857 resultados para Brand Loyalty, Functional Approach, Definition, Qualitative
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In this paper, we report a novel approach using peptide CALNN and its derivative CALNNGGRRRRRRRR (CALNNR(8)) to functionalize gold nanoparticles for intracellular component targeting. The translocation is effected by the nanoparticle diameter and CALNNR8 surface coverage. The intracellular distributions of the complexes are change from the cellular nucleus to the endoplasmic reticulum by increasing the density of CALNNR8 at a constant nanoparticle diameter. Additionally, increasing the nanoparticle diameter at a constant density of CALNNR8 leads to less cellular internalization.
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Zooplankton plays a vital role in marine ecosystems. Variations in the zooplankton species composition, biomass, and secondary production will change the structure and function of the ecosystem. How to describe this process and make it easier to be modeled in the Yellow Sea ecosystem is the main purpose of this paper. The zooplankton functional groups approach, which is considered a good method of linking the structure of food webs and the energy flow in the ecosystems, is used to describe the main contributors of secondary produciton of the Yellow Sea ecosystem. The zooplankton can be classified into six functional groups: giant crustaceans, large copepods, small copepods, chaetognaths, medusae, and salps. The giant crustaceans, large copepods, and small copepods groups, which are the main food resources for fish, are defined depending on the size spectrum. Medusae and chaetognaths are the two gelatinous carnivorous groups, which compete with fish for food. The salps group, acting as passive filter-feeders, competes with other species feeding on phytoplankton, but their energy could not be efficiently transferred to higher trophic levels. From the viewpoint of biomass, which is the basis of the food web, and feeding activities, the contributions of each functional group to the ecosystem were evaluated; the seasonal variations, geographical distribution patterns, and species composition of each functional group were analyzed. The average zooplankton biomass was 2.1 g dry wt m(-2) in spring, to which the giant crustaceans, large copepods, and small copepods contributed 19, 44, and 26%, respectively. High biomasses of the large copepods and small copepods were distributed at the coastal waters, while the giant crustaceans were mainly located at offshore area. In summer, the mean biomass was 3.1 g dry wt m(-2), which was mostly contributed by the giant crustaceans (73%), and high biomasses of the giant crustaceans, large copepods, and small copepods were all distributed in the central part of the Yellow Sea. During autumn, the mean biomass was 1.8 g dry wt m(-2), which was similarly constituted by the giant crustaceans, large copepods, and small copepods (36, 33, and 23%, respectively), and high biomasses of the giant crustaceans and large copepods occurred in the central part of the Yellow Sea, while the small copepods were mainly located at offshore stations. The giant crustaceans and large copepods dominated the zooplankton biomass (2.9 g dry wt m(-2)) in winter, contributing respectively 57 and 27%, and they, as well as the small copepods, were all mainly located in the central part of the Yellow Sea. The chaetognaths group was mainly located in the northern part of the Yellow Sea during all seasons, but contributed less to the biomass compared with the other groups. The medusae and salps groups were distributed unevenly, with sporadic dynamics, mainly along the coastline and at the northern part of the Yellow Sea. No more than 10 species belonging to the respective functional groups dominated the zooplankton biomass and controlled the dynamics of the zooplankton community. The clear picture of the seasonal and spatial variations of each zooplankton functional group makes the complicated Yellow Sea ecosystem easier to be understood and modeled. (C) 2010 Elsevier Ltd. All rights reserved.
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An investigation in innovation management and entrepreneurial management is conducted in this thesis. The aim of the research is to explore changes of innovation styles in the transformation process from a start-up company to a more mature phase of business, to predict in a second step future sustainability and the probability of success. As businesses grow in revenue, corporate size and functional complexity, various triggers, supporters and drivers affect innovation and company's success. In a comprehensive study more than 200 innovative and technology driven companies have been examined and compared to identify patterns in different performance levels. All of them have been founded under the same formal requirements of the Munich Business Plan Competition -a research approach which allowed a unique snapshot that only long-term studies would be able to provide. The general objective was to identify the correlation between different factors, as well as different dimensions, to incremental and radical innovations realised. The 12 hypothesis were formed to prove have been derived from a comprehensive literature review. The relevant academic and practitioner literature on entrepreneurial, innovation, and knowledge management as well as social network theory revealed that the concept of innovation has evolved significantly over the last decade. A review of over 15 innovation models/frameworks contributed to understand what innovation in context means and what the dimensions are. It appears that the complex theories of innovation can be described by the increasing extent of social ingredients in the explanation of innovativeness. Originally based on tangible forms of capital, and on the necessity of pull and technology push, innovation management is today integrated in a larger system. Therefore, two research instruments have been developed to explore the changes in innovations styles. The Innovation Management Audits (IMA Start-up and IMA Mature) provided statements related to product/service development, innovativeness in various typologies, resources for innovations, innovation capabilities in conjunction to knowledge and management, social networks as well as the measurement of outcomes to generate high-quality data for further exploration. In obtaining results the mature companies have been clustered in the performance level low, average and high, while the start-up companies have been kept as one cluster. Firstly, the analysis exposed that knowledge, the process of acquiring knowledge, interorganisational networks and resources for innovations are the most important driving factors for innovation and success. Secondly, the actual change of the innovation style provides new insights about the importance of focusing on sustaining success and innovation ii 16 key areas. Thirdly, a detailed overview of triggers, supporters and drivers for innovation and success for each dimension support decision makers in putting their company in the right direction. Fourthly, a critical review of contemporary strategic management in conjunction to the findings provides recommendation of how to apply well-known management tools. Last but not least, the Munich cluster is analysed providing an estimation of the success probability of the different performance cluster and start-up companies. For the analysis of the probability of success of the newly developed as well as statistically and qualitative validated ICP Model (Innovativeness, Capabilities & Potential) has been developed and applied. While the model was primarily developed to evaluate the probability of success of companies; it has equal application in the situation to measure innovativeness to identify the impact of various strategic initiatives within small or large enterprises. The main findings of the model are that competitor, and customer orientation and acquiring knowledge important for incremental and radical innovation. Formal and interorganisation networks are important to foster innovation but informal networks appear to be detrimental to innovation. The testing of the ICP model h the long term is recommended as one subject of further research. Another is to investigate some of the more intangible aspects of innovation management such as attitude and motivation of mangers. IV
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Integrating connectivity patterns into marine ecosystem management is a fundamental step, specially for stock subjected to the combined impacts of human activities (overfishing, habitat degradation, etc.) and climate changes. Thus, management of marine resources must incorporates the spatial scales over which the populations are connected. Notwithstanding, studying these dynamics remains a crucial and hard task and the predictions of the temporal and spatial patterns of these mechanisms are still particularly challenging. This thesis aims to puzzle over the red mullet Mullus barbatus population connectivity in the Western Mediterranean Sea, by implementing a multidisciplinary approach. Otolith sclerochronology, larval dispersal modelling and genetic techniques were gathered in this study. More particularly, this research project focused on early life history stages of red mullet and their role in the characterization of connectivity dynamics. The results show that M. barbatus larval dispersal distances can reach a range of 200 km. The differences in early life traits (i.e. PLD, spawning and settlement dates) observed between various areas of the Western Mediterranean Sea suggest a certain level of larval patchiness, likely due to the occurrence of different spawning pulses during the reproductive period. The dispersal of individuals across distant areas, even not significant in demographic terms, is accountable for the maintenance of the genetic flow among different demes. Fluctuations in the level of exchange among different areas, due to the variability of the source-sink dynamics, could have major implications in the population connectivity patterns. These findings highlight the reliability of combining several approaches and represent a benchmark for the definition of a proper resource management, with considerable engagements in effectively assuring the beneficial effects of the existent and future conservation strategies.
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C.J.Price, D.R.Pugh, N.A.Snooke, J.E.Hunt, M.S.Wilson, Combining Functional and Structural Reasoning for Safety Analysis of Electrical Designs, Knowledge Engineering Review, vol 12:3, pp.271-287, 1997.
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Lee M.H., Model-Based Reasoning: A Principled Approach for Software Engineering, Software - Concepts and Tools,19(4), pp179-189, 2000.
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M. H. Lee, and S. M. Garrett, Qualitative modelling of unknown interface behaviour, International Journal of Human Computer Studies, Vol. 53, No. 4, pp. 493-515, 2000
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Lee M.H., Qualitative Circuit Models in Failure Analysis Reasoning, AI Journal. vol 111, pp239-276.1999.
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King, R.D., Garrett, S.M., Coghill, G.M. (2005). On the use of qualitative reasoning to simulate and identify metabolic pathways. Bioinformatics 21(9):2017-2026 RAE2008
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Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop
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Prescott, S. (2005). The Cambrian Muse: Welsh Identity and Hanoverian Loyalty in the Poems of Jane Brereton (1685-1740). Eighteenth -Century Studies. 38(4), pp.587-603. RAE2008
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Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências da Comunicação, com especialização em Marketing e Comunicação Estratégica.
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Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais, especialidade em Psicologia
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Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais, especialidade em Psicologia