Nuclear astrophysics: nucleosynthesis in the Universe

Nuclear astrophysics is a relatively young science; it is about half a century old. It is a multidisciplinary subject, since it combines nuclear physics with astrophysics and observations in astronomy. It also addresses fundamental issues in astrobiology through the formation of elements, in particular those required for a carbon-based life. In this paper, a rapid overview of nucleosynthesis is given, mainly from the point of view of nuclear physics. A short historical introduction is followed by the definition of the relevant nuclear parameters, such as nuclear reaction cross sections, astrophysical S-factors, the energy range defined by the Gamow peak and reaction rates. The different astrophysical scenarios that are the sites of nucleosynthesis, and different processes, cycles and chains that are responsible for the building of complex nuclei from the elementary hydrogen nuclei are then briefly described. Received 28 February 2012, accepted 5 April 2012, first published online 9 May 2012

From inflation to dark energy through a dynamical Λ: an attempt at alleviating fundamental cosmic puzzles

After decades of successful hot big-bang paradigm, cosmology still lacks a framework in which the early inflationary phase of the universe smoothly matches the radiation epoch and evolves to the present “quasi” de Sitter spacetime. No less intriguing is that the current value of the effective vacuum energy density is vastly smaller than the value that triggered inflation. In this paper, we propose a new class of cosmologies capable of overcoming, or highly alleviating, some of these acute cosmic puzzles. Powered by a decaying vacuum energy density, the spacetime emerges from a pure nonsingular de Sitter vacuum stage, “gracefully” exits from inflation to a radiation phase followed by dark matter and vacuum regimes, and, finally, evolves to a late-time de Sitter phase.

Too Big to Ignore: Global Partnership on Small-Scale Fisheries Research - the Latin America and Caribbean network

Too Big to Ignore (TBTI; www.toobigtoignore.net) is a research network and knowledge mobilization partnership established to elevate the profile of small-scale fisheries (SSF), to argue against their marginalization in national and international policies, and to develop research and governance capacity to address global fisheries challenges. Network participants and partners are conducting global and comparative analyses, as well as in-depth studies of SSF in the context of local complexity and dynamics, along with a thorough examination of governance challenges, to encourage careful consideration of this sector in local, regional and global policy arenas. Comprising 15 partners and 62 researchers from 27 countries, TBTI conducts activities in five regions of the world. In Latin America and the Caribbean (LAC) region, we are taking a participative approach to investigate and promote stewardship and self-governance in SSF, seeking best practices and success stories that could be replicated elsewhere. As well, the region will focus to promote sustainable livelihoods of coastal communities. Key activities include workshops and stakeholder meetings, facilitation of policy dialogue and networking, as well as assessing local capacity needs and training. Currently, LAC members are putting together publications that examine key issues concerning SSF in the region and best practices, with a first focus on ecosystem stewardship. Other planned deliverables include comparative analysis, a regional profile on the top research issues on SSF, and a synthesis of SSF knowledge in LAC

A Fuzzy Ontology-Driven Approach to Semantic Interoperability in e-Government Big Data

With the increasing production of information from e-government initiatives, there is also the need to transform a large volume of unstructured data into useful information for society. All this information should be easily accessible and made available in a meaningful and effective way in order to achieve semantic interoperability in electronic government services, which is a challenge to be pursued by governments round the world. Our aim is to discuss the context of e-Government Big Data and to present a framework to promote semantic interoperability through automatic generation of ontologies from unstructured information found in the Internet. We propose the use of fuzzy mechanisms to deal with natural language terms and present some related works found in this area. The results achieved in this study are based on the architectural definition and major components and requirements in order to compose the proposed framework. With this, it is possible to take advantage of the large volume of information generated from e-Government initiatives and use it to benefit society.