41 research outputs found

    Evidence gathering in support of sustainable Scottish inshore fisheries: work package (4) final report: a pilot study to define the footprint and activities of Scottish inshore fisheries by identifying target fisheries, habitats and associated fish stocks

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    [Extract from Executive Summary] This work was conducted under Work package 4 of the European Fisheries Funded program “Evidence Gathering in Support of Sustainable Scottish Inshore Fisheries”. The overall aim of the program was to work in partnership with Marine Scotland Fisheries Policy and with the Scottish Inshore Fisheries Groups to help develop inshore fisheries management. Specifically the program aims were to establish the location of fishing activities within inshore areas; to identify catch composition and associated fishery impacts; to define the environmental footprint and availability of stocks; to develop economic value within local fisheries and; to establish an information resource base to assist the development of inshore fisheries management provisions.Publisher PD

    Under-Ice Light Field in the Western Arctic Ocean During Late Summer

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    The Arctic is no longer a region dominated by thick multi-year ice (MYI), but by thinner, more dynamic, first-year-ice (FYI). This shift towards a seasonal ice cover has consequences for the under-ice light field, as sea-ice and its snow cover are a major factor influencing radiative transfer and thus, biological activity within- and under the ice. This work describes in situ measurements of light transmission through different types of sea-ice (MYI and FYI) performed during two expeditions to the Chukchi sea in August 2018 and 2019, as well as a simple characterisation of the biological state of the ice microbial system. Our analysis shows that, in late summer, two different states of FYI exist in this region: 1) FYI in an enhanced state of decay, and 2) robust FYI, more likely to survive the melt season. The two FYI types have different average ice thicknesses: 0.74 ± 0.07 m (N = 9) and 0.93 ± 0.11 m (N = 9), different average values of transmittance: 0.15 ± 0.04 compared to 0.09 ± 0.02, and different ice extinction coefficients: 1.49 ± 0.28 and 1.12 ± 0.19 m−1. The measurements performed over MYI present different characteristics with a higher average ice thickness of 1.56 ± 0.12 m, lower transmittance (0.05 ± 0.01) with ice extinction coefficients of 1.24 ± 0.26 m−1 (N = 12). All ice types show consistently low salinity, chlorophyll a concentrations and nutrients, which may be linked to the timing of the measurements and the flushing of melt-water through the ice. With continued Arctic warming, the summer ice will continue to retreat, and the decayed variant of FYI, with a higher scattering of light, but a reduced thickness, leading to an overall higher light transmittance, may become a more relevant ice type. Our results suggest that in this scenario, more light would reach the ice interior and the upper-ocean

    Green tea consumption and lung cancer risk: the Ohsaki study

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    We examined the risk of lung cancer in relation to green tea consumption in a population-based cohort study in Japan among 41 440 men and women, aged 40–79 years, who completed a questionnaire in 1994 regarding green tea consumption and other health-related lifestyle factors. During the follow-up period of 7 years (from 1995 to 2001), 302 cases of lung cancer were identified, and the Cox proportional hazards regression model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). The multivariable-adjusted HRs of lung cancer incidence for green tea consumption of 1 or 2, 3 or 4, and 5 or more cups/day as compared to less than 1 cup/day were 1.14 (95% CI: 0.80–1.62), 1.18 (95% CI: 0.83–1.66), and 1.17 (95% CI: 0.85–1.61), respectively (P for trend=0.48). This cohort study has found no evidence that green tea consumption is associated with lung cancer

    Effect of Database Generation on Damage Consequences’ Assessment Based on Random Forests

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    Recently, the application of machine learning has been explored to assess the main damage consequences without employing flooding sensors. This method can be the base of a new generation of onboard decision support systems to help the master during the progressive flooding of the ship. In particular, the application of random forests has been found suitable to assess the final fate of the ship and the damaged compartments’ set and estimate the time-to-flood. Random forests have to be trained using a database of precalculated progressive flooding simulations. In the present work, multiple options for database generation were tested and compared: three based on Monte Carlo (MC) sampling based on different probability distributions of the damage parameters and a parametric one. The methods were tested on a barge geometry to highlight the main effects on the damage consequences’ assessment in order to ease the further development of flooding-sensor-agnostic decision support systems for flooding emergencies

    Forecasting large explosions at Bezymianny Volcano using thermal satellite data

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    Large volcanic explosions pose a severe risk to life and cargo by injecting ash into local and international air traffic routes. Prior to exploding, Bezymianny (Kamchatka) commonly shows an increase in lava extrusion rate, which can be detected by satellites as an increase in thermal radiance. Here we present the first method of forecasting explosive eruptions based solely on satellite data. A pattern recognition algorithm using Advanced Very High Resolution Radiometer (AVHRR) data has been developed based on known precursory trends of increasing radiance prior to 19 explosions at Bezymianny Volcano in 1993–2008. The algorithm retrospectively forecasts 89% of the explosions (100% of the explosions that show precursory increases in thermal radiance), with 71% of alerts issued in the 30 days beforehand. The method also provides the probability of an explosion occurring within a given number of days after an alert is triggered by the algorithm. When applied to independent data, the algorithm correctly provided alerts before the 16 December 2009, 31 May 2010 and 13 April 2011 explosions
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