The adoption of market-based instruments for resource management: Three case studies

Market-based instruments (MBIs) for resource management create financial incentives for people and businesses to use resources more efficiently, within a regulatory context designed to ensure that ecological, social and cultural objectives are also met. Three case studies were done to identify factors influencing the adoption or rejection of market-based instruments in New Zealand. Case studies included Individual Transferable Quota (ITQ) for New Zealand's inshore fisheries, Transferable Water Permits (TWPs) in Tasman District and Waikato Region, and charges for occupation of coastal space at both the national and regional levels in New Zealand. This paper provides a summary of findings from these case studies. These include: MBIs are difficult to implement if they threaten the position of existing users. It is important to have clear objectives. Norms and values can be an obstacle to MBIs, especially where they help to protect the interests of key stakeholders, but value-based opposition can be overcome if practical concerns are addressed.market-based instruments, ITQ, transferable water permits, coastal occupation charges, Agribusiness, Agricultural and Food Policy, Consumer/Household Economics, Crop Production/Industries, Environmental Economics and Policy, Farm Management,

Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography

We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on deep learning, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based on the design and selection/extraction of hand-crafted radiomics features, followed by the classifier learning process. Experimental results, based on a dataset of 48 annotated chest CTs, show that the deep learning model produces a mean 5-year mortality prediction accuracy of 68.5%, while radiomics produces a mean accuracy that varies between 56% to 66% (depending on the feature selection/extraction method and classifier). The successful development of the proposed models has the potential to make a profound impact in preventive and personalised healthcare.Comment: 9 page