The technology used in renewable energy production is resulting in a material increase in the demand for many minerals and metals. While the mining industry contributes to global carbon dioxide emissions, the industry is also critical to lowering global carbon emissions across the broader economy. Here we test the impact of a hypothetical international carbon taxation regime on a subsection of the mining industry compared to other sectors. A financial model was developed to calculate the cost of carbon taxes for 23 commodities across three industries. The findings show that, given any level of taxation tested, most mining industry commodities would not add more than 30% of their present product value. Comparatively, commodities such as coal could be taxed at more than 150% of their current product value under more intense carbon pricing initiatives, thereby accelerating the transition to renewable energy sources and the consequent demand benefits for mined metals.
Coincidence counting methods were used to examine the desorption of secondary ions from a CsI surface via keV atomic and polyatomic projectile impacts. A correlation between the emission of I− and CsI−2 secondary ions was attributed to the common chemical origin of the ions. The degree to which I− and CsI−2 were correlated was observed to change as a function of the kinetic energy and complexity of the primary ion as well as the yield of I−. This is attributed to a change in the relative importance of competing ion formation processes as a function of the energy in the desorption site.
The use of machine learning (ML) has become more widespread in many areas of consumer financial services, including credit underwriting and pricing of loans. ML’s ability to automatically learn nonlinearities and interactions in training data is perceived to facilitate faster and more accurate credit decisions, and ML is now a viable challenger to traditional credit modeling methodologies. In this mini review, we further the discussion of ML in consumer finance by proposing uniform definitions of key ML and legal concepts related to discrimination and interpretability. We use the United States legal and regulatory environment as a foundation to add critical context to the broader discussion of relevant, substantial, and novel ML methodologies in credit underwriting, and we review numerous strategies to mitigate the many potential adverse implications of ML in consumer finance.
Background With the rates of non-alcoholic fatty liver disease (NAFLD) on the rise, the necessity of identifying patients at risk of cirrhosis and its complications is becoming ever more important. Liver biopsy remains the gold standard for assessing fibrosis, although the costs, risks, and availability prohibit its widespread use for at-risk patients. Fibroscan has proven to be a non-invasive and accurate way of assessing fibrosis, although the availability of this modality is often limited in the primary care setting. The Fibrosis-4 (FIB-4) and Non-Alcoholic Fatty Liver Disease Fibrosis Score (NFS) are scoring systems which incorporate commonly measured lab parameters and BMI to predict fibrosis. In this study, we compared FIB-4 and NFS values to fibroscan scores to assess the accuracy of these inexpensive and readily available scoring systems for detecting fibrosis. Aims The aim of this study was to determine if non-invasive and inexpensive scoring systems (FIB-4 and NFS) can be used to rule out fibrosis in non-alcoholic fatty liver disease with comparable efficacy to fibroscan. Ultimately, we aim to demonstrate that these scoring systems can be used as an alternative to fibroscan in some patients. Methods Data was collected from 317 patient charts from the Vancouver General Hepatology Clinic. 93 patients were removed from the study due to insufficient data (missing Fibroscan score or lab work necessary for FIB-4/NFS). For the remaining 224 patients, FIB-4 and NFS were calculated and compared to fibrosis scores both independently and in combination. Results: Using a NFS score cut-off of -1.455 and a fibroscan score cut-off of ≥8.7kPa, the NFS had a sensitivity of 71.9%, a specificity of 75%, and a negative predictive value of 94.1%. For a fibroscan score cut-off of ≥8.0kPa, the NFS had a sensitivity of 66.7%, a specificity of 75.7%, and a negative predictive value of 91.5%. Using a fibroscan score cut-off of ≥8.7kPa, the FIB-4 score had a sensitivity of 53.1%, specificity of 84.9%, and a negative predictive value of 91.6%. For a cut-off of ≥8.0kPa, it had a sensitivity of 51.3%, and 85.9%, and a negative predictive value of 89.3%. Conclusions: The NFS and FIB-4 are non-invasive scoring systems that have high sensitivity and negative predictive value for fibrosis when compared to fibroscan scores. These findings suggest that the NFS and FIB-4 can provide adequate reassurance to rule-out fibrosis in select patients, and has promising use in the primary care setting where fibroscan access is often limited. Funding Agencies None
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