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We describe an ultra-wide-bandwidth, low-frequency receiver recently installed on the Parkes radio telescope. The receiver system provides continuous frequency coverage from 704 to 4032 MHz. For much of the band (
), the system temperature is approximately 22 K and the receiver system remains in a linear regime even in the presence of strong mobile phone transmissions. We discuss the scientific and technical aspects of the new receiver, including its astronomical objectives, as well as the feed, receiver, digitiser, and signal processor design. We describe the pipeline routines that form the archive-ready data products and how those data files can be accessed from the archives. The system performance is quantified, including the system noise and linearity, beam shape, antenna efficiency, polarisation calibration, and timing stability.
The science of studying diamond inclusions for understanding Earth history has developed significantly over the past decades, with new instrumentation and techniques applied to diamond sample archives revealing the stories contained within diamond inclusions. This chapter reviews what diamonds can tell us about the deep carbon cycle over the course of Earth’s history. It reviews how the geochemistry of diamonds and their inclusions inform us about the deep carbon cycle, the origin of the diamonds in Earth’s mantle, and the evolution of diamonds through time.
Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.
To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.
Clinical isolates of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumoniae were obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.
Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.
Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.
Under the European Union’s Solvency II regulations, insurance firms are required to use a one-year VaR (Value at Risk) approach. This involves a one-year projection of the balance sheet and requires sufficient capital to be solvent in 99.5% of outcomes. The Solvency II Internal Model risk calibrations require annual changes in market indices/term structure for the estimation of risk distribution for each of the Internal Model risk drivers. This presents a significant challenge for calibrators in terms of:
Robustness of the calibration that is relevant to the current market regimes and at the same time able to represent the historically observed worst crisis;
Stability of the calibration model year on year with arrival of new information.
The above points need careful consideration to avoid credibility issues with the Solvency Capital Requirement (SCR) calculation, in that the results are subject to high levels of uncertainty.
For market risks, common industry practice to compensate for the limited number of historic annual data points is to use overlapping annual changes. Overlapping changes are dependent on each other, and this dependence can cause issues in estimation, statistical testing, and communication of uncertainty levels around risk calibrations.
This paper discusses the issues with the use of overlapping data when producing risk calibrations for an Internal Model. A comparison of the overlapping data approach with the alternative non-overlapping data approach is presented. A comparison is made of the bias and mean squared error of the first four cumulants under four different statistical models. For some statistical models it is found that overlapping data can be used with bias corrections to obtain similarly unbiased results as non-overlapping data, but with significantly lower mean squared errors. For more complex statistical models (e.g. GARCH) it is found that published bias corrections for non-overlapping and overlapping datasets do not result in unbiased cumulant estimates and/or lead to increased variance of the process.
In order to test the goodness of fit of probability distributions to the datasets, it is common to use statistical tests. Most of these tests do not function when using overlapping data, as overlapping data breach the independence assumption underlying most statistical tests. We present and test an adjustment to one of the statistical tests (the Kolmogorov Smirnov goodness-of-fit test) to allow for overlapping data.
Finally, we explore the methods of converting “high”-frequency (e.g. monthly data) to “low”-frequency data (e.g. annual data). This is an alternative methodology to using overlapping data, and the approach of fitting a statistical model to monthly data and then using the monthly model aggregated over 12 time steps to model annual returns is explored. There are a number of methods available for this approach. We explore two of the widely used approaches for aggregating the time series.
Myanmar is a country in political and economic transition. Facing a wide-variety of natural hazards and ongoing conflict, the country’s under-developed infrastructure has resulted in high disaster risk. Following the devastation of Cyclone Nargis in 2008 and increased global focus on disaster management and risk reduction, Myanmar has begun development of national disaster policies. Myanmar’s Action Plan for Disaster Risk Reduction addressed multiple stages of disaster development and has made progress towards national projects, however, has struggled to implement community-based preparedness and response initiatives. This article analyses Myanmar’s disaster strategy, though the use of a disaster development framework and suggests areas for possible improvement. In particular, the article aims to generate discussion regarding methods of supporting objective evaluation of risk reduction initiatives in developing countries. (Disaster Med Public Health Preparedness. 2018;12:422–426)
Excavations at the 109 hectare site of Kurd Qaburstan on the Erbil plain in the Kurdistan Region of Iraq were conducted by the Johns Hopkins University in 2013 and 2014. The Middle Bronze Age (Old Babylonian period) is the main period of occupation evident on the site, and the project therefore aims to study the character of a north Mesopotamian urban centre of the early second millennium b.c. On the high mound, excavations revealed three phases of Mittani (Late Bronze) period occupation, including evidence of elite residential architecture. On the low mound and the south slope of the high mound, Middle Bronze evidence included domestic remains with numerous ceramic vessels left in situ. Also dating to the Middle Bronze period is evidence of a city wall on the site edges. Later occupations include a cemetery, perhaps of Achaemenid date, on the south slope of the high mound and a Middle Islamic settlement on the southern lower town. Faunal and archaeobotanical analysis provide information on the plant and animal economy of the second millennium b.c. occupations, and geophysical results have documented a thirty-one hectare expanse of dense Middle Bronze Age architecture in the northern lower town.
We present low-frequency spectral energy distributions of 60 known radio pulsars observed with the Murchison Widefield Array telescope. We searched the GaLactic and Extragalactic All-sky Murchison Widefield Array survey images for 200-MHz continuum radio emission at the position of all pulsars in the Australia Telescope National Facility (ATNF) pulsar catalogue. For the 60 confirmed detections, we have measured flux densities in 20 × 8 MHz bands between 72 and 231 MHz. We compare our results to existing measurements and show that the Murchison Widefield Array flux densities are in good agreement.
Accurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.
Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximal N = 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of -0.49 and -0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.
Clavate (club-shaped) structures rimming mid-Cretaceous Burmese amber from Myanmar, previously misdiagnosed as fungal sporocarps, are shown to be domichnia (crypts) of martesiine bivalves (Pholadidae: Martesiinae). They are similar in form to Teredolites clavatus Leymerie, 1842 and Gastrochaenolites lapidicus Kelly & Bromley, 1984; however, the former identification is preferable, given that they are martesiine crypts in amber as opposed to a lithic substrate. Cross-cutting relationships between the clavate features and inclusions in the amber demonstrate that the features post-date hardening of the resin. The fills of the crypts are variable, including sand grade sediment of very fine to coarse sand grainsize, and sparry calcite cements. In some cases, the articulated valves of the pholadid bivalve responsible are visible inside the borings. However, one remarkable specimen contains two pairs of articulated shells ‘floating’ in amber, not associated with crypts; an observation that suggests that the resin was still liquid or soft when the bivalves were trapped in the resin. One individual is associated with an irregular sediment-filled feature and shows shell breakage. Formation of a solid rim around a liquid central volume has been documented in subaqueous bodies of resin in modern swamp forests, and argues for a close proximity between the amber-producing trees and a brackish water habitat for the bivalves. The presence of pyrite as thin films and crystal groups within Burmese amber is further consistent with such a depositional environment. Comparison of the size of pholadid body fossils with growth rates of modern equivalents allows the duration of boring activities to be estimated and suggests that small fossil pholadids in Burmese amber became trapped and died within 1–2 weeks of having settled on the resin. Larger examples present within well-formed domichnia formed in hardened resin. Since ‘hardground’ describes early lithified sediment as a substrate and ‘woodground’ describes wood as a substrate, the term ‘amberground’ is used here to described borings in an amber substrate.
In their 2013 book Reimagining Business History, Philip Scranton and Patrick Fridenson called on business historians to reassess militarization and the “two-way exchanges” between the military and the private sector. The call is timely. The extensive business-historical scholarship on the relationship between companies and war sensibly focuses on companies that profited from their involvement in the military-industrial complex.1 The business-historical literature is virtually silent, however, on the role of business in preventing wars from starting in the first place. In other words, business historians have missed a productive opportunity to engage with capitalist peace theory (CPT), an increasingly important theory in the discipline of international relations (IR). Many IR scholars now argue that the mutual economic interdependence characteristic of global capitalism reduces the likelihood of war. Their research suggests that while extensive cross-border economic linkages do not preclude the possibility of war, the creation of a transnational community of economic interests tends, ceteris paribus, to reduce the frequency, duration, and intensity of warfare.2