588 research outputs found

    Design, Testing, Analysis, and Material Properties of Carbon Fiber Reinforced Polymers

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    Rose-Hulman Institute of Technology excels in many fields, however in the field of Carbon Fiber Reinforced Polymers (CFRPs) there is a significant lack of knowledge. Over the 2015 summer I started accumulating and building a knowledge base for the institute to help future students who have interest in this ever expanding field. This report covers many of the learnings I found in the structures of fiber reinforced polymers, manufacturing processes and controls, testing procedures and standards, important considerations in the design process of CFRPs, and analysis capabilities and methods. This report is organized as a series of short guides to assist students with the individual subject matters at hand. While many of these are bolstered by the knowledge provided in other parts of the document, due to the multifaceted approach to the problem, this is a much easier way to communicate the information to undergraduate science and engineering students trying to work reliably with CFRPs or other composite materials. Special thanks to the Rose-Hulman Institute of Technology Mechanical Engineering Department for supporting this project

    Novel machine learning techniques for anomaly intrusion detection

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    Voters prefer candidates who send their children to public over private school

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    From presidential races to local elections, decisions about where politicians send their children to school can attract public attention. But do these choices actually impact how voters cast their ballots? In new research, Leslie K. Finger, Thomas Gift, and Andrew Miner use an original survey experiment to examine how voters view politicians who send their children to public versus private school. They find that voters are more likely support candidates whose children attend public school — an outcome they trace to voters perceiving these candidates as warmer and more committed to public services

    In silico identification of small molecule agonist binding sites on KCC2

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    Purpose: Potassium-Chloride Cotransporter 2 (KCC2) is a neuronal membrane protein specific to the central nervous system. It is responsible for removing Cl- ions from the intracellular space, maintaining a normal Cl- gradient essential for proper function at inhibitory synapses. Dysregulation causes an upward shift in the Cl- reversal potential resulting in a hyperexcitable state of the postsynaptic neuron. Existing literature indicates that KCC2 may be involved in the addiction pathway of a variety of drugs of abuse, including opioids and alcohol. This makes KCC2 an attractive potential drug target when treating substance use disorders. A novel direct KCC2 agonist, VU0500469, was recently identified experimentally; however, no binding sites were identified or characterized. The goal of this project is to identify likely binding sites of this protein-ligand pair via computer simulation. Methods: A 3D model of human KCC2 was obtained from RCSB Protein Databank. VU0500469 was reconstructed manually. Protein-ligand computational simulations were run using AutoDock Tools and AutoDock Vina, GNINA, and P2Rank to identify direct interactions between VU0500469, and KCC2. Results: Results between simulations were then compared, and several possible VU0500469 binding pocket sites were successfully identified. We plan to further investigate molecular binding dynamics using CHARMM. Conclusion: The binding sites identified may represent targets for the development of additional KCC2 agonists

    A ternary knowledge relation on secrets

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    ABSTRACT The paper introduces and studies the ternary relation "secret a reveals at least as much information about secret c as secret b ." In spite of its seeming simplicity, this relation has many non-trivial properties. The main result is a complete infinite axiomatization of the propositional theory of this relation
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