A Robust, Fully Adaptive Hybrid Level-Set/Front-Tracking Method for Two-Phase Flows with an Accurate Surface Tension Computation

We present a variable time step, fully adaptive in space, hybrid method for the accurate simulation of incompressible two-phase flows in the presence of surface tension in two dimensions. The method is based on the hybrid level set/front-tracking approach proposed in [H. D. Ceniceros and A. M. Roma, J. Comput. Phys., 205, 391400, 2005]. Geometric, interfacial quantities are computed from front-tracking via the immersed-boundary setting while the signed distance (level set) function, which is evaluated fast and to machine precision, is used as a fluid indicator. The surface tension force is obtained by employing the mixed Eulerian/Lagrangian representation introduced in [S. Shin, S. I. Abdel-Khalik, V. Daru and D. Juric, J. Comput. Phys., 203, 493-516, 2005] whose success for greatly reducing parasitic currents has been demonstrated. The use of our accurate fluid indicator together with effective Lagrangian marker control enhance this parasitic current reduction by several orders of magnitude. To resolve accurately and efficiently sharp gradients and salient flow features we employ dynamic, adaptive mesh refinements. This spatial adaption is used in concert with a dynamic control of the distribution of the Lagrangian nodes along the fluid interface and a variable time step, linearly implicit time integration scheme. We present numerical examples designed to test the capabilities and performance of the proposed approach as well as three applications: the long-time evolution of a fluid interface undergoing Rayleigh-Taylor instability, an example of bubble ascending dynamics, and a drop impacting on a free interface whose dynamics we compare with both existing numerical and experimental data.

The Use of On-Line Computer adaptive Testing to raise Standardized Test Scores

Nursing school graduates are under pressure to pass the RN-NCLEX Exam on the first attempt since New York State monitors the results and uses them to evaluate the school’s nursing programs. Since the RN-NCLEX Exam is a standardized test, we sought a method to make our students better test takers. The use of on-line computer adaptive testing has raised our student’s standardized test scores at the end of the nursing course.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved.

Adaptive device with underlying mechanism defined by a programming language

An adaptive device is made up of an underlying mechanism, for instance, an automaton, a grammar, a decision tree, etc., to which is added an adaptive mechanism, responsible for allowing a dynamic modification in the structure of the underlying mechanism. This article aims to investigate if a programming language can be used as an underlying mechanism of an adaptive device, resulting in an adaptive language.

Conception of adaptive programming languages

Adaptive devices show the characteristic of dynamically change themselves in response to input stimuli with no interference of external agents. Occasional changes in behaviour are immediately detected by the devices, which right away react spontaneously to them. Chronologically such devices derived from researches in the field of formal languages and automata. However, formalism spurred applications in several other fields. Based on the operation of adaptive automata, the elementary ideas generanting programming adaptive languages are presented.

Adaptive languages and a new programming style

A programming style can be seen as a particular model of shaping thought or a special way of codifying language to solve a problem. Adaptive languages have the basic feature of allowing the expression of programs which self-modifying through adaptive actions at runtime. The conception of such languages calls for a new programming style, since the application of adaptive technology in the field of programming languages suggests a new way of thinking. With the adaptive style, programming language codes can be structured in such a way that the codified program therein modifies or adapts itself towards the needs of the problem. The adaptive programming style may be a feasible alternate way to obtain self-modifying consistent codes, which allow its use in modern applications for self-modifying code.

Using adaptive automata in a multi-paradigm programming environment

In this paper the architecture of an experimental multiparadigmatic programming environment is sketched, showing how its parts combine together with application modules in order to perform the integration of program modules written in different programming languages and paradigms. Adaptive automata are special self-modifying formal state machines used as a design and implementation tool in the representation of complex systems. Adaptive automata have been proven to have the same formal power as Turing Machines. Therefore, at least in theory, arbitrarily complex systems may be modeled with adaptive automata. The present work briefly introduces such formal tool and presents case studies showing how to use them in two very different situations: the first one, in the name management module of a multi-paradigmatic and multi-language programming environment, and the second one, in an application program implementing an adaptive automaton that accepts a context-sensitive language.

Testing unit root based on partially adaptive estimation

This paper proposes unit tests based on partially adaptive estimation. The proposed tests provide an intermediate class of inference procedures that are more efficient than the traditional OLS-based methods and simpler than unit root tests based on fully adptive estimation using nonparametric methods. The limiting distribution of the proposed test is a combination of standard normal and the traditional Dickey-Fuller (DF) distribution, including the traditional ADF test as a special case when using Gaussian density. Taking into a account the well documented characteristic of heavy-tail behavior in economic and financial data, we consider unit root tests coupled with a class of partially adaptive M-estimators based on the student-t distributions, wich includes te normal distribution as a limiting case. Monte Carlo Experiments indicate that, in the presence of heavy tail distributions or innovations that are contaminated by outliers, the proposed test is more powerful than the traditional ADF test. We apply the proposed test to several macroeconomic time series that have heavy-tailed distributions. The unit root hypothesis is rejected in U.S. real GNP, supporting the literature of transitory shocks in output. However, evidence against unit roots is not found in real exchange rate and nominal interest rate even haevy-tail is taken into a account.

The Brazilian credit market for small and medium-sized firms: an adaptive marketing approach

Neste trabalho, o mercado brasileiro de crédito para pequenas e médias empresas (PMEs) é analisado sob a perspectiva do marketing adaptativo, em que se assume que atividades mercadológicas como segmentação, gestão de relacionamento com clientes, apreçamento e desenvolvimento de produtos, são determinadas pela utilidade obtida por agentes de mercado ao atenderem a demanda. Identifica-se que a existência de assimetria de informações e de custos de transação limita e direciona as atividades de marketing no mercado estudado. A partir de uma amostra com 65.535 propostas de crédito, recebidas e avaliadas por um grande banco brasileiro entre janeiro de 2004 e setembro de 2006, estima-se a utilidade do banco em operações de crédito. Adicionalmente, 17.149 transações de empréstimos concedidos pelo banco ao segmento de pequenas empresas entre abril de 2006 e março de 2007, são investigadas. Finalmente, um conjunto de dados com 1,636 registros obtidos pela junção das bases de dados de propostas e de transações mencionados, é analisado em termos das relações entre taxas de juros e os totais de cobertura oferecidas por meio de garantias de crédito. Os resultados revelam a existência de um ambiente de marketing adaptativo, em que os pequenos tomadores de crédito produtivo são racionados, e aceitam pagar taxas de juros mais elevadas do que outros segmentos. Produtos de créditos baseados em garantias líquidas e com altas taxas de juros são desenvolvidos para suprir de maneira oportuna este segmento racionado de pequenas empresas. Ademais, a utilidade do banco em operações de crédito é afetada pela informação privada que captura ao longo de relacionamentos mantidos com seus cientes. Os resultados implicam que o sistema de marketing financeiro brasileiro não desempenha papel formativo no desenvolvimento econômico, que seria de fomento ao crédito produtivo por meio de empréstimos a baixo custo para pequenas e médias empresas. Um sistema formativo de marketing é improvável em um ambiente com informação imperfeita, como o mercado de crédito brasileiro. O estudo traz informações úteis àqueles interessados no desenvolvimento de mercados de crédito produtivo, tais como profissionais de instituições financeiras; agentes responsáveis por políticas públicas e monetárias de fomento ao crédito; e empreendedores de pequeno e médio porte que necessitem de financiamento externo para seus negócios.

A unit root test based on partially adaptive estimation

This paper constructs a unit root test baseei on partially adaptive estimation, which is shown to be robust against non-Gaussian innovations. We show that the limiting distribution of the t-statistic is a convex combination of standard normal and DF distribution. Convergence to the DF distribution is obtaineel when the innovations are Gaussian, implying that the traditional ADF test is a special case of the proposed testo Monte Carlo Experiments indicate that, if innovation has heavy tail distribution or are contaminated by outliers, then the proposed test is more powerful than the traditional ADF testo Nominal interest rates (different maturities) are shown to be stationary according to the robust test but not stationary according to the nonrobust ADF testo This result seems to suggest that the failure of rejecting the null of unit root in nominal interest rate may be due to the use of estimation and hypothesis testing procedures that do not consider the absence of Gaussianity in the data.Our results validate practical restrictions on the behavior of the nominal interest rate imposed by CCAPM, optimal monetary policy and option pricing models.

Light exercise causes adaptive changes in muscles of young Brasileiro de Hipismo horses

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)