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Mineral and hydrocarbon exploration relies heavily on geological and geotechnical information extracted from drill cores. Traditional drill-core characterization is based purely on the subjective expertise of a geologist. New technologies can provide automatic mineral analysis and high-resolution drill core images in a non-destructive manner. However, automated rock mass characterization presents a significant challenge due to its lack of generalization and robustness. To date, the automated estimation of rock quality designation (RQD), a key parameter for rock mass classification, is based mostly on digital image processing techniques with significant user biases. Alternatively, we propose using computer vision and machine learning-based algorithms for drill core characterization using drill core images to determine the RQD. A convolutional neural network (CNN) is used to detect and classify intact and non-intact cores, and to filter out empty tray areas and non-rock objects present in the core trays. The model calculates the length of the detected intact cores and estimates the RQD. We train the CNN model with thousands of sandstone core images from different drill holes in South Australia. The proposed method is tested on 540 sandstone core rows and 90 limestone core rows (~ 1 m each), which produces average error rates of 2.58% and 3.17%, respectively. Highlights: Core tray images are automatically analyzed for estimating rock quality designation. A convolutional neural network for core image classification is developed. The proposed approach is tested on sandstone and limestone images and results in an error rate of 3% approximately. [ABSTRACT FROM AUTHOR]

Background: delivering appropriate care for patients with multimorbidity and polypharmacy is increasingly challenging. Challenges for individual healthcare professions are known, but only little is known about overall healthcare team implementation of best practice for these patients. Objective: to explore current approaches to multimorbidity management, and perceived barriers and enablers to deliver appropriate medications management for community-dwelling patients with multimorbidity and polypharmacy, from a broad range of healthcare professional (HCP) perspectives in Australia. Methods: this qualitative study used semi-structured interviews to gain in-depth understanding of HCPs' perspectives on the management of multimorbidity and polypharmacy. The interview guide was based on established principles for the management of multimorbidity in older patients. HCPs in rural and metropolitan Victoria and South Australia were purposefully selected to obtain a maximum variation sample. Twenty-six HCPs, from relevant medical, dentistry, nursing, pharmacy and allied health backgrounds, were interviewed between October 2013 and February 2014. Fourteen were prescribers and 12 practiced in primary care. Interviews were digitally audio-taped, transcribed verbatim and analysed using a constant comparison approach. Results: most participants did not routinely use structured approaches to incorporate patients' preferences in clinical decision-making, address conflicting prescriber advice, assess patients' adherence to treatment plans or seek to optimise care plans. Most HCPs were either unaware of medical decision aids and measurements tools to support these processes or disregarded them as not being user-friendly. Challenges with coordination and continuity of care, pressures of workload and poorly defined individual responsibilities for care, all contributed to participants' avoiding ownership of multimorbidity management. Potential facilitators of improved care related to improved culture, implementation of electronic health records, greater engagement of pharmacists, nurses and patients, families in care provision, and the use of care coordinators. Conclusion: extensive shortcomings exist in team-based care for the management of multimorbidity. Delegating coordination and review responsibilities to specified HCPs may support improved overall care. [ABSTRACT FROM AUTHOR]