23 resultados para Stated preference methods


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"Series title: Springerbriefs in applied sciences and technology, ISSN 2191-530X"

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"Series title: Springerbriefs in applied sciences and technology, ISSN 2191-530X"

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Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.

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This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

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Dissertação de mestrado em Técnicas de Caracterização e Análise Química

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"Series: Solid mechanics and its applications, vol. 226"

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In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.

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Background: Neonates show visual preference for their mother's face/voice and shift their attention from their mother to a stranger's face/voice after habituation. Aim: To assess neonate's mother versus stranger's face/voice visual preference, namely mother's anxiety and depression during the third pregnancy trimester and neonate's: 1) visual preference for the mother versus the stranger's face/voice (pretest visual preference), 2) habituation to the mother's face/voice and 3) visual preference for the stranger versus the mother's face/voice (posttest visual preference). Method: Mothers (N=100) filled out the Edinburgh Postnatal Depression Scale (EPDS) and the State Anxiety Inventory (STAI) both at the third pregnancy trimester and childbirth, and the “preference and habituation to the mother's face/voice versus stranger” paradigm was administered to their newborn 1 to 5 days after childbirth. Results: Neonates of anxious/depressed mothers during the third pregnancy trimester contrarily to neonates of non-anxious/non-depressed mothers did not look 1) longer at their mother's than at the stranger's face/voice at the pretest visual preference (showing no visual preference for the mother), nor 2) longer at the stranger's face/voice in the posttest than in the pretest visual preference (not improving their attention to the stranger's after habituation). Conclusion: Infants exposed to mother's anxiety/depression at the third gestational trimester exhibit less perceptual/social competencies at birth.