Institution
Missouri University of Science and Technology
Education•Rolla, Missouri, United States•
About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Artificial neural network & Control theory. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.
Topics: Artificial neural network, Control theory, Nonlinear system, Ionization, Finite element method
Papers published on a yearly basis
Papers
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TL;DR: Virtual sensors, which are at the core of this sensor cloud architecture, assist in creating a multiuser environment on top of resource-constrained physical wireless sensors and can help in supporting multiple applications.
Abstract: Newer models for interacting with wireless sensors such as Internet of Things and Sensor Cloud aim to overcome restricted resources and efficiency. The Missouri S&T (science and technology) sensor cloud enables different networks, spread in a huge geographical area, to connect together and be employed simultaneously by multiple users on demand. Virtual sensors, which are at the core of this sensor cloud architecture, assist in creating a multiuser environment on top of resource-constrained physical wireless sensors and can help in supporting multiple applications.
175 citations
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TL;DR: In this paper, the effects of nano-CaCO 3 and nano-SiO 2 contents on flowability, heat of hydration, mechanical properties, phase change, and pore structure of ultra-high strength concrete (UHSC) were investigated.
Abstract: Nanomaterials have attracted much interest in cement-based materials during the past decade. In this study, the effects of different nano-CaCO 3 and nano-SiO 2 contents on flowability, heat of hydration, mechanical properties, phase change, and pore structure of ultra-high strength concrete (UHSC) were investigated. The dosages of nano-CaCO 3 were 0, 1.6%, 3.2%, 4.8%, and 6.4%, by the mass of cementitious materials, while the dosages of nano-SiO 2 were 0, 0.5%, 1.0%, 1.5%, and 2%. The results indicated that both nano-CaCO 3 and nano-SiO 2 decreased the flowability and increased the heat of hydration with the increase of their contents. The optimal dosages to enhance compressive and flexural strengths were 1.6%–4.8% for the nano-CaCO 3 and 0.5%–1.5% for the nano-SiO 2 . Although compressive and flexural strengths were comparable for the two nanomaterials after 28 d, their strength development tendencies with age were different. UHSC mixtures with nano-SiO 2 showed continuous and sharp increase in strength with age up to 7 d, while those with nano-CaCO 3 showed almost constant strength between 3 and 7 d, but sharp increase thereafter. Thermal gravimetry (TG) analysis demonstrated that the calcium hydroxide (CH) content in UHSC samples decreased significantly with the increase of nano-SiO 2 content, but remained almost constant for those with nano-CaCO 3 . Mercury intrusion porosimetry (MIP) results showed that both porosity and critical pore size decreased with the increase of hydration time as well as the increase of nanoparticles content to an optimal threshold, beyond which porosity decreased. The difference between them was that nano-CaCO 3 mainly reacted with C 3 A to form carboaluminates, while nano-SiO 2 reacted with Ca(OH) 2 to form C S H. Both nano-CaCO 3 and nano-SiO 2 demonstrated nucleation and filling effects and resulted in less porous and more homogeneous structure.
174 citations
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TL;DR: Results show that redeveloped neural network models that use the recent relevant variables generate higher profits with lower risks than the buy-and-hold strategy, conventional linear regression, and the random walk model, as well as the neural networks that use constant relevant variables.
174 citations
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TL;DR: The results of this work indicate that the pore network model and expressions presented could allow one, for a given porous adsorbent, adsorbate, ligand (active site), and interstitial column fluid velocity, to determine in an a priori manner the values of the intraparticle interstitial velocity and pore diffusivity within the monolith or within the porous Adsorbent particles as the fractional saturation of the active sites changes.
174 citations
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TL;DR: In this paper, the authors used friction stir processing (FSP) to create a microstructure with ultrafine grains (0.68-μm grain size) in an as-cast Al 8.9Zn-2.09Sc (wt.%) alloy.
174 citations
Authors
Showing all 9433 results
Name | H-index | Papers | Citations |
---|---|---|---|
Robert Stone | 160 | 1756 | 167901 |
Tobin J. Marks | 159 | 1621 | 111604 |
Jeffrey R. Long | 118 | 425 | 68415 |
Xiao-Ming Chen | 108 | 596 | 42229 |
Mark C. Hersam | 107 | 659 | 46813 |
Michael Schulz | 100 | 759 | 50719 |
Christopher J. Chang | 98 | 307 | 36101 |
Marco Cavaglia | 93 | 372 | 60157 |
Daniel W. Armstrong | 93 | 759 | 35819 |
Sajal K. Das | 85 | 1124 | 29785 |
Ming-Liang Tong | 79 | 364 | 23537 |
Ludwig J. Gauckler | 78 | 517 | 25926 |
Rodolphe Clérac | 78 | 506 | 22604 |
David W. Fahey | 77 | 315 | 30176 |
Kai Wang | 75 | 519 | 22819 |