11 resultados para Endemic plants


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XX Symposium of Brazilian Medicinal Plants & X International Congress of Ethnopharmacology. S. Paulo, Brasil.

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The section at Cristo Rei shows sandy beds with intercalated clayey lenses (IVb division from the Lisbon Miocene series) that correspond to a major regression event dated from between ca. 17.6 and 17 Ma. They also correspond to a distal position (relatively to the typical fluviatile facies in Lisbon), nearer the basin's axis. Geologic data and paleontological analysis (plant fossils, fishes, crocodilians, land mammals) allow the reconstruction of environments that were represented in the concerned area: estuary with channels and ox-bows; upstream, areas occupied by brackish waters where Gryphaea griphoides banks developped; still farther upstream, freshwaters sided by humid forests and low mountain subtropical forests under warm temperate and rainy conditions, as well as not far away, seasonally dry environments (low density tree or shrub cover, or steppe).

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Palaeodiversity 3: 59–87; Stuttgart 30 December 2010

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Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

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Dissertation presented to obtain the degree of Doctorate in Biochemistry by Instituto de Tecnologia Química e Biológica of Universidade Nova de Lisboa

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A thesis submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy in Sanitary Engineering in the Faculty of Sciences and Technology of the New University of Lisbon

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Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

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Dissertation presented to obtain the Ph.D degree in Biochemistry

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Thesis submitted to obtain the Doctoral degree in Energy and Bioenergy

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Climate change is emerging as one of the major threats to natural communities of the world’s ecosystems; and biodiversity hotspots, such as Madeira Island, might face a challenging future in the conservation of endangered land snails’ species. With this thesis, progresses have been made in order to properly understand the impact of climate on these vulnerable taxa; and species distribution models coupled with GIS and climate change scenarios have become crucial to understand the relations between species distribution and environmental conditions, identifying threats and determining biodiversity vulnerability. With the use of MaxEnt, important changes in the species suitable areas were obtained. Laurel forest species, highly dependent on precipitation and relative humidity, may face major losses on their future suitable areas, leading to the possible extinction of several endangered species, such as Leiostyla heterodon. Despite the complexity of the biological systems, the intrinsic uncertainty of species distribution models and the lack of information about land snails’ functional traits, this analysis contributed to a pioneer study on the impacts of climate change on endemic species of Madeira Island. The future inclusion of predictions of the effect of climate change on species distribution as part of IUCN assessments could contribute to species prioritizing, promoting specific management actions and maximizing conservation investment.

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Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.