High temperature cobalt-base alloy Patent

High temperature cobalt-base alloy resistant to corrosion by liquid metals and to sublimation in vacuum environmen

Nasa developments in cobalt-base superalloys

Chemical, mechanical and physical properties of cobalt-refractory-metal superalloys for high temperature aerospace application

Effect of variations in silicon and iron content on embrittlement of L-605 /HS-25/

Silicon and iron content effects on ductility and tensile strength of cobalt alloy after agin

Development of a cobalt-tungsten ferromagnetic, high-temperature, structural alloy

Cobalt-tungsten ferromagnetic, high temperature structural alloy for rotor applications in space power generator

Semantically enhancing multimedia lifelog events

Lifelogging is the digital recording of our everyday behaviour in order to identify human activities and build applications that support daily life. Lifelogs represent a unique form of personal multimedia content in that they are temporal, synchronised, multi-modal and composed of multiple media. Analysing lifelogs with a view to supporting content-based access, presents many challenges. These include the integration of heterogeneous input streams from different sensors, structuring a lifelog into events, representing events, and interpreting and understanding lifelogs. In this paper we demonstrate the potential of semantic web technologies for analysing lifelogs by automatically augmenting descriptions of lifelog events. We report on experiments and demonstrate how our re- sults yield rich descriptions of multi-modal, multimedia lifelog content, opening up even greater possibilities for managing and using lifelogs

Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

Exploring the self-assembly and energy transfer of dynamic supramolecular iridium-porphyrin systems

EZ-C acknowledges the University of St Andrews for financial support. IDWS acknowledges support from EPSRC (EP/J009016) and the European Research Council (grant 321305). IDWS also acknowledges support from a Royal Society Wolfson research merit award. DJ acknowledges the European Research Council (grant: 278845) and the RFI Lumomat for financial support.We present the first examples of dynamic supramolecular systems composed of cyclometalated Ir(III) complexes of the form of [Ir(C^N)2(N^N)]PF6 (where C^N is mesppy = 2-phenyl-4-mesitylpyridinato and dFmesppy = 2-(4,6-difluorophenyl)-4-mesitylpyridinato and N^N is 4,4':2',2'':4'',4'''-quaterpyridine, qpy) and zinc tetraphenylporphyrin (ZnTPP), assembled through non-covalent interactions between the distal pyridine moieties of the qpy ligand located on the iridium complex and the zinc of the ZnTPP. The assemblies have been comprehensively characterized by a series of analytical techniques (1H NMR titration experiments, 2D COSY and HETCOR NMR spectra and low temperature 1H NMR spectroscopy) and the crystal structures have been elucidated by X-ray diffraction. The optoelectronic properties of the assemblies and the electronic interaction between the iridium and porphyrin chromophoric units have been explored with detailed photophysical measurements, supported by time-dependent density functional theory (TD-DFT) calculations.PostprintPeer reviewe

A multinuclear solid state NMR, density functional theory and X-Ray diffraction study of hydrogen bonding in Group I hydrogen dibenzoates

An NMR crystallographic approach incorporating multinuclear solid state NMR (SSNMR), X-ray structure determinations and density functional theory (DFT) are used to characterise the H bonding arrangements in benzoic acid (BZA) and the corresponding Group I alkali metal hydrogen dibenzoates (HD) systems. Since the XRD data often cannot precisely confirm the proton position within the hydrogen bond, the relationship between the experimental SSNMR parameters and the ability of gauge included plane augmented wave (GIPAW) DFT to predict them becomes a powerful constraint that can assist with further structure refinement. Both the 1H and 13C MAS NMR methods provide primary descriptions of the H bonding via accurate measurements of the 1H and 13C isotropic chemical shifts, and the individual 13C chemical shift tensor elements; these are unequivocally corroborated by DFT calculations, which together accurately describe the trend of the H bonding strength as the size of the monovalent cation changes. In addition, 17O MAS and DOR NMR form a powerful combination to characterise the O environments, with the DOR technique providing highly resolved 17O NMR data which helps verify unequivocally the number of inequivalent O positions for the conventional 17O MAS NMR to process. Further multinuclear MAS and static NMR studies involving the quadrupolar 7Li, 39K, 87Rb and 133Cs nuclei, and the associated DFT calculations, provide trends and a corroboration of the H bond geometry which assist in the understanding of these arrangements. Even though the crystallographic H positions in each H bonding arrangement reported from the single crystal X-ray studies are prone to uncertainty, the good corroboration between the measured and DFT calculated chemical shift and quadrupole tensor parameters for the Group I alkali species suggest that these reported H positions are reliable

Random matrix ensembles of time correlation matrices to analyze visual lifelogs

Visual lifelogging is the process of automatically recording images and other sensor data for the purpose of aiding memory recall. Such lifelogs are usually created using wearable cameras. Given the vast amount of images that are maintained in a visual lifelog, it is a significant challenge for users to deconstruct a sizeable collection of images into meaningful events. In this paper, random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using SenseCam lifelog data streams to identify such events. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to “distinct significant events” in the visual lifelogs. Finally, the cross-correlation matrix C is cleaned by separating the noisy part from the non-noisy part. Overall, the RMT technique is shown to be useful to detect major events in SenseCam images