A Simple maxwell Based Model in Order to Represent the frequency-dependent viscosity measured by ultrasound

A previous work showed that viscosity values measured high frequency by ultrasound agreed with the values at low frequency by the rotational viscometer when conditions are met, such as relatively low frequency viscosity. However, these conditions strongly reduce the range of the measurement cell. In order to obtain a measurement range and sensitivity high frequency must used, but it causes a frequency-dependent decrease on the viscosity values. This work introduces a new simple in order to represent this frequency-dependent behavior.model is based on the Maxwell model for viscoelastic , but using a variable parameter. This parameter has physical meaning because it represents the linear behavior the apparent elasticity measured along with the viscosity by .Automotive oils SAE 90 and SAE 250 at 22.5±0.5oC viscosities at low frequency of 0.6 and 6.7 Pa.s, respectively,tested in the range of 1-5 MHz. The model was used in to fit the obtained data using an algorithm of non-linear in Matlab. By including the viscosity at low frequency an unknown fitting parameter, it is possible to extrapolate its . Relative deviations between the values measured by the and extrapolated using the model for the SAE 90 and SAE 250 oils were 5.0% and 15.7%, respectively.©2008 IEEE.

Equivalence between supersymmetric self-dual and Maxwell-Chern-Simons models coupled to a matter spinor superfield

We study the duality of the supersymmetric self-dual and Maxwell-Chern-Simons theories coupled to a fermionic matter superfield, using a master action. This approach evades the difficulties inherent to the quartic couplings that appear when matter is represented by a scalar superfield. The price is that the spinorial matter superfield represents a unusual supersymmetric multiplet, whose main physical properties we also discuss. (C) 2009 Elsevier B.V. All rights reserved.

Quantum equivalence between the self-dual and the Maxwell-Chern-Simons models nonlinearly coupled to U(1) scalar fields

The use of master actions to prove duality at quantum level becomes cumbersome if one of the dual fields interacts nonlinearly with other fields. This is the case of the theory considered here consisting of U(1) scalar fields coupled to a self-dual field through a linear and a quadratic term in the self-dual field. Integrating perturbatively over the scalar fields and deriving effective actions for the self-dual and the gauge field we are able to consistently neglect awkward extra terms generated via master action and establish quantum duality up to cubic terms in the coupling constant. The duality holds for the partition function and some correlation functions. The absence of ghosts imposes restrictions on the coupling with the scalar fields.

The benefits of tree-based models for stock selection

The identification of the primary drivers of stock returns has been of great interest to both financial practitioners and academics alike for many decades. Influenced by classical financial theories such as the CAPM (Sharp, 1964; Lintner, 1965) and APT (Ross, 1976), a linear relationship is conventionally assumed between company characteristics as derived from their financial accounts and forward returns. Whilst this assumption may be a fair approximation to the underlying structural relationship, it is often adopted for the purpose of convenience. It is actually quite rare that the assumptions of distributional normality and a linear relationship are explicitly assessed in advance even though this information would help to inform the appropriate choice of modelling technique. Non-linear models have nevertheless been applied successfully to the task of stock selection in the past (Sorensen et al, 2000). However, their take-up by the investment community has been limited despite the fact that researchers in other fields have found them to be a useful way to express knowledge and aid decision-making...

Moore meets maxwell

Moore's Law has driven the semiconductor revolution enabling over four decades of scaling in frequency, size, complexity, and power. However, the limits of physics are preventing further scaling of speed, forcing a paradigm shift towards multicore computing and parallelization. In effect, the system is taking over the role that the single CPU was playing: high-speed signals running through chips but also packages and boards connect ever more complex systems. High-speed signals making their way through the entire system cause new challenges in the design of computing hardware. Inductance, phase shifts and velocity of light effects, material resonances, and wave behavior become not only prevalent but need to be calculated accurately and rapidly to enable short design cycle times. In essence, to continue scaling with Moore's Law requires the incorporation of Maxwell's equations in the design process. Incorporating Maxwell's equations into the design flow is only possible through the combined power that new algorithms, parallelization and high-speed computing provide. At the same time, incorporation of Maxwell-based models into circuit and system-level simulation presents a massive accuracy, passivity, and scalability challenge. In this tutorial, we navigate through the often confusing terminology and concepts behind field solvers, show how advances in field solvers enable integration into EDA flows, present novel methods for model generation and passivity assurance in large systems, and demonstrate the power of cloud computing in enabling the next generation of scalable Maxwell solvers and the next generation of Moore's Law scaling of systems. We intend to show the truly symbiotic growing relationship between Maxwell and Moore!

James Clerk Maxwell e a unidade do mundo: modelos e metáforas na construção de teorias científicas

Esta é uma pesquisa sobre o uso de metáforas na construção de modelos por parte do físico escocês James Clerk Maxwell. O objetivo da pesquisa foi buscar compreender de que maneira o uso de metáforas e modelos é legítimo na ciência e em que medida contribui para seu sucesso. Além disso, busca compreender em que medida o uso de artifícios como modelos e analogias entre ramos distintos da ciência são impulsionadores de sucesso explicativo e preditivo da teoria do físico estudado. Explora as crenças teológicas e filosóficas do autor, que vê o mundo como unidade, permitindo a analogia entre ramos distintos da física. Seus desenvolvimentos em torno de teorias como calor, cores, óptica, magnetismo e eletricidade permitem evidenciar essa visão em todo o seu trabalho. Maxwell é considerado inaugurador de nova metodologia com o uso de modelos e metáforas. Explora o desenvolvimento da teoria das cores, da descrição matemática da estabilidade dos anéis de Saturno e o desenvolvimento da teoria dos gases como preâmbulo à discussão da teoria do eletromagnetismo. Descreve o desenvolvimento teórico do eletromagnetismo em seus diversos momentos. A construção da teoria do eletromagnetismo evidencia paulatino abandono do mecanicismo, uso intenso de modelos e metáforas temporários e ênfase na quantificação e no uso de experimentos. Discute o relacionamento de Maxwell com as discussões filosóficas, sociais e teológicas de sua época, seu engajamento em atividades práticas nesse sentido e suas influências científicas e filosóficas. Descreve e discute os textos filosóficos do cientista, em que se evidenciam sua ontologia, suas crenças teológicas e sua concepção de analogias. Discute a questão do uso de analogias em ciência e compara diversos autores que abordam o tema. A metodologia utilizada foi a de levantamento bibliográfico com análise crítica da literatura do autor e de seus comentadores, além de comentário crítico sobre os textos primários e secundários. Conclui que o sucesso científico de Maxwell deve-se à sua aposta numa unidade do mundo garantida por Deus, bem como na unidade entre o mundo e a mente humana, posturas que mostraram ser bem-sucedidas quando aplicadas à metodologia científica. Conclui também pela legitimidade e necessidade do uso de metáforas e modelos no empreendimento científico.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

DNA methylation profiling in cell models of diabetic nephropathy

We have previously identified differentially expressed genes in cell models of diabetic nephropathy and renal biopsies. Here we have performed quantitative DNA methylation profiling in cell models of diabetic nephropathy. Over 3,000 CpG units in the promoter regions of 192 candidate genes were assessed in unstimulated human mesangial cells (HMCs) and proximal tubular epithelial cells (PTCs) compared to HMCs or PTCs exposed to appropriate stimuli. A total of 301 CpG units across 38 genes (similar to 20%) were identified as differentially methylated in unstimulated HMCs versus PTCs. Analysis of amplicon methylation values in unstimulated versus stimulated cell models failed to demonstrate a >20% difference between amplicons. In conclusion, our results demonstrate that specific DNA methylation signatures are present in HMCs and PTCs, and standard protocols for exposure of renal cells to stimuli that alter gene expression may be insufficient to replicate possible alterations in DNA methylation profiles in diabetic nephropathy.

Stability results for the time-harmonic Maxwell equations with impedance boundary conditions

We consider the time-harmonic Maxwell equations with constant coefficients in a bounded, uniformly star-shaped polyhedron. We prove wavenumber-explicit norm bounds for weak solutions. This result is pivotal for convergence proofs in numerical analysis and may be a tool in the analysis of electromagnetic boundary integral operators.

Destructive weighted Poisson cure rate models

In this paper, we develop a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow a compound weighted Poisson distribution. This model is more flexible in terms of dispersion than the promotion time cure model. Moreover, it gives an interesting and realistic interpretation of the biological mechanism of the occurrence of event of interest as it includes a destructive process of the initial risk factors in a competitive scenario. In other words, what is recorded is only from the undamaged portion of the original number of risk factors.

Numerical prediction of three-dimensional time-dependent viscoelastic extrudate swell using differential and algebraic models

This study investigates the numerical simulation of three-dimensional time-dependent viscoelastic free surface flows using the Upper-Convected Maxwell (UCM) constitutive equation and an algebraic explicit model. This investigation was carried out to develop a simplified approach that can be applied to the extrudate swell problem. The relevant physics of this flow phenomenon is discussed in the paper and an algebraic model to predict the extrudate swell problem is presented. It is based on an explicit algebraic representation of the non-Newtonian extra-stress through a kinematic tensor formed with the scaled dyadic product of the velocity field. The elasticity of the fluid is governed by a single transport equation for a scalar quantity which has dimension of strain rate. Mass and momentum conservations, and the constitutive equation (UCM and algebraic model) were solved by a three-dimensional time-dependent finite difference method. The free surface of the fluid was modeled using a marker-and-cell approach. The algebraic model was validated by comparing the numerical predictions with analytic solutions for pipe flow. In comparison with the classical UCM model, one advantage of this approach is that computational workload is substantially reduced: the UCM model employs six differential equations while the algebraic model uses only one. The results showed stable flows with very large extrudate growths beyond those usually obtained with standard differential viscoelastic models. (C) 2010 Elsevier Ltd. All rights reserved.

COM-Poisson cure rate survival models and an application to a cutaneous melanoma data

In this paper, we develop a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models discussed in the literature. Next, we discuss the maximum likelihood estimation of the parameters of this cure rate survival model. Finally, we illustrate the usefulness of this model by applying it to a real cutaneous melanoma data. (C) 2009 Elsevier B.V. All rights reserved.

On duality of the noncommutative supersymmetric Maxwell-Chern-Simons theory

We study the possibility of establishing the dual equivalence between the noncommutative supersymmetric Maxwell-Chern-Simons theory and the noncommutative supersymmetric self-dual theory. It turns to be that whereas in the commutative case the Maxwell-Chern-Simons theory can be mapped into the sum of the self-dual theory and the Chern-Simons theory, in the noncommutative case such a mapping is possible only for the theory with modified Maxwell term. (c) 2008 Elsevier B.V. All rights reserved.