. As far as the mechanical properties are concerned, specimens reinforced with coated PAN-derived fibres provided higher strengths and damage tolerance than uncoated pitch-derived fibres, due to the higher degree of fibre pull-out. On the other hand, the weaker fibre/matrix interface resulted in lower interlaminar shear, off-axis strength and ablation resistance

Is spark plasma sintering suitable for the densification of continuous carbon fibre - UHTCMCs?

For the first time we show that spark plasma sintering can efficiently replace hot pressing for the densification of UHTCMCs, in the present case ZrB2/SiC composites reinforced with continuous carbon fibres. To this purpose, the same materials were first produced by hot pressing as baseline samples and then by spark plasma sintering (SPS) to compare microstructure and basic mechanical properties. A special emphasis was given to the study of interfaces, in case of both coated and uncoated carbon fibres. SPS allowed for faster sintering but required an adjustment of the temperature to avoid fibre degradation compared to hot pressing. With similar porosity levels, we observed a slight decrease of flexural strength (300 vs 470 MPa), and an improvement of fracture toughness (15 vs 10 MPa√m) for SPSed samples. SPS was proved to be an effective method for the consolidation of continuous fibre reinforced UHTC composites

Machine learning for automatic prediction of the quality of electrophysiological recordings

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters