51 |
A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie? Xu B, Lin BQ Energy Policy, 98, 328, 2016 |
52 |
An artificial intelligence approach to predict a lower heating value of municipal solid waste Ozveren U Energy Sources Part A-recovery Utilization and Environmental Effects, 38(19), 2906, 2016 |
53 |
Random sample consensus combined with partial least squares regression (RANSAC-PLS) for microbial metabolomics data mining and phenotype improvement Teoh ST, Kitamura M, Nakayama Y, Putri S, Mukai Y, Fukusaki E Journal of Bioscience and Bioengineering, 122(2), 168, 2016 |
54 |
A new grid-scale model simulating the spatiotemporal distribution of PM2.5-PAHs for exposure assessment Lee CL, Huang HC, Wang CC, Sheu CC, Wu CC, Leung SY, Lai RS, Lin CC, Wei YF, Lai IC, Jiang H, Choug WL, Chung WY, Huang MS, Huang SK Journal of Hazardous Materials, 314, 286, 2016 |
55 |
Multiple regression analysis to assess the role of plankton on the distribution and speciation of mercury in water of a contaminated lagoon Stoichev T, Tessier E, Amouroux D, Almeida CM, Basto MCP, Vasconcelos VM Journal of Hazardous Materials, 318, 711, 2016 |
56 |
Active learning assisted strategy of constructing hybrid models in repetitive operations of membrane filtration processes: Using case of mixture of bentonite clay and sodium alginate Liu Y, Chou CP, Chen JH, Lai JY Journal of Membrane Science, 515, 245, 2016 |
57 |
Heat and mass transfer performance analysis and cooling capacity prediction of earth to air heat exchanger Niu FX, Yu YB, Yu DH, Li HR Applied Energy, 137, 211, 2015 |
58 |
Stability Study on Ethylene Oxide Industrial Reaction from the Management of Critical Process Variables Ribeiro LG, Taqueda MES Chemical Engineering & Technology, 38(12), 2235, 2015 |
59 |
An efficient copula-based method of identifying regression models of non-monotonic relationships in processing plants Ahooyi TM, Arbogast JE, Soroush M Chemical Engineering Science, 136, 106, 2015 |
60 |
Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression Hu JM, Wang JZ Energy, 93, 1456, 2015 |