Naturalistic decision making: a model to overcome methodological challenges in the study of critical care nurses` decision making about patients` hemodynamic status

The quality of critical care nurses' decision making about patients' hemodynamic status in the immediate period after cardiac surgery is important for the patients' well-being and, at times, survival. The way nurses respond to hemodynamic cues varies according to the nurses' skills, experiences, and knowledge. Variability in decisions is also associated with the inherent complexity of hemodynamic monitoring. Previous methodological approaches to the study of hemodynamic assessment and treatment decisions have ignored the important interplay between nurses, the task, and the environment in which these decisions are made. The advantages of naturalistic decision making as a framework for studying the manner in which nurses make decisions are presented.

Geometric shape errors in forging: developing a metric and an inverse model

The complexity of the forging process ensures that there is inherent variability in the geometric shape of a forged part. While knowledge of shape error, comparing the desired versus the measured shape, is significant in measuring part quality the question of more interest is what can this error suggest about the forging process set-up? The first contribution of this paper is to develop a shape error metric which identifies geometric shape differences that occur from a desired forged part. This metric is based on the point distribution deformable model developed in pattern recognition research. The second contribution of this paper is to propose an inverse model that identifies changes in process set-up parameter values by analysing the proposed shape error metric. The metric and inverse models are developed using two sets of simulated hot-forged parts created using two different die pairs (simple and 'M'-shaped die pairs). A neural network is used to classify the shape data into three arbitrarily chosen levels for each parameter and it is accurate to at least 77 per cent in the worst case for the simple die pair data and has an average accuracy of approximately 80 per cent when classifying the more complex 'M'-shaped die pair data.

Effect of dietary omega-3 fatty acid deficiency on heart rate variability in hooded rats

Introduction: Recent reports in adult humans suggest that heart rate variability is modulated by the concentration of omega-3 polyunsaturated fatty acids (PUFA) contained in blood cell membranes. Material and methods: Hurst analysis of ECG data was conducted on 12 male adult hooded (Long-Evans) rats, representing the 3rd generation to be fed diets that were either deficient in, or supplemented with, omega-3 PUFA. ECG data were obtained from surface electrodes and 4000 beats were analyzed for each animal. Results: Dietary manipulation, despite leading to large changes in tissue omega- 3 PUFA levels, did not significantly affect the complexity of heart rate dynamics, with Hurst exponent (H) values of 0.15±0.02 and 0.12±0.03, for animals fed omega- 3 fatty acid-adequate and -deficient diets, respectively. Mean heart rate was also unaffected by the diets. A power calculation revealed that about one hundred animals per group would have been required to avoid a type II error. Conclusions: According to this model of dietary PUFA manipulation, omega-3 fatty acids are unlikely to exert a large effect on the autonomic functions that control heart rate variability. Prospective studies into the effect of omega-3 fatty acids on HRV should consider the need for large sample size as estimated by the results contained in this report.

Multivariate modelling of variability in sheet metal forming

The inherent variability in incoming material and process conditions in sheet metal forming makes quality control and the maintenance of consistency extremely difficult. A single FEM simulation is successful at predicting the formability for a given system, however lacks the ability to capture the variability in an actual production process due to the numerical deterministic nature. This paper investigates a probabilistic analytical model where the variation of five input parameters and their relationship to the sensitivity of springback in a stamping process is examined. A range of sheet tensions are investigated, simulating different operating windows in an attempt to highlight robust regions where the distribution of springback is small. A series of FEM simulations were also performed, to compare with the findings from the analytical model using AutoForm Sigma v4.04 and to validate the analytical model assumptions.

Results show that an increase in sheet tension not only decreases springback, but more importantly reduces the sensitivity of the process to variation. A relative sensitivity analysis has been performed where the most influential parameters and the changes in sensitivity at various sheet tensions have been investigated. Variation in the material parameters, yield stress and n-value were the most influential causes of springback variation, when compared to process input parameters such as friction, which had a small effect. The probabilistic model presented allows manufacturers to develop a more comprehensive assessment of the success of their forming processes by capturing the effects of inherent variation.

Exposure estimation, uncertainty and variability in occupational hygiene retrospective assessment

This thesis reports on a quantitative exposure assessment and on an analysis of the attributes of the data used in the estimations, in particular distinguishing between its uncertainty and variability. A retrospective assessment of exposure to benzene was carried out for a case control study of leukaemia in the Australian petroleum industry. The study used the mean of personal task-based measurements (Base Estimates) in a deterministic algorithm and applied factors to model back to places, times etc for which no exposure measurements were available. Mean daily exposures were estimated, on an individual subject basis, by summing the task-based exposures. These mean exposures were multiplied by the years spent on each job to provide exposure estimates in ppm-years. These were summed to provide a Cumulative Estimate for each subject. Validation was completed for the model and key inputs. Exposures were low, most jobs were below TWA of 5 ppm benzene. Exposures in terminals were generally higher than at refineries. Cumulative Estimates ranged from 0.005 to 50.9 ppm-years, with 84 percent less than 10 ppm-years. Exposure probability distributions were developed for tanker drivers using Monte Carlo simulation of the exposure estimation algorithm. The outcome was a lognormal distribution of exposure for each driver. These provide the basis for alternative risk assessment metrics e.g. the frequency of short but intense exposures which provided only a minimal contribution to the long-term average exposure but may increase risk of leukaemia. The effect of different inputs to the model were examined and their significance assessed using Monte Carlo simulation. The Base Estimates were the most important determinant of exposure in the model. The sources of variability in the measured data were examined, including the effect of having censored data and the between and within-worker variability. The sources of uncertainty in the exposure estimates were analysed and consequential improvements in exposure assessment identified. Monte Carlo sampling was also used to examine the uncertainties and variability associated with the tanker drivers' exposure assessment, to derive an estimate of the range and to put confidence intervals on the daily mean exposures. The identified uncertainty was less than the variability associated with the estimates. The traditional approach to exposure estimation typically derives only point estimates of mean exposure. The approach developed here allows a range of exposure estimates to be made and provides a more flexible and improved basis for risk assessment.

On the influence of building design, occupants and heat waves on comfort and greenhouse gas emissions in naturally ventilated offices. A study based on the EN 15251 adaptive thermal comfort model in Athens, Greece

According to the Intergovernmental Panel on Climate Change the buildings sector has the largest mitigation potential for CO2 emissions. Especially in office buildings, where internal heat loads and a relatively high occupant density occur at the same time with solar heat gains, overheating has become a common problem. In Europe the adaptive thermal comfort model according to EN 15251 provides a method to evaluate thermal comfort in naturally ventilated buildings. However, especially in the context of the climate change and the occurrence of heat waves within the last decade, the question arises, how thermal comfort can be maintained without additional cooling, especially in warm climates. In this paper a parametric study for a typical cellular naturally ventilated office room has been conducted, using the building simulation software EnergyPlus. It is based on the Mediterranean climate of Athens, Greece. Adaptive thermal comfort is evaluated according to EN 15251. Variations refer to different building design priorities, and they consider the variability of occupant behaviour and internal heat loads by using an ideal and worst case scenario. The influence of heat waves is considered by comparing measured temperatures for an average and an exceptionally hot year within the last decade. Since the use of building controls for shading affects thermal as well as visual comfort, daylighting and view are evaluated as well. Conclusions are drawn regarding the influence and interaction of building design, occupants and heat waves on comfort and greenhouse gas emissions in naturally ventilated offices, and related optimisation potential.

A pose-wise linear illumination manifold model for face recognition using video

The objective of this work is to recognize faces using video sequences both for training and novel input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. There are three major areas of novelty: (i) illumination generalization is achieved by combining coarse histogram correction with fine illumination manifold-based normalization; (ii) pose robustness is achieved by decomposing each appearance manifold into semantic Gaussian pose clusters, comparing the corresponding clusters and fusing the results using an RBF network; (iii) a fully automatic recognition system based on the proposed method is described and extensively evaluated on 600 head motion video sequences with extreme illumination, pose and motion pattern variation. On this challenging data set our system consistently demonstrated a very high recognition rate (95% on average), significantly outperforming state-of-the-art methods from the literature.

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. © 2010 World Scientific Publishing Company.

Step by step analyzing, modeling and simulation of single and double array PV system in different environmental variability

This research analyses the mathematical model of the PV array with all the parameters involved, in the sequential steps. Then it is going to simulate the I-V and P-V characteristics of PV array in different climatology conditions, by using both MATLAB M-file and Simulink. Finally the capability of this method is demonstrated in simulated results, which are performed for both Single and Double Array PV system.

A statistical model for combustion resonance from a DI diesel engine with applications

Introduced in this paper is a Bayesian model for isolating the resonant frequency from combustion chamber resonance. The model shown in this paper focused on characterising the initial rise in the resonant frequency to investigate the rise of in-cylinder bulk temperature associated with combustion. By resolving the model parameters, it is possible to determine: the start of pre-mixed combustion, the start of diffusion combustion, the initial resonant frequency, the resonant frequency as a function of crank angle, the in-cylinder bulk temperature as a function of crank angle and the trapped mass as a function of crank angle. The Bayesian method allows for individual cycles to be examined without cycle-averaging|allowing inter-cycle variability studies. Results are shown for a turbo-charged, common-rail compression ignition engine run at 2000 rpm and full load.

Towards powerful experimental and statistical approaches to study intraindividual variability in labile traits

There is a long-standing interest in behavioural ecology, exploring the causes and correlates of consistent individual differences in mean behavioural traits ('personality') and the response to the environment ('plasticity'). Recently, it has been observed that individuals also consistently differ in their residual intraindividual variability (rIIV). This variation will probably have broad biological and methodological implications to the study of trait variation in labile traits, such as behaviour and physiology, though we currently need studies to quantify variation in rIIV, using more standardized and powerful methodology. Focusing on activity rates in guppies (Poecilia reticulata), we provide a model example, from sampling design to data analysis, in how to quantify rIIV in labile traits. Building on the doubly hierarchical generalized linear model recently used to quantify individual differences in rIIV, we extend the model to evaluate the covariance between individual mean values and their rIIV. After accounting for time-related change in behaviour, our guppies substantially differed in rIIV, and it was the active individuals that tended to be more consistent (lower rIIV). We provide annotated data analysis code to implement these complex models, and discuss how to further generalize the model to evaluate covariances with other aspects of phenotypic variation.