The use of acoustic emission monitoring to rank paper materials with respect to their fracture toughness

In this study, a simplified Acoustic Emission (AE) equipment, in essence an AE signal conditioner and a USB (Universal Serial Bus) data acquisition system, is used to study what happens in paper structures during mechanical loading. By the use of such equipment, some parameters that can be extracted are e.g. the stress and strain at onset of AE, the stress and strain at the onset of rapid AE defined as some numerical factor (larger then one) times the initial emission rate, the emission rate at the first stage of loading and the stress and strain at final failure i.e. when the specimen loses its load carrying ability.In this study however, the interest is focused on one particular parameter i.e. the elastic strain energy density W c at onset of AE. This is a parameter with a clear physical meaning and in this study, the correlation between this parameter and a fracture toughness measure, is investigated.The conclusion is that when nine different paper materials (with a large span regarding properties) are considered, there is a correlation (however not linear) between these two parameters.

Local models for exploratory analysis of hydrological extremes

Trend analysis is widely used for detecting changes in hydrological data. Parametric methods for this employ pre-specified models and associated tests to assess significance, whereas non-parametric methods generally apply rank tests to the data. Neither approach is suitable for exploratory analysis, because parametric models impose a particular, perhaps unsuitable, form of trend, while testing may confirm that trend is present but does not describe its form. This paper describes semi-parametric approaches to trend analysis using local likelihood fitting of annual maximum and partial duration series and illustrates their application to the exploratory analysis of changes in extremes in sea level and river flow data. Bootstrap methods are used to quantify the variability of estimates.

Risk mitigation framework for a robust design process

The increasing complexity of new manufacturing processes and the continuously growing range of fabrication options mean that critical decisions about the insertion of new technologies must be made as early as possible in the design process. Mitigating the technology risks under limited knowledge is a key factor and major requirement to secure a successful development of the new technologies. In order to address this challenge, a risk mitigation methodology that incorporates both qualitative and quantitative analysis is required. This paper outlines the methodology being developed under a major UK grand challenge project - 3D-Mintegration. The main focus is on identifying the risks through identification of the product key characteristics using a product breakdown approach. The assessment of the identified risks uses quantification and prioritisation techniques to evaluate and rank the risks. Traditional statistical process control based on process capability and six sigma concepts are applied to measure the process capability as a result of the risks that have been identified. This paper also details a numerical approach that can be used to undertake risk analysis. This methodology is based on computational framework where modelling and statistical techniques are integrated. Also, an example of modeling and simulation technique is given using focused ion beam which is among the investigated in the project manufacturing processes.

In vitro-in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks

The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro-in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis model. The release of probucol to the aqueous (micellar) phase was monitored during the progress of lipolysis. These release profiles compared with plasma profiles obtained in a previous bioavailability study conducted in mini-pigs at the same conditions. The release rate and extent of release from the oil formulation were found to be significantly lower than from SMEDDS and SNEDDS. The rank order of probucol released (SMEDDS approximately SNEDDS > oil formulation) was similar to the rank order of bioavailability from the in vivo study. The employed neuro-fuzzy model (AFM-IVIVC) achieved significantly high prediction ability for different data formations (correlation greater than 0.91 and prediction error close to zero), without employing complex configurations. These preliminary results suggest that the dynamic lipolysis model combined with the AFM-IVIVC can be a useful tool in the prediction of the in vivo behavior of lipid-based formulations.

The performance and persistence of exchange-traded funds: evidence for iShares MSCI country-specific ETFs

The aim of this paper is to investigate the performance and persistence of 20 iShares MSCI country-specific exchange-traded funds (ETFs) in comparison with S&P 500 index over the period July 2001 to June 2006. There are several studies analysing mutual funds performance in past years, but very little is known about ETFs. In our analysis the Sharpe, Treynor and Sortino ratios are used as risk-adjusted performance measures. To evaluate performance persistence and therefore if there is any relationship among past performance and future performance, we apply to the Spearman Rank Correlation Coefficient and the Winner-loser Contingency Table. The main findings are at two levels. First, ETFs can beat the U.S. market index based on risk-adjusted performance measures. Second, there is evidence of ETFs performance persistence based on annual return.