Airborne Radiometric Mapping in the Mole Tableland, Northern New South Wales

Airborne radiometric mapping was undertaken in The Mole Tableland Area in Northern New South Wales. This work comprised the production of coloured maps of radiometric parameters such as total radiation count (TC), potassium concentration (K), equivalent uranium concentration (eU). and equivalent thorium concentration (eTh). Maps of ratio parameters namely eU eU/eTh, and eTh/K were also created. Two types of maps were produced from the airborne radiometric survey: maps of corrected data and of corrected-and-deconvoluted data. In general, maps of corrected data resemble the geological map whereas maps of corrected-and-deconvoluted data enhance patterns of local anomalies which represent an abundance of radioelements K, U, and Th. In a number of cases these may be correlated with areas of mineralization. An experimental ground radiometric investigation was also conducted. Over 70 kilometres was traversed on foot with a portable --1 -ray spectrometer, imitating the time-and-distance integration of the radiometric data. This was undertaken to provide correlation to the airborne data. The correlation coefficients obtained from the investigation were used for converting the normalized airborne radiometric counts to radioelement concentrations. Attenuation of radiometric signal due to soil cover or alluvial sediments was also studied. High eTh/K ratio values were found to be diagnostic of soil cover on a granite with high U and Th

Shock-induced transition of quartz to stishovite

The transformation of quartz to stishovite has been studied by X-ray and optical examination of a series of experimentally shock-loaded specimens of a quartz-copper mixture. Shock pressures of 68 to 260 kb and peak temperatures of 320° to 870°K were achieved. Stishovite was identified from quartz shock-loaded above 90 kb; the quantity increases with increasing pressure, but is not dependent on temperature. The formation of stishovite under shock conditions appears to be intimately related to a short-range order phase

Multi-generational oxidation model to simulate secondary organic aerosol in a 3-D air quality model

Multi-generational gas-phase oxidation of organic vapors can influence the abundance, composition and properties of secondary organic aerosol (SOA). Only recently have SOA models been developed that explicitly represent multi-generational SOA formation. In this work, we integrated the statistical oxidation model (SOM) into SAPRC-11 to simulate the multi-generational oxidation and gas/particle partitioning of SOA in the regional UCD/CIT (University of California, Davis/California Institute of Technology) air quality model. In the SOM, evolution of organic vapors by reaction with the hydroxyl radical is defined by (1) the number of oxygen atoms added per reaction, (2) the decrease in volatility upon addition of an oxygen atom and (3) the probability that a given reaction leads to fragmentation of the organic molecule. These SOM parameter values were fit to laboratory smog chamber data for each precursor/compound class. SOM was installed in the UCD/CIT model, which simulated air quality over 2-week periods in the South Coast Air Basin of California and the eastern United States. For the regions and episodes tested, the two-product SOA model and SOM produce similar SOA concentrations but a modestly different SOA chemical composition. Predictions of the oxygen-to-carbon ratio qualitatively agree with those measured globally using aerosol mass spectrometers. Overall, the implementation of the SOM in a 3-D model provides a comprehensive framework to simulate the atmospheric evolution of organic aerosol

Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 1: Assessing the influence of constrained multi-generational ageing

Multi-generational oxidation of volatile organic compound (VOC) oxidation products can significantly alter the mass, chemical composition and properties of secondary organic aerosol (SOA) compared to calculations that consider only the first few generations of oxidation reactions. However, the most commonly used state-of-the-science schemes in 3-D regional or global models that account for multi-generational oxidation (1) consider only functionalization reactions but do not consider fragmentation reactions, (2) have not been constrained to experimental data and (3) are added on top of existing parameterizations. The incomplete description of multi-generational oxidation in these models has the potential to bias source apportionment and control calculations for SOA. In this work, we used the statistical oxidation model (SOM) of Cappa and Wilson (2012), constrained by experimental laboratory chamber data, to evaluate the regional implications of multi-generational oxidation considering both functionalization and fragmentation reactions. SOM was implemented into the regional University of California at Davis / California Institute of Technology (UCD/CIT) air quality model and applied to air quality episodes in California and the eastern USA. The mass, composition and properties of SOA predicted using SOM were compared to SOA predictions generated by a traditional two-product model to fully investigate the impact of explicit and self-consistent accounting of multi-generational oxidation. Results show that SOA mass concentrations predicted by the UCD/CIT-SOM model are very similar to those predicted by a two-product model when both models use parameters that are derived from the same chamber data. Since the two-product model does not explicitly resolve multi-generational oxidation reactions, this finding suggests that the chamber data used to parameterize the models captures the majority of the SOA mass formation from multi-generational oxidation under the conditions tested. Consequently, the use of low and high NOx yields perturbs SOA concentrations by a factor of two and are probably a much stronger determinant in 3-D models than multi-generational oxidation. While total predicted SOA mass is similar for the SOM and two-product models, the SOM model predicts increased SOA contributions from anthropogenic (alkane, aromatic) and sesquiterpenes and decreased SOA contributions from isoprene and monoterpene relative to the two-product model calculations. The SOA predicted by SOM has a much lower volatility than that predicted by the traditional model, resulting in better qualitative agreement with volatility measurements of ambient OA. On account of its lower-volatility, the SOA mass produced by SOM does not appear to be as strongly influenced by the inclusion of oligomerization reactions, whereas the two-product model relies heavily on oligomerization to form low-volatility SOA products. Finally, an unconstrained contemporary hybrid scheme to model multi-generational oxidation within the framework of a two-product model in which ageing reactions are added on top of the existing two-product parameterization is considered. This hybrid scheme formed at least 3 times more SOA than the SOM during regional simulations as a result of excessive transformation of semi-volatile vapors into lower volatility material that strongly partitions to the particle phase. This finding suggests that these hybrid multi-generational schemes should be used with great caution in regional models

Towards an integrated crowdsourcing definition

Crowdsourcing is a relatively recent concept that encompasses many practices. This diversity leads to the blurring of the limits of crowdsourcing that may be identified virtually with any type of internet-based collaborative activity, such as co-creation or user innovation. Varying definitions of crowdsourcing exist, and therefore some authors present certain specific examples of crowdsourcing as paradigmatic, while others present the same examples as the opposite. In this article, existing definitions of crowdsourcing are analysed to extract common elements and to establish the basic characteristics of any crowdsourcing initiative. Based on these existing definitions, an exhaustive and consistent definition for crowdsourcing is presented and contrasted in 11 cases.Estelles Arolas, E.; González-Ladrón-De-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science. 32(2):189-200. doi:10.1177/0165551512437638S189200322Vukovic, M., & Bartolini, C. (2010). Towards a Research Agenda for Enterprise Crowdsourcing. Leveraging Applications of Formal Methods, Verification, and Validation, 425-434. doi:10.1007/978-3-642-16558-0_36Brabham, D. C. (2008). Crowdsourcing as a Model for Problem Solving. Convergence: The International Journal of Research into New Media Technologies, 14(1), 75-90. doi:10.1177/1354856507084420Vukovic, M. (2009). Crowdsourcing for Enterprises. 2009 Congress on Services - I. doi:10.1109/services-i.2009.56Doan, A., Ramakrishnan, R., & Halevy, A. Y. (2011). Crowdsourcing systems on the World-Wide Web. Communications of the ACM, 54(4), 86. doi:10.1145/1924421.1924442Brabham, D. C. (2008). Moving the crowd at iStockphoto: The composition of the crowd and motivations for participation in a crowdsourcing application. First Monday, 13(6). doi:10.5210/fm.v13i6.2159Huberman, B. A., Romero, D. M., & Wu, F. (2009). Crowdsourcing, attention and productivity. Journal of Information Science, 35(6), 758-765. doi:10.1177/0165551509346786Andriole, S. J. (2010). Business impact of Web 2.0 technologies. Communications of the ACM, 53(12), 67. doi:10.1145/1859204.1859225Denyer, D., Tranfield, D., & van Aken, J. E. (2008). Developing Design Propositions through Research Synthesis. Organization Studies, 29(3), 393-413. doi:10.1177/0170840607088020Egger, M., Smith, G. D., & Altman, D. G. (Eds.). (2001). Systematic Reviews in Health Care. doi:10.1002/9780470693926Tatarkiewicz, W. (1980). A History of Six Ideas. doi:10.1007/978-94-009-8805-7Cosma, G., & Joy, M. (2008). Towards a Definition of Source-Code Plagiarism. IEEE Transactions on Education, 51(2), 195-200. doi:10.1109/te.2007.906776Brabham, D. C. (2009). Crowdsourcing the Public Participation Process for Planning Projects. Planning Theory, 8(3), 242-262. doi:10.1177/1473095209104824Alonso, O., & Lease, M. (2011). Crowdsourcing 101. Proceedings of the fourth ACM international conference on Web search and data mining - WSDM ’11. doi:10.1145/1935826.1935831Bederson, B. B., & Quinn, A. J. (2011). Web workers unite! addressing challenges of online laborers. Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems - CHI EA ’11. doi:10.1145/1979742.1979606Grier, D. A. (2011). Not for All Markets. Computer, 44(5), 6-8. doi:10.1109/mc.2011.155Heer, J., & Bostock, M. (2010). Crowdsourcing graphical perception. Proceedings of the 28th international conference on Human factors in computing systems - CHI ’10. doi:10.1145/1753326.1753357Heymann, P., & Garcia-Molina, H. (2011). Turkalytics. Proceedings of the 20th international conference on World wide web - WWW ’11. doi:10.1145/1963405.1963473Kazai, G. (2011). In Search of Quality in Crowdsourcing for Search Engine Evaluation. Advances in Information Retrieval, 165-176. doi:10.1007/978-3-642-20161-5_17La Vecchia, G., & Cisternino, A. (2010). Collaborative Workforce, Business Process Crowdsourcing as an Alternative of BPO. Lecture Notes in Computer Science, 425-430. doi:10.1007/978-3-642-16985-4_40Liu, E., & Porter, T. (2010). Culture and KM in China. VINE, 40(3/4), 326-333. doi:10.1108/03055721011071449Oliveira, F., Ramos, I., & Santos, L. (2010). Definition of a Crowdsourcing Innovation Service for the European SMEs. Lecture Notes in Computer Science, 412-416. doi:10.1007/978-3-642-16985-4_37Porta, M., House, B., Buckley, L., & Blitz, A. (2008). Value 2.0: eight new rules for creating and capturing value from innovative technologies. Strategy & Leadership, 36(4), 10-18. doi:10.1108/10878570810888713Ribiere, V. M., & Tuggle, F. D. (Doug). (2010). Fostering innovation with KM 2.0. VINE, 40(1), 90-101. doi:10.1108/03055721011024955Sloane, P. (2011). The brave new world of open innovation. Strategic Direction, 27(5), 3-4. doi:10.1108/02580541111125725Wexler, M. N. (2011). Reconfiguring the sociology of the crowd: exploring crowdsourcing. International Journal of Sociology and Social Policy, 31(1/2), 6-20. doi:10.1108/01443331111104779Whitla, P. (2009). Crowdsourcing and Its Application in Marketing Activities. Contemporary Management Research, 5(1). doi:10.7903/cmr.1145Yang, J., Adamic, L. A., & Ackerman, M. S. (2008). Crowdsourcing and knowledge sharing. Proceedings of the 9th ACM conference on Electronic commerce - EC ’08. doi:10.1145/1386790.1386829Brabham, D. C. (2010). MOVING THE CROWD AT THREADLESS. Information, Communication & Society, 13(8), 1122-1145. doi:10.1080/13691181003624090Giudice, K. D. (2010). Crowdsourcing credibility: The impact of audience feedback on Web page credibility. Proceedings of the American Society for Information Science and Technology, 47(1), 1-9. doi:10.1002/meet.14504701099Stewart, O., Huerta, J. M., & Sader, M. (2009). Designing crowdsourcing community for the enterprise. 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Real-Time Black Carbon Emission Factor Measurements from Light Duty Vehicles

Eight light-duty gasoline low emission vehicles (LEV I) were tested on a Chassis dynamometer using the California Unified Cycle (UC) at the Haagen-Smit vehicle test facility at the California Air Resources Board in El Monte, CA during September 2011. The UC includes a cold start phase followed by a hot stabilized running phase. In addition, a light-duty gasoline LEV vehicle and ultralow emission vehicle (ULEV), and a light-duty diesel passenger vehicle and gasoline direct injection (GDI) vehicle were tested on a constant velocity driving cycle. A variety of instruments with response times ≥0.1 Hz were used to characterize how the emissions of the major particulate matter components varied for the LEVs during a typical driving cycle. This study focuses primarily on emissions of black carbon (BC). These measurements allowed for the determination of BC emission factors throughout the driving cycle, providing insights into the temporal variability of BC emission factors during different phases of a typical driving cycle

Children and Virtual Reality: Emerging Possibilities and Challenges

Virtual Reality is fast becoming a reality, with estimates that over 200m headsets will have been sold by 2020, and the market value for VR hardware and software reaching well over $20bn by then. Key players in the market currently include PlayStation with PSVR, Facebook with Oculus Rift, Google Cardboard and Daydream, Mattel with Viewmaster, and many other brands investing in content production for various audiences. One of those audiences is young people and children. “Children and Virtual Reality” is a collaboration between Dubit, Turner, WEARVR and the COST (European Cooperation in Science and Technology) Action DigiLitEY. Dubit, Turner and WEARVR are companies that specialise in digital, TV and VR content, with an interest in developing best practices around VR and children. DigiLitEY is a five year (2013-2017) academic network that focuses on existing and emerging communicative technologies for young children. This includes wearable technologies, 3D printers, robots, augmented reality, toys and games and relevant aspects of the Internet of Things. This report brings together industry research into the effects of VR on 8 to 12 year olds, and ideas that arose from a COST funded Think Tank to explore what the research findings might mean for the use of VR by under 8s

Long-term particulate matter modeling for health effect studies in California – Part 2: Concentrations and sources of ultrafine organic aerosols

Organic aerosol (OA) is a major constituent of ultrafine particulate matter (PM_0. 1). Recent epidemiological studies have identified associations between PM_0. 1 OA and premature mortality and low birth weight. In this study, the source-oriented UCD/CIT model was used to simulate the concentrations and sources of primary organic aerosols (POA) and secondary organic aerosols (SOA) in PM_0. 1 in California for a 9-year (2000–2008) modeling period with 4 km horizontal resolution to provide more insights about PM_0. 1 OA for health effect studies. As a related quality control, predicted monthly average concentrations of fine particulate matter (PM_2. 5) total organic carbon at six major urban sites had mean fractional bias of −0.31 to 0.19 and mean fractional errors of 0.4 to 0.59. The predicted ratio of PM_2. 5 SOA ∕ OA was lower than estimates derived from chemical mass balance (CMB) calculations by a factor of 2–3, which suggests the potential effects of processes such as POA volatility, additional SOA formation mechanism, and missing sources. OA in PM_0. 1, the focus size fraction of this study, is dominated by POA. Wood smoke is found to be the single biggest source of PM_0. 1 OA in winter in California, while meat cooking, mobile emissions (gasoline and diesel engines), and other anthropogenic sources (mainly solvent usage and waste disposal) are the most important sources in summer. Biogenic emissions are predicted to be the largest PM_0. 1 SOA source, followed by mobile sources and other anthropogenic sources, but these rankings are sensitive to the SOA model used in the calculation. Air pollution control programs aiming to reduce the PM_0. 1 OA concentrations should consider controlling solvent usage, waste disposal, and mobile emissions in California, but these findings should be revisited after the latest science is incorporated into the SOA exposure calculations. The spatial distributions of SOA associated with different sources are not sensitive to the choice of SOA model, although the absolute amount of SOA can change significantly. Therefore, the spatial distributions of PM_0. 1 POA and SOA over the 9-year study period provide useful information for epidemiological studies to further investigate the associations with health outcomes

Early Neutrophilia Marked by Aerobic Glycolysis Sustains Host Metabolism and Delays Cancer Cachexia

An elevated neutrophil–lymphocyte ratio negatively predicts the outcome of patients with cancer and is associated with cachexia, the terminal wasting syndrome. Here, using murine model systems of colorectal and pancreatic cancer we show that neutrophilia in the circulation and multiple organs, accompanied by extramedullary hematopoiesis, is an early event during cancer progression. Transcriptomic and metabolic assessment reveals that neutrophils in tumor-bearing animals utilize aerobic glycolysis, similar to cancer cells. Although pharmacological inhibition of aerobic glycolysis slows down tumor growth in C26 tumor-bearing mice, it precipitates cachexia, thereby shortening the overall survival. This negative effect may be explained by our observation that acute depletion of neutrophils in pre-cachectic mice impairs systemic glucose homeostasis secondary to altered hepatic lipid processing. Thus, changes in neutrophil number, distribution, and metabolism play an adaptive role in host metabolic homeostasis during cancer progression. Our findings provide insight into early events during cancer progression to cachexia, with implications for therapy