Preliminary Grade and Volume Model of Alluvial Sn-Au Placers

Summary The following preliminary points are made during this study of Sn-Au placer deposits: Grade and volume model of Sn-Au placers is developed from 63 deposits found worldwide The model is preliminary because: (1) it includes some highly uncertain data, (2) most S.E. Asia deposits are absent, (3) significant departure from the expected lognormal distribution in volume is found, and (4) data are from deposits worked using small-volume and large-volume methods The model is for use in mineral resource assessment Sn grades and deposit volumes are not significantly correlated

Current Concepts and Trends in Human-Automation Interaction

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The purpose of this panel was to provide a general overview and discussion of some of the most current and controversial concepts and trends in human-automation interaction. The panel was composed of eight researchers and practitioners. The panelists are well-known experts in the area and offered differing views on a variety of different human-automation topics. The range of concepts and trends discussed in this panel include: general taxonomies regarding stages and levels of automation and function allocation, individualized adaptive automation, automation-induced complacency, economic rationality and the use of automation, the potential utility of false alarms, the influence of different types of false alarms on trust and reliance, and a system-wide theory of trust in multiple automated aids

THE EFFECTS OF AUTOMATION EXPERTISE AND SYSTEM CONFIDENCE ON TRUST BEHAVIORS

Trust in automation is more likely to be appropriate when information about the automation's capability is available. The goal of this study was to determine how automation expertise and system confidence affected automation trust behaviors. Forty-one participants completed a target detection task while receiving advice from an imperfect diagnostic aid that varied in expertise (expert vs. novice) and confidence (75% vs. 50% vs. 25%, no aid). Results showed that participants were more willing to comply with the highly confident expert aid than the highly confident novice aid. Furthermore, participants were more apt to generate false alarms as system confidence increased. These results suggest that, similar to interpersonal relationships, humans appraise automation features such as confidence and expertise when deciding to comply with automation. Implications and direction for future research are discussed

Sources of pro-cyclicality in east Asian financial systems

Procyclicality is a normal feature of economic systems, but financial sector weaknesses can exacerbate it sufficiently to pose a threat to macroeconomic and financial stability. These include shortcomings in bank risk management and governance, in supervision and in terms of dependence on volatile sources of funds. The paper tests econometrically for the importance of such features leading to pro-cyclicality in the financial systems of 11 East Asian countries. This analysis makes it possible to identify specific policy measures for East Asian countries that could limit the extent to which financial systems exacerbate pro-cyclicality

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p