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This paper aims to examine the process of reservoir sedimentation, namely to try to estimate the sediment volume deposited in the main Portuguese reservoirs. The changes in fluvial sediment transport induced by the construction of dams on the major Portuguese rivers are described. Based on the values of dead volumes, the current situation of 166 Portuguese reservoirs is analysed to obtain an order of magnitude of available sediments and pre-select reservoirs that could potentially be integrated into a project for the artificial sand nourishment of beaches. This work was carried out as a starting point in order to implement a national plan for the use of reservoir sediments as an "added value" to the economy, which is as yet generally unexploited. © 2014 Taylor & Francis Group, London.

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Abstract
Traditional Collaborative Filtering algorithms for recommendation are designed for stationary data. Likewise, conventional evaluation methodologies are only applicable in offline experiments, where data and models are static. However, in real world systems, user feedback is continuously being generated, at unpredictable rates. One way to deal with this data stream is to perform online model updates as new data points become available. This requires algorithms able to process data at least as fast as it is generated. One other issue is how to evaluate algorithms in such a streaming data environment. In this paper we introduce a simple but fast incremental Matrix Factorization algorithm for positive-only feedback. We also contribute with a prequential evaluation protocol for recommender systems, suitable for streaming data environments. Using this evaluation methodology, we compare our algorithm with other state-of-the-art proposals. Our experiments reveal that despite its simplicity, our algorithm has competitive accuracy, while being significantly faster.

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This paper introduces new classes of bivariate time series models being useful to fit count data time series with a finite range of counts.. Motivation comes mainly from the comparison of schemes for monitoring tourism demand, stock data, production and environmental processes. All models are based on the bivariate binomial distribution of Type II. First, a new family of bivariate integer-valued GARCH models is proposed. Then, a new bivariate thinning operation is introduced and explained in detail. The new thinning operation has a number of advantages including the fact that marginally it behaves as the usual binomial thinning operation and also that allows for both positive and negative cross-correlations. Based upon this new thinning operation, a bivariate extension of the binomial autoregressive model of order one is introduced. Basic probabilistic and statistical properties of the model are discussed. Parameter estimation and forecasting are also covered. The performance of these models is illustrated through an empirical application to a set of rainy days time series collected from 2000 up to 2010 in the German cities of Bremen and Cuxhaven.

Abstract
Programmers resort to user-level parallel frameworks in order to exploit the parallelism provided by multiprocessor platforms. While such general frameworks do not support the stringent timing requirements of real-time systems, they offer a useful model of computation based on the standard fork/join, for which the analysis of timing properties makes sense. Very few works analyse the schedulability of synchronous parallel real-time tasks, which is a generalisation of the standard fork/join model. This paper proposes to narrow the gap by presenting a model that analyses the response-time of synchronous parallel real-time tasks. The model under consideration targets tasks with fixed priorities, composed of several segments with an arbitrary number of parallel and independent units of execution. We contribute to the state-of-the-art by analysing the response-time behaviour of synchronous parallel tasks. To accomplish this, we take into account concepts previously proposed in the literature and define new concepts such as carry-out decomposition and sliding window technique in order to compute the worst-case workload in a window of interest. Results show that the proposed approach is significantly better than current approaches, improving the state-of-the-art analysis of parallel real-time tasks. Copyright © 2014 ACM.

Abstract