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Książki na temat „NNos”

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Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 maja 2024

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1

Nguynen, Khoac Mnan. Nnoi lòng. [S.l: s.n., 1987.

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2

Centers for Disease Control (U.S.), red. NNIS manual: NNIS, National Nosocomial Infections Surveillance System. Atlanta, Ga: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, 1988.

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3

Buck, Pearl S. Nnoi đau thre kky. [Hà Nuoi]: NXB Văn học, 1996.

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4

Đno, Văn Trọn. Nnoi nisem mang theo. San Jose, CA (675 N. 1st St., Suite 611, San Jose 95112): Tusan báo Yêu, 1988.

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5

Du nnes Eis: Novelle. Berlin: Contumax-Verl., 2009.

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6

Anh, Hoàng Xuyên. Nnoi lòng cô phụ. San Jose, CA: NXB Chki Thkao, 1995.

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7

Đno, Thị Khôi Nguyên. Tuan cùng nnoi nhwo. [United States: s.n., 1989.

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8

Phương, Viuet. Trôi trong nnoi nhwo. Toronto, Ont: Làng Văn, 2000.

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9

Nguynen, Tran Hưng. Muot nnoi buson riêng: Tuap truyuen. Glendale, CA: Đại Nam, 1992.

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10

Ninh, Bkao. Nnoi buson chiren tranh: Truyuen dài. [Westminster, CA]: Hông Lĩnh, 1992.

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11

Hoàng, Ngọc Sơn. Khurat sau nnoi buson: Titeu thuyret. Hà Nuoi: NXB Huoi nhà văn, 2002.

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12

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

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13

Duras, Marguerite. Nnoi đam mê ckua Lol V. Stein. [Đà Nmang?]: NXB Đà Nmang, 2001.

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14

Hoàng, Lại Giang. Nnoi brat hạnh tình yêu: Titeu thuyret. Wyd. 2. Houston, Tex: Đa Nguyên, 1990.

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15

Tuan, Nguyen Anh. Nnoi nisem đưxong xa: Tuap truyuen ngoan. Hà Nuoi: NXB Huoi nhà văn, 2002.

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16

Lucien, Anya-Noa, red. Mfubub Bibel a nkòbò Ewondo: Nnom Amvoe. [Cameroon: s.n., 1992.

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17

Võ, Đoac Danh. Nnoi nisem U Minh Hạ: Tuap bút ký. Thành phro Hso Chí Minh: NXB Trke, 2008.

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18

Gikam thiteu nnoi đau: Tâm-svu vwoi đsong-bào txung chịu nhzung nnoi đau làm chran-đuong lương-tâm nhân-loại. California: NXB Nam-Văn, 2006.

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19

Nurses, International Council of, red. Professional and socio-economic welfare responsibilities within NNAs: Reference document. Geneva: International Council of Nurses, 1996.

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20

Quê hương thương ghét: Nnoi lòng ngưxoi Viuet hkai ngoại. [Springfield, Va.]: Vietnam Books, 2001.

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21

Canzoneri, Matthew B. The cost of nominal inertia in NNS models. Cambridge, MA: National Bureau of Economic Research, 2004.

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22

Mackelworth, I. Managing third generation NNGOs: Meeting the challenges of the contemporary environment. Manchester: UMIST, 1997.

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23

Malʹi︠a︡n, Vilʹdana. V pomoshchʹ lideru NNO: Posobie dli︠a︡ negosudarstvennykh nekommercheskikh organizat︠s︡iĭ. Tashkent: Kaunterpart Uzbekistan, 2003.

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24

No, they won't "just sound like each other": NNS-NNS negotiated interaction and attention to phonological form on targeted L2 pronunciation tasks. Frankfurt am Main: Peter Lang, 2009.

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25

Sicola, Laura. No, they won't "just sound like each other": NNS-NNS negotiated interaction and attention to phonological form on targeted L2 pronunciation tasks. Frankfurt am Main: Peter Lang, 2009.

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26

Laos. Service national de la statistique., Laos. Hygiene and Prevention Department. i UNICEF, red. National maternal and child nutrition survey (MICS3-NNS) report, the Lao PDR, 2006: Final report. [Vientiane]: Department of Statistics, Ministry of Planning and Investment, 2009.

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27

Brar, Rita S. K. NNGOs and SNGOs partnership as a strategy for institutional development: Case studies: Dfid, Oxfam UK, ActionAid UK, PRIA India. Oxford: Oxford Brookes University, 2003.

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28

Burke, Stefanie. Play maths!: Independent reception and KS1 activities to support the objectives of the NNS framework/Early Learning Goals. Exeter: Devon Curriculum Services, 2000.

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29

Tapia, Fermín. Jñ'o tíbio jndye nno jñ'o m'a jñ'o konà'nè n'a yo jñondá Tzjó[superscript n] noa: Cuentos y leyendas en idioma amuzgo de San Pedro Amuzgos, Oaxaca. [Mexico City]: Consejo Nacional para la Cultura y las Artes, 2008.

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30

McLaren, Anya T. Increased expression of hypoxia inducible factor-1alpha (HIF-1alpha) and neuronal nitric oxide synthase (nNOS) in the cerebral cortex of anemic rats. 2006.

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31

Lester, Barry M., i Edward Z. Tronick. Nicu Network Neurobehavioral Scales (Nnns). Brookes Publishing, 2004.

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32

N, Nieson. Nns Red Book. NNS Publishing Company, 1988.

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33

Lester, Barry M., i Edward Z. Tronick. NICU Network Neurobehavioral Scale (NNNS) Manual. Brookes Publishing Company, 2004.

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34

Tronick, Edward, i Barry M. Lester. The Nicu Network Neurobehavioral Scale (Nnns. Brookes Publishing Company, 2004.

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35

Lester, Barry M., i Edward Z. Tronick. NICU Network Neurobehavioral Scale (Nnns) Kit:. Brookes Publishing Company, 2005.

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36

Vokins, Michael. Nuffield Advanced Level Chemistry (NNS). Wyd. 4. Longman, 2000.

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37

Nuffield Advanced Level Chemistry (NNS). Wyd. 4. Longman, 2000.

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38

PUBLISHER, PRENTICE HALL. SPSH 2010 NATL PREN HALL REALIDADES 1 PACKAGE NNDS 2010 BU2 G8. PRENTICE HALL, 2010.

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39

Bradford-Alptogan, Ingrid M. To compare gender differnces in NNS/NNS conversational interaction between EFL learners whose first language is Arabic and EFL learners whose first language is Japanese. 1996.

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40

Guelachvili, G., i K. N. Rao. N2O (NNO) (Numerical Data and Functional Relationships in Science and Technology). Springer, 1998.

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41

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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42

Raff, Lionel, Ranga Komanduri, Martin Hagan i 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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43

Hqny, Unicef. National Nutrition Information Systems : The Fundamentals Series, Module2: How Does an NNIS Support a Country¿s Nutrition Programmes? United Nations Children's Fund, The (UNICEF), 2022.

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44

Coloring, Cartoon. Dẹnnis and Gnashẹr Unlẹashed! Coloring Book: JUMBO dẹnnis and Gnashẹr Unlẹashed! Colouring Book for Children, Amazing Colouring Book for Boys and Girls Ages 2-4, 4-8 and Toddlers. Independently Published, 2022.

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45

Rusetskaya, Lyudmila. The writing problems and writing processes of MA in ELT students at Thames Valley University: An investigation into perceptions of NS and NNS student writers. 1993.

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46

Williams, John, i Francis Bonnet. Analgesics in anaesthetic practice. Redaktor Michel M. R. F. Struys. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0018.

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Each year, approximately 230 million major surgical procedures are undertaken worldwide, with over three-quarters of the patients complaining of pain postoperatively and 10% complaining of severe pain. Pain is not, however, just an unpleasant sensory consequence of surgery, but can also have significant physiological implications impacting negatively on well-being and postoperative outcome. Postoperative pain may also result in changes within the central nervous system, leading to the development of chronic pain states lasting in excess of 3–6 months. Adequate analgesia has proven to be effective when employed in the perioperative period at combating many of these adverse effects. An understanding of the basic physiological and pharmacological mechanisms responsible for producing, transmitting, and sustaining pain has allowed for a variety of effective analgesic agents to be fashioned and used clinically to treat pain. Morphine, the archetypal opioid analgesic, is the most familiar of these agents with a long history of use and evidence of effectiveness; morphine possesses a number-needed-to-treat (NNT) to reduce pain by 50% of around 3 when given in doses of between 10 and 15 mg. Non-steroidal agents and paracetamol are similarly effective in the immediate postoperative period with NNTs of between 2 and 4. More recently, a number of analgesic adjuncts such as gabapentin, pregabalin, ketamine, clonidine, and nefopam have found favour for the treatment of acute postoperative pain. None of these agents, however, are without side-effects, ensuring that the search for effective analgesic agents continues to be a vibrant area of research with new analgesic agents continuing to be developed.
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