965 resultados para combined stage sintering model


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The effect of deformation parameters on the flow behavior of a Ti6Al4V alloy has been studied to understand the deformation mechanisms during hot compression. Cylindrical samples with partially equiaxed grains were deformed in the α+β phase region at different thermo-mechanical conditions. To develop components with tailored properties, the physically based Estrin and Mecking (EM) model for the work hardening/dynamic recovery combined with the Avrami equation for dynamic recrystallization was used to predict the flow stress at varying process conditions. The EM model revealed good predictability up to the peak strain, however, at strain rates below 0.01s-1, a higher B value was observed due to the reduced density of dislocation tangles. In contrast, the flow softening model revealed higher value of constants a and b at high strain rates due to the reduction in the volume fraction of dynamic recrystallization and larger peak strain. The predicted flow stress using the combined EM+Avrami model revealed good agreement with the measured flow stress resulted in very low average absolute relative error value. The microstructural analysis of the samples suggests the formation of coarse equiaxed grains together with the increased β phase fraction at low strain rate leads to a higher flow softening.

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Climate model projections show that climate change will further increase the risk of flooding in many regions of the world. There is a need for climate adaptation, but building new infrastructure or additional retention basins has its limits, especially in densely populated areas where open spaces are limited. Another solution is the more efficient use of the existing infrastructure. This research investigates a method for real-time flood control by means of existing gated weirs and retention basins. The method was tested for the specific study area of the Demer basin in Belgium but is generally applicable. Today, retention basins along the Demer River are controlled by means of adjustable gated weirs based on fixed logic rules. However, because of the high complexity of the system, only suboptimal results are achieved by these rules. By making use of precipitation forecasts and combined hydrological-hydraulic river models, the state of the river network can be predicted. To fasten the calculation speed, a conceptual river model was used. The conceptual model was combined with a Model Predictive Control (MPC) algorithm and a Genetic Algorithm (GA). The MPC algorithm predicts the state of the river network depending on the positions of the adjustable weirs in the basin. The GA generates these positions in a semi-random way. Cost functions, based on water levels, were introduced to evaluate the efficiency of each generation, based on flood damage minimization. In the final phase of this research the influence of the most important MPC and GA parameters was investigated by means of a sensitivity study. The results show that the MPC-GA algorithm manages to reduce the total flood volume during the historical event of September 1998 by 46% in comparison with the current regulation. Based on the MPC-GA results, some recommendations could be formulated to improve the logic rules.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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We propose a stage-structured integrodifference model for blowflies' growth and dispersion taking into account the density dependence of fertility and survival rates and the non-overlap of generations. We assume a discrete-time, stage-structured, model. The spatial dynamics is introduced by means of a redistribution kernel. We treat one and two dimensional cases, the latter on the semi-plane, with a reflexive boundary. We analytically show that the upper bound for the invasion front speed is the same as in the one-dimensional case. Using laboratory data for fertility and survival parameters and dispersal data of a single generation from a capture-recapture experiment in South Africa, we obtain an estimate for the velocity of invasion of blowflies of the species Chrysomya albiceps. This model predicts a speed of invasion which was compared to actual observational data for the invasion of the focal species in the Neotropics. Good agreement was found between model and observations.

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Aircraft composite structures must have high stiffness and strength with low weight, which can guarantee the increase of the pay-load for airplanes without losing airworthiness. However, the mechanical behavior of composite laminates is very complex due the inherent anisotropy and heterogeneity. Many researchers have developed different failure progressive analyses and damage models in order to predict the complex failure mechanisms. This work presents a damage model and progressive failure analysis that requires simple experimental tests and that achieves good accuracy. Firstly, the paper explains damage initiation and propagation criteria and a procedure to identify the material parameters. In the second stage, the model was implemented as a UMAT (User Material Subroutine), which is linked to finite element software, ABAQUS (TM), in order to predict the composite structures behavior. Afterwards, some case studies, mainly off-axis coupons under tensile or compression loads, with different types of stacking sequence were analyzed using the proposed material model. Finally, the computational results were compared to the experimental results, verifying the capability of the damage model in order to predict the composite structure behavior. (C) 2011 Elsevier Ltd. All rights reserved.

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Information on how species distributions and ecosystem services are impacted by anthropogenic climate change is important for adaptation planning. Palaeo data suggest that Abies alba formed forests under significantly warmer-than-present conditions in Europe and might be a native substitute for widespread drought-sensitive temperate and boreal tree species such as beech (Fagus sylvatica) and spruce (Picea abies) under future global warming conditions. Here, we combine pollen and macrofossil data, modern observations, and results from transient simulations with the LPX-Bern dynamic global vegetation model to assess past and future distributions of A. alba in Europe. LPX-Bern is forced with climate anomalies from a run over the past 21 000 years with the Community Earth System Model, modern climatology, and with 21st-century multimodel ensemble results for the high-emission RCP8.5 and the stringent mitigation RCP2.6 pathway. The simulated distribution for present climate encompasses the modern range of A. alba, with the model exceeding the present distribution in north-western and southern Europe. Mid-Holocene pollen data and model results agree for southern Europe, suggesting that at present, human impacts suppress the distribution in southern Europe. Pollen and model results both show range expansion starting during the Bølling–Allerød warm period, interrupted by the Younger Dryas cold, and resuming during the Holocene. The distribution of A. alba expands to the north-east in all future scenarios, whereas the potential (currently unrealized) range would be substantially reduced in southern Europe under RCP8.5. A. alba maintains its current range in central Europe despite competition by other thermophilous tree species. Our combined palaeoecological and model evidence suggest that A. alba may ensure important ecosystem services including stand and slope stability, infrastructure protection, and carbon sequestration under significantly warmer-than-present conditions in central Europe.

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Though E2F1 is deregulated in most human cancers by mutations of the p16-cyclin D-Rb pathway, it also exhibits tumor suppressive activity. A transgenic mouse model overexpressing E2F1 under the control of the bovine keratin 5 (K5) promoter exhibits epidermal hyperplasia and spontaneously develops tumors in the skin and other epithelial tissues after one year of age. In a p53-deficient background, aberrant apoptosis in K5 E2F1 transgenic epidermis is reduced and tumorigenesis is accelerated. In sharp contrast, K5 E2F1 transgenic mice are resistant to papilloma formation in the DMBA/TPA two-stage carcinogenesis protocol. K5 E2F4 and K5 DP1 transgenic mice were also characterized and both display epidermal hyperplasia but do not develop spontaneous tumors even in cooperation with p53 deficiency. These transgenic mice do not have increased levels of apoptosis in their skin and are more susceptible to papilloma formation in the two-stage carcinogenesis model. These studies show that deregulated proliferation does not necessarily lead to tumor formation and that the ability to suppress skin carcinogenesis is unique to E2F1. E2F1 can also suppress skin carcinogenesis when okadaic acid is used as the tumor promoter and when a pre-initiated mouse model is used, demonstrating that E2F1's tumor suppressive activity is not specific for TPA and occurs at the promotion stage. E2F1 was thought to induce p53-dependent apoptosis through upregulation of p19ARF tumor suppressor, which inhibits mdm2-mediated p53 degradation. Consistent with in vitro studies, the overexpression of E2F1 in mouse skin results in the transcriptional activation of the p19ARF and the accumulation of p53. Inactivation of either p19ARF or p53 restores the sensitivity of K5 E2F1 transgenic mice to DMBA/TPA carcinogenesis, demonstrating that an intact p19ARF-p53 pathway is necessary for E2F1 to suppress carcinogenesis. Surprisingly, while p53 is required for E2F1 to induce apoptosis in mouse skin, p19ARF is not, and inactivation of p19ARF actually enhances E2F1-induced apoptosis and proliferation in transgenic epidermis. This indicates that ARF is important for E2F1-induced tumor suppression but not apoptosis. Senescence is another potential mechanism of tumor suppression that involves p53 and p19ARF. K5 E2F1 transgenic mice initiated with DMBA and treated with TPA show an increased number of senescence cells in their epidermis. These experiments demonstrate that E2F1's unique tumor suppressive activity in two-stage skin carcinogenesis can be genetically separated from E2F1-induced apoptosis and suggest that senescence utilizing the p19ARF-p53 pathway plays a role in tumor suppression by E2F1. ^

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Three distinct, spatially separated crustal terranes have been recognised in the Shackleton Range, East Antarctica: the Southern, Eastern and Northern Terranes. Mafic gneisses from the Southern Terrane provide geochemical evidence for a within-plate, probably back-arc origin of their protoliths. A plume-distal ridge origin in an incipient ocean basin is the favoured interpretation for the emplacement site of these rocks at c. 1850 Ma, which, together with a few ocean island basalts, were subsequently incorporated into an accretionary continental arc/supra-subduction zone tectonic setting. Magmatic underplating resulted in partial melting of the lower crust, which caused high-temperature granulite-facies metamorphism in the Southern Terrane at c. 1710-1680 Ma. Mafic and felsic gneisses there are characterised by isotopically depleted, positive Nd and Hf initials and model ages between 2100 and 2000 Ma. They may be explained as juvenile additions to the crust towards the end of the Palaeoproterozoic. These juvenile rocks occur in a narrow, c. 150 km long E-W trending belt, inferred to trace a suture that is associated with a large Palaeoproterozoic accretionary orogenic system. The Southern Terrane contains many features that are similar to the Australo-Antarctic Mawson Continent and may be its furthermost extension into East Antarctica. The Eastern Terrane is characterised by metagranitoids that formed in a continental volcanic arc setting during a late Mesoproterozoic orogeny at c. 1060 Ma. Subsequently, the rocks experienced high-temperature metamorphism during Pan-African collisional tectonics at 600 Ma. Isotopically depleted zircon grains yielded Hf model ages of 1600-1400 Ma, which are identical to Nd model ages obtained from juvenile metagranitoids. Most likely, these rocks trace the suture related to the amalgamation of the Indo-Antarctic and West Gondwana continental blocks at ~600 Ma. The Eastern Terrane is interpreted as the southernmost extension of the Pan-African Mozambique/Maud Belt in East Antarctica and, based on Hf isotope data, may also represent a link to the Ellsworth-Whitmore Mountains block in West Antarctica and the Namaqua-Natal Province of southern Africa. Geochemical evidence indicates that the majority of the protoliths of the mafic gneisses in the Northern Terrane formed as oceanic island basalts in a within-plate setting. Subsequently the rocks were incorporated into a subduction zone environment and, finally, accreted to a continental margin during Pan-African collisional tectonics. Felsic gneisses there provide evidence for a within-plate and volcanic arc/collisional origin. Emplacement of granitoids occurred at c. 530 Ma and high-temperature, high-pressure metamorphism took place at 510-500 Ma. Enriched Hf and Nd initials and Palaeoproterozoic model ages for most samples indicate that no juvenile material was added to the crust of the Northern Terrane during the Pan-African Orogeny but recycling of older crust or mixing of crustal components of different age must have occurred. Isotopically depleted mafic gneisses, which are spatially associated with eclogite-facies pyroxenites, yielded late Mesoproterozoic Nd model ages. These rocks occur in a narrow, at least 100 km long, E-W trending belt that separates alkaline ocean island metabasalts and within-plate metagranitoids from volcanic arc metabasalts and volcanic arc/syn-collisional metagranitoids in the Northern Terrane. This belt is interpreted to trace the late Neoproterozoic/early Cambrian Pan-African collisional suture between the Australo-Antarctic and the combined Indo-Antarctic/West Gondwana continental blocks that formed during the final amalgamation of Gondwana.

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A model of the cognitive process of natural language processing has been developed using the formalism of generalized nets. Following this stage-simulating model, the treatment of information inevitably includes phases, which require joint operations in two knowledge spaces – language and semantics. In order to examine and formalize the relations between the language and the semantic levels of treatment, the language is presented as an information system, conceived on the bases of human cognitive resources, semantic primitives, semantic operators and language rules and data. This approach is applied for modeling a specific grammatical rule – the secondary predication in Russian. Grammatical rules of the language space are expressed as operators in the semantic space. Examples from the linguistics domain are treated and several conclusions for the semantics of the modeled rule are made. The results of applying the information system approach to the language turn up to be consistent with the stages of treatment modeled with the generalized net.

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This paper presents a vision that allows the combined use of model-driven engineering, run-time monitoring, and animation for the development and analysis of components in real-time embedded systems. Key building block in the tool environment supporting this vision is a highly-customizable code generation process. Customization is performed via a configuration specification which describes the ways in which input is provided to the component, the ways in which run-time execution information can be observed, and how these observations drive animation tools. The environment is envisioned to be suitable for different activities ranging from quality assurance to supporting certification, teaching, and outreach and will be built exclusively with open source tools to increase impact. A preliminary prototype implementation is described.

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A two-stage hybrid model for data classification and rule extraction is proposed. The first stage uses a Fuzzy ARTMAP (FAM) classifier with Q-learning (known as QFAM) for incremental learning of data samples, while the second stage uses a Genetic Algorithm (GA) for rule extraction from QFAM. Given a new data sample, the resulting hybrid model, known as QFAM-GA, is able to provide prediction pertaining to the target class of the data sample as well as to give a fuzzy if-then rule to explain the prediction. To reduce the network complexity, a pruning scheme using Q-values is applied to reduce the number of prototypes generated by QFAM. A 'don't care' technique is employed to minimize the number of input features using the GA. A number of benchmark problems are used to evaluate the effectiveness of QFAM-GA in terms of test accuracy, noise tolerance, model complexity (number of rules and total rule length). The results are comparable, if not better, than many other models reported in the literature. The main significance of this research is a usable and useful intelligent model (i.e., QFAM-GA) for data classification in noisy conditions with the capability of yielding a set of explanatory rules with minimum antecedents. In addition, QFAM-GA is able to maximize accuracy and minimize model complexity simultaneously. The empirical outcome positively demonstrate the potential impact of QFAM-GA in the practical environment, i.e., providing an accurate prediction with a concise justification pertaining to the prediction to the domain users, therefore allowing domain users to adopt QFAM-GA as a useful decision support tool in assisting their decision-making processes.