998 resultados para inventory models


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Neurological disorders are a major concern in modern societies, with increasing prevalence mainly related with the higher life expectancy. Most of the current available therapeutic options can only control and ameliorate the patients’ symptoms, often be-coming refractory over time. Therapeutic breakthroughs and advances have been hampered by the lack of accurate central nervous system (CNS) models. The develop-ment of these models allows the study of the disease onset/progression mechanisms and the preclinical evaluation of novel therapeutics. This has traditionally relied on genetically engineered animal models that often diverge considerably from the human phenotype (developmentally, anatomically and physiologically) and 2D in vitro cell models, which fail to recapitulate the characteristics of the target tissue (cell-cell and cell-matrix interactions, cell polarity). The in vitro recapitulation of CNS phenotypic and functional features requires the implementation of advanced culture strategies that enable to mimic the in vivo struc-tural and molecular complexity. Models based on differentiation of human neural stem cells (hNSC) in 3D cultures have great potential as complementary tools in preclinical research, bridging the gap between human clinical studies and animal models. This thesis aimed at the development of novel human 3D in vitro CNS models by integrat-ing agitation-based culture systems and a wide array of characterization tools. Neural differentiation of hNSC as 3D neurospheres was explored in Chapter 2. Here, it was demonstrated that human midbrain-derived neural progenitor cells from fetal origin (hmNPC) can generate complex tissue-like structures containing functional dopaminergic neurons, as well as astrocytes and oligodendrocytes. Chapter 3 focused on the development of cellular characterization assays for cell aggregates based on light-sheet fluorescence imaging systems, which resulted in increased spatial resolu-tion both for fixed samples or live imaging. The applicability of the developed human 3D cell model for preclinical research was explored in Chapter 4, evaluating the poten-tial of a viral vector candidate for gene therapy. The efficacy and safety of helper-dependent CAV-2 (hd-CAV-2) for gene delivery in human neurons was evaluated, demonstrating increased neuronal tropism, efficient transgene expression and minimal toxicity. The potential of human 3D in vitro CNS models to mimic brain functions was further addressed in Chapter 5. Exploring the use of 13C-labeled substrates and Nucle-ar Magnetic Resonance (NMR) spectroscopy tools, neural metabolic signatures were evaluated showing lineage-specific metabolic specialization and establishment of neu-ron-astrocytic shuttles upon differentiation. Chapter 6 focused on transferring the knowledge and strategies described in the previous chapters for the implementation of a scalable and robust process for the 3D differentiation of hNSC derived from human induced pluripotent stem cells (hiPSC). Here, software-controlled perfusion stirred-tank bioreactors were used as technological system to sustain cell aggregation and dif-ferentiation. The work developed in this thesis provides practical and versatile new in vitro ap-proaches to model the human brain. Furthermore, the culture strategies described herein can be further extended to other sources of neural phenotypes, including pa-tient-derived hiPSC. The combination of this 3D culture strategy with the implemented characterization methods represents a powerful complementary tool applicable in the drug discovery, toxicology and disease modeling.

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This work project is based on the MIES (Map of Innovation and Social Entrepreneurship in Portugal) database and it aims to understand the characteristics of social business models in the context of the portuguese market, by determining whether they follow the proposed characteristics by John Elkington and Pamela Hartigan, and then adding to their matrix. Furthermore, it tries to determine success patterns by comparing a group of successful social ventures with a group of less successful ones, with the objective of increasing the knowledge of social entrepreneurship as it applies to Portugal and provide a framework for future study.

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A large-scale inventory of trees > 10cm DBH was conducted in the upland "terra firme" rain forest of the Distrito Agropecuário da SUFRAMA (Manaus Free Zone Authority Agricultural District) approximately 65Km north of the city of Manaus (AM), Srasil. Thegeneral appearance and structure of the forest is described together with local topography and soil texture. Thepreliminary results of the Inventory provide a minimum estimate of 698 tree species in 53 families in the 40Km radius sampled, including 17 undescribed species. Themost numerically abundant families, Lecythidaceae, Leguminosae, 5apotaceae and Burseraceae as also among the most species rich families. One aspect of this diverse assemblage is the proliferation of species within certain genera, Including 26 genera In 17 families with 6 or more species or morphospecies. Most species have very low abundances of less than 1 tree per hectare. While more abundant species do exist at densities ranging up to a mean of 12 trees per ha, many have clumped distributions leading to great variation in local species abundance. The degree of similarity between hectare samples based int the Coefficient of Community similarity Index varies widely over different sample hectares for five ecologically different families. Soil texture apparently plays a significant role In determining species composition in the different one hectare plots examined while results for other variable were less consistent. Greater differences in similarity indices are found for comparisons with a one hectare sample within the same formation approximately 40Km to the south. It is concluded that homogeneity of tree community composition within this single large and diverse yet continuous upland forest formation can not be assumed.

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This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies.

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Developing and implementing data-oriented workflows for data migration processes are complex tasks involving several problems related to the integration of data coming from different schemas. Usually, they involve very specific requirements - every process is almost unique. Having a way to abstract their representation will help us to better understand and validate them with business users, which is a crucial step for requirements validation. In this demo we present an approach that provides a way to enrich incrementally conceptual models in order to support an automatic way for producing their correspondent physical implementation. In this demo we will show how B2K (Business to Kettle) system works transforming BPMN 2.0 conceptual models into Kettle data-integration executable processes, approaching the most relevant aspects related to model design and enrichment, model to system transformation, and system execution.

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ETL conceptual modeling is a very important activity in any data warehousing system project implementation. Owning a high-level system representation allowing for a clear identification of the main parts of a data warehousing system is clearly a great advantage, especially in early stages of design and development. However, the effort to model conceptually an ETL system rarely is properly rewarded. Translating ETL conceptual models directly into something that saves work and time on the concrete implementation of the system process it would be, in fact, a great help. In this paper we present and discuss a hybrid approach to this problem, combining the simplicity of interpretation and power of expression of BPMN on ETL systems conceptualization with the use of ETL patterns to produce automatically an ETL skeleton, a first prototype system, which has the ability to be executed in a commercial ETL tool like Kettle.

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This work reports the implementation and verification of a new so lver in OpenFOAM® open source computational library, able to cope with integral viscoelastic models based on the integral upper-convected Maxwell model. The code is verified through the comparison of its predictions with analytical solutions and numerical results obtained with the differential upper-convected Maxwell model

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In this study, we concentrate on modelling gross primary productivity using two simple approaches to simulate canopy photosynthesis: "big leaf" and "sun/shade" models. Two approaches for calibration are used: scaling up of canopy photosynthetic parameters from the leaf to the canopy level and fitting canopy biochemistry to eddy covariance fluxes. Validation of the models is achieved by using eddy covariance data from the LBA site C14. Comparing the performance of both models we conclude that numerically (in terms of goodness of fit) and qualitatively, (in terms of residual response to different environmental variables) sun/shade does a better job. Compared to the sun/shade model, the big leaf model shows a lower goodness of fit and fails to respond to variations in the diffuse fraction, also having skewed responses to temperature and VPD. The separate treatment of sun and shade leaves in combination with the separation of the incoming light into direct beam and diffuse make sun/shade a strong modelling tool that catches more of the observed variability in canopy fluxes as measured by eddy covariance. In conclusion, the sun/shade approach is a relatively simple and effective tool for modelling photosynthetic carbon uptake that could be easily included in many terrestrial carbon models.

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This review deals with the recent developments and present status of the theoretical models for the simulation of the performance of lithium ion batteries. Preceded by a description of the main materials used for each of the components of a battery -anode, cathode and separator- and how material characteristics affect battery performance, a description of the main theoretical models describing the operation and performance of a battery are presented. The influence of the most relevant parameters of the models, such as boundary conditions, geometry and material characteristics are discussed. Finally, suggestions for future work are proposed.

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Programa Doutoral em Engenharia Industrial e de Sistemas.

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Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

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Currently in Portugal academic excellence in higher education is given recognition through merit prizes. Because honours students can be seen as having the potential to achieve imporant levels of professional expertise, especially if they have some personal skills, it is important to know not only the marks of the students but also the personal characteristics that contribute to academic success and which may also be important for career success. Some theoretical models of giftedness and excellence agree with this idea and this work considers the latest contribution of Renzulli (2005) where it is pointed out that excellent achievements result from the combination of motivational, intellectual and creative factors as well as from co-cognitive factors which are most associated with personality functioning in a particular context. The main purpose of this paper is to analyze how honours students differ from their classmates in various psychological dimensions: Cognition-creativity, Motivation and Learning Strategies, Persistence, Social Interaction, Drive for Excellence and Cultural Interest. These dimensions were measured by the Inventory of Psychological Characteristics Associated with Academic Performance (ICPADA), which was constructed taking into consideration the previous study of Scaeger et al. (2012). The sample included 914 Portuguese higher educatoin students from a first cycle degree in the Bologna process. Participants were selected from three different fields of study: Social and Human Sciences; Science and Technology; Arts and Humanities. The data collected through the administration of the ICPADA was presented, and the results revealed a higher self-perception by honours students in all areas analyzed, with the exception of the dimension of social interaction. In addition an interaction effect was revealed for persistence, social interaction, and cultural interest. The field of study and whether the participants were honours students or not were also taken into account. Some implications for future studies are presented here along with possible interventions for honours students.

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A preliminary survey of the spider fauna in natural and artificial forest gap formations at “Porto Urucu”, a petroleum/natural gas production facility in the Urucu river basin, Coari, Amazonas, Brazil is presented. Sampling was conducted both occasionally and using a protocol composed of a suite of techniques: beating trays (32 samples), nocturnal manual samplings (48), sweeping nets (16), Winkler extractors (24), and pitfall traps (120). A total of 4201 spiders, belonging to 43 families and 393 morphospecies, were collected during the dry season, in July, 2003. Excluding the occasional samples, the observed richness was 357 species. In a performance test of seven species richness estimators, the Incidence Based Coverage Estimator (ICE) was the best fit estimator, with 639 estimated species. To evaluate differences in species richness associated with natural and artificial gaps, samples from between the center of the gaps up to 300 meters inside the adjacent forest matrix were compared through the inspection of the confidence intervals of individual-based rarefaction curves for each treatment. The observed species richness was significantly higher in natural gaps combined with adjacent forest than in the artificial gaps combined with adjacent forest. Moreover, a community similarity analysis between the fauna collected under both treatments demonstrated that there were considerable differences in species composition. The significantly higher abundance of Lycosidae in artificial gap forest is explained by the presence of herbaceous vegetation in the gaps themselves. Ctenidae was significantly more abundant in the natural gap forest, probable due to the increase of shelter availability provided by the fallen trees in the gaps themselves. Both families are identified as potential indicators of environmental change related to the establishment or recovery of artificial gaps in the study area.

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Depression is an extremely heterogeneous disorder. Diverse molecular mechanisms have been suggested to underlie its etiology. To understand the molecular mechanisms responsible for this complex disorder, researchers have been using animal models extensively, namely mice from various genetic backgrounds and harboring distinct genetic modifications. The use of numerous mouse models has contributed to enrich our knowledge on depression. However, accumulating data also revealed that the intrinsic characteristics of each mouse strain might influence the experimental outcomes, which may justify some conflicting evidence reported in the literature. To further understand the impact of the genetic background, we performed a multimodal comparative study encompassing the most relevant parameters commonly addressed in depression, in three of the most widely used mouse strains: Balb/c, C57BL/6, and CD-1. Moreover, female mice were selected for this study taken into account the higher prevalence of depression in women and the fewer animal studies using this gender. Our results show that Balb/c mice have a more pronounced anxious-like behavior than CD-1 and C57BL/6 mice, whereas C57BL/6 animals present the strongest depressive-like trait. Furthermore, C57BL/6 mice display the highest rate of proliferating cells and brain-derived neurotrophic factor (Bdnf) expression levels in the hippocampus, while hippocampal dentate granular neurons of Balb/c mice show smaller dendritic lengths and fewer ramifications. Of notice, the expression levels of inducible nitric oxide synthase (iNos) predict 39.5% of the depressive-like behavior index, which suggests a key role of hippocampal iNOS in depression. Overall, this study reveals important interstrain differences in several behavioral dimensions and molecular and cellular parameters that should be considered when preparing and analyzing experiments addressing depression using mouse models. It further contributes to the literature by revealing the predictive value of hippocampal iNos expression levels in depressive-like behavior, irrespectively of the mouse strain.

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Dissertação de mestrado em Engenharia de Sistemas