326 research outputs found
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
Helping Voice Shoppers Make Purchase Decisions
No full textOnline shoppers have a lot of information at their disposal when making a purchase decision. They can look at images of the product, read reviews, make comparisons with other products, do research online, read expert reviews, and more. Voice shopping (purchasing items via a Voice assistant such as Amazon Alexa or Google Assistant) is different. Voice introduces novel challenges as the communication channel is limited in terms of the amount of information people can and are willing to absorb. Because of this, the system should choose the single most effective nugget of information to help the customer, and present the information succinctly. In this paper we report on a within-subject user study (N = 24), in which we employed three template-based methods that use information from customer reviews, product attributes and search relevance signals to generate helpful supporting information. Our results suggest that: (1) supporting information from customer reviews significantly improves participants perception of system effectiveness (helping them make good decisions); (2) supporting information based on search relevance signals improves user perception of system transparency (providing insight into how the system works). We discuss the implications of our findings for providing supporting information for customers shopping by Voice.Web Information System
Acute 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
Which Hashtag to use? Building a Hashtag recommender system and understanding the textual features surrounding Hashtags
No full textHashtags are community-based tags on twitter that are used to annotate tweets and make them findable. To make a user's participation on the social platform more relevant, recommending a hashtag would help a user participate better. This study is an attempt to build a recommender system for hashtag recommendation, and to further study the textual features around hashtags, which assist in their retrieval. The suggested system performs better for tweets with longer text; those with a URL, with multiple hashtags and those that have user mentions.Master of Science in Information Scienc
Characterizing and Understanding User Perception of System Initiative for Conversational Systems to Support Collaborative Search
No full textPopular messaging applications such as Slack, Discord, and Microsoft Teams have given rise to thousands of chatbots and in-app integrations to facilitate collaborations. We utilized this design framework to study how searchbots (i.e., chatbots that perform specific searches) can facilitate collaborative search. More specifically, we investigated a design space for searchbot's that can engage in a mixed-initiative interaction. In this dissertation, we present a Wizard of Oz (WoZ) study to investigate the implications of envisioning a conversational search system capable of engaging in mixed-initiative interactions to support collaborative search. The Wizard plays the role of a conversational search system that can search for information, send relevant web results, and message users. We investigated three Wizard conditions: bot\_info, bot\_dialog, and bot\_task, which differ in how the Wizard can intervene in a conversation. The intervention modes follow the mixed-initiative framework by ~\cite{chu1998evidential}, originally developed based on human conversations. Broadly, we report on three investigations: (1) participants perceptions of the searchbot across the different levels of inititive; (2) the Wizards' motivations to take the initiative; and (3) the Wizards' characterization of the appropriateness of their interventions. Our results suggest that participants' collaborations can enhance when the searchbot can take limited initiative and align with the participants' search strategy. Additionally, in the characterization of motivations and timings, the Wizard presented a wide array of themes to provide search assistance and promote collaborations. Finally, while the participants did not prefer the advanced capabilities of the searchbot, our characterization of their motivations and timing helps us understand the complex activities the searchbot can cater to support collaborations.Doctor of Philosoph
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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