915 resultados para models of computation
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In extensions of the standard model with a heavy fourth generation, one important question is what makes the fourth-generation lepton sector, particularly the neutrinos, so different from the lighter three generations. We study this question in the context of models of electroweak symmetry breaking in warped extra dimensions, where the flavor hierarchy is generated by choosing the localization of the zero-mode fermions in the extra dimension. In this setup the Higgs sector is localized near the infrared brane, whereas the Majorana mass term is localized at the ultraviolet brane. As a result, light neutrinos are almost entirely Majorana particles, whereas the fourth-generation neutrino is mostly a Dirac fermion. We show that it is possible to obtain heavy fourth-generation leptons in regions of parameter space where the light neutrino masses and mixings are compatible with observation. We study the impact of these bounds, as well as the ones from lepton flavor violation, on the phenomenology of these models.
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We discuss the intriguing possibility that dark energy may change its equation of state in situations where large dark energy fluctuations are present. We show indications of this dynamical mutation in some generic models of dark energy.
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In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.
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Salicylaldehyde 2-chlorobenzoyl hydrazone (H(2)LASSBio-466), salicylaldehyde 4-chlorobenzoyl hydrazone (H(2)LASSBio-1064) and their complexes [Zn(LASSBio-466) H(2)O](2) (1) and [Zn(HLASSBio-1064) Cl](2) (2) were evaluated in animal models of peripheral and central nociception, and acute inflammation. All studied compounds significantly inhibited acetic acid-induced writhing response. Upon coordination the anti-nociceptive activity was favored in the complex 1. H(2)LASSBio-466 inhibited only the first phase of the formalin test, while 1 was active in the second phase, like indomethacin, indicating its ability to inhibit nociception associated with the inflammatory response. Hence coordination to zinc(II) altered the pharmacological profile of H(2)LASSBio-466. H(2)LASSBio-1064 inhibited both phases but this effect was not improved by coordination. The studied compounds did not increase the latency of response in the hot plate model, indicating their lack of central anti-nociceptive activity. All compounds showed levels of inhibition of zymosan-induced peritonitis comparable or superior to indomethacin, indicating an expressive anti-inflammatory profile.
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Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.
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Southeastern Brazil has seen dramatic landscape modifications in recent decades, due to expansion of agriculture and urban areas; these changes have influenced the distribution and abundance of vertebrates. We developed predictive models of ecological and spatial distributions of capybaras (Hydrochoerus hydrochaeris) using ecological niche modeling. Most Occurrences of capybaras were in flat areas with water bodies Surrounded by sugarcane and pasture. More than 75% of the Piracicaba River basin was estimated as potentially habitable by capybara. The models had low omission error (2.3-3.4%), but higher commission error (91.0-98.5%); these ""model failures"" seem to be more related to local habitat characteristics than to spatial ones. The potential distribution of capybaras in the basin is associated with anthropogenic habitats, particularly with intensive land use for agriculture.
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There is evidence that cognitions (beliefs) and mood contribute to physical disability and work status in people with chronic pain. However, most of the current evidence comes from North America and Europe. This study examined the contribution of demographic, pain and psychosocial factors to disability and work status in chronic pain patients in two matched samples from quite different countries (Australia and Brazil). Data were collected from 311 chronic pain patients in each country. The results suggest that although demographic and pain variables (especially pain levels) contribute disability, self-efficacy beliefs made a significant contribution to disability in both samples. Age and educational level also contributed to unemployment in both samples. But there were some differences, with self-efficacy and physical disability contributing to work status only in the Brazilian sample. In contrast, depression was the only psychological risk factor for unemployment in the Australian sample. Catastrophising and pain acceptance did not contribute to disability or unemployment in either sample. These findings confirm key aspects of biopsychosocial models of pain in two culturally and linguistically different chronic pain samples from different countries. They suggest that different chronic pain populations may share more similarities than differences. (C) 2008 European Federation of Chapters of the International Association for the Study of Pain. Published
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A role for the occipital or retrosplenial cortex in nociceptive processing has not been demonstrated yet, but connections from these cortices to brain structures involved in descending pain-inhibitory mechanisms were already demonstrated. This study demonstrated that the electrical stimulation of the occipital or retrosplenial cortex produces antinociception in the rat tail-flick and formalin tests. Bilateral lesions of the dorsolateral funiculus abolished the effect of cortical stimulation in the tail-flick test. Injection of glutamate into the same targets was also antinociceptive in the tail-flick test. No rats stimulated in the occipital or retrosplenial cortex showed any change in motor performance on the Rota-rod test, or had epileptiform changes in the EEG recording during or up to 3 hours after stimulation. The antinociception induced by occipital cortex stimulation persisted after neural block of the retrosplenial cortex. The effect of retrosplenial cortex stimulation also persisted after neural block of the occipital cortex. We conclude that stimulation of the occipital or retrosplenial cortex in rats leads to antinociception activating distinct descending pain-inhibitory mechanisms, and this is unlikely to result from a reduced motor performance or a postictal phenomenon. Perspective: This study presents evidence that stimulation of the retrosplenial or occipital cortex produces antinociception in rat models of acute pain. These findings enhance our understanding of the role of the cerebral cortex in control of pain. (C) 2010 by the American Pain Society
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This paper presents an investigation of design code provisions for steel-concrete composite columns. The study covers the national building codes of United States, Canada and Brazil, and the transnational EUROCODE. The study is based on experimental results of 93 axially loaded concrete-filled tubular steel columns. This includes 36 unpublished, full scale experimental results by the authors and 57 results from the literature. The error of resistance models is determined by comparing experimental results for ultimate loads with code-predicted column resistances. Regression analysis is used to describe the variation of model error with column slenderness and to describe model uncertainty. The paper shows that Canadian and European codes are able to predict mean column resistance, since resistance models of these codes present detailed formulations for concrete confinement by a steel tube. ANSI/AISC and Brazilian codes have limited allowance for concrete confinement, and become very conservative for short columns. Reliability analysis is used to evaluate the safety level of code provisions. Reliability analysis includes model error and other random problem parameters like steel and concrete strengths, and dead and live loads. Design code provisions are evaluated in terms of sufficient and uniform reliability criteria. Results show that the four design codes studied provide uniform reliability, with the Canadian code being best in achieving this goal. This is a result of a well balanced code, both in terms of load combinations and resistance model. The European code is less successful in providing uniform reliability, a consequence of the partial factors used in load combinations. The paper also shows that reliability indexes of columns designed according to European code can be as low as 2.2, which is quite below target reliability levels of EUROCODE. (C) 2009 Elsevier Ltd. All rights reserved.
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This paper presents the experimental results of 32 axially loaded concrete-filled steel tubular columns (CFT). The load was introduced only on the concrete core by means of two high strength steel cylinders placed at the column ends to evaluate the passive confinement provided by the steel tube. The columns were filled with structural concretes with compressive strengths of 30, 60, 80 and 100 MPa. The outer diameter (D) of the column was 114.3 mm, and the length/diameter (L/D) ratios considered were 3, 5, 7 and 10. The wall thicknesses of the tubes (t) were 3.35 mm and 6.0 mm, resulting in diameter/thickness (D/t) ratios of 34 and 19, respectively. The force vs. axial strain curves obtained from the tests showed, in general, a good post-peak behavior of the CFT columns, even for those columns filled with high strength concrete. Three analytical models of confinement for short concrete-filled columns found in the literature were used to predict the axial capacity of the columns tested. To apply these models to slender columns, a correction factor was introduced to penalize the calculated results, giving good agreement with the experimental values. Additional results of 63 CFT columns tested by other researchers were also compared to the predictions of the modified analytical models and presented satisfactory results. (C) 2009 Elsevier Ltd. All rights reserved.
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The health sector requires continuous investments to ensure the improvement of products and services from a technological standpoint, the use of new materials, equipment and tools, and the application of process management methods. Methods associated with the process management approach, such as the development of reference models of business processes, can provide significant innovations in the health sector and respond to the current market trend for modern management in this sector (Gunderman et al. (2008) [4]). This article proposes a process model for diagnostic medical X-ray imaging, from which it derives a primary reference model and describes how this information leads to gains in quality and improvements. (C) 2010 Elsevier Ireland Ltd. All rights reserved.
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This paper presents a free software tool that supports the next-generation Mobile Communications, through the automatic generation of models of components and electronic devices based on neural networks. This tool enables the creation, training, validation and simulation of the model directly from measurements made on devices of interest, using an interface totally oriented to non-experts in neural models. The resulting model can be exported automatically to a traditional circuit simulator to test different scenarios.
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This work examines the effect of weld strength mismatch on fracture toughness measurements defined by J and CTOD fracture parameters using single edge notch bend (SE(B)) specimens. A central objective of the present study is to enlarge on previous developments of J and CTOD estimation procedures for welded bend specimens based upon plastic eta factors (eta) and plastic rotational factors (r (p) ). Very detailed non-linear finite element analyses for plane-strain models of standard SE(B) fracture specimens with a notch located at the center of square groove welds and in the heat affected zone provide the evolution of load with increased crack mouth opening displacement required for the estimation procedure. One key result emerging from the analyses is that levels of weld strength mismatch within the range +/- 20% mismatch do not affect significantly J and CTOD estimation expressions applicable to homogeneous materials, particularly for deeply cracked fracture specimens with relatively large weld grooves. The present study provides additional understanding on the effect of weld strength mismatch on J and CTOD toughness measurements while, at the same time, adding a fairly extensive body of results to determine parameters J and CTOD for different materials using bend specimens with varying geometries and mismatch levels.
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Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.
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In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.