171 research outputs found

    Small-scale fisheries access to fishing opportunities in the European Union: is the common fisheries policy the right step to SDG14b?

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    The profile of small-scale fisheries has been raised through a dedicated target within the United Nations Sustainable Development Goals (SDG14b) that calls for the provision of ‘access of small-scale artisanal fishers to marine resources and markets’. By focusing on access to fisheries resources in the context of European Union, in this article we demonstrate that the potential for small-scale fishing sectors to benefit from fishing opportunities remains low due to different mechanisms at play including legislative gaps in the Common Fisheries Policy, and long-existing local structures somewhat favouring the status quo of distributive injustice. Consequently, those without access to capital and authority are faced by marginalizing allocation systems, impacting the overall resilience of fishing communities. Achieving SDG14b requires an overhaul in the promulgation of policies emanating from the present nested governance systems.info:eu-repo/semantics/publishedVersio

    A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA

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    Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Estimation) to facilitate further usage and research by the wider academic community

    Iteratively regularized Newton-type methods for general data misfit functionals and applications to Poisson data

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    We study Newton type methods for inverse problems described by nonlinear operator equations F(u)=gF(u)=g in Banach spaces where the Newton equations F(un;un+1un)=gF(un)F'(u_n;u_{n+1}-u_n) = g-F(u_n) are regularized variationally using a general data misfit functional and a convex regularization term. This generalizes the well-known iteratively regularized Gauss-Newton method (IRGNM). We prove convergence and convergence rates as the noise level tends to 0 both for an a priori stopping rule and for a Lepski{\u\i}-type a posteriori stopping rule. Our analysis includes previous order optimal convergence rate results for the IRGNM as special cases. The main focus of this paper is on inverse problems with Poisson data where the natural data misfit functional is given by the Kullback-Leibler divergence. Two examples of such problems are discussed in detail: an inverse obstacle scattering problem with amplitude data of the far-field pattern and a phase retrieval problem. The performence of the proposed method for these problems is illustrated in numerical examples

    Solving the chemical master equation using sliding windows

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    <p>Abstract</p> <p>Background</p> <p>The chemical master equation (CME) is a system of ordinary differential equations that describes the evolution of a network of chemical reactions as a stochastic process. Its solution yields the probability density vector of the system at each point in time. Solving the CME numerically is in many cases computationally expensive or even infeasible as the number of reachable states can be very large or infinite. We introduce the sliding window method, which computes an approximate solution of the CME by performing a sequence of local analysis steps. In each step, only a manageable subset of states is considered, representing a "window" into the state space. In subsequent steps, the window follows the direction in which the probability mass moves, until the time period of interest has elapsed. We construct the window based on a deterministic approximation of the future behavior of the system by estimating upper and lower bounds on the populations of the chemical species.</p> <p>Results</p> <p>In order to show the effectiveness of our approach, we apply it to several examples previously described in the literature. The experimental results show that the proposed method speeds up the analysis considerably, compared to a global analysis, while still providing high accuracy.</p> <p>Conclusions</p> <p>The sliding window method is a novel approach to address the performance problems of numerical algorithms for the solution of the chemical master equation. The method efficiently approximates the probability distributions at the time points of interest for a variety of chemically reacting systems, including systems for which no upper bound on the population sizes of the chemical species is known a priori.</p

    Social Pedagogy: An Approach Without Fixed Recipes

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    A historical and theoretical reconstruction of the specificity and peculiarity of the discipline of social pedagogy, as it has developed in Denmark. Social pedagogy takes its departure from the idea that the individual person and the community are complementary but at the same time opposed to each other, so the task of social pedagogy is rebalancing the dynamics between the two. Social pedagogy is also characterised as a discipline with three dimensions: a practical dimension, a theoretical dimension and a professional dimension. The professional’s task is neither to apply theory in practice nor to uphold the usual practice; it is to mediate between theory and practice. The specificity of the discipline gives rise to particular challenges and dilemmas that theorists make understandable and transparent and practitioners have to deal with. A big challenge for social pedagogy is the quest for evidence-based methods that overrides the specificity of the social pedagogical approach. Balancing different forms of knowledge implies that programmes and methods are used as inspiration that can be contained in a social pedagogical approach

    The handbook for standardised field and laboratory measurements in terrestrial climate-change experiments and observational studies

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    Climate change is a worldwide threat to biodiversity and ecosystem structure, functioning, and services. To understand the underlying drivers and mechanisms, and to predict the consequences for nature and people, we urgently need better understanding of the direction and magnitude of climate‐change impacts across the soil–plant–atmosphere continuum. An increasing number of climate‐change studies is creating new opportunities for meaningful and high‐quality generalisations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re‐use, synthesis, and upscaling. Many of these challenges relate to a lack of an established “best practice” for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change

    Nutritional correlates of koala persistence in a low-density population

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    It is widely postulated that nutritional factors drive bottom-up, resource-based patterns in herbivore ecology and distribution. There is, however, much controversy over the roles of different plant constituents and how these influence individual herbivores and herbivore populations. The density of koala (Phascolarctos cinereus) populations varies widely and many attribute population trends to variation in the nutritional quality of the eucalypt leaves of their diet, but there is little evidence to support this hypothesis. We used a nested design that involved sampling of trees at two spatial scales to investigate how leaf chemistry influences free-living koalas from a low-density population in south east New South Wales, Australia. Using koala faecal pellets as a proxy for koala visitation to trees, we found an interaction between toxins and nutrients in leaves at a small spatial scale, whereby koalas preferred trees with leaves of higher concentrations of available nitrogen but lower concentrations of sideroxylonals (secondary metabolites found exclusively in eucalypts) compared to neighbouring trees of the same species. We argue that taxonomic and phenotypic diversity is likely to be important when foraging in habitats of low nutritional quality in providing diet choice to tradeoff nutrients and toxins and minimise movement costs. Our findings suggest that immediate nutritional concerns are an important priority of folivores in low-quality habitats and imply that nutritional limitations play an important role in constraining folivore populations. We show that, with a careful experimental design, it is possible to make inferences about populations of herbivores that exist at extremely low densities and thus achieve a better understanding about how plant composition influences herbivore ecology and persistence.IW and WF received a grant from New South Wales (NSW) Department of Environment, Climate Change & Water
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