Validity of the Holmes–Wright lantern as a color vision test for the rail industry

AbstractA simulated field test was designed to determine whether the Holmes–Wright A lantern (HWA) is a valid color vision test for the rail industry. The simulation replicated viewing rail signal lights at 0.8 km distance under daylight conditions. Using the worst-normal as the maximum number of allowable errors on the simulation, 94% of the color-defectives failed both tests on the first trial and 92% failed at the second session. The HWA had a higher false negative rate than a false alarm rate. The majority of individuals who had discrepancies on the two tests were mild deutans. Results from the Ishihara test were marginally better at predicting performance on the simulation

Toward source region tomography with inter-source interferometry: Shear wave velocity from 2018 West Bohemia swarm earthquakes

The concept of seismic interferometry embraces the construction of waves traveling between receivers or sources with cross‐correlation techniques. In the present study cross correlations of coda waves are used to measure traveltimes of shear waves between earthquake locations for five event clusters of the 2018 West Bohemia earthquake swarm. With the help of a high‐quality earthquake catalog, I was able to determine the shear wave velocity in the region of the five clusters separately. The shear wave velocities range between 3.5 and 4.2 km/s. The resolution of this novel method is given by the extent of the clusters and better than for a comparable classical tomography. It is suggested to use the method in a tomographic inversion and map the shear wave velocity in the source region with unprecedented resolution. Furthermore, the influence of focal mechanisms and the attenuation properties on the polarity and location of the maxima in the cross‐correlation functions is discussed. The intracluster ratio of P wave to S wave velocity is approximately fixed at 1.68

Temporal trends in mode, site and stage of presentation with the introduction of colorectal cancer screening: a decade of experience from the West of Scotland

background:  Population colorectal cancer screening programmes have been introduced to reduce cancer-specific mortality through the detection of early-stage disease. The present study aimed to examine the impact of screening introduction in the West of Scotland. methods:  Data on all patients with a diagnosis of colorectal cancer between January 2003 and December 2012 were extracted from a prospectively maintained regional audit database. Changes in mode, site and stage of presentation before, during and after screening introduction were examined. results:  In a population of 2.4 million, over a 10-year period, 14 487 incident cases of colorectal cancer were noted. Of these, 7827 (54%) were males and 7727 (53%) were socioeconomically deprived. In the postscreening era, 18% were diagnosed via the screening programme. There was a reduction in both emergency presentation (20% prescreening vs 13% postscreening, P0.001) and the proportion of rectal cancers (34% prescreening vs 31% pos-screening, P0.001) over the timeframe. Within non-metastatic disease, an increase in the proportion of stage I tumours at diagnosis was noted (17% prescreening vs 28% postscreening, P0.001). conclusions:  Within non-metastatic disease, a shift towards earlier stage at diagnosis has accompanied the introduction of a national screening programme. Such a change should lead to improved outcomes in patients with colorectal cancer

Sharp transition towards shared vocabularies in multi-agent systems

What processes can explain how very large populations are able to converge on the use of a particular word or grammatical construction without global coordination? Answering this question helps to understand why new language constructs usually propagate along an S-shaped curve with a rather sudden transition towards global agreement. It also helps to analyze and design new technologies that support or orchestrate self-organizing communication systems, such as recent social tagging systems for the web. The article introduces and studies a microscopic model of communicating autonomous agents performing language games without any central control. We show that the system undergoes a disorder/order transition, going trough a sharp symmetry breaking process to reach a shared set of conventions. Before the transition, the system builds up non-trivial scale-invariant correlations, for instance in the distribution of competing synonyms, which display a Zipf-like law. These correlations make the system ready for the transition towards shared conventions, which, observed on the time-scale of collective behaviors, becomes sharper and sharper with system size. This surprising result not only explains why human language can scale up to very large populations but also suggests ways to optimize artificial semiotic dynamics.Comment: 12 pages, 4 figure

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

© 2018 The Author(s) Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer's accuracy of 98.8% (errors of omissions = 1.2%), and user's accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer's accuracy of 80% (errors of omissions = 20%), and user's accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA's Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated fromsuch products. Thereby, the overarching goal of this study was to develop a high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, 10 time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the three timeperiods over 12 months (monsoon: Days of the Year (DOY) 151–300; winter: DOY 301–365 plus 1–60; and summer: DOY 61–150), taking the every 8-day data from Landsat-8 and 7 for the years 2013–2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the five agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledgebase for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N = 2179) in five AEZs. The classification was performed on GEE for each of the five AEZs using well-established knowledge-base and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N = 1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full-resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, atwww.croplands. org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

Systematic event generator tuning for the LHC

In this article we describe Professor, a new program for tuning model parameters of Monte Carlo event generators to experimental data by parameterising the per-bin generator response to parameter variations and numerically optimising the parameterised behaviour. Simulated experimental analysis data is obtained using the Rivet analysis toolkit. This paper presents the Professor procedure and implementation, illustrated with the application of the method to tunes of the Pythia 6 event generator to data from the LEP/SLD and Tevatron experiments. These tunes are substantial improvements on existing standard choices, and are recommended as base tunes for LHC experiments, to be themselves systematically improved upon when early LHC data is available.Comment: 28 pages. Submitted to European Physical Journal C. Program sources and extra information are available from http://projects.hepforge.org/professor

Effects of partial triple excitations in atomic coupled cluster calculations

In this article we study the effects of higher body excitations in the relativistic CC calculations for atoms and ions with one valence electron using Fock-space CCSD, CCSD(T) and its unitary variants. The present study demonstrates that CCSD(T) estimates the ionization potentials (IPs) and the valence electron removal energies quite accurately for alkali atoms and singly ionized alkaline earth ions, but yields unphysical energy levels for atoms and/or ions with partially filled sub-shell like C II. We further demonstrate that the higher body excitation effects can be incorporated more effectively through the unitary coupled cluster theory (UCC) compared to the CCSD(T) method.Comment: 5 EPS figures, Latex 2