Search results for "E6"

showing 10 items of 100 documents

Control of 3-D tectonic inheritance on fold-and-thrust belts: insights from 3-D numerical models and application to the Helvetic nappe system

2020

We apply three-dimensional (3-D) thermo-mechanical numerical simulations of the shortening of the upper crustal region of a passive margin in order to investigate the control of 3-D laterally variable inherited structures on fold-and-thrust belt evolution and associated nappe formation. We consider tectonic inheritance by employing an initial model configuration with basement horst and graben structures having laterally variable geometry and with sedimentary layers having different mechanical strength. We use a visco-plastic rheology with a temperature-dependent flow law and a Drucker–Prager yield criterion. The models show the folding, detachment (shearing off) and horizontal transport of …

lcsh:Geologylcsh:Stratigraphylcsh:QE1-996.5lcsh:QE640-699Solid Earth
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Editorial NoteA case of plagiarism: "Modelling of the wave fields by the modification of the matrix method in anisotropic media" published in Solid E…

2018

No abstract available.

lcsh:Geologylcsh:Stratigraphylcsh:QE1-996.5lcsh:QE640-699
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Squirt flow due to interfacial water films in hydrate bearing sediments

2018

Sediments containing gas hydrate dispersed in the pore space are known to show a characteristic seismic anomaly which is a high attenuation along with increasing seismic velocities. Currently, this observation cannot be fully explained albeit squirt-flow type mechanisms on the microscale have been speculated to be the cause. Recent major findings from in situ experiments, using the gas in excess and water in excess formation method, and coupled with high-resolution synchrotron-based X-ray micro-tomography, have revealed the systematic presence of thin water films between the quartz grains and the encrusting hydrate. The data obtained from these experiments underwent an image processing proc…

lcsh:Geologylcsh:Stratigraphylcsh:QE1-996.5lcsh:QE640-699Solid Earth
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Post-entrapment modification of residual inclusion pressure and its implications for Raman elastic thermobarometry

2020

Residual pressure can be preserved in mineral inclusions, e.g. quartz-in-garnet, after exhumation due to differential expansion between inclusion and host crystals. Raman spectroscopy has been applied to infer the residual pressure and provides information on the entrapment temperature and pressure conditions. However, the amount of residual pressure relaxation cannot be directly measured. An underestimation or overestimation of residual pressure may lead to significant errors between calculated and actual entrapment pressure. This study focuses on three mechanisms responsible for the residual pressure modification: (1) viscous creep; (2) plastic yield; (3) proximity of inclusion to the thi…

lcsh:Geologylcsh:Stratigraphylcsh:QE1-996.5lcsh:QE640-699
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Blends using brabender farinograph and E6 Haubelt flourgraph

2010

The aim of this work is to establish correlations between the values of the Brabender Farinograph and the E6 Haubert Flourgraph. We analyzed two types of flours, white flour with additives (WF add) and brown flour with additives (BF add). The following parameters were varied: water absorption capacity, development time, the stability, the degree of softening and the quality number of wheat and potato flour mixture. A statistical analysis was made from linear regression equations. The obtained values for the E6 Flourgraph are comparable with Farinograph values and units. Hydration capacity values, calculated for a standard consistency of 500 HE, UF, obtained on these two devices are in a clo…

potato Laura varietylcsh:Food processing and manufacturelcsh:TP368-456additivated brown flourFarinograph Brabenderadditivated white flourwater absorbtionFlourgraph E6 HaubeltAnnals of the University Dunarea de Jos of Galati. Fascicle VI: Food Technology
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Mainz Institute of Multiscale Modeling

Computational methods and data-driven modeling have become indispensable tools across the sciences. The highly interdisciplinary Mainz Institute for Multiscale Modeling brings together researchers from different areas in natural and life sciences with researchers in mathematics and computer science. Our research follows two main thrusts: developing multiscale models informed by simulation and experiment, and pushing the boundaries of computational methods. M3ODEL has been established in July 2019 as one of the Top-level Research Area funded through the Research Initiative of the State of Rhineland-Palatinate, and aims to facilitate and connect computational and modeling-oriented research ac…

PE1 PE6 PE11 LS2
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Image and Signal Processing Group

The ISP research group, http://isp.uv.es, has a long tradition in statistical analysis of data coming from imaging systems. These measurements depend on the properties of the scenes and the physics of the imaging process, and their relevance depends on the (natural or artificial) observer that will analyze the data. Our distinct approach to signal, image and vision processing combines machine learning theory with the understanding of the underlying physics and biological vision. Applications mainly focus on optical remote sensing and computational visual neuroscience. Empirical statistical inference, also known as machine learning, is a field of computer science interested in making predict…

SH7_10 PE6_11 PE1
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IT Security

Analysis of various security aspects of communication protocols, in particular Key Exchange Protocols (IKE & IKEv2) and VPN protocols (IPsec). Evaluation of security levels in IoT/IoE networks. Design and deployment of computer systems, that measures the level of cyberthreats in close physical proximity of the actor. Detection of anomalies, trends and threats in IT security by utilizing machine learning, statistical methods, game theory, semantic networks.

PE6_5
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Centre of Intelligent Signal Processing and Wireless Networks

Machine learning, in-network signal processing and artificial intelligence for wireless networks.

PE6
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Centre for Artificial Intelligence Research

Artificial morality and causality, deep reinforcement learning to optimize hydropower production and other complex systems, deep information understanding and knowledge (data mining and big data).

PE6
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