Development and characterization of transparent glass matrix composites

Glass matrix composites based on NextelTM alumina fibre reinforced borosilicate glass have been fabricated to improve their mechanical property and fracture toughness. In this work, a novel processing technique, which is called “sandwich” hot-pressing, has been used. It consists of arranging the reinforcing fibres in two directions with a periodic interspacing between glass slides, and submitting the material to a heat-treatment for consolidation into highly dense and transparent composites, which were proved by XRD analysis and SEM observations. These composites’ mechanical, optical and microstructural properties were studied and compared to those of the unidirectional fibre reinforced borosilicate glass composite and unreinforced glass matrix produced under the same conditions. Furthermore, a hybrid sol-gel technique has been employed for coating the fibres with a smooth and crack free ZrO2 interfacial layer to provide a weak bonding at the fibre/matrix interface to promote fibre pull-out during fracture. ZrO2 coated and uncoated fibre-reinforced borosilicate glass matrix composites were fabricated, with different sizes of optical windows including 4x4, 5x5 and 6x6 cm2. Moreover, a geometry based equation was derived to evaluate the expected light transmittance of the composites. These multi-directional fibre reinforced glass matrix composites retained at least 50% of the light transmittance and higher flexural strength compared with the unreinforced glass matrix. The highest measured flexural strength value of these composites was 56 ± 7 MPa. The composites reinforced by ZrO2 coated fibres had higher flexural strength (approx. 36%) and lower standard deviation (approx. 47%) compared with those reinforced by uncoated fibres. The introduction of a ZrO2 interfacial layer was to improve the mechanical properties and to retain the composites’ integrity, which was proved by the observations of fibre pull-out and crack deflection upon failure during mechanical tests. To investigate the microstructure of the interfacial layer in the composite, SEM, FIB-SIMS and TEM were employed. The present composites show potential for applications in architecture and special machinery requiring strong transparent windows

A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.Comment: Data available at http://www.cs.cornell.edu/people/pabo/movie-review-data

On the power laws of language: word frequency distributions

About eight decades ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. Over the years, this phenomenon has been documented and studied extensively. For many corpora, however, the empirical distribution barely resembles a power law: when plotted on a loglog scale, the distribution is concave and appears to be composed of two differently sloped straight lines joined by a smooth curve. A simple generative model is proposed to capture this phenomenon. Theword frequency distributions produced by this model are shown to match the observations both analytically and empirically. © 2017 Copyright held by the owner/author(s)

Thumbs up? Sentiment Classification using Machine Learning Techniques

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.Comment: To appear in EMNLP-200

An epilepsy-associated mutation of salt-inducible kinase 1 increases the susceptibility to epileptic seizures and interferes with adrenocorticotropic hormone therapy for infantile spasms in mice（Salt-induced kinase 1遺伝子のてんかん関連変異はてんかん発作の感受性を高めるとともに、マウスの点頭てんかんに対するACTHの効果を減弱させる。）

信州大学(Shinshu university)博士（医学）次の雑誌に発表。 /International Journal of Molecular Sciences 23(14) :7927(2022); doi:10.3390/ijms23147927 © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).ThesisPANG BO. An epilepsy-associated mutation of salt-inducible kinase 1 increases the susceptibility to epileptic seizures and interferes with adrenocorticotropic hormone therapy for infantile spasms in mice（Salt-induced kinase 1遺伝子のてんかん関連変異はてんかん発作の感受性を高めるとともに、マウスの点頭てんかんに対するACTHの効果を減弱させる。）. 信州大学, 2021, 博士論文. 博士（医学）, 甲第1301号, 令和03年09月30日授与.doctoral thesi