892 resultados para Big Mac
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Pós-graduação em Ciência da Informação - FFC
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Nowadays the companies generate great amount of data from different sources, however some of them produce more data than they can analyze. Big Data is a set of data that grows very fast, collected several times during a short period of time. This work focus on the importance of the correct management of Big Data in an industrial plant. Through a case study based on a company that belongs to the pulp and paper area, the problems resolutions are going to be presented with the usage of appropriate data management. In the final chapters, the results achieved by the company are discussed, showing how the correct choice of data to be monitored and analyzed brought benefits to the company, also best practices will be recommended for the Big Data management
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Two of the five subspecies of the western big-eared bat, Corynorhinus townsendii, are listed as federally endangered with the remaining three being of conservation concern. Knowing the degree of connectivity among populations would aid in the establishment of sound conservation and management plans for this taxon. For this purpose, we have developed and characterized eight polymorphic microsatellite markers.
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We developed and characterized 15 microsatellite markers for Rafinesque’s big-eared bat, Corynorhinus rafinesquii. In a population from Tennessee, the number of alleles per locus ranged from three to 13 and observed heterozygosities were 0.35 to 0.97 per locus. These loci will provide appropriate variability for estimation of population connectivity, demographic parameters, and genetic diversity for this species of concern.
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Townsend’s big-eared bat, Corynorhinus townsendii, is distributed broadly across western North America and in two isolated, endangered populations in central and eastern United States. There are five subspecies of C. townsendii; C. t. pallescens, C. t. australis, C. t. townsendii, C. t. ingens, and C. t. virginianus with varying degrees of concern over the conservation status of each. The aim of this study was to use mitochondrial and microsatellite DNA data to examine genetic diversity, population differentiation, and dispersal of three C. townsendii subspecies. C. t. virginianus is found in isolated populations in the eastern United States and was listed as endangered under the Endangered Species Act in 1979. Concern also exists about declining populations of two western subspecies, C. t. pallescens and C. t. townsendii. Using a comparative approach, estimates of the genetic diversity within populations of the endangered subspecies, C. t. virginianus, were found to be significantly lower than within populations of the two western subspecies. Further, both classes of molecular markers revealed significant differentiation among regional populations of C. t. virginianus with most genetic diversity distributed among populations. Genetic diversity was not significantly different between C. t. townsendii and C. t. pallescens. Some populations of C. t. townsendii are not genetically differentiated from populations of C. t. pallescens in areas of sympatry. For the western subspecies gene flow appears to occur primarily through male dispersal. Finally, geographic regions representing significantly differentiated and genetically unique populations of C. townsendii virginianus are recognized as distinct evolutionary significant units.
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The University of Nebraska-Lincoln National Agri-Marketing Association (NAMA) Marketing Team brought home the Big XII Championship as a national finalist at the 2009 NAMA Annual Conference held April 14-17 in Atlanta, Georgia.
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The Rose-ringed parakeet (Psittacula krameri [Scopdi]) has been reported (Roberts, 1974; Bashir, 1978; Beg, 1978; and DeGrazio, 1978) as a serious bird pest of maize, sunflower, rape seeds, and fruit crops, particularly citrus, mangoes, and guavas, in Pakistan. Estimated annual losses to maize grown for seed alone amount to about 97,000 tons, worth about Pak. Rs. 150 million or US $15 million (Roberts, 1978). Paradoxically, this handsome bright green parakeet is highly esteemed in the pet trade; and limited numbers are also marketed locally and sometimes exported to neighboring countries, particularly the Arab Gulf Emirates, as caged pets. Traditional control methods aimed at scaring or chasing birds from the crops, usually with noise-making devices, are costly; furthermore, they have largely been unsuccessful and time consuming because they require human patrolling before and after normal working hours. They provide at best only temporary relief. The aim of this study was to develop a new decoy trap based on the Modified Australian Crow Trap (MAC), which we propose to call the PAROTRAP, and to evaluate its effectiveness and potential in capturing live parakeets in the field as a possible solution to the parakeet problem, as well as promoting the economic exploitation of trapped parakeets for the pet trade. The study was undertaken during March and June 1979 as a part of the UNDP/FAO Project No. PAK/71/554, assisting Pakistan Vertebrate Pest Control Centre in developing and improving control techniques to prevent or reduce bird damage to important crops. Our earlier trials showed that parakeets could be induced to enter a conventionally designed MAC trap, and that after some time they learned how to escape from it. Therefore, a series of minor modifications were introduced and field tested.
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Pós-graduação em Ciência da Informação - FFC
Resumo:
Nowadays the companies generate great amount of data from different sources, however some of them produce more data than they can analyze. Big Data is a set of data that grows very fast, collected several times during a short period of time. This work focus on the importance of the correct management of Big Data in an industrial plant. Through a case study based on a company that belongs to the pulp and paper area, the problems resolutions are going to be presented with the usage of appropriate data management. In the final chapters, the results achieved by the company are discussed, showing how the correct choice of data to be monitored and analyzed brought benefits to the company, also best practices will be recommended for the Big Data management
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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
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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.