Adaptive local learning techniques for multiple-step-ahead wind speed forecasting

A massive deployment of wind energy in power systems is expected in the near future. However, a still open issue is how to integrate wind generators into existing electrical grids by limiting their side effects on network operations and control. In order to attain this objective, accurate short and medium-term wind speed forecasting is required.This paper discusses and compares a physical (white-box) model (namely a limited-area non hydrostatic model developed by the European consortium for small-scale modeling) with a family of local learning techniques (black-box) for short and medium term forecasting. Also, an original model integrating machine learning techniques with physical knowledge modeling (grey-box) is proposed.A set of experiments on real data collected from a set of meteorological sensors located in the south of Italy supports the methodological analysis and assesses the potential of the different forecasting approaches.     

On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines

In the last years, the interest in methods and techniques for circumventing the security of the available digital video broadcasting systems is continuously increasing. Digital TV providers are struggling to restrict access to their video contents only to authorized users, by deploying more and more sophisticated conditional access systems. At the state-of-the-art, the most significant menace is the card-sharing activity which exploits a known weakness allowing an authorized subscriber to provide access to digital contents to a potentially large group of unauthorized ones connected over a communication network. This is usually realized by using ad hoc customized devices. Detecting the presence of these illegal systems on a network, by recognizing their related traffic is an issue of primary importance. Unfortunately, to avoid the identification of such traffic, payload obfuscation strategies based on encryption are often used, hindering packet inspection techniques.This paper presents a strategy for the detection of card-sharing traffic, empowered by machine-learning-driven traffic classification techniques and based on the natural capability of wavelet analysis to decompose a traffic time series into several component series associated with particular time and frequency scales and hence allowing its observation at different frequency component levels and with different resolutions. These ideas have been used for the proof-of-concept implementation of an SVM-based binary classification scheme that relies only on time regularities of the traffic and not on the packet contents and hence is immune to payload obfuscation techniques.     

Automatic mapping of parallel applications on multicore architectures using the Servet benchmark suite

Servet is a suite of benchmarks focused on detecting a set of parameters with high influence on the overall performance of multicore systems. These parameters can be used for autotuning codes to increase their performance on multicore clusters. Although Servet has been proved to detect accurately cache hierarchies, bandwidths and bottlenecks in memory accesses, as well as the communication overhead among cores, up to now the impact of the use of this information on application performance optimization has not been assessed. This paper presents a novel algorithm that automatically uses Servet for mapping parallel applications on multicore systems and analyzes its impact on three testbeds using three different parallel programming models: message-passing, shared memory and partitioned global address space (PGAS). Our results show that a suitable mapping policy based on the data provided by this tool can significantly improve the performance of parallel applications without source code modification.     

Characterization and Technological Properties of Lactic Acid Bacteria Isolated from Traditional Argentinean Goat’s Milk Products

A total of 286 lactic acid bacteria isolated from goat’s dairy products in northwest of Argentina were characterized. Lactobacilli (38%) and cocci (62%) were identified according to morphological, physiological, and relevant technological properties. L. plantarum (14%) and Enterococcus (34%) were the predominant species. S. thermophilus, Pediococcus and L. plantarum were the highest acid producers. Eight strains of L. fermentum produced bacteriocins or metabolites similar to bacteriocins. The API-ZYM test was applied to 39 isolates. Eight strains were selected from their both technological properties and enzymatic activities for use as starter or adjunct culture in the manufacture of artisanal goat cheeses.     

On the regional incidence of highway investments in the USA

The objective of this paper was to investigate the regional incidence of the aggregate effects of investment in highways in the US taking into consideration the presence of regional spillovers. The empirical results are based on VAR estimates at both the aggregate and state levels using output, employment, and investment, as well as different measures of highway investments. Empirical results allow us to establish several stylized facts. First, investment in highways affects private sector variables positively at the aggregate level as well as in most states. Second, the regional spillover effects are very significant for all private sector variables. Third, these spillovers have a clear geographical pattern in that they tend to be more important in western states and the corridor between the Great Lakes and the Gulf Coast.     

Shape-Resonant Superconductivity in Nanofilms: from Weak to Strong Coupling

Ultrathin superconductors of different materials are becoming a powerful platform to find mechanisms for enhancement of superconductivity, exploiting shape resonances in different superconducting properties. Here, we evaluate the superconducting gap and its spatial profile, the multiple gap components, and the chemical potential, of generic superconducting nanofilms, considering the pairing attraction and its energy scale as tunable parameters, from weak to strong coupling, at fixed electron density. Superconducting properties are evaluated at mean field level as a function of the thickness of the nanofilm, in order to characterize the shape resonances in the superconducting gap. We find that the most pronounced shape resonances are generated for weakly coupled superconductors, while approaching the strong coupling regime the shape resonances are rounded by a mixing of the subbands due to the large energy gaps extending over large energy scales. Finally, we find that the spatial profile, transverse to the nanofilm, of the superconducting gap acquires a flat behavior in the shape resonance region, indicating that a robust and uniform multigap superconducting state can arise at resonance.