A three dimensional numerical investigation of hillslope flow processes

Physically based distributed models of catchment hydrology are likely to be made available as engineering tools in the near future. Although these models are based on theoretically acceptable equations of continuity, there are still limitations in the present modelling strategy. Of interest to this thesis are the current modelling assumptions made concerning the effects of soil spatial variability, including formations producing distinct zones of preferential flow. The thesis contains a review of current physically based modelling strategies and a field based assessment of soil spatial variability. In order to investigate the effects of soil nonuniformity a fully three dimensional model of variability saturated flow in porous media is developed. The model is based on a Galerkin finite element approximation to Richards equation. Accessibility to a vector processor permits numerical solutions on grids containing several thousand node points. The model is applied to a single hillslope segment under various degrees of soil spatial variability. Such variability is introduced by generating random fields of saturated hydraulic conductivity using the turning bands method. Similar experiments are performed under conditions of preferred soil moisture movement. The results show that the influence of soil variability on subsurface flow may be less significant than suggested in the literature, due to the integrating effects of three dimensional flow. Under conditions of widespread infiltration excess runoff, the results indicate a greater significance of soil nonuniformity. The recognition of zones of preferential flow is also shown to be an important factor in accurate rainfall-runoff modelling. Using the results of various fields of soil variability, experiments are carried out to assess the validity of the commonly used concept of `effective parameters'. The results of these experiments suggest that such a concept may be valid in modelling subsurface flow. However, the effective parameter is observed to be event dependent when the dominating mechanism is infiltration excess runoff.

Verification and validation of numerical models of the transport of insulation debris

Damage to insulation materials located near to a primary circuit coolant leak may compromise the operation of the emergency core cooling system (ECCS). Insulation material in the form of mineral wool fiber agglomerates (MWFA) maybe transported to the containment sump strainers, where they may block or penetrate the strainers. Though the impact of MWFA on the pressure drop across the strainers is minimal, corrosion products formed over time may also accumulate in the fiber cakes on the strainers, which can lead to a significant increase in the strainer pressure drop and result in cavitation in the ECCS. An experimental and theoretical study performed by the Helmholtz-Zentrum Dresden-Rossendorf and the Hochschule Zittau/Görlitz is investigating the phenomena that maybe observed in the containment vessel during a primary circuit coolant leak. The study entails the generation of fiber agglomerates, the determination of their transport properties in single and multi-effect experiments and the long-term effect that corrosion and erosion of the containment internals by the coolant has on the strainer pressure drop. The focus of this paper is on the verification and validation of numerical models that can predict the transport of MWFA. A number of pseudo-continuous dispersed phases of spherical wetted agglomerates represent the MWFA. The size, density, the relative viscosity of the fluid-fiber agglomerate mixture and the turbulent dispersion all affect how the fiber agglomerates are transported. In the cases described here, the size is kept constant while the density is modified. This definition affects both the terminal velocity and volume fraction of the dispersed phases. Note that the relative viscosity is only significant at high concentrations. Three single effect experiments were used to provide validation data on the transport of the fiber agglomerates under conditions of sedimentation in quiescent fluid, sedimentation in a horizontal flow and suspension in a horizontal flow. The experiments were performed in a rectangular column for the quiescent fluid and a racetrack type channel that provided a near uniform horizontal flow. The numerical models of sedimentation in the column and the racetrack channel found that the sedimentation characteristics are consistent with the experiments. For channel suspension, the heavier fibers tend to accumulate at the channel base even at high velocities, while lighter phases are more likely to be transported around the channel.

Wealth effects of mergers and acquisitions for US firms: using alternative pricing models

This empirical study employs a different methodology to examine the change in wealth associated with mergers and acquisitions (M&As) for US firms. Specifically, we employ the standard CAPM, the Fama-French three-factor model and the Carhart four-factor models within the OLS and GJR-GARCH estimation methods to test the behaviour of the cumulative abnormal returns (CARs). Whilst the standard CAPM captures the variability of stock returns with the overall market, the Fama-French factors capture the risk factors that are important to investors. Additionally, augmenting the Fama-French three-factor model with the Carhart momentum factor to generate the four-factor captures additional pricing elements that may affect stock returns. Traditionally, estimates of abnormal returns (ARs) in M&As situations rely on the standard OLS estimation method. However, the standard OLS will provide inefficient estimates of the ARs if the data contain ARCH and asymmetric effects. To minimise this problem of estimation efficiency we re-estimated the ARs using GJR-GARCH estimation method. We find that there is variation in the results both as regards the choice models and estimation methods. Besides these variations in the estimated models and the choice of estimation methods, we also tested whether the ARs are affected by the degree of liquidity of the stocks and the size of the firm. We document significant positive post-announcement cumulative ARs (CARs) for target firm shareholders under both the OLS and GJR-GARCH methods across all three methodologies. However, post-event CARs for acquiring firm shareholders were insignificant for both sets of estimation methods under the three methodologies. The GJR-GARCH method seems to generate larger CARs than those of the OLS method. Using both market capitalization and trading volume as a measure of liquidity and the size of the firm, we observed strong return continuations in the medium firms relative to small and large firms for target shareholders. We consistently observed market efficiency in small and large firm. This implies that target firms for small and large firms overreact to new information resulting in a more efficient market. For acquirer firms, our measure of liquidity captures strong return continuations for small firms under the OLS estimates for both CAPM and Fama-French three-factor models, whilst under the GJR-GARCH estimates only for Carhart model. Post-announcement bootstrapping simulated CARs confirmed our earlier results.

Reducing suffering in animal models and procedures involving seizures, convulsions and epilepsy

This report is based on discussions and submissions from an expert working group consisting of veterinarians, animal care staff and scientists with expert knowledge relevant to the field and aims to facilitate the implementation of the Three Rs (replacement, reduction and refinement) in the use of animal models or procedures involving seizures, convulsions and epilepsy. Each of these conditions will be considered, the specific welfare issues discussed, and practical measures to reduce animal use and suffering suggested. The emphasis is on refinement since this has the greatest potential for immediate implementation, and some general issues for refinement are summarised to help achieve this, with more detail provided on a range of specific refinements.

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

Outsourcing and offshoring engineering projects:understanding the value, sourcing models, and coordination practices

In this paper, we review recent developments in the field of outsourcing and offshoring and the implications for engineering management. We examine three aspects involved in outsourcing and offshoring, namely, sourcing models, coordination, and value extracted from outsourcing projects. We conclude that additional research is needed on recent trends in outsourcing and the impact of such change process on the practice of engineering management. © 2011 IEEE.

The expression of P-glycoprotein does influence the distribution of novel fluorescent compounds in solid tumour models

Solid tumours display a complex drug resistance phenotype that involves inherent and acquired mechanisms. Multicellular resistance is an inherent feature of solid tumours and is known to present significant barriers to drug permeation in tumours. Given this barrier, do acquired resistance mechanisms such as P-glycoprotein (P-gp) contribute significantly to resistance? To address this question, the multicellular tumour spheroid (MCTS) model was used to examine the influence of P-gp on drug distribution in solid tissue. Tumour spheroids (TS) were generated from either drug-sensitive MCF7WT cells or a drug-resistant, P-gp-expressing derivative MCF7Adr. Confocal microscopy was used to measure time courses and distribution patterns of three fluorescent compounds; calcein-AM, rhodamine123 and BODIPY-taxol. These compounds were chosen because they are all substrates for P-gp-mediated transport, exhibit high fluorescence and are chemically dissimilar. For example, BODIPY-taxol and rhodamine 123 showed high accumulation and distributed extensively throughout the TSWT, whereas calcein-AM accumulation was restricted to the outermost layers. The presence of P-gp in TSAdr resulted in negligible accumulation, regardless of the compound. Moreover, the inhibition of P-gp by nicardipine restored intracellular accumulation and distribution patterns to levels observed in TSWT. The results demonstrate the effectiveness of P-gp in modulating drug distribution in solid tumour models. However, the penetration of agents throughout the tissue is strongly determined by the physico-chemical properties of the individual compounds.

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Online learning in discrete hidden Markov models

We present and analyze three different online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare their performance with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of the generalization error we draw learning curves in simplified situations and compare the results. The performance for learning drifting concepts of one of the presented algorithms is analyzed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented. © 2006 American Institute of Physics.

An examination of NPD models in the context of business models

Most prior new product diffusion (NPD) models do not specifically consider the role of the business model in the process. However, the context of NPD in today's market has been changed dramatically by the introduction of new business models. Through reinterpretation and extension, this paper empirically examines the feasibility of applying Bass-type NPD models to products that are commercialized by different business models. More specifically, the results and analysis of this study consider the subscription business model for service products, the freemium business model for digital products, and a pre-paid and post-paid business model that is widely used by mobile network providers. The paper offers new insights derived from implementing the models in real-life cases. It also highlights three themes for future research.

The challenges of representing human behaviour in simulation models:some case examples from supply chain, evacuation modelling and rail disruption

As more of the economy moves from traditional manufacturing to the service sector, the nature of work is becoming less tangible and thus, the representation of human behaviour in models is becoming more important. Representing human behaviour and decision making in models is challenging, both in terms of capturing the essence of the processes, and also the way that those behaviours and decisions are or can be represented in the models themselves. In order to advance understanding in this area, a useful first step is to evaluate and start to classify the various types of behaviour and decision making that are required to be modelled. This talk will attempt to set out and provide an initial classification of the different types of behaviour and decision making that a modeller might want to represent in a model. Then, it will be useful to start to assess the main methods of simulation in terms of their capability in representing these various aspects. The three main simulation methods, System Dynamics, Agent Based Modelling and Discrete Event Simulation all achieve this to varying degrees. There is some evidence that all three methods can, within limits, represent the key aspects of the system being modelled. The three simulation approaches are then assessed for their suitability in modelling these various aspects. Illustration of behavioural modelling will be provided from cases in supply chain management, evacuation modelling and rail disruption.

Investigation of skewing effects on the vibration reduction of three-phase switched reluctance motors

Switched reluctance motors (SRMs) are gaining in popularity because of their robustness, low cost, and excellent high-speed characteristics. However, they are known to cause vibration and noise primarily due to the radial pulsating force resulting from their double-saliency structure. This paper investigates the effect of skewing the stator and/or rotor on the vibration reduction of the three-phase SRMs by developing four 12/8-pole SRMs, including a conventional SRM, a skewed rotor-SRM (SR-SRM), a skewed stator-SRM (SS-SRM), and a skewed stator and rotor-SRM (SSR-SRM). The radial force distributed on the stator yoke under different skewing angles is extensively studied by the finite-element method and experimental tests on the four prototypes. The inductance and torque characteristics of the four motors are also compared, and a control strategy by modulating the turn-ON and turn-OFF angles for the SR-SRM and the SS-SRM are also presented. Furthermore, experimental results validate the numerical models and the effectiveness of the skewing in reducing the motor vibration. Test results also suggest that skewing the stator is more effective than skewing the rotor in the SRMs.

Kelet-közép-európai devizaárfolyamok előrejelzése határidős árfolyamok segítségével (Forecasting the exchange rates of three Central-Eastern European currencies with forward exchange rates)

Írásunkban azt vizsgáljuk, hogy a hosszú lejáratú határidős árfolyamok stacionaritását feltételező hibakorrekciós modellek, amelyeknek korábbi számítások szerint - a világ devizapiaci forgalmának mintegy 75 százalékát kitevő fejlett ipari országokra alkalmazva - kitűnő a mintán kívüli előrejelző erejük, hogyan képesek három keletközép- európai ország devizaárfolyamát előrejelezni. A három vizsgálat alá vont deviza (cseh, magyar, lengyel) esetében az eredmények relációnként nagyon eltérnek, és összességében kedvezőtlenebbek, mint a fejlett ipari országokra kapott eredmények, amit a nem teljesen rugalmas árfolyamrezsim, a rendelkezésre álló adatsor rövidsége, az eurózóna-csatlakozáshoz kapcsolódó bizonytalanságok, a devizakockázati és a határidős kamatprémium létezése, továbbá a Balassa-Samuelson-hatás együttes befolyásaként tudunk értelmezni. JEL kód: E43, F31, F47. /===/ This paper studies whether models that assume long-maturity forward exchange rates are stationary (which proved in earlier studies to provide superior forecasting ability when applied to exchange rates of major currencies) are capable of forecasting the Euro exchange rates of three Central-East European currencies (the Czech koruna, Hungarian forint and Polish zloty). The results for the three currencies differ from each other and are generally much worse than those obtained earlier for major currencies. These unfavourable results are attributed to the consequences of managed exchange-rate systems, to the short time series available, to uncertainties related to future Euro-zone entry, to the existence of a foreign exchange and term premium, and to the Balassa–Samuelson effect.

Derivatív pénzügyi termékek árdinamikája és az új típusú kamatlábmodellek (Interest rate models and the price processes of financial derivatives)

Ennek a cikknek az a célja, hogy áttekintést adjon annak a folyamatnak néhány főbb állomásáról, amit Black, Scholes és Merton opcióárazásról írt cikkei indítottak el a 70-es évek elején, és ami egyszerre forradalmasította a fejlett nyugati pénzügyi piacokat és a pénzügyi elméletet. / === / This review article compares the development of financial theory within and outside Hungary in the last three decades starting with the Black-Scholes revolution. Problems like the term structure of interest rate volatilities which is in the focus of many research internationally has not received the proper attention among the Hungarian economists. The article gives an overview of no-arbitrage pricing, the partial differential equation approach and the related numerical techniques, like the lattice methods in pricing financial derivatives. The relevant concepts of the martingal approach are overviewed. There is a special focus on the HJM framework of the interest rate development. The idea that the volatility and the correlation can be traded is a new horizon to the Hungarian capital market.