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Capturing carbon dioxide (CO(2)) emissions from industrial sources and injecting the emissions deep underground in geologic formations is one method being considered to control CO(2) concentrations in the atmosphere. Sequestering CO(2) underground has its own set of environmental risks, including the potential migration of CO(2) out of the storage reservoir(More)
Clustering is a fundamental machine learning application, which partitions data into homogeneous groups. K-means and its variants are the most widely used class of clustering algorithms today. However, the original k-means algorithm can only be applied to numeric data. For categorical data, the data has to be converted into numeric data through 1-of-K(More)
The risk of CO(2) leakage from a properly permitted deep geologic storage facility is expected to be very low. However, if leakage occurs it could potentially impact potable groundwater quality. Dissolved CO(2) in groundwater decreases pH, which can mobilize naturally occurring trace metals commonly contained in aquifer sediments. Observing such processes(More)
To improve treatment outcomes in non-small cell lung cancer (NSCLC), preclinical models that can better predict individual patient response to novel therapies are urgently needed. Using freshly resected tumor tissue, we describe an optimized ex vivo explant culture model that enables concurrent evaluation of NSCLC response to therapy while maintaining the(More)
Silhouette is one of the most popular and effective internal measures for the evaluation of clustering validity. Simplified Silhouette is a computationally simplified version of Silhouette. However, to date Simplified Silhouette has not been systematically analysed in a specific clustering algorithm. This paper analyses the application of Simplified(More)
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