Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Lloyd P M Johnston"'
Autor:
Petr O Ilyinskii, Grigoriy I Kovalev, Conlin P O'Neil, Christopher J Roy, Alicia M Michaud, Natalia M Drefs, Mikhail A Pechenkin, Fen-Ni Fu, Lloyd P M Johnston, Dmitry A Ovchinnikov, Takashi Kei Kishimoto
Publikováno v:
PLoS ONE, Vol 13, Iss 6, p e0197694 (2018)
We previously reported that synthetic vaccine particles (SVP) encapsulating antigens and TLR agonists resulted in augmentation of immune responses with minimal production of systemic inflammatory cytokines. Here we evaluated two different polymer for
Externí odkaz:
https://doaj.org/article/9f01f61eeda54a439de2914fa8f41d1c
Publikováno v:
AIChE Journal. 46:946-954
The structure of individual reverse osmosis modules, the configuration of the module network, and the operating conditions were optimized for seawater and brackish water desalination. The system model included simple mathematical equations to predict
Autor:
Mark A. Kramer, Lloyd P. M. Johnston
Publikováno v:
AIChE Journal. 44:591-602
The method of recursive state density estimation (RSDE) is developed for determining the probability distribution of the states of a system from measurements that contain both random noise and gross errors. The technique is based on the expectation m
Publikováno v:
Macromolecules. 30:8191-8204
In this work, both the implicit penultimate model and a version of the terminal bootstrap model were fitted to extensive (k) over bar(p) data covering a temperature range of 17.9-57.2 degrees C, for the copolymerization of styrene and methyl methacry
Autor:
Lloyd P. M. Johnston, Mark A. Kramer
Publikováno v:
AIChE Journal. 41:2415-2426
A maximum likelihood rectification (MLR) technique that poses the data-rectification problem in a probabilistic framework and maximizes the probability of the estimated plant states given the measurements is proposed. This approach does not divide th
Autor:
Mark A. Kramer, Lloyd P. M. Johnston
Publikováno v:
AIChE Journal. 40:1639-1649
Elliptical basis function (EBF) networks are introduced as a new nonparametric method of estimating probability density functions for process data. Unlike Parzen window density estimators that use identical hyperspherical basis functions, the EBF met
Publikováno v:
ACS Symposium Series ISBN: 9780841235458
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e733fecd86154c9d4d33955f8d50406c
https://doi.org/10.1021/bk-1998-0685.ch008
https://doi.org/10.1021/bk-1998-0685.ch008
Autor:
KRZYSZTOF MATYJASZEWSKI, Leo Radom, Ming Wah Wong, Addy Pross, Anne Ghosez-Giese, Bernd Giese, Dennis P. Curran, Gerard van Koten, Robert A. Gossage, David M. Grove, Johann T. B. H. Jastzebski, Sabine Beuermann, Michael Buback, Paul A. Clay, David I. Christie, Robert G. Gilbert, Johan P. A. Heuts, Michelle L. Coote, Thomas P. Davis, Lloyd P. M. Johnston, Mikiharu Kamachi, M. K. Georges, R. P. N. Veregin, K. Daimon, Takeshi Fukuda, Atsushi Goto, Kohji Ohno, Yoshinobu Tsujii, B. Yamada, Y. Miura, Y. Nobukane, M. Aota, Yucheng Zhu, I. Q. Li, B. A. Howell, D. B. Priddy, D. Benoit, S. Grimaldi, J. P. Finet, P. Tordo, M. Fontanille, Y. Gnanou, Stefan A. F. Bon, Frank A. C. Bergman, J. J. G. Steven van Es, Bert Klumperman, Anton L. German, D. M. Haddleton, A. J. Shooter, A. M. Heming, M. C. Crossman, D. J. Duncalf, S. R. Morsley, Mitsuo Sawamoto, Masami Kamigaito, B. B. Wayland, S. Mukerjee, G. Poszmik, D. C. Woska, L. Basickes, A. A. Gridnev, M. Fryd, S. D. Ittel, Labros D. Arvanitopoulos, Michael P. Greuel, Brian M. King, Anne K. Shim, H. James Harwood, Gra