992 resultados para Inter-modality re-weighting


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The present thesis in focused on the minimization of experimental efforts for the prediction of pollutant propagation in rivers by mathematical modelling and knowledge re-use. Mathematical modelling is based on the well known advection-dispersion equation, while the knowledge re-use approach employs the methods of case based reasoning, graphical analysis and text mining. The thesis contribution to the pollutant transport research field consists of: (1) analytical and numerical models for pollutant transport prediction; (2) two novel techniques which enable the use of variable parameters along rivers in analytical models; (3) models for the estimation of pollutant transport characteristic parameters (velocity, dispersion coefficient and nutrient transformation rates) as functions of water flow, channel characteristics and/or seasonality; (4) the graphical analysis method to be used for the identification of pollution sources along rivers; (5) a case based reasoning tool for the identification of crucial information related to the pollutant transport modelling; (6) and the application of a software tool for the reuse of information during pollutants transport modelling research. These support tools are applicable in the water quality research field and in practice as well, as they can be involved in multiple activities. The models are capable of predicting pollutant propagation along rivers in case of both ordinary pollution and accidents. They can also be applied for other similar rivers in modelling of pollutant transport in rivers with low availability of experimental data concerning concentration. This is because models for parameter estimation developed in the present thesis enable the calculation of transport characteristic parameters as functions of river hydraulic parameters and/or seasonality. The similarity between rivers is assessed using case based reasoning tools, and additional necessary information can be identified by using the software for the information reuse. Such systems represent support for users and open up possibilities for new modelling methods, monitoring facilities and for better river water quality management tools. They are useful also for the estimation of environmental impact of possible technological changes and can be applied in the pre-design stage or/and in the practical use of processes as well.

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In the field of observational methodology the observer is obviously a central figure, and close attention should be paid to the process through which he or she acquires, applies, and maintains the skills required. Basic training in how to apply the operational definitions of categories and the rules for coding, coupled with the opportunity to use the observation instrument in real-life situations, can have a positive effect in terms of the degree of agreement achieved when one evaluates intra- and inter-observer reliability. Several authors, including Arias, Argudo, & Alonso (2009) and Medina and Delgado (1999), have put forward proposals for the process of basic and applied training in this context. Reid y De Master (1982) focuses on the observer's performance and how to maintain the acquired skills, it being argued that periodic checks are needed after initial training because an observer may, over time, become less reliable due to the inherent complexity of category systems. The purpose of this subsequent training is to maintain acceptable levels of observer reliability. Various strategies can be used to this end, including providing feedback about those categories associated with a good reliability index, or offering re-training in how to apply those that yield lower indices. The aim of this study is to develop a performance-based index that is capable of assessing an observer's ability to produce reliable observations in conjunction with other observers.

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Invocatio: Deo moderatore.

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Invocatio: Q.F.F.Q.S.

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Invocatio: J.N.J.F.

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Invocatio: Q.F.F.S.

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Invocatio: D.D.

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Invocatio: I.N.J.C.

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Invocatio: D.O.D.

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Invocatio: B.C.D.

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Invocatio: I.N.J.C.

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Invocatio: Q.F.E.I.S.T.

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Invocatio: I.N.J.