降雨触发滑坡的非线性模型与斜坡演化的混沌效应

This paper analyzes landsliding process by nonlinear theories, especially the influence mechanism of external factors (such as rainfall and groundwater) on slope evolution. The author investigates landslide as a consequence of the catastrophic slide of initially stationary or creeping slope triggered by a small perturbation. A fully catastrophe analysis is done for all possible scenarios when a continuous change is imposed to the control parameters. As the slip surface continues and erosion due to rainfall occurs, control parameters of the slip surface may evolve such that a previously stable slope may become unstable (e.g. catastrophe occurs), when a small perturbation is imposed. Thus the present analysis offers a plausible explanation to why slope failure occurs at a particular rainfall, which is not the largest in the history of the slope. It is found, by analysis on the nonlinear dynamical model of the evolution process of slope built, that the relationship between the action of external environment factors and the response of the slope system is complicatedly nonlinear. When the nonlinear action of slope itself is equivalent to the acting ability of external environment, the chaotic phenomenon appears in the evolution process of slope, and its route leading to chaos is realized with bifurcation of period-doublings. On the basis of displacement time series of the slope, a nonlinear dynamic model is set up by improved Backus generalized linear inversion theory in this paper. Due to the equivalence between autonomous gradient system and catastrophe model, a standard cusp catastrophe model can be obtained through variable substitution. The method is applied to displacement data of Huangci landslide and Wolongsi landslide, to show how slopes evolve before landsliding. There is convincing statistical evidence to believe that the nonlinear dynamic model can make satisfied prediction results. Most important of all, we find that there is a sudden fall of D, which indicates the occurrence of catastrophe (when D=0).

INVESTIGATION OF BUBBLE DYNAMICS AND HEATING DURING FOCUSED ULTRASOUND INSONATION IN TISSUE-MIMICKING MATERIALS

The deposition of ultrasonic energy in tissue can cause tissue damage due to local heating. For pressures above a critical threshold, cavitation will occur in tissue and bubbles will be created. These oscillating bubbles can induce a much larger thermal energy deposition in the local region. Traditionally, clinicians and researchers have not exploited this bubble-enhanced heating since cavitation behavior is erratic and very difficult to control. The present work is an attempt to control and utilize this bubble-enhanced heating. First, by applying appropriate bubble dynamic models, limits on the asymptotic bubble size distribution are obtained for different driving pressures at 1 MHz. The size distributions are bounded by two thresholds: the bubble shape instability threshold and the rectified diffusion threshold. The growth rate of bubbles in this region is also given, and the resulting time evolution of the heating in a given insonation scenario is modeled. In addition, some experimental results have been obtained to investigate the bubble-enhanced heating in an agar and graphite based tissue- mimicking material. Heating as a function of dissolved gas concentrations in the tissue phantom is investigated. Bubble-based contrast agents are introduced to investigate the effect on the bubble-enhanced heating, and to control the initial bubble size distribution. The mechanisms of cavitation-related bubble heating are investigated, and a heating model is established using our understanding of the bubble dynamics. By fitting appropriate bubble densities in the ultrasound field, the peak temperature changes are simulated. The results for required bubble density are given. Finally, a simple bubbly liquid model is presented to estimate the shielding effects which may be important even for low void fraction during high intensity focused ultrasound (HIFU) treatment.

Thermodynamic analysis of ligand-induced changes in protein thermal unfolding applied to high-throughput determination of ligand affinities with extrinsic fluorescent dyes.

The quantification of protein-ligand interactions is essential for systems biology, drug discovery, and bioengineering. Ligand-induced changes in protein thermal stability provide a general, quantifiable signature of binding and may be monitored with dyes such as Sypro Orange (SO), which increase their fluorescence emission intensities upon interaction with the unfolded protein. This method is an experimentally straightforward, economical, and high-throughput approach for observing thermal melts using commonly available real-time polymerase chain reaction instrumentation. However, quantitative analysis requires careful consideration of the dye-mediated reporting mechanism and the underlying thermodynamic model. We determine affinity constants by analysis of ligand-mediated shifts in melting-temperature midpoint values. Ligand affinity is determined in a ligand titration series from shifts in free energies of stability at a common reference temperature. Thermodynamic parameters are obtained by fitting the inverse first derivative of the experimental signal reporting on thermal denaturation with equations that incorporate linear or nonlinear baseline models. We apply these methods to fit protein melts monitored with SO that exhibit prominent nonlinear post-transition baselines. SO can perturb the equilibria on which it is reporting. We analyze cases in which the ligand binds to both the native and denatured state or to the native state only and cases in which protein:ligand stoichiometry needs to treated explicitly.

Evaluación económica de las decisiones sobre fertilidad del suelo mediante el uso de modelos dinámicos

p.209-217

Residential consumption of gas and electricity in the U.S.: The role of prices and income

We study the residential demand for electricity and gas, working with nationwide household-level data that cover recent years, namely 1997-2007. Our dataset is a mixed panel/multi-year cross-sections of dwellings/households in the 50 largest metropolitan areas in the United States as of 2008. We estimate static and dynamic models of electricity and gas demand. We find strong household response to energy prices, both in the short and long term. From the static models, we get estimates of the own price elasticity of electricity demand in the -0.860 to -0.667 range, while the own price elasticity of gas demand is -0.693 to -0.566. These results are robust to a variety of checks. Contrary to earlier literature (Metcalf and Hassett, 1999; Reiss and White, 2005), we find no evidence of significantly different elasticities across households with electric and gas heat. The price elasticity of electricity demand declines with income, but the magnitude of this effect is small. These results are in sharp contrast to much of the literature on residential energy consumption in the United States, and with the figures used in current government agency practice. Our results suggest that there might be greater potential for policies which affect energy price than may have been previously appreciated. (C) 2011 Elsevier B.V. All rights reserved.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Cyclic behaviour of RC elements with plain reinforcing bars

O comportamento cíclico das estruturas de betão armado é fortemente condicionado pelo mecanismo de aderência entre o betão e o aço. O escorregamento relativo entre os dois materiais, resultante da degradação progressiva da aderência em elementos solicitados por ações cíclicas, é uma causa frequente de danos graves e até do colapso de estruturas devido à ocorrência de sismos. Entre as estruturas existentes de betão armado que foram dimensionadas e construídas antes da entrada em vigor dos regulamentos sísmicos atuais, muitas foram construídas com armadura lisa, e portanto, possuem fracas propriedades de aderência. A informação disponível na literatura sobre o comportamento cíclico de elementos estruturais de betão armado com armadura lisa é reduzida e a influência das propriedades da aderência associadas a este tipo de armadura no comportamento cíclico das estruturas existentes não se encontra ainda devidamente estudada. O objectivo principal desta tese foi estudar a influência do escorregamento na resposta cíclica de elementos estruturais de betão armado com armadura lisa. Foram realizados ensaios cíclicos em elementos do tipo nó viga-pilar, construídos à escala real, representativos de ligações interiores em edifícios existentes sem pormenorização específica para resistir às ações sísmicas. Para comparação, foi realizado o ensaio de um nó construído com armadura nervurada. Foi ainda realizado o ensaio cíclico de uma viga de betão armado recolhida de uma estrutura antiga. Foram elaborados modelos numéricos não-lineares para simular a resposta dos elementos ensaiados, concentrando especial atenção no mecanismo do escorregamento. Os resultados obtidos no âmbito desta tese contribuem para o avanço do conhecimento sobre o comportamento cíclico de elementos estruturais de betão armado com armadura lisa. As análises numéricas realizadas comprovam a necessidade de incluir os efeitos do escorregamento na modelação numérica deste tipo de estruturas de forma a representar com rigor a sua resposta às ações cíclicas.

Implementation of a service quality strategy: application to the Algarve hotel industry

Tese dout., Doctor of Philisophy, Sheffield Hallam University, 2001

A greenhouse climate multivariable predictive controller

A multivariable predictive controller was implemented to regulate the air temperature, humidity and CO2 concentration for a greenhouse located in the north of Portugal. The controller outputs are computed in order to optimise the future behaviour of the greenhouse environment, concerning the set-point accuracy and the minimization of energy inputs.

Análise estática não linear de um edifício em betão armado

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil

Modelling of a wastewater treatment plant using GPS-X

Dissertation to obtain the degree of Master in Chemical and Biochemical Engineering

Analyse des risques d'extinction des populations naturelles

A review of extinction risk analysis and viability methods is presented. The importance of environmental, demographic and genetic uncertainties, as well as the role of catastrophes are successively considered, and different approaches aiming at the integration of these risk factors in predictive population dynamic models are discussed.

Identification, Weak Instruments and Statistical Inference in Econometrics

We discuss statistical inference problems associated with identification and testability in econometrics, and we emphasize the common nature of the two issues. After reviewing the relevant statistical notions, we consider in turn inference in nonparametric models and recent developments on weakly identified models (or weak instruments). We point out that many hypotheses, for which test procedures are commonly proposed, are not testable at all, while some frequently used econometric methods are fundamentally inappropriate for the models considered. Such situations lead to ill-defined statistical problems and are often associated with a misguided use of asymptotic distributional results. Concerning nonparametric hypotheses, we discuss three basic problems for which such difficulties occur: (1) testing a mean (or a moment) under (too) weak distributional assumptions; (2) inference under heteroskedasticity of unknown form; (3) inference in dynamic models with an unlimited number of parameters. Concerning weakly identified models, we stress that valid inference should be based on proper pivotal functions —a condition not satisfied by standard Wald-type methods based on standard errors — and we discuss recent developments in this field, mainly from the viewpoint of building valid tests and confidence sets. The techniques discussed include alternative proposed statistics, bounds, projection, split-sampling, conditioning, Monte Carlo tests. The possibility of deriving a finite-sample distributional theory, robustness to the presence of weak instruments, and robustness to the specification of a model for endogenous explanatory variables are stressed as important criteria assessing alternative procedures.

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system