Livros: "NNF" – Grafiati

1

(India), National Neonatology Forum, e Indian Academy of Pediatrics, eds. IAP-NNF guidelines 2006 on level II neonatatal care. Mumbai: Indian Academy of Padiatrics, 2007.

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2

Césaire, Michèle. La nef. Paris: Editions theatrales, 1992.

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3

Agassi, Nelly. Nof Shaʼul. [Tel Aviv]: Muzeʼon Tel Aviv le-omanut, 2004.

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4

Avishay, Ayal, e Universiṭat Ḥefah Galeryah le-omanut, eds. Tavnit nof: Heʻarah ʻal tsilum ha-nof ha-meḳomi. Ḥefah: ha-Galeryah le-omanut, Universiṭat Ḥefah, 2004.

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5

Ofomata, Chinedum E. Lee nna m. Enugu: Format Publishers, 2009.

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6

Dekalo-Dor, Mali. Nof ʻim menorot. [Tel Aviv]: Galeryah Bograshov, 1991.

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7

D, Maphalla K. P. Nna ke mang? Pietermaritzburg: Reach Out Publishers, 1992.

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8

Amichai, Yehuda. Nof galui ʻenayim. Yerushalayim: Shoḳen, 1992.

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9

Flint, Wendy. NSF development framework. Leicester: National Youth Agency, 2003.

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10

Ofomata, Chinedum E. Maka nne m. Enugu: Format Publishers, 2009.

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11

Alṿail, Aryeh. Ot, demut, nof. En-Ḥarod: Mishkan le-omanut ʻa. sh. Ḥayim Atar, 2015.

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12

Sambu, Aadama. Nna maakunda kumpabooroo. Dakar: PAPA, 1999.

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13

Amoako, B. O. Etire nni safoa. Accra: Bureau of Ghana Languages, 1988.

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14

Ferraris, Zoë. Meḥapśim et Nuf. אור יהודה: כנרת, 2010.

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15

Michailov, Boris. Drucksache N.F.4. Düsseldorf: Richter Verlag, 2000.

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16

Georgiev, Sergeĭ. Pokhishchenie brillianta "Nuf-nuf": Zakhvatyvai︠u︡shchai︠a︡ povestʹ, gde kazhdoe slovo - chisteĭshai︠a︡ pravda. Moskva: OLMA-PRESS, 2004.

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17

Fichman, Jacob. Raze nof: Mivḥar shirim. Tel-Aviv: ʻEmdah/Bitan, 2001.

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18

Otladisang, Obakeng. Ya le nna Babelona. Gaborone: Longman Botswana, 1993.

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19

Li, Ping, e Karen Marrongelle. Having Success with NSF. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118431108.

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20

Guelachvili, G., ed. Linear Triatomic Molecules - NNO. Berlin/Heidelberg: Springer-Verlag, 1998. http://dx.doi.org/10.1007/b53036.

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21

Nourissier, François. Un siècle NRF: Iconographie. Paris: Gallimard, 2000.

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22

Zakharov, Yu D. General armii N.F. Vatutin. Moskva: Voenizdat, 1985.

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23

Hamelin, Claude. Nef des fous: Poèmes. Montréal: Teichtner, 1992.

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24

United States. Federal Bureau of Investigation. FBI file on NNC. [Wilmington, Del.]: Scholarly Resources, 1987.

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25

Service, United States Forest, ed. Forest facts: Hoosier N.F. [Washington, D.C.]: USDA Forest Service, Hoosier National Forest, 1999.

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26

Brisset, Laurence. La NRF de Paulhan. [Paris]: Gallimard, 2003.

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27

Service, United States Forest, ed. Forest facts: Hoosier N.F. [Washington, D.C.]: USDA Forest Service, Hoosier National Forest, 1999.

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28

Bahat, Ofer. Gamla: Ṭeṿaʻ ṿe-nof. [Tel Aviv]: Miśrad ha-biṭaḥon, 1991.

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29

D, Zakharov I͡U. General armii N.F. Vatutin. Moskva: Voen. izd-vo, 1985.

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30

Service, United States Forest, ed. Forest facts: Hoosier N.F. [Washington, DC]: USDA Forest Service, Hoosier National Forest, 1998.

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31

Ticho, Anna. Anah Ṭikho: Nof ḥayehah. 8^a ed. Yerushalayim: Muzeʼon Yiśraʼel, 2019.

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32

Hobb, Robin. La nef du crépuscule. Paris: J'ai lu, 2008.

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33

D, Zakharov I︠U︡. General armii N.F. Vatutin. Moskva: Voen. izd-vo, 1985.

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34

Huret, Jean-Etienne. Les cartonnages NRF: Bibliographie. Paris: Librairie Nicaise, 1997.

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35

Service, United States Forest, ed. Forest facts: Hoosier N.F. [Washington, D.C.]: USDA Forest Service, Hoosier National Forest, 1999.

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36

Rivaz, Dominique de. Tache: (ta[ch]) n.f. Lausanne: L'Age d'homme, 2002.

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37

Gil, Rachel. Nof ʻim ishah gevohah. [Tel Aviv]: ha-Ḳibuts ha-meʼuḥad, 2008.

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38

Badius, Josse. La nef des folles. Paris: Classiques Garnier, 2013.

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39

Zenia, Salem. Yella zik-nni: Timucuha. Tizi-Ouzou: Editions de L'Odyssée, 2008.

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40

Service, United States Forest, ed. Forest facts: Hoosier N.F. [Washington, D.C.]: USDA Forest Service, Hoosier National Forest, 1999.

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41

Fichman, Jacob. Raze nof: Mivḥar shirim. Tel-Aviv: ʻEmdah/Bitan, 2001.

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42

Hartwick, John M. National wealth and NNP. Québec, Qué: Département d'économique, Faculté des sciences sociales, Université Laval, 1993.

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43

Luce, Guilbeault, ed. La Nef des sorcières. Montréal: L'Hexagone, 1992.

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44

Service, United States Forest, ed. Forest facts: Hoosier N.F. [Washington, DC]: USDA Forest Service, Hoosier National Forest, 1998.

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45

Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.

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46

Raff, Lionel, Ranga Komanduri, Martin Hagan e Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.

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47

Nof. Ramat Gan: ha-Muzeʾon le-omanut Yiśreʾelit, 1996.

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48

Bourges, Elémir. Nef. Creative Media Partners, LLC, 2022.

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49

Bourges, Elémir. Nef. Creative Media Partners, LLC, 2018.

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50

Bourges, Elémir. Nef. Creative Media Partners, LLC, 2022.

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