Optimization of green ammonia distribution systems for intercontinental energy transport.

Green ammonia is a promising hydrogen derivative which enables intercontinental transport of dispatchable renewable energy. This research describes the development of a model which optimizes a global green ammonia network, considering the costs of production, storage, and transport. In generating the model, we show economies of scale for green ammonia production are small beyond 1 million tonnes per annum (MMTPA), although benefits accrue up to a production rate of 10 MMTPA if a production facility is serviced by a new port or requires a long pipeline.

Bayesian networks for clinical decision support in lung cancer care.

Survival prediction and treatment selection in lung cancer care are characterised by high levels of uncertainty. Bayesian Networks (BNs), which naturally reason with uncertain domain knowledge, can be applied to aid lung cancer experts by providing personalised survival estimates and treatment selection recommendations. Based on the English Lung Cancer Database (LUCADA), we evaluate the feasibility of BNs for these two tasks, while comparing the performances of various causal discovery approaches to uncover the most feasible network structure from expert knowledge and data.

Survival prediction and treatment recommendation with Bayesian techniques in lung cancer.

In this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm achieved an area under the curve value of 81%, outperforming the Bayesian Networks learned by the M(3) and K2 structure learning algorithms.

Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction.