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Items: 1 to 20 of 112

1.

BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.

Pi E, Mantri N, Ngai SM, Lu H, Du L.

PLoS One. 2013 Dec 13;8(12):e82413. doi: 10.1371/journal.pone.0082413. eCollection 2013.

2.

Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.

Pi E, Qu L, Tang X, Peng T, Jiang B, Guo J, Lu H, Du L.

PLoS One. 2015 Jul 8;10(7):e0131489. doi: 10.1371/journal.pone.0131489. eCollection 2015.

3.

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly.

Hsieh KC, Chen YJ, Lu HK, Lee LC, Huang YC, Chen YY.

Nutr J. 2013 Feb 6;12:21. doi: 10.1186/1475-2891-12-21.

5.

Development of a threshold model to predict germination of Populus tomentosa seeds after harvest and storage under ambient condition.

Wang WQ, Cheng HY, Song SQ.

PLoS One. 2013 Apr 26;8(4):e62868. doi: 10.1371/journal.pone.0062868. Print 2013.

6.
7.

[Seed germinating characteristics of endangered plant Magnolia officinalis].

Shu X, Yang Z, Duan H, Yang X, Yu H.

Zhongguo Zhong Yao Za Zhi. 2010 Feb;35(4):419-22. Chinese.

PMID:
20450036
8.

Germination parameterization and development of an after-ripening thermal-time model for primary dormancy release of Lithospermum arvense seeds.

Chantre GR, Batlla D, Sabbatini MR, Orioli G.

Ann Bot. 2009 Jun;103(8):1291-301. doi: 10.1093/aob/mcp070. Epub 2009 Mar 29.

9.

Effects of temperature and photoperiod on postponing bermudagrass (Cynodon dactylon [L.] Pers.) turf dormancy.

Esmaili S, Salehi H.

J Plant Physiol. 2012 Jun 15;169(9):851-8. doi: 10.1016/j.jplph.2012.01.022. Epub 2012 Mar 31.

PMID:
22465814
10.

An improved method for estimating human circadian phase derived from multichannel ambulatory monitoring and artificial neural networks.

Kolodyazhniy V, Späti J, Frey S, Götz T, Wirz-Justice A, Kräuchi K, Cajochen C, Wilhelm FH.

Chronobiol Int. 2012 Oct;29(8):1078-97. doi: 10.3109/07420528.2012.700669. Epub 2012 Aug 14.

PMID:
22891656
11.

Seed dormancy and germination of Ficus lundellii and tropical forest restoration.

Garcia X, Hong TD, Ellis RH.

Tree Physiol. 2006 Jan;26(1):81-5.

PMID:
16203717
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14.

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21.

Nelofer R, Ramanan RN, Rahman RN, Basri M, Ariff AB.

J Ind Microbiol Biotechnol. 2012 Feb;39(2):243-54. doi: 10.1007/s10295-011-1019-3. Epub 2011 Aug 11.

PMID:
21833714
15.

QSPR studies on soot-water partition coefficients of persistent organic pollutants by using artificial neural network.

Jiao L.

Chemosphere. 2010 Jul;80(6):671-5. doi: 10.1016/j.chemosphere.2010.04.013. Epub 2010 May 10.

PMID:
20452639
16.

An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area.

DiRusso SM, Sullivan T, Holly C, Cuff SN, Savino J.

J Trauma. 2000 Aug;49(2):212-20; discussion 220-3.

PMID:
10963531
17.
18.

[Effects of storage conditions and sowing methods on seed germination of Leymus chinensis].

Ma HY, Liang ZW.

Ying Yong Sheng Tai Xue Bao. 2007 May;18(5):997-1002. Chinese.

PMID:
17650847
19.

Differential metabolic responses of perennial grass Cynodon transvaalensis×Cynodon dactylon (C₄) and Poa Pratensis (C₃) to heat stress.

Du H, Wang Z, Yu W, Liu Y, Huang B.

Physiol Plant. 2011 Mar;141(3):251-64. doi: 10.1111/j.1399-3054.2010.01432.x. Epub 2010 Dec 22.

PMID:
21114672
20.

Characterizing Ipomopsis rubra (Polemoniaceae) germination under various thermal scenarios with non-parametric and semi-parametric statistical methods.

Pérez HE, Kettner K.

Planta. 2013 Oct;238(4):771-84. doi: 10.1007/s00425-013-1935-8. Epub 2013 Jul 30.

PMID:
23897298
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