727 research outputs found
Réflexions sur la comprovisation de Sandeep Bhagwati : théorie et pratique
No full textLa « comprovisation » est un terme utilisé par Sandeep Bhagwati depuis 2004, créé par la combinaison des mots « composition » et « improvisation » et servant à clarifier la coexistence systématique entre ces deux pratiques. Cet article démontre de quelle façon ce concept de Bhagwati peut être utilisé comme un concept artistique, une façon collective de faire de la musique, une façon de travailler et un modèle d’entraînement. La comprovisation propose une nouvelle formulation des tâches et des rôles des participants dans l’idée d’une pièce collaborative, une formulation consistente dans le processus de répétition d’une oeuvre mise en scène. Avec cette idée, on s’approche considérablement de l’idée de base de « faire de la musique ensemble » (Alfred Schütz, Christopher Small). Ce qui est nouveau ici est l’orientation transculturelle ou interculturelle. La comprovisation invite à expérimenter avec les frontières des musiques et des cultures actuelles, avec leur matérialité, leur sociabilité, leur performabilité et leur potentiel créatif.“Comprovisation” is a term created by combining “composition” and “improvisation” used by Sandeep Bhagwati since 2004 on various occasions. It is meant to clarify the systematic coexistence between the areas of composition and improvisation. This article shows how Bhagwati’s concept of comprovisation can be used as an artistic concept, a form of playing music collectively, a way of working and as a training model. Comprovisation brings a new formulation of tasks and location of roles of the participants to the idea of collaborative work that is consistent in the process of rehearsal for staged performance. With it, one comes close to the realization of the basic idea of “making music together” (Alfred Schütz, Christopher Small). What is above all new here is its transcultural or intercultural orientation. Comprovisation invites the testing of and experimentation with the boundaries of available musics and cultures, their materiality, sociability, performativity and their creative potential
Proceedings of ASME Turbo Expo 2013: Power for Land, Sea and Air, Volume 1A: Combustion, Fuels and Emissions
No full textShahrokh Etemad (with Sandeep Alavandi and Benjamin Baird) is a contributing author, Fuel Flexible Rich Catalytic Lean Burn System for Low Btu Fuels
Filament surgery and temporal grid adaptivity extensions to a parallel tree code for simulation and diagnosis in 3D vortex dynamics
No full textAcute Ethanol Administration Rapidly Increases Phosphorylation of Conventional Protein Kinase C in Specific Mammalian Brain Regions in Vivo
No full textBackground
Protein kinase C (PKC) is a family of isoenzymes that regulate a variety of functions in the central nervous system including neurotransmitter release, ion channel activity, and cell differentiation. Growing evidence suggests that specific isoforms of PKC influence a variety of behavioral, biochemical, and physiological effects of ethanol in mammals. The purpose of this study was to determine whether acute ethanol exposure alters phosphorylation of conventional PKC isoforms at a threonine 674 (p-cPKC) site in the hydrophobic domain of the kinase, which is required for its catalytic activity.
Methods
Male rats were administered a dose range of ethanol (0, 0.5, 1, or 2 g/kg, intragastric) and brain tissue was removed 10 minutes later for evaluation of changes in p-cPKC expression using immunohistochemistry and Western blot methods.
Results
Immunohistochemical data show that the highest dose of ethanol (2 g/kg) rapidly increases p-cPKC immunoreactivity specifically in the nucleus accumbens (core and shell), lateral septum, and hippocampus (CA3 and dentate gyrus). Western blot analysis further showed that ethanol (2 g/kg) increased p-cPKC expression in the P2 membrane fraction of tissue from the nucleus accumbens and hippocampus. Although p-cPKC was expressed in numerous other brain regions, including the caudate nucleus, amygdala, and cortex, no changes were observed in response to acute ethanol. Total PKC? immunoreactivity was surveyed throughout the brain and showed no change following acute ethanol injection
Characterizing collagen mimetic peptides for orthogonal self-assembly
No full textA computational design of collagen mimetic peptides (CMPs) that self-assemble orthogonally (mutually exclusively), in the presence of other pre-existing collagen trimer mixtures, in vitro, has been proposed. The orthogonality in self-assembly was brought about by orthogonal patterning of ionic salt bridges and residues, along the collagen trimers’ axial length. Through the aid of circular dichroism spectroscopy alone, a novel experimental protocol was set-up to rapidly assess the level of cross-talk that may arise in such designed ‘heterogeneous monomer to trimer folding’ mixture environments. It is shown that the designed collagen mimetic peptides are stable and hetero-specific within their composite 3 chain peptide ecosystem. We experimentally demonstrate the extent to which loss in specificity could possibly occur, upon moving to a higher order ‘more than 3 monomers in solution’ peptide ensemble. Although the desired level of multi-state orthogonality was not achieved in the current design, the experimental results obtained were used to estimate the stability and specificity barrier threshold that one might run into, if one were to instead design orthogonal systems where-in specificity is incorporated during the computational design stage itself a priori. A Pareto frontier plot indicating the specificity versus stability trade-off is plotted. We conclude that a bottom-up design approach, incorporating design of specificity during the sequence design stage, would be a better way forward for achieving self-assembling orthogonality. In contrast to the complex chaperone assisted protein folding systems existing in nature, our method is a simplistic first step towards the complementary approach of modular synthetic collagen molecule design.Ph.D.Includes bibliographical referencesby Sandeep Vishwanath Belur
Resin and steel-reinforced resin used as injection materials in bolted connections
No full textInjection bolts are bolts in which the cavity produced by the clearance between the bolt and the wall of the hole is completely filled up with a two-component resin. Filling of the clearance is carried out through a small hole in the head of the bolt. After injection and complete curing, the connection is slip resistant. Recently the injection material, typically an epoxy resin, was modified at TU Delft by adding steel shots (spherical particles) to mitigate the effects of resin compliance in the shear connection of reusable composite (steel-concrete) structures. Experimental compressive material tests on unconfined/confined resin and steel-reinforced resin are evaluated in this chapter. The uniaxial model which combines damage mechanics and the Ramberg-Osgood relationship is proposed to describe the uniaxial compressive behavior of resin and steel-reinforced resin. First-order numerical homogenization is employed as a high-fidelity model, where a combined nonlinear isotropic/kinematic cyclic hardening model is employed to define the steel plasticity, the linear Drucker-Prager plastic criterion was used to simulate resin damage, and the cohesive surfaces reflecting the relationship between traction and displacement at the interface. The linear Drucker-Prager plastic model is used as a low-fidelity model.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Steel & Composite Structure
Anomaly-Based DNN Model for Intrusion Detection in IoT and Model Explanation: Explainable Artificial Intelligence
No full textIoT has gained immense popularity recently with advancements in technologies and big data. IoT network is dynamically increasing with the addition of devices, and the big data is generated within the network, making the network vulnerable to attacks. Thus, network security is essential, and an intrusion detection system is needed. In this paper, we proposed a deep learning-based model for detecting intrusions or attacks in IoT networks. We constructed a DNN model, applied a filter method for feature reduction, and tuned the model with different parameters. We also compared the performance of DNN with other machine learning techniques in terms of accuracy, and the proposed DNN model with weight decay of 0.0001 and dropout rate of 0.01 achieved an accuracy of 0.993, and the reduced loss on the NSL-KDD dataset having five classes. DL models are a black box and hard to understand, so we explained the model predictions using LIME.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit
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