Modelling the effect of pressure on the critical shear stress of MgO single crystals

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Automated Semantic Knowledge Acquisition From Sensor Data

The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended k-means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors

Modeling viscosity of (Mg,Fe)O at lowermost mantle conditions

The viscosity of the lower mantle results from the rheological behavior of its two main constituent minerals, aluminous (Mg,Fe)SiO_3 bridgmanite and (Mg,Fe)O ferropericlase. Understanding the transport properties of lower mantle aggregates is of primary importance in geophysics and it is a challenging task, due to the extreme time-varying conditions to which such aggregates are subjected. In particular, viscosity is a crucial transport property that can vary over several orders of magnitude. It thus has a first-order control on the structure and dynamics of the mantle. Here we focus on the creep behavior of (Mg,Fe)O at the bottom of the lower mantle, where the presence of thermo-chemical anomalies such as ultralow-velocity zones (ULVZ) may significantly alter the viscosity contrast characterizing this region. Two different iron concentrations of (Mg_(1–x)Fe_x)O are considered: one mirroring the average composition of ferropericlase throughout most of the lower mantle (x = 0.20) and another representing a candidate magnesiowüstite component of ULVZs near the base of the mantle (x = 0.84). The investigated pressure-temperature conditions span from 120 GPa and 2800 K, corresponding to the average geotherm at this depth, to core-mantle boundary conditions of 135 GPa and 3800 K. In this study, dislocation creep of (Mg,Fe)O is investigated by dislocation dynamics (DD) simulations, a modeling tool which considers the collective motion and interactions of dislocations. To model their behavior, a 2.5 dimensional dislocation dynamics approach is employed. Within this method, both glide and climb mechanisms can be taken into account, and the interplay of these features results in a steady-state condition. This allows the retrieval of the creep strain rates at different temperatures, pressures, applied stresses and iron concentrations across the (Mg,Fe)O solid solution, providing information on the viscosity for these materials. A particularly low viscosity is obtained for magnesiowüstite with respect to ferropericlase, the difference being around 10 orders of magnitude. Thus, the final section of this work is devoted to the assessment of the dynamic implications of such a weak phase within ULVZs, in terms of the viscosity contrast with respect to the surrounding lowermost mantle

A reference architecture for federating IoT infrastructures supporting semantic interoperability

: The Internet-of-Things (IoT) is unanimously identified as one of the main pillars of future smart scenarios. However, despite the growing number of IoT deployments, the majority of IoT applications tend to be self-contained, thereby forming vertical silos. Indeed, the ability to combine and synthesize data streams and services from diverse IoT platforms and testbeds, holds the promise to increase the potential of smart applications in terms of size, scope and targeted business context. This paper describes the system architecture for the FIESTA-IoT platform, whose main aim is to federate a large number of testbeds across the planet, in order to offer experimenters the unique experience of dealing with a large number of semantically interoperable data sources. This system architecture was developed by following the Architectural Reference Model (ARM) methodology promoted by the IoT-A project (FP7 “light house” project on Architecture for the Internet of Things). Through this process, the FIESTAIoT architecture is composed of a set of Views that deals with a “logical” functional decomposition (Functional View, FV) and data structuring and annotation, data flows and inter-functional component interactions (Information View, IV)

Formation of GEMS from shock-accelerated crystalline dust in superbubbles

Interplanetary dust particles (IDPs) contain enigmatic sub-micron components called GEMS (Glass with Embedded Metal and Sulfides). The compositions and structures of GEMS indicate that they have been processed by exposure to ion- izing radiation but details of the actual irradiation environment(s) have remained elusive. Here we propose a mechanism and astrophysical site for GEMS formation that explains for the first time the following key properties of GEMS; they are stoichiometrically enriched in oxygen and systematically deple- ted in S, Mg, Ca and Fe (relative to solar abundances), most have normal (solar) oxygen isotopic compositions, they exhibit a strikingly narrow size distribution (0.1-0.5 $\mu$m diameter), and some of them contain ``relict'' crystals within their glass matrices. We show that these properties are incon- sistent with amorphization by particles accelerated by diffusive shock accel- eration. Instead, we propose that GEMS are formed from crystalline grains that condense in outflows from massive stars in OB associations, are accelerated in encounters with frequent supernova shocks inside the associated superbubble, and are implanted with atoms from the hot gas in the SB interior. We thus rev- erse the usual roles of target and projectile. Rather than being bombarded at rest by energetic ions, grains are accelerated and bombarded by a nearly mono- velocity beam of atoms as viewed in their rest frame. Meyer, Drury and Ellison have proposed that galactic cosmic rays originate from ions sputtered from such accelerated dust grains. We suggest that GEMS are surviving members of a pop- ulation of fast grains that constitute the long-sought source material for gal- actic cosmic rays. Thus, representatives of the GCR source material may have been awaiting discovery in cosmic dust labs for the last thirty years.Comment: 27 pages, 10 figures, accepted for publication in Astrophysical Journa

Context-aware management for sensor networks

The wide field of wireless sensor networks requires that hun- dreds or even thousands of sensor nodes have to be main- tained and configured. With the upcoming initatives such as Smart Home and Internet of Things, we need new mecha- nism to discover and manage this amount of sensors. In this paper, we describe a middleware architecture that uses con- text information of sensors to supply a plug-and-play gate- way and resource management framework for heterogeneous sensor networks. Our main goals are to minimise the effort for network engineers to configure and maintain the network and supply a unified interface to access the underlying het- erogeneous network. Based on the context information such as battery status, routing information, location and radio signal strength the gateway will configure and maintain the sensor network. The sensors are associated to nearby base stations using an approach that is adapted from the 802.11 WLAN association and negotiation mechanism to provide registration and connectivity services for the underlying sen- sor devices. This abstracted connection layer can be used to integrate the underlying sensor networks into high-level ser- vices and applications such as IP-based networks and Web services

Static Safety for an Actor Dedicated Process Calculus by Abstract Interpretation

The actor model eases the definition of concurrent programs with non uniform behaviors. Static analysis of such a model was previously done in a data-flow oriented way, with type systems. This approach was based on constraint set resolution and was not able to deal with precise properties for communications of behaviors. We present here a new approach, control-flow oriented, based on the abstract interpretation framework, able to deal with communication of behaviors. Within our new analyses, we are able to verify most of the previous properties we observed as well as new ones, principally based on occurrence counting

Multiscale modeling of the effective viscoplastic behavior of Mg 2 SiO 4 wadsleyite: bridging atomic and polycrystal scales

The viscoplastic behavior of polycrystalline Mg2SiO4 wadsleyite aggregates, a major high pressure phase of the mantle transition zone of the Earth (depth range: 410–520 km), is obtained by properly bridging several scale transition models. At the very fine nanometric scale corresponding to the dislocation core structure, the behavior of thermally activated plastic slip is modeled for strain-rates relevant for laboratory experimental conditions, at high pressure and for a wide range of temperatures, based on the Peierls–Nabarro–Galerkin model. Corresponding single slip reference resolved shear stresses and associated constitutive equations are deduced from Orowan’s equation in order to describe the average viscoplastic behavior at the grain scale, for the easiest slip systems. These data have been implemented in two grain-polycrystal scale transition models, a mean-field one (the recent Fully-Optimized Second-Order Viscoplastic Self-Consistent scheme of [1]) allowing rapid evaluation of the effective viscosity of polycrystalline aggregates, and a full-field (FFT based [2, 3]) method allowing investigating stress and strain-rate localization in typical microstructures and heterogeneous activation of slip systems within grains. Calculations have been performed at pressure and temperatures relevant for in-situ conditions. Results are in very good agreement with available mechanical tests conducted at strain-rates typical for laboratory experiments.This work was supported by the European Research Council under the Seventh Framework Programme (FP 7), ERC (grant number 290424 RheoMan) and under the Horizon 2020 research and innovation programme (grant number 787198 TimeMan)