| 1 |
Comprehensive understanding of Saccharomyces cerevisiae phenotypes with whole-cell model WM_S288C
Ye C, Xu N, Gao C, Liu GQ, Xu JZ, Zhang WG, Chen XL, Nielsen J, Liu LM
Biotechnology and Bioengineering, 117(5), 1562, 2020 |
| 2 |
A comparison of Measure-Correlate-Predict Methodologies using LiDAR as a candidate site measurement device for the Mediterranean Island of Malta
Mifsud MD, Sant T, Farrugia RN
Renewable Energy, 127, 947, 2018 |
| 3 |
Optimal interconnections to address partial shading losses in solar photovoltaic arrays
Pareek S, Chaturvedi N, Dahiya R
Solar Energy, 155, 537, 2017 |
| 4 |
Implications of applying solar industry best practice resource estimation on project financing
Pacudan R
Energy Policy, 95, 489, 2016 |
| 5 |
A novel hybrid model based on artificial neural networks for solar radiation prediction
Wu YJ, Wang JZ
Renewable Energy, 89, 268, 2016 |
| 6 |
Comparison of feature selection methods using ANNs in MCP-wind speed methods. A case study
Carta JA, Cabrera P, Matias JM, Castellano F
Applied Energy, 158, 490, 2015 |
| 7 |
Determination of extreme wind values using the Gumbel distribution
Kang D, Ko K, Huh J
Energy, 86, 51, 2015 |
| 8 |
Performance analysis of the first method for long-term turbulence intensity estimation at potential wind energy sites
Casella L
Renewable Energy, 74, 106, 2015 |
| 9 |
Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure-correlate-predict
Weekes SM, Tomlin AS, Vosper SB, Skea AK, Gallani ML, Standen JJ
Renewable Energy, 81, 760, 2015 |
| 10 |
Prediction of current and the maximum power of solar cell via voltage generated by light and irradiance using analytically invertible function
Liu CS
Solar Energy, 113, 340, 2015 |
