Pareto Optimization of Water Resources Using the Nexus Approach

The continuously growing population in combination with the escalating urbanization and economic growth increase the pressure on water, energy and food resources of our planet. This entails an urgent need for proper water resources management within the water-energy-food (WEF) nexus concept. The WEF nexus considers water, energy and food as three continuously interconnected sectors, whose complex interactions lead to an increased number of trade-offs and potential conflicts. Computational modeling can be used to quantify these interactions, reduce trade-offs and promote synergies. We investigate the water resources in the Upper Blue Nile River (UBNR) basin, one of the two main sources of the Nile, using the Hydronomeas tool. Hydronomeas is based on the parameterization-simulation-optimization method; optimization is implemented in two levels, using a holistic approach and multiple criteria. We assign various targets, constraints and priorities to the UBNR system of reservoirs, hydropower plants and irrigation projects and derive a Pareto front that contains alternative, optimal solutions, for which improvement of one objective can be achieved only at the expense of another. By visualizing the trade-offs between the conflicting objectives of hydropower and irrigation, we aim to help decision makers understand changes due to different management policies and thus, achieve greater efficiency in water resources management in the Nile region.     

Verification of a CFD model for indoor airflow and heat transfer

The SST k–ω based model is applied to calculate air-flow velocities and temperatures in a model office room. Calculations are compared with experiments and with the results of the standard k–ε, the RNG k–ε model and the laminar model. It is concluded that (a) all the three tested turbulent models predict satisfactorily the main qualitative features of the flow and the layered type of temperature fields and (b) computations with the SST k–ω based model show the best agreement with measurements. The use of this model is proposed combined with a suitable grid.     

Fuzzy relation equations and fuzzy inference systems: an insideapproach

This paper investigates and extends the use of fuzzy relation equations for the representation and study of fuzzy inference systems. Using the generalized sup-t (t is a triangular norm) composition of fuzzy relations and the study of sup-t fuzzy relation equations, interesting results are provided concerning the completeness and the theoretical soundness of the representation, as well as the ability to mathematically formulate and satisfy application-oriented design demands. Furthermore, giving a formal study of fuzzy partitions and some useful aspects of fuzzy associations and fuzzy systems, the paper can be used as a theoretical background for designing consistent fuzzy inference systems.     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.