Institution
Lehigh University
Education•Bethlehem, Pennsylvania, United States•
About: Lehigh University is a education organization based out in Bethlehem, Pennsylvania, United States. It is known for research contribution in the topics: Catalysis & Fracture mechanics. The organization has 12684 authors who have published 26550 publications receiving 770061 citations.
Papers published on a yearly basis
Papers
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TL;DR: Results show that Monte Carlo cost-to-go estimation reduces computation time 65% in large instances with little or no loss in solution quality, and compares results to the perfect information case from solving exact a posteriori solutions for sampled vehicle routing problems.
271 citations
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TL;DR: The development of highly active, selective and stable supported metal catalysts for levulinic acid catalytic hydrogenation and on the beneficial effects of metal nano-alloying are reported.
Abstract: The catalytic hydrogenation of levulinic acid, a key platform molecule in many biorefinery schemes, into γ-valerolactone is considered as one of the pivotal reactions to convert lignocellulose-based biomass into renewable fuels and chemicals. Here we report on the development of highly active, selective and stable supported metal catalysts for this reaction and on the beneficial effects of metal nano-alloying. Bimetallic random alloys of gold-palladium and ruthenium-palladium supported on titanium dioxide are prepared with a modified metal impregnation method. Gold-palladium/titanium dioxide shows a marked,~27-fold increase in activity (that is, turnover frequency of 0.1 s(-1)) compared with its monometallic counterparts. Although ruthenium-palladium/titanium dioxide is not only exceptionally active (that is, turnover frequency of 0.6 s(-1)), it shows excellent, sustained selectivity to γ-valerolactone (99%). The dilution and isolation of ruthenium by palladium is thought to be responsible for this superior catalytic performance. Alloying, furthermore, greatly improves the stability of both supported nano-alloy catalysts.
271 citations
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02 Sep 2011TL;DR: This paper crawled active Twitter users, their followers/ following information and their most recent 100 tweets, and evaluated the detection scheme based on the suggested user and content-based features, showing that among the four classifiers, the Random Forest classifier produces the best results.
Abstract: Social networking sites have become very popular in recent years. Users use them to find new friends, updates their existing friends with their latest thoughts and activities. Among these sites, Twitter is the fastest growing site. Its popularity also attracts many spammers to infiltrate legitimate users' accounts with a large amount of spam messages. In this paper, we discuss some user-based and content-based features that are different between spammers and legitimate users. Then, we use these features to facilitate spam detection. Using the API methods provided by Twitter, we crawled active Twitter users, their followers/ following information and their most recent 100 tweets. Then, we evaluated our detection scheme based on the suggested user and content-based features. Our results show that among the four classifiers we evaluated, the Random Forest classifier produces the best results. Our spam detector can achieve 95.7% precision and 95.7% F-measure using the Random Forest classifier.
271 citations
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TL;DR: In this article, the authors investigated factors related to students' engagement during a collaborative AR, forensic science mystery game using mobile devices and found that neither gender nor interest in science was an important predictor of variability in flow experience.
Abstract: Current studies have reported that secondary students are highly engaged while playing mobile augmented reality (AR) learning games. Some researchers have posited that players' engagement may indicate a flow experience, but no research results have confirmed this hypothesis with vision-based AR learning games. This study investigated factors related to students' engagement – as characterized by flow theory – during a collaborative AR, forensic science mystery game using mobile devices. School Scene Investigators: The Case of the Stolen Score Sheets is a vision-based AR game played inside the school environment with Quick Response codes. A mixed methods approach was employed with 68 urban middle school students. Data sources included pre- and post-surveys, field observations and group interviews. Results showed that neither gender nor interest in science was an important predictor of variability in flow experience. Gaming attitude uniquely predicted 23% of the variance in flow experience. Student flow experience features included a flash of intensity, a sense of discovery and the desire for higher performance. The findings demonstrated a potential for mobile AR science games to increase science interest and help students learn collaboration skills. Implications for future research concerning mobile AR science games are discussed.
271 citations
Authors
Showing all 12785 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yang Yang | 171 | 2644 | 153049 |
Gang Chen | 167 | 3372 | 149819 |
Yi Yang | 143 | 2456 | 92268 |
Mark D. Griffiths | 124 | 1238 | 61335 |
Michael Gill | 121 | 810 | 86338 |
Masaki Mori | 110 | 2200 | 66676 |
Kai Nan An | 109 | 953 | 51638 |
James R. Rice | 108 | 278 | 68943 |
Vinayak P. Dravid | 103 | 817 | 43612 |
Andrew M. Jones | 103 | 764 | 37253 |
Israel E. Wachs | 103 | 427 | 32029 |
Demetrios N. Christodoulides | 100 | 704 | 51093 |
Bert M. Weckhuysen | 100 | 767 | 40945 |
José Luis García Fierro | 100 | 1027 | 47228 |
Mordechai Segev | 99 | 729 | 40073 |