3 research outputs found

    Indicators for monitoring and assessment of Environmental management systems in ports

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    Ports are no longer content with being the connection of ocean and land transportation, of which they are keys in international logistics and supply chains. Ports have also become industrial production areas. As ports continue to evolve as production areas, they are becoming significant sources of water pollution, solid waste, and noise and air pollution. Due to this increase in environmental impacts, the majority of the world ports have made commitments to development of proactive procedures for a sustainable development by adopting an environmentally responsible approach to preserve and protect the environment. This is despite the need of a diagnostic tool which allows monitoring and evaluation of the progress of environmental management in the different sectors of the port. The present study evaluated the different activities and environmental aspects related to the shipping industry and identified the main indicators to assess and develop an environmental management system (EMS) in order to achieve sustainable development

    IRIT at e-Risk

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    International audienceIn this paper, we present the method we developed when participating to the e-Risk pilot task. We use machine learning in order to solve the problem of early detection of depressive users in social media relying on various features that we detail in this paper. We submitted 4 models which differences are also detailed in this paper. Best results were obtained when using a combination of lexical and statistical features
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