Anaerobic threshold estimation by statistical modelling.

Anaerobic threshold (AT) is usually estimated as a change point problem by visual analysis of the cardiorespiratory response to incremental dynamic exercise. In this study, two phase linear (TPL) models of the linear-linear and linear-quadratic type were used for the estimation of AT. The correlation coefficient between the classical and statistical approaches was 0.88, and 0.89 after outlier exclusion. The TPL models provide a simple method for estimating AT that can be easily implemented using a digital computer for the automatic pattern recognition of AT.

Estimates of covariance functions for growth of Nelore cattle applying a parametric correlation structure to model within-animal correlations

A total of 20,065 weights recorded on 3016 Nelore animals were used to estimate covariance functions for growth from birth to 630 days of age, assuming a parametric correlation structure to model within-animal correlations. The model of analysis included fixed effects of contemporary groups and age of dam as quadratic covariable. Mean trends were taken into account by a cubic regression on orthogonal polynomials of animal age. Genetic effects of the animal and its dam and maternal permanent environmental effects were modelled by random regressions on Legendre polynomials of age at recording. Changes in direct permanent environmental effect variances were modelled by a polynomial variance function, together with a parametric correlation function to account for correlations between ages. Stationary and nonstationary models were used to model within-animal correlations between different ages. Residual variances were considered homogeneous or heterogeneous, with changes modelled by a step or polynomial function of age at recording. Based on Bayesian information criterion, a model with a cubic variance function combined with a nonstationary correlation function for permanent environmental effects, with 49 parameters to be estimated, fitted best. Modelling within-animal correlations through a parametric correlation structure can describe the variation pattern adequately. Moreover, the number of parameters to be estimated can be decreased substantially compared to a model fitting random regression on Legendre polynomial of age. © 2004 Elsevier B.V. All rights reserved.

An equilibrium approach to modelling social interaction

The aim of this work is to put forward a statistical mechanics theory of social interaction, generalizing econometric discrete choice models. After showing the formal equivalence linking econometric multinomial logit models to equilibrium statical mechanics, a multi- population generalization of the Curie-Weiss model for ferromagnets is considered as a starting point in developing a model capable of describing sudden shifts in aggregate human behaviour. Existence of the thermodynamic limit for the model is shown by an asymptotic sub-additivity method and factorization of correlation functions is proved almost everywhere. The exact solution for the model is provided in the thermodynamical limit by nding converging upper and lower bounds for the system's pressure, and the solution is used to prove an analytic result regarding the number of possible equilibrium states of a two-population system. The work stresses the importance of linking regimes predicted by the model to real phenomena, and to this end it proposes two possible procedures to estimate the model's parameters starting from micro-level data. These are applied to three case studies based on census type data: though these studies are found to be ultimately inconclusive on an empirical level, considerations are drawn that encourage further refinements of the chosen modelling approach, to be considered in future work.

Modelling and analysis of internally BFRP reinforced beams at elevated temperatures

The aim of the work is to conduct a finite element model analysis on a small – size concrete beam and on a full size concrete beam internally reinforced with BFRP exposed at elevated temperatures. Experimental tests performed at Kingston University have been used to compare the results from the numerical analysis for the small – size concrete beam. Once the behavior of the small – size beam at room temperature is investigated and switching to the heating phase reinforced beams are tested at 100°C, 200°C and 300°C in loaded condition. The aim of the finite element analysis is to reflect the three – point bending test adopted into the oven during the exposure of the beam at room temperature and at elevated temperatures. Performance and deformability of reinforced beams are straightly correlated to the material properties and a wide analysis on elastic modulus and coefficient of thermal expansion is given in this work. Develop a good correlation between the numerical model and the experimental test is the main objective of the analysis on the small – size concrete beam, for both modelling the aim is also to estimate which is the deterioration of the material properties due to the heating process and the influence of different parameters on the final result. The focus of the full – size modelling which involved the last part of this work is to evaluate the effect of elevated temperatures, the material deterioration and the deflection trend on a reinforced beam characterized by a different size. A comparison between the results from different modelling has been developed.

Techniques for Lagrangian modelling of dispersion in geophysical flows

Basic concepts and definitions relative to Lagrangian Particle Dispersion Models (LPDMs)for the description of turbulent dispersion are introduced. The study focusses on LPDMs that use as input, for the large scale motion, fields produced by Eulerian models, with the small scale motions described by Lagrangian Stochastic Models (LSMs). The data of two different dynamical model have been used: a Large Eddy Simulation (LES) and a General Circulation Model (GCM). After reviewing the small scale closure adopted by the Eulerian model, the development and implementation of appropriate LSMs is outlined. The basic requirement of every LPDM used in this work is its fullfillment of the Well Mixed Condition (WMC). For the dispersion description in the GCM domain, a stochastic model of Markov order 0, consistent with the eddy-viscosity closure of the dynamical model, is implemented. A LSM of Markov order 1, more suitable for shorter timescales, has been implemented for the description of the unresolved motion of the LES fields. Different assumptions on the small scale correlation time are made. Tests of the LSM on GCM fields suggest that the use of an interpolation algorithm able to maintain an analytical consistency between the diffusion coefficient and its derivative is mandatory if the model has to satisfy the WMC. Also a dynamical time step selection scheme based on the diffusion coefficient shape is introduced, and the criteria for the integration step selection are discussed. Absolute and relative dispersion experiments are made with various unresolved motion settings for the LSM on LES data, and the results are compared with laboratory data. The study shows that the unresolved turbulence parameterization has a negligible influence on the absolute dispersion, while it affects the contribution of the relative dispersion and meandering to absolute dispersion, as well as the Lagrangian correlation.

Total internal reflection fluorescence cross-correlation spectroscopy: theory and application for studying boundary slip phenomenon

I present a new experimental method called Total Internal Reflection Fluorescence Cross-Correlation Spectroscopy (TIR-FCCS). It is a method that can probe hydrodynamic flows near solid surfaces, on length scales of tens of nanometres. Fluorescent tracers flowing with the liquid are excited by evanescent light, produced by epi-illumination through the periphery of a high NA oil-immersion objective. Due to the fast decay of the evanescent wave, fluorescence only occurs for tracers in the ~100 nm proximity of the surface, thus resulting in very high normal resolution. The time-resolved fluorescence intensity signals from two laterally shifted (in flow direction) observation volumes, created by two confocal pinholes are independently measured and recorded. The cross-correlation of these signals provides important information for the tracers’ motion and thus their flow velocity. Due to the high sensitivity of the method, fluorescent species with different size, down to single dye molecules can be used as tracers. The aim of my work was to build an experimental setup for TIR-FCCS and use it to experimentally measure the shear rate and slip length of water flowing on hydrophilic and hydrophobic surfaces. However, in order to extract these parameters from the measured correlation curves a quantitative data analysis is needed. This is not straightforward task due to the complexity of the problem, which makes the derivation of analytical expressions for the correlation functions needed to fit the experimental data, impossible. Therefore in order to process and interpret the experimental results I also describe a new numerical method of data analysis of the acquired auto- and cross-correlation curves – Brownian Dynamics techniques are used to produce simulated auto- and cross-correlation functions and to fit the corresponding experimental data. I show how to combine detailed and fairly realistic theoretical modelling of the phenomena with accurate measurements of the correlation functions, in order to establish a fully quantitative method to retrieve the flow properties from the experiments. An importance-sampling Monte Carlo procedure is employed in order to fit the experiments. This provides the optimum parameter values together with their statistical error bars. The approach is well suited for both modern desktop PC machines and massively parallel computers. The latter allows making the data analysis within short computing times. I applied this method to study flow of aqueous electrolyte solution near smooth hydrophilic and hydrophobic surfaces. Generally on hydrophilic surface slip is not expected, while on hydrophobic surface some slippage may exists. Our results show that on both hydrophilic and moderately hydrophobic (contact angle ~85°) surfaces the slip length is ~10-15nm or lower, and within the limitations of the experiments and the model, indistinguishable from zero.

Quantitative 3D strain analysis in analogue experiments: Integration of X-ray computed tomography and digital volume correlation techniques

The combination of scaled analogue experiments, material mechanics, X-ray computed tomography (XRCT) and Digital Volume Correlation techniques (DVC) is a powerful new tool not only to examine the 3 dimensional structure and kinematic evolution of complex deformation structures in scaled analogue experiments, but also to fully quantify their spatial strain distribution and complete strain history. Digital image correlation (DIC) is an important advance in quantitative physical modelling and helps to understand non-linear deformation processes. Optical non-intrusive (DIC) techniques enable the quantification of localised and distributed deformation in analogue experiments based either on images taken through transparent sidewalls (2D DIC) or on surface views (3D DIC). X-ray computed tomography (XRCT) analysis permits the non-destructive visualisation of the internal structure and kinematic evolution of scaled analogue experiments simulating tectonic evolution of complex geological structures. The combination of XRCT sectional image data of analogue experiments with 2D DIC only allows quantification of 2D displacement and strain components in section direction. This completely omits the potential of CT experiments for full 3D strain analysis of complex, non-cylindrical deformation structures. In this study, we apply digital volume correlation (DVC) techniques on XRCT scan data of “solid” analogue experiments to fully quantify the internal displacement and strain in 3 dimensions over time. Our first results indicate that the application of DVC techniques on XRCT volume data can successfully be used to quantify the 3D spatial and temporal strain patterns inside analogue experiments. We demonstrate the potential of combining DVC techniques and XRCT volume imaging for 3D strain analysis of a contractional experiment simulating the development of a non-cylindrical pop-up structure. Furthermore, we discuss various options for optimisation of granular materials, pattern generation, and data acquisition for increased resolution and accuracy of the strain results. Three-dimensional strain analysis of analogue models is of particular interest for geological and seismic interpretations of complex, non-cylindrical geological structures. The volume strain data enable the analysis of the large-scale and small-scale strain history of geological structures.

Modelling susceptibility of grassland soil to macropore flow

Investigating preferential flow, including macropore flow, is crucial to predicting and preventing point sources of contamination in soil, for example in the vicinity of pumping wells. With a view to advancing groundwater protection, this study aimed (i) to quantify the strength of macropore flow in four representative natural grassland soils on the Swiss plateau, and (ii) to define the parameters that significantly control macropore flow in grassland soil. For each soil type we selected three measurement points on which three successive irrigation experiments were carried out, resulting in a total of 36 irrigations. The strength of macropore flow, parameterized as the cumulated water volume flowing from macropores at a depth of 1 m in response to an irrigation of 60 mm h−1 intensity and 1 h duration, was simulated using the dual-permeability MACRO model. The model calibration was based on the key soil parameters and fine measurements of water content at different depths. Modelling results indicate high performance of macropore flow in all investigated soil types except in gleysols. The volume of water that flowed from macropores and was hence expected to reach groundwater varied between 81% and 94% in brown soils, 59% and 67% in para-brown soils, 43% and 56% in acid brown soils, and 22% and 35% in gleysols. These results show that spreading pesticides and herbicides in pumping well protection zones poses a high risk of contamination and must be strictly prohibited. We also found that organic carbon content was not correlated with the strength of macropore flow, probably due to its very weak variation in our study, while saturated water content showed a negative correlation with macropore flow. The correlation between saturated hydraulic conductivity (Ks) and macropore flow was negative as well, but weak. Macropore flow appears to be controlled by the interaction between the bulk density of the uppermost topsoil layer (0–0.10 m) and the macroporosity of the soil below. This interaction also affects the variations in Ks and saturated water content. Further investigations are needed to better understand the combined effect of all these processes including the exchange between micropore and macropore domains.

(Table 1) Correlation of precipitation and winter mass balance (bw) at Storbreen, Norway

Measurements of winter balance (bw) and summer balance (bs) have been carried out at Storbreen since 1949. Here we apply a simple mass balance model to study the climate sensitivity and to reconstruct the mass balance series prior to 1949. The model is calibrated and validated with data from an automatic weather station (AWS) operating in the ablation zone of Storbreen since 2001. Regression analysis revealed that bw was best modelled using precipitation data southwest of the glacier. Results from the model compared well with reported mass balance values for the period 1949-2006, obtained correlations (r) for bw and bs varied between 0.83 and 0.87 depending on model set up. Reconstruction of the mass balance series for the period 1924/1925-1948/1949 suggested a cumulative mass deficit of c. 30 m w.e. mainly due to highly negative summer balances, but also lower bw than the average for 1949-2006. Calculated change in specific mass balance for a ±1°C change in air temperature was ±0.55 m w.e., whereas a ±10 % increase in precipitation represented a change of ± 0.20 m w.e. Model results further indicated that for a 2°C warming, the ablation season will be extended by c. 30 days and that the period of ice melt at the AWS location will increase from c. 40 to c. 80 days.

Modelling the Impact of Built Environment, Geographical Scales and Latent Constructs On Individual Travel Behaviour

La relación entre la estructura urbana y la movilidad ha sido estudiada desde hace más de 70 años. El entorno urbano incluye múltiples dimensiones como por ejemplo: la estructura urbana, los usos de suelo, la distribución de instalaciones diversas (comercios, escuelas y zonas de restauración, parking, etc.). Al realizar una revisión de la literatura existente en este contexto, se encuentran distintos análisis, metodologías, escalas geográficas y dimensiones, tanto de la movilidad como de la estructura urbana. En este sentido, se trata de una relación muy estudiada pero muy compleja, sobre la que no existe hasta el momento un consenso sobre qué dimensión del entorno urbano influye sobre qué dimensión de la movilidad, y cuál es la manera apropiada de representar esta relación. Con el propósito de contestar estas preguntas investigación, la presente tesis tiene los siguientes objetivos generales: (1) Contribuir al mejor entendimiento de la compleja relación estructura urbana y movilidad. y (2) Entender el rol de los atributos latentes en la relación entorno urbano y movilidad. El objetivo específico de la tesis es analizar la influencia del entorno urbano sobre dos dimensiones de la movilidad: número de viajes y tipo de tour. Vista la complejidad de la relación entorno urbano y movilidad, se pretende contribuir al mejor entendimiento de la relación a través de la utilización de 3 escalas geográficas de las variables y del análisis de la influencia de efectos inobservados en la movilidad. Para el análisis se utiliza una base de datos conformada por tres tipos de datos: (1) Una encuesta de movilidad realizada durante los años 2006 y 2007. Se obtuvo un total de 943 encuestas, en 3 barrios de Madrid: Chamberí, Pozuelo y Algete. (2) Información municipal del Instituto Nacional de Estadística: dicha información se encuentra enlazada con los orígenes y destinos de los viajes recogidos en la encuesta. Y (3) Información georeferenciada en Arc-GIS de los hogares participantes en la encuesta: la base de datos contiene información respecto a la estructura de las calles, localización de escuelas, parking, centros médicos y lugares de restauración. Se analizó la correlación entre e intra-grupos y se modelizaron 4 casos de atributos bajo la estructura ordinal logit. Posteriormente se evalúa la auto-selección a través de la estimación conjunta de las elecciones de tipo de barrio y número de viajes. La elección del tipo de barrio consta de 3 alternativas: CBD, Urban y Suburban, según la zona de residencia recogida en las encuestas. Mientras que la elección del número de viajes consta de 4 categorías ordinales: 0 viajes, 1-2 viajes, 3-4 viajes y 5 o más viajes. A partir de la mejor especificación del modelo ordinal logit. Se desarrolló un modelo joint mixed-ordinal conjunto. Los resultados indican que las variables exógenas requieren un análisis exhaustivo de correlaciones con el fin de evitar resultados sesgados. ha determinado que es importante medir los atributos del BE donde se realiza el viaje, pero también la información municipal es muy explicativa de la movilidad individual. Por tanto, la percepción de las zonas de destino a nivel municipal es considerada importante. En el contexto de la Auto-selección (self-selection) es importante modelizar conjuntamente las decisiones. La Auto-selección existe, puesto que los parámetros estimados conjuntamente son significativos. Sin embargo, sólo ciertos atributos del entorno urbano son igualmente importantes sobre la elección de la zona de residencia y frecuencia de viajes. Para analizar la Propensión al Viaje, se desarrolló un modelo híbrido, formado por: una variable latente, un indicador y un modelo de elección discreta. La variable latente se denomina “Propensión al Viaje”, cuyo indicador en ecuación de medida es el número de viajes; la elección discreta es el tipo de tour. El modelo de elección consiste en 5 alternativas, según la jerarquía de actividades establecida en la tesis: HOME, no realiza viajes durante el día de estudio, HWH tour cuya actividad principal es el trabajo o estudios, y no se realizan paradas intermedias; HWHs tour si el individuo reaiza paradas intermedias; HOH tour cuya actividad principal es distinta a trabajo y estudios, y no se realizan paradas intermedias; HOHs donde se realizan paradas intermedias. Para llegar a la mejor especificación del modelo, se realizó un trabajo importante considerando diferentes estructuras de modelos y tres tipos de estimaciones. De tal manera, se obtuvieron parámetros consistentes y eficientes. Los resultados muestran que la modelización de los tours, representa una ventaja sobre la modelización de los viajes, puesto que supera las limitaciones de espacio y tiempo, enlazando los viajes realizados por la misma persona en el día de estudio. La propensión al viaje (PT) existe y es específica para cada tipo de tour. Los parámetros estimados en el modelo híbrido resultaron significativos y distintos para cada alternativa de tipo de tour. Por último, en la tesis se verifica que los modelos híbridos representan una mejora sobre los modelos tradicionales de elección discreta, dando como resultado parámetros consistentes y más robustos. En cuanto a políticas de transporte, se ha demostrado que los atributos del entorno urbano son más importantes que los LOS (Level of Service) en la generación de tours multi-etapas. la presente tesis representa el primer análisis empírico de la relación entre los tipos de tours y la propensión al viaje. El concepto Propensity to Travel ha sido desarrollado exclusivamente para la tesis. Igualmente, el desarrollo de un modelo conjunto RC-Number of trips basado en tres escalas de medida representa innovación en cuanto a la comparación de las escalas geográficas, que no había sido hecha en la modelización de la self-selection. The relationship between built environment (BE) and travel behaviour (TB) has been studied in a number of cases, using several methods - aggregate and disaggregate approaches - and different focuses – trip frequency, automobile use, and vehicle miles travelled and so on. Definitely, travel is generated by the need to undertake activities and obtain services, and there is a general consensus that urban components affect TB. However researches are still needed to better understand which components of the travel behaviour are affected most and by which of the urban components. In order to fill the gap in the research, the present dissertation faced two main objectives: (1) To contribute to the better understanding of the relationship between travel demand and urban environment. And (2) To develop an econometric model for estimating travel demand with urban environment attributes. With this purpose, the present thesis faced an exhaustive research and computation of land-use variables in order to find the best representation of BE for modelling trip frequency. In particular two empirical analyses are carried out: 1. Estimation of three dimensions of travel demand using dimensions of urban environment. We compare different travel dimensions and geographical scales, and we measure self-selection contribution following the joint models. 2. Develop a hybrid model, integrated latent variable and discrete choice model. The implementation of hybrid models is new in the analysis of land-use and travel behaviour. BE and TB explicitly interact and allow richness information about a specific individual decision process For all empirical analysis is used a data-base from a survey conducted in 2006 and 2007 in Madrid. Spatial attributes describing neighbourhood environment are derived from different data sources: National Institute of Statistics-INE (Administrative: municipality and district) and GIS (circular units). INE provides raw data for such spatial units as: municipality and district. The construction of census units is trivial as the census bureau provides tables that readily define districts and municipalities. The construction of circular units requires us to determine the radius and associate the spatial information to our households. The first empirical part analyzes trip frequency by applying an ordered logit model. In this part is studied the effect of socio-economic, transport and land use characteristics on two travel dimensions: trip frequency and type of tour. In particular the land use is defined in terms of type of neighbourhoods and types of dwellers. Three neighbourhood representations are explored, and described three for constructing neighbourhood attributes. In particular administrative units are examined to represent neighbourhood and circular – unit representation. Ordered logit models are applied, while ordinal logit models are well-known, an intensive work for constructing a spatial attributes was carried out. On the other hand, the second empirical analysis consists of the development of an innovative econometric model that considers a latent variable called “propensity to travel”, and choice model is the choice of type of tour. The first two specifications of ordinal models help to estimate this latent variable. The latent variable is unobserved but the manifestation is called “indicators”, then the probability of choosing an alternative of tour is conditional to the probability of latent variable and type of tour. Since latent variable is unknown we fit the integral over its distribution. Four “sets of best variables” are specified, following the specification obtained from the correlation analysis. The results evidence that the relative importance of SE variables versus BE variables depends on how BE variables are measured. We found that each of these three spatial scales has its intangible qualities and drawbacks. Spatial scales play an important role on predicting travel demand due to the variability in measures at trip origin/destinations within the same administrative unit (municipality, district and so on). Larger units will produce less variation in data; but it does not affect certain variables, such as public transport supply, that are more significant at municipality level. By contrast, land-use measures are more efficient at district level. Self-selection in this context, is weak. Thus, the influence of BE attributes is true. The results of the hybrid model show that unobserved factors affect the choice of tour complexity. The latent variable used in this model is propensity to travel that is explained by socioeconomic aspects and neighbourhood attributes. The results show that neighbourhood attributes have indeed a significant impact on the choice of the type of tours either directly and through the propensity to travel. The propensity to travel has a different impact depending on the structure of each tour and increases the probability of choosing more complex tours, such as tours with many intermediate stops. The integration of choice and latent variable model shows that omitting important perception and attitudes leads to inconsistent estimates. The results also indicate that goodness of fit improves by adding the latent variable in both sequential and simultaneous estimation. There are significant differences in the sensitivity to the latent variable across alternatives. In general, as expected, the hybrid models show a major improvement into the goodness of fit of the model, compared to a classical discrete choice model that does not incorporate latent effects. The integrated model leads to a more detailed analysis of the behavioural process. Summarizing, the effect that built environment characteristics on trip frequency studied is deeply analyzed. In particular we tried to better understand how land use characteristics can be defined and measured and which of these measures do have really an impact on trip frequency. We also tried to test the superiority of HCM on this field. We can concluded that HCM shows a major improvement into the goodness of fit of the model, compared to classical discrete choice model that does not incorporate latent effects. And consequently, the application of HCM shows the importance of LV on the decision of tour complexity. People are more elastic to built environment attributes than level of services. Thus, policy implications must take place to develop more mixed areas, work-places in combination with commercial retails.

Modelling Saharan dust transport into the Mediterranean basin with CMAQ

The need for a better quantification of the influence of Saharan dust transport processes on the air quality modelling in the Mediterranean basin led to the formulation of a dust emission module (DEM) integrated into the Air Quality Risk Assessment System for the Iberian Peninsula (SERCA). This paper is focused on the formulation of DEM based on the GOCART aerosol model, along with its integration and execution into the air quality model. It also addresses the testing of the module and its evaluation by contrasting results against satellite products such as MODIS and CALIPSO and ground-level observations of aerosol optical thickness (AOT) and concentration levels of PM10 for different periods in July 2007. DEM was found capable of reproducing the spatial (horizontal and vertical) and temporal profiles of Saharan dust outbreaks into the Mediterranean basin and the Atlantic coast of Africa. Moreover, it was observed that its combination with CMAQ increased the correlation degree between observed and modelled PM10 concentrations at the selected monitoring locations. DEM also enhanced CMAQ capabilities to reproduce observed AOT, although significant underestimations remain. The implementation of CMAQ + DEM succeeded in capturing Saharan dust transport into the Iberian Peninsula, with contributions up to 25 and 14 μg m−3 in 1 h and 24 h average PM10 respectively. The general improvement of total PM10 predictions in Spain are however moderate. The analysis of model performance for the main PM components points out that remaining PM10 underestimation is due to dust local sources missing in the inventories and misrepresentation of organic aerosol processes, which constitutes the main areas for future improvement of CMAQ capabilities to simulate particulate matter within SERCA.

Laser ablation modelling of aluminium, silver and crystalline silicon for applications in photovoltaic technologies

Laser material processing is being extensively used in photovoltaic applications for both the fabrication of thin film modules and the enhancement of the crystalline silicon solar cells. The two temperature model for thermal diffusion was numerically solved in this paper. Laser pulses of 1064, 532 or 248 nm with duration of 35, 26 or 10 ns were considered as the thermal source leading to the material ablation. Considering high irradiance levels (108–109 W cm−2), a total absorption of the energy during the ablation process was assumed in the model. The materials analysed in the simulation were aluminium (Al) and silver (Ag), which are commonly used as metallic electrodes in photovoltaic devices. Moreover, thermal diffusion was also simulated for crystalline silicon (c-Si). A similar trend of temperature as a function of depth and time was found for both metals and c-Si regardless of the employed wavelength. For each material, the ablation depth dependence on laser pulse parameters was determined by means of an ablation criterion. Thus, after the laser pulse, the maximum depth for which the total energy stored in the material is equal to the vaporisation enthalpy was considered as the ablation depth. For all cases, the ablation depth increased with the laser pulse fluence and did not exhibit a clear correlation with the radiation wavelength. Finally, the experimental validation of the simulation results was carried out and the ability of the model with the initial hypothesis of total energy absorption to closely fit experimental results was confirmed.

Regional subsidence modelling in Murcia city (SE Spain) using 1-D vertical finite element analysis and 2-D interpolation of ground surface displacements

Subsidence is a hazard that may have natural or anthropogenic origin causing important economic losses. The area of Murcia city (SE Spain) has been affected by subsidence due to groundwater overexploitation since the year 1992. The main observed historical piezometric level declines occurred in the periods 1982–1984, 1992–1995 and 2004–2008 and showed a close correlation with the temporal evolution of ground displacements. Since 2008, the pressure recovery in the aquifer has led to an uplift of the ground surface that has been detected by the extensometers. In the present work an elastic hydro-mechanical finite element code has been used to compute the subsidence time series for 24 geotechnical boreholes, prescribing the measured groundwater table evolution. The achieved results have been compared with the displacements estimated through an advanced DInSAR technique and measured by the extensometers. These spatio-temporal comparisons have showed that, in spite of the limited geomechanical data available, the model has turned out to satisfactorily reproduce the subsidence phenomenon affecting Murcia City. The model will allow the prediction of future induced deformations and the consequences of any piezometric level variation in the study area.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

Modelling the behaviour of the new issue market

This paper analyses the time series behaviour of the initial public offering (IPO) market using an equilibrium model of demand and supply that incorporates the number of new issues, average underpricing, and general market conditions. Model predictions include the existence of serial correlation in both the number of new issues and the average level of underpricing, as well as interactions between these variables and the impact of general market conditions. The model is tested using 40 years of monthly IPO data. The empirical results are generally consistent with predictions.