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On the elusivity of dynamic optimisation problems. (2023)
Journal Article
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2023. On the elusivity of dynamic optimisation problems. Swarm and evolutionary computation [online], 78, article 101289. Available from: https://doi.org/10.1016/j.swevo.2023.101289

The field of dynamic optimisation continuously designs and compares algorithms with adaptation abilities that deal with changing problems during their search process. However, restarting the search algorithm after a detected change is sometimes a bet... Read More about On the elusivity of dynamic optimisation problems..

Multi-criteria material selection for casing pipe in shale gas wells application. (2022)
Journal Article
MOHAMMED, A.I., BARTLETT, M., OYENEYIN, B., KAYVANTASH, K. and NJUGUNA, J. 2022. Multi-criteria material selection for casing pipe in shale gas wells application. Journal of petroleum exploration and production technology [online], 12(12), pages 3183-3199. Available from: https://doi.org/10.1007/s13202-022-01506-0

The conventional method of casing selection is based on availability and/or order placement to manufacturers based on certain design specifications to meet the anticipated downhole conditions. This traditional approach is very much dependent on exper... Read More about Multi-criteria material selection for casing pipe in shale gas wells application..

An application of FEA and machine learning for the prediction and optimisation of casing buckling and deformation responses in shale gas wells in an in-situ operation. (2021)
Journal Article
MOHAMMED, A.I., BARTLETT, M., OYENEYIN, B., KAYVANTASH, K. and NJUGUNA, J. 2021. An application of FEA and machine learning for the prediction and optimisation of casing buckling and deformation responses in shale gas wells in an in-situ operation. Journal of natural gas science and engineering [online], 95, article 104221. Available from: https://doi.org/10.1016/j.jngse.2021.104221

This paper proposes a novel way to study the casing structural integrity using two approaches of finite element analysis (FEA) and machine learning. The approach in this study is unique, as it captures the pertinent parameters influencing the casing... Read More about An application of FEA and machine learning for the prediction and optimisation of casing buckling and deformation responses in shale gas wells in an in-situ operation..

Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe. (2020)
Journal Article
DE LUCIA, C., PAZIENZA, P. and BARTLETT, M. 2020. Does good ESG lead to better financial performances by firms? Machine learning and logistics regression models of public enterprises in Europe. Sustainability [online], 12(13), article ID 5317. Available from: https://doi.org/10.3390/su12135317

The increasing awareness of climate change and human capital issues is shifting companies towards aspects other than traditional financial earnings. In particular, the changing behaviors towards sustainability issues of the global community and the a... Read More about Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe..

Prediction of casing critical buckling during shale gas hydraulic fracturing. (2019)
Journal Article
MOHAMMED, A.I., OYENEYIN, B., BARTLETT, M. and NJUGUNA, J. 2020. Prediction of casing critical buckling during shale gas hydraulic fracturing. Journal of petroleum science and engineering [online], 185, article ID 106655. Available from: https://doi.org/10.1016/j.petrol.2019.106655

Casing deformation during volume fracturing in shale gas horizontal wells is caused by both existing and induced stresses. These stresses jointly alter and compound the stress field around the casing leading to inefficient well stimulation as planned... Read More about Prediction of casing critical buckling during shale gas hydraulic fracturing..

Bayesian network structure learning with integer programming: polytopes, facets and complexity. (2017)
Journal Article
CUSSENS, J., JĂ„RVISALO, M., KORHONEN, J.H. and BARTLETT, M. 2017. Bayesian network structure learning with integer programming: polytopes, facets and complexity. Journal of artificial intelligence research [online], 58, pages 185-229. Available from: https://doi.org/10.1613/jair.5203

The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research. Recent years have witnessed several major algorithmic advances in structure learning for Bayesian networks - arguably the... Read More about Bayesian network structure learning with integer programming: polytopes, facets and complexity..

Integer linear programming for the Bayesian network structure learning problem. (2015)
Journal Article
BARTLETT, M. and CUSSENS, J. 2017. Integer linear programming for the Bayesian network structure learning problem. Artificial intelligence [online], 244, pages 258-271. Available from: https://doi.org/10.1016/j.artint.2015.03.003

Bayesian networks are a commonly used method of representing conditional probability relationships between a set of variables in the form of a directed acyclic graph (DAG). Determination of the DAG which best explains observed data is an NP-hard prob... Read More about Integer linear programming for the Bayesian network structure learning problem..