Exploratory analysis of multivariate drill core time series measurements

Demand for mineral resources is increasing, necessitating exploitation of lower grade and more heterogeneous orebodies. The high variability inherent in such orebodies leads to an increase in the cost, complexity and environmental footprint associated with mining and mineral processing. Enhanced knowledge of orebody characteristics is thus vital for mining companies to optimize profitability. We present a pilot study to investigate prediction of geometallurgical variables from drill sensor data. A comparison is made of the performance of multilayer perceptron (MLP) and multiple linear regression models (MLR) for predicting a geometallurgical variable. This comparison is based on simulated data that are physically realistic, having been derived from models fitted to the one available drill core. The comparison is made in terms of the mean and standard deviation (over repeated samples from the population) of the mean absolute error, root mean square error, and coefficient of determination. The best performing model depends on the form of the response variable and the sample size. The standard deviation of performance measures tends to be higher for the MLP, and MLR appears to offer a more consistent performance for the test cases considered. References R. M. Balabin and S. V. Smirnov. Interpolation and extrapolation problems of multivariate regression in analytical chemistry: Benchmarking the robustness on near-infrared (NIR) spectroscopy data”. Analyst 137.7 (2012), pp. 1604–1610. doi: 10.1039/c2an15972d C. M. Bishop. Pattern recognition and machine learning. Springer, 2006. url: https://link.springer.com/book/9780387310732 J. B. Boisvert, M. E. Rossi, K. Ehrig, and C. V. Deutsch. Geometallurgical modeling at Olympic dam mine, South Australia”. Math. 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Well-being, turnover intention, and stigma attitudes of mental health transition-to-practice nurses : A cross-sectional study

There is global recognition that mental health nursing can be stressful and have detrimental effects on nurses' well-being and retention. With substantial nursing shortages, there is an urgent need to attract and retain nurses to sustain this workforce and provide effective mental healthcare. Mental health transition programs provide vital recruitment pathways and support novice registered nurses, enrolled nurses and experienced registered generalist nurses moving into this field. There is little evidence, however, on the well-being, resilience, and retention of nurses transitioning into mental health. The primary aims for this cross-sectional study were to describe demographic characteristics, perceived stress, well-being, resilience, mental illness stigma attitudes, work satisfaction, and turnover intention of four nurse cohorts entering mental health transition programs: generalist registered nurses, graduate and post-graduate registered nurses, and enrolled nurses; to explore relationships between these variables; and explore differences between these four nurse cohorts. Findings (n = 87) included overall moderate perceived stress, moderate well-being and resilience, high work satisfaction, low stigma, and low turnover intention. Higher turnover intention was associated with lower age and work satisfaction, and higher perceived stress. Generalist RNs had significantly higher stress and stigmatizing attitudes than Enrolled Nurses. Secondary analysis of well-being scores identified 14 nurses with scores indicating depression, with significantly lower resilience and work satisfaction, and significantly higher stress than the rest of the sample. To help prevent attrition, it is vital that mental health services provide tailored well-being initiatives during transition and intervene early to provide support for nurses with mental distress

An adaptive two-arm clinical trial using early endpoints to inform decision making : design for a study of sub-acromial spacers for repair of rotator cuff tendon tears

Background There is widespread concern across the clinical and research communities that clinical trials, powered for patient-reported outcomes, testing new surgical procedures are often expensive and time-consuming, particularly when the new intervention is shown to be no better than the standard. Conventional (non-adaptive) randomised controlled trials (RCTs) are perceived as being particularly inefficient in this setting. Therefore, we have developed an adaptive group sequential design that allows early endpoints to inform decision making and show, through simulations and a worked example, that these designs are feasible and often preferable to conventional non-adaptive designs. The methodology is motivated by an ongoing clinical trial investigating a saline-filled balloon, inserted above the main joint of the shoulder at the end of arthroscopic debridement, for treatment of tears of rotor cuff tendons. This research question and setting is typical of many studies undertaken to assess new surgical procedures. Methods Test statistics are presented based on the setting of two early outcomes, and methods for estimation of sequential stopping boundaries are described. A framework for the implementation of simulations to evaluate design characteristics is also described. Results Simulations show that designs with one, two and three early looks are feasible and, with appropriately chosen futility stopping boundaries, have appealing design characteristics. A number of possible design options are described that have good power and a high probability of stopping for futility if there is no evidence of a treatment effect at early looks. A worked example, with code in R, provides a practical demonstration of how the design might work in a real study. Conclusions In summary, we show that adaptive designs are feasible and could work in practice. We describe the operating characteristics of the designs and provide guidelines for appropriate values for the stopping boundaries for the START:REACTS (Sub-acromial spacer for Tears Affecting Rotator cuff Tendons: a Randomised, Efficient, Adaptive Clinical Trial in Surgery) study

Supervoid Origin of the Cold Spot in the Cosmic Microwave Background

We use a WISE-2MASS-Pan-STARRS1 galaxy catalog to search for a supervoid in the direction of the Cosmic Microwave Background Cold Spot. We obtain photometric redshifts using our multicolor data set to create a tomographic map of the galaxy distribution. The radial density profile centred on the Cold Spot shows a large low density region, extending over 10's of degrees. Motivated by previous Cosmic Microwave Background results, we test for underdensities within two angular radii, $5^\circ$, and $15^\circ$. Our data, combined with an earlier measurement by Granett et al 2010, are consistent with a large $R_{\rm void}=(192 \pm 15)h^{-1} Mpc$ $(2\sigma)$ supervoid with $\delta \simeq -0.13 \pm 0.03$ centered at $z=0.22\pm0.01$. Such a supervoid, constituting a $\sim3.5 \sigma$ fluctuation in the $\Lambda CDM$ model, is a plausible cause for the Cold Spot.Comment: 4 pages, 2 figures, Proceedings of IAU 306 Symposium: Statistical Challenges in 21st Century Cosmolog

The Cold Spot in the Cosmic Microwave Background: the Shadow of a Supervoid

Standard inflationary hot big bang cosmology predicts small fluctuations in the Cosmic Microwave Background (CMB) with isotropic Gaussian statistics. All measurements support the standard theory, except for a few anomalies discovered in the Wilkinson Microwave Anisotropy Probe maps and confirmed recently by the Planck satellite. The Cold Spot is one of the most significant of such anomalies, and the leading explanation of it posits a large void that imprints this extremely cold area via the linear Integrated Sachs-Wolfe (ISW) effect due to the decay of gravitational potentials over cosmic time, or via the Rees- Sciama (RS) effect due to late-time non-linear evolution. Despite several observational campaigns targeting the Cold Spot region, to date no suitably large void was found at higher redshifts z>0.3. Here we report the detection of an R=(192±15)h −1Mpc size supervoid of depth δ=−0.13±0.03, and centred at redshift z=0.22. This supervoid, possibly the largest ever found, is large enough to significantly affect the CMB via the non-linear RS effect, as shown in our Lemaitre-Tolman-Bondi framework. This discovery presents the first plausible explanation for any of the physical CMB anomalies, and raises the possibility that local large-scale structure could be responsible for other anomalies as well