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This article proposes an adaptable hybrid model for recommending effective investments in different scenarios. Currently, a wide variety of methodologies are used for company valuation, especially those that take into account financial statements. However, for private held companies, there is no method that would be capable of predicting, with full certainty, the future success of an investment. The Optimal Capital Investment Case-Base Reasoning (OCI-CBR) consists of a case-based reasoning system that uses a classification algorithm to prune the case base according to a projected increase in certain company attributes. Once the cases have been pruned and the case is fed with the most profitable investment opportunities, the case-based reasoning system recommends optimal investments to potential investors. The complete model is conceived as an intelligent hybrid model that optimizes the case base by employing different algorithms for data retrieval and reuse. The system makes recommendations based on the investor's preferences and the investment decisions of other investors with similar profiles or interests. • Hybrid decision support system for capital investment. • Machine learning pruning techniques for fault detection and diagnosis of companies. • A CBR methodology is used to make recommendations for optimal investment. [ABSTRACT FROM AUTHOR]

This work addresses the use of deep neural networks (DNNs) in automatic language identification (LID) focused on short test utterances. Motivated by their recent success in acoustic modelling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from the short-term acoustic features. We show how DNNs are particularly suitable to perform LID in real-time applications, due to their capacity to emit a language identification posterior at each new frame of the test utterance. We then analyse different aspects of the system, such as the amount of required training data, the number of hidden layers, the relevance of contextual information and the effect of the test utterance duration. Finally, we propose several methods to combine frame-by-frame posteriors. Experiments are conducted on two different datasets: the public NIST Language Recognition Evaluation 2009 (3 s task) and a much larger corpus (of 5 million utterances) known as Google 5M LID, obtained from different Google Services. Reported results show relative improvements of DNNs versus the i -vector system of 40% in LRE09 3 second task and 76% in Google 5M LID. [ABSTRACT FROM AUTHOR]

Abstract: The recent statements of both the European Union and the US Presidency pushed in the direction of using renewable forms of energy, in order to act against climate changes induced by the growing concentration of carbon dioxide in the atmosphere. In this paper, a survey regarding methods and tools presently available to determine potential and exploitable energy in the most important renewable sectors (i.e., solar, wind, wave, biomass and geothermal energy) is presented. Moreover, challenges for each renewable resource are highlighted as well as the available tools that can help in evaluating the use of a mix of different sources. [ABSTRACT FROM AUTHOR]

Hydrogen production from renewable energies is a key part in the energy transition to realize a sustainable energy economy for both developed and developing nations. For Algeria, successful energy transition toward a hydrogen economy will require the establishment of its potential. This study was conducted to estimate the potential for producing hydrogen from renewable resources in Algeria. The renewable energies considered are: solar photovoltaic and wind. To accomplish this objective, first, we analyzed renewable resource data both statistically and graphically using Geographical Information System (GIS), a computer-based information system utilized to create and visualize the spatial distribution of the geographic information. Then, the study will evaluate the availability of renewable electricity production potential from these key renewable resources. The potential for the hydrogen production, via the electrolysis process with wind and solar photovoltaic electricity, is described with maps showing it per unit area in each region. Finally, the results of the estimated hydrogen potential from both resources for each region are compared and significant conclusions are drawn. [ABSTRACT FROM AUTHOR]

The present study describes the distinct bone marrow (BM) and peripheral blood (PB) monocyte subpopulations detected by seven-colour flow cytometry. Mononuclear phagocytes were identified as viable CD172a+ SWC8- CD203a- mononuclear leukocytes. After that, monocyte subpopulations were differentiated by using CD14, CD163 and SLA-DR markers. Four distinct monocyte subpopulations were found in the BM and PB. Based on the discovered populations two possible maturation pathways have been proposed. The first pathway was characterised by release of CD14hi CD163- SLA-DR- BM monocytes into the PB where they matured into CD14low CD163+ SLA-DR+ monocytes. In the alternative pathway the monocytes finalised their phenotypical maturation in the BM and then they were released into the PB as CD14low CD163+ SLA-DR+ cells. In Salmonella-infected piglets, the population of CD14low CD163+ SLA-DR+ monocytes was elevated in the BM and mesenteric lymph nodes (MLN), suggesting the role of this population in pathogenesis of Salmonella infection in pigs. [ABSTRACT FROM AUTHOR]