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Institution

Missouri University of Science and Technology

EducationRolla, 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: Control theory & Artificial neural network. 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.


Papers
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Journal ArticleDOI
26 Jan 2016-ACS Nano
TL;DR: Tuning the vascular barrier leakiness through ND surface group functionalization could provide an alternative strategy for the cancer nanomedicine to traverse across the vascular Barrier.
Abstract: Cancer nanomedicine vehicles are required to cross the vascular barrier to reach the tumor site in order to ensure the successful delivery of their therapeutic load. Here, nanodiamond (ND) variants were shown to induce surface dependent vascular barrier leakiness. The ND-induced leakiness was found to be mediated by the increase in intracellular reactive oxygen species (ROS) and Ca2+. These then in turn triggered the loss in endothelial cell–endothelial cell connections of the vascular barrier and also triggered their quasi-stable cytoskeletal remodelling. This ND driven increase in leakiness allowed more doxorubicin drug to penetrate through the vascular barrier to reach the cancer cells. This increase in the doxorubicin penetration subsequently led to an increase in the cancer killing effect. Overall, tuning the vascular barrier leakiness through ND surface group functionalization could provide an alternative strategy for the cancer nanomedicine to traverse across the vascular barrier.

125 citations

Journal ArticleDOI
TL;DR: The procedure of the traditional Most Probable Point (MPP) based reliability analysis method is combined with the collaborative disciplinary analyses to automatically satisfy the interdisciplinary consistency when conducting reliability analysis.
Abstract: Traditional Multidisciplinary Design Optimization (MDO) generates deterministic optimal designs, which are frequently pushed to the limits of design constraint boundaries, leaving little or no room to accommodate uncertainties in system input, modeling, and simulation. As a result, the design solution obtained may be highly sensitive to the variations of system input which will lead to performance loss and the solution is often risky (high likelihood of undesired events). Reliability-based design is one of the alternative techniques for design under uncertainty. The natural method to perform reliability analysis in multidisciplinary systems is the all-in-one approach where the existing reliability analysis techniques are applied directly to the system-level multidisciplinary analysis. However, the all-in-one reliability analysis method requires a double loop procedure and therefore is generally very time consuming. To improve the efficiency of reliability analysis under the MDO framework, a collaborative reliability analysis method is proposed in this paper. The procedure of the traditional Most Probable Point (MPP) based reliability analysis method is combined with the collaborative disciplinary analyses to automatically satisfy the interdisciplinary consistency when conducting reliability analysis. As a result, only a single loop procedure is required and all the computations are conducted concurrently at the individual discipline-level. Compared with the existing reliability analysis methods in MDO, the proposed method is efficient and therefore provides a cheaper tool to evaluate design feasibility in MDO under uncertainty. Two examples are used for the purpose of verification.

125 citations

Journal ArticleDOI
TL;DR: The neural network model is found to possess better performance than the regression model for turbine power curve estimation under complicated influence factors.
Abstract: This paper examines and compares regression and artificial neural network models used for the estimation of wind turbine power curves. First, characteristics of wind turbine power generation are investigated. Then, models for turbine power curve estimation using both regression and neural network methods are presented and compared. The parameter estimates for the regression model and training of the neural network are completed with the wind farm data, and the performances of the two models are studied. The regression model is shown to be function dependent, and the neural network model obtains its power curve estimation through learning. The neural network model is found to possess better performance than the regression model for turbine power curve estimation under complicated influence factors.

125 citations

Proceedings ArticleDOI
26 Jul 2009
TL;DR: In this paper, a particle swarm optimization (PSO) is proposed to solve the V2G, as PSO has been demonstrated to reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit.
Abstract: An automatic Vehicle-to-Grid (V2G) technology can contribute to the utility grid. V2G technology has drawn great interest in the recent years. Success of the sophisticated automatic V2G research depends on efficient scheduling of gridable vehicles in constrained parking lots. Parking lots have constraints of space and current limits for V2G. However, V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to solve the V2G, as PSO has been demonstrated to reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO optimizes the on/off states of power generating units easily. Vehicles are presented by signed integer number instead of 0/1 to reduce the dimension of the problem. Typical discrete version of PSO has less balance between local and global searching abilities to optimize the number of charging/discharging gridable vehicles in the constrained system. In the same model, balanced PSO is proposed to optimize the V2G part in the constrained parking lots. Finally, results show a considerable amount of profit for using proper scheduling of gridable vehicles in constrained parking lots.

125 citations

Journal ArticleDOI
TL;DR: In this paper, the ratio of aluminum to nickel in the AlFeCoCrNi system was decreased to observe the transition of the solid solution from a body centered cubic to a face centered cubic structure.

124 citations


Authors

Showing all 9433 results

NameH-indexPapersCitations
Robert Stone1601756167901
Tobin J. Marks1591621111604
Jeffrey R. Long11842568415
Xiao-Ming Chen10859642229
Mark C. Hersam10765946813
Michael Schulz10075950719
Christopher J. Chang9830736101
Marco Cavaglia9337260157
Daniel W. Armstrong9375935819
Sajal K. Das85112429785
Ming-Liang Tong7936423537
Ludwig J. Gauckler7851725926
Rodolphe Clérac7850622604
David W. Fahey7731530176
Kai Wang7551922819
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202330
2022162
20211,047
20201,180
20191,195
20181,108