A new period of volcanogenic massive sulfide formation in the Yilgarn: A volcanological study of the ca 2.76 Ga Hollandaire VMS deposit, Yilgarn Craton, Western Australia

The Archean Hollandaire volcanogenic massive sulfide deposit is a felsic–siliciclastic VMS deposit located in the Murchison Domain of the Youanmi Terrane, Yilgarn Craton, Western Australia. It is hosted in a succession of turbidites, mudstones and coherent rhyodacite sills and has been metamorphosed to upper greenschist/lower amphibolite facies and includes a pervasive S1 deformational fabric. The coherent rhyodacitic sills are interpreted as syndepositional based on geochemical similarities with well-known VMS-associated felsic rocks and similar foliations to the metasediments. We offer several explanations for the absence of textural evidence (e.g. breccias) for syn-depositional origins: 1) the subaqueous sediments were dehydrated by long-lived magmatism such that no pore-water remained to drive quench fragmentation; 2) pore-space occlusion by burial and/or, 3) alteration overprinting and obscuring of primary breccias at contact margins. Mineralisation occurs by sub-seafloor replacement of original host rocks in two ore bodies, Hollandaire Main (~125 x >500 m and ~8 m thick) and Hollandaire West (~100 x 470 m and ~5 m thick), and occurs in three main textural styles, massive sulfides, which are exclusively hosted in turbidites and mudstones, and stringer and disseminated sulfides, which are also hosted in coherent rhyodacite. Most sulfides have textures consistent with remobilisation and recrystallisation. Hydrothermal metamorphism has altered the hangingwall and footwall to similar degrees, with significant gains in Mg, Mn and K and losses in Na, Ca and Sr. Garnet and staurolite porphyryoblasts also exhibit a footprint around mineralisation, extending up to 30 m both above and below the ore zone. High precision thermal ionisation mass spectrometry of zircons extracted from the coherent rhyodacite yield an age of 2759.5 ± 0.9 Ma, which along with geochemical comparisons, places the succession within the 2760–2735 Ma Greensleeves Formation of the Polelle Group of the Murchison Supergroup. Geochemical and geochronological evidence link the coherent rhyodacite sills to the Peter Well Granodiorite pluton ~2 km to the W, which acted as the heat engine driving hydrothermal circulation during VMS mineralisation. This study highlights the importance of both: detailed physical volcanological studies from which an accurate assessment of timing relationships, particularly the possibility of intrusions dismembering ore horizons, can be made; and identifying synvolcanic plutons and other similar suites, for VMS exploration targets in the Youanmi Terrane and worldwide.

Machine learning approaches to analysing textual injury surveillance data: A systematic review

Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.

Biomimicking natural nanopatterned topology by 3D laser lithography

Natural nanopatterned surfaces (nNPS) present on insect wings have demonstrated bactericidal activity [1, 2]. Fabricated nanopatterned surfaces (fNPS) derived by characterization of these wings have also shown superior bactericidal activity [2]. However bactericidal NPS topologies vary in both geometry and chemical characteristics of the individual features in different insects and fabricated surfaces, rendering it difficult to ascertain the optimum geometrical parameters underling bactericidal activity. This situation calls for the adaptation of new and emerging techniques, which are capable of fabricating and characterising comparable structures to nNPS from biocompatible materials. In this research, CAD drawn nNPS representing an area of 10 μm x10 μm was fabricated on a fused silica glass by Nanoscribe photonic professional GT 3D laser lithography system using two photon polymerization lithography. The glass was cleaned with acetone and isopropyl alcohol thrice and a drop of IP-DIP photoresist from Nanoscribe GmbH was cast onto the glass slide prior to patterning. Photosensitive IP-DIP resist was polymerized with high precision to make the surface nanopatterns using a 780 nm wavelength laser. Both moving-beam fixedsample (MBFS) and fixed-beam moving-sample (FBMS) fabrication approaches were tested during the fabrication process to determine the best approach for the precise fabrication of the required nanotopological pattern. Laser power was also optimized to fabricate the required fNPS, where this was changed from 3mW to 10mW to determine the optimum laser power for the polymerization of the photoresist for fabricating FNPS...

Building instance knowledge network for word sense disambiguation

In this paper, a new high precision focused word sense disambiguation (WSD) approach is proposed, which not only attempts to identify the proper sense for a word but also provides the probabilistic evaluation for the identification confidence at the same time. A novel Instance Knowledge Network (IKN) is built to generate and maintain semantic knowledge at the word, type synonym set and instance levels. Related algorithms based on graph matching are developed to train IKN with probabilistic knowledge and to use IKN for probabilistic word sense disambiguation. Based on the Senseval-3 all-words task, we run extensive experiments to show the performance enhancements in different precision ranges and the rationality of probabilistic based automatic confidence evaluation of disambiguation. We combine our WSD algorithm with five best WSD algorithms in senseval-3 all words tasks. The results show that the combined algorithms all outperform the corresponding algorithms.

Accelerating Pseudo-Marginal MCMC using Gaussian Processes

Pseudo-marginal methods such as the grouped independence Metropolis-Hastings (GIMH) and Markov chain within Metropolis (MCWM) algorithms have been introduced in the literature as an approach to perform Bayesian inference in latent variable models. These methods replace intractable likelihood calculations with unbiased estimates within Markov chain Monte Carlo algorithms. The GIMH method has the posterior of interest as its limiting distribution, but suffers from poor mixing if it is too computationally intensive to obtain high-precision likelihood estimates. The MCWM algorithm has better mixing properties, but less theoretical support. In this paper we propose to use Gaussian processes (GP) to accelerate the GIMH method, whilst using a short pilot run of MCWM to train the GP. Our new method, GP-GIMH, is illustrated on simulated data from a stochastic volatility and a gene network model.

Wide area kinematic positioning with BeiDou triple frequency signals

A key challenge of wide area kinematic positioning is to overcome the effects of the varying hardware biases in code signals of the BeiDou system. Based on three geometryfree/ionosphere-free combinations, the elevation-dependent code biases are modelled for all BeiDou satellites. Results from the data sets of 30-day for 5 baselines of 533 to 2545 km demonstrate that the wide-lane (WL) integer-fixing success rates of 98% to 100% can be achieved within 25 min. Under the condition of HDOP of less than 2, the overall RMS statistics show that ionospheric-free WL single-epoch solutions achieve 24 to 50 cm in the horizontal direction. Smoothing processing over the moving window of 20 min reduces the RMS values by a factor of about 2. Considering distance-independent nature, the above results show the potential that reliable and high precision positioning services could be provided in a wide area based on a sparsely distributed ground network.