A comparative study of manufacturing practices and performance variables

The reported study was conducted to compare and contrast current manufacturing practices between two countries, Australia and Malaysia, and identify the practices that significantly influence their manufacturing performances. The results are based on data collected from surveys using a standard questionnaire in both countries. Evidence indicates that product quality and reliability is the main competitive factor for manufacturers. Maintaining a supplier rating system and regularly updating it with field failure and warranty data and making use of product data management are found to be effective manufacturing practices. In terms of the investigated manufacturing performance, Australian manufacturers are marginally ahead of their Malaysian counterparts. However, Malaysian manufacturers came out ahead on most dimensions of advanced quality and manufacturing practices, particularly in the adoption of product data management, effective supply chains and relationships with suppliers and customers.

Within- and between-day repeatability and variability in children's physiological responses during submaximal treadmill exercise

The purpose of this study was to verify within- and between-day repeatability and variability in children's oxygen uptake (VO^sub 2^), gross economy (GE; VO^sub 2^ divided by speed) and heart rate (HR) during treadmill walking based on self-selected speed (SS). Fourteen children (10.1 ± 1.4 years) undertook three testing sessions over 2 days in which four walking speeds, including SS were tested. Within- and between-day repeatability were assessed using the Bland and Altman method, and coefficients of variability (CV) were determined for each child across exercise bouts and averaged to obtain a mean group CV value for VO^sub 2^, GE, and HR per speed. Repeated measures analysis of variance showed no statistically significant differences in within- or between-day CV for VO^sub 2^, GE, or HR at any speed. Repeatability within- and between-day for VO^sub 2^, GE, and HR for all speeds was verified. These results suggest that submaximal VO^sub 2^ during treadmill walking is stable and reproducible at a range of speeds based on children's SS.

Generating rules with predicates, terms and variables from the pruned neural networks

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.

Inequalities in cardiovascular disease mortality : the role of behavioural, physiological and social risk factors

Background: While the relationship between socioeconomic disadvantage and cardiovascular disease (CVD) is well established, the role that traditional cardiovascular risk factors play in this association remains unclear. We examined the association between education attainment and CVD mortality and the extent to which behavioural, social and physiological factors explained this relationship. Methods: Adults (n=38 355) aged 40-69 years living in Melbourne, Australia were recruited in 1990-1994. Subjects with baseline CVD risk factor data ascertained through questionnaire and physical measurement were followed for an average of 9.4 years with CVD deaths verified by review of medical records and autopsy reports. Results: CVD mortality was higher for those with primary education only compared to those who had completed tertiary education, with a hazard ratio (HR) of 1.66 (95% confidence interval [CI] 1.11-2.49) after adjustment for age, country of birth and gender. Those from the lowest educated group had a more adverse cardiovascular risk factor profile compared to the highest educated group, and adjustment for these risk factors reduced the HR to 1.18 (95% CI 0.78-1.77). In analysis of individual risk factors, smoking and waist circumference explained most of the difference in CVD mortality between the highest and lowest education groups. Conclusions: Most of the excess CVD mortality in lower socioeconomic groups can be explained by known risk factors, particularly smoking and overweight. While targeting cardiovascular risk factors should not divert efforts from addressing the underlying determinants of health inequalities, it is essential that known risk factors are addressed effectively among lower socioeconomic groups.

Distribution profiles of key weather variables in Australian capital cities : implications for energy efficient building design

Local climate is a critical element in the design of energy efficient buildings. In this paper, ten years of historical weather data in Australia's eight capital cities were profiled and analysed to characterize the variations of climatic variables in Australia. The method of descriptive statistics was employed. Either the pattern of cumulative distribution and/or the profile of percentage distribution are presented. It was found that although weather variables vary with different locations, there is often a good, nearly linear relation between a weather variable and its cumulative percentage for the majority of middle part of the cumulative curves. By comparing the slopes of these distribution profiles, it may be possible to determine the relative range of changes of the particular weather variables for a given city. The implications of these distribution profiles of key weather variables on energy efficient building design are also discussed.

Dynamics of in vitro polymer degradation of polycaprolactone-based scaffolds : accelerated versus simulated physiological conditions

The increasing use of biodegradable devices in tissue engineering and regenerative medicine means it is essential to study and understand their degradation behaviour. Accelerated degradation systems aim to achieve similar degradation profiles within a shorter period of time, compared with standard conditions. However, these conditions only partially mimic the actual situation, and subsequent analyses and derived mechanisms must be treated with caution and should always be supported by actual long-term degradation data obtained under physiological conditions. Our studies revealed that polycaprolactone (PCL) and PCL-composite scaffolds degrade very differently under these different degradation conditions, whilst still undergoing hydrolysis. Molecular weight and mass loss results differ due to the different degradation pathways followed (surface degradation pathway for accelerated conditions and bulk degradation pathway for simulated physiological conditions). Crystallinity studies revealed similar patterns of recrystallization dynamics, and mechanical data indicated that the scaffolds retained their functional stability, in both instances, over the course of degradation. Ultimately, polymer degradation was shown to be chiefly governed by molecular weight, crystallinity susceptibility to hydrolysis and device architecture considerations whilst maintaining its thermodynamic equilibrium.

Overeating as a clue to weight gain: behavioural and physiological interactions in the control of food intake

It has been estimated that 25-50% of people in most affluent societies are either obese or overweight. These disorders are the result of an imbalance between calorific intake and energy expenditure over a prolonged time period. These types of disorders are among the most common health problems in industrialized societies. Addressing these issues and offering new strategies, this thorough new study draws together contributions from interdisciplinary and international group of specialists, includes recent research on genetic influences, features discussions of epidemiological studies and covers both biological and social aspects of obesity.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Physiological investigation of periosteum structure and its application in periosteum tissue engineering

Although many different materials, techniques and methods, including artificial or engineered bone substitutes, have been used to repair various bone defects, the restoration of critical-sized bone defects caused by trauma, surgery or congenital malformation is still a great challenge to orthopedic surgeons. One important fact that has been neglected in the pursuit of resolutions for large bone defect healing is that most physiological bone defect healing needs the periosteum and stripping off the periosteum may result in non-union or non-healed bone defects. Periosteum plays very important roles not only in bone development but also in bone defect healing. The purpose of this project was to construct a functional periosteum in vitro using a single stem cell source and then test its ability to aid the repair of critical-sized bone defect in animal models. This project was designed with three separate but closely-linked parts which in the end led to four independent papers. The first part of this study investigated the structural and cellular features in periostea from diaphyseal and metaphyseal bone surfaces in rats of different ages or with osteoporosis. Histological and immunohistological methods were used in this part of the study. Results revealed that the structure and cell populations in periosteum are both age-related and site-specific. The diaphyseal periosteum showed age-related degeneration, whereas the metaphyseal periosteum is more destructive in older aged rats. The periosteum from osteoporotic bones differs from normal bones both in terms of structure and cell populations. This is especially evident in the cambial layer of the metaphyseal area. Bone resorption appears to be more active in the periosteum from osteoporotic bones, whereas bone formation activity is comparable between the osteoporotic and normal bone. The dysregulation of bone resorption and formation in the periosteum may also be the effect of the interaction between various neural pathways and the cell populations residing within it. One of the most important aspects in periosteum engineering is how to introduce new blood vessels into the engineered periosteum to help form vascularized bone tissues in bone defect areas. The second part of this study was designed to investigate the possibility of differentiating bone marrow stromal cells (BMSCs) into the endothelial cells and using them to construct vascularized periosteum. The endothelial cell differentiation of BMSCs was induced in pro-angiogenic media under both normoxia and CoCl2 (hypoxia-mimicking agent)-induced hypoxia conditions. The VEGF/PEDF expression pattern, endothelial cell specific marker expression, in vitro and in vivo vascularization ability of BMSCs cultured in different situations were assessed. Results revealed that BMSCs most likely cannot be differentiated into endothelial cells through the application of pro-angiogenic growth factors or by culturing under CoCl2-induced hypoxic conditions. However, they may be involved in angiogenesis as regulators under both normoxia and hypoxia conditions. Two major angiogenesis-related growth factors, VEGF (pro-angiogenic) and PEDF (anti-angiogenic) were found to have altered their expressions in accordance with the extracellular environment. BMSCs treated with the hypoxia-mimicking agent CoCl2 expressed more VEGF and less PEDF and enhanced the vascularization of subcutaneous implants in vivo. Based on the findings of the second part, the CoCl2 pre-treated BMSCs were used to construct periosteum, and the in vivo vascularization and osteogenesis of the constructed periosteum were assessed in the third part of this project. The findings of the third part revealed that BMSCs pre-treated with CoCl2 could enhance both ectopic and orthotopic osteogenesis of BMSCs-derived osteoblasts and vascularization at the early osteogenic stage, and the endothelial cells (HUVECs), which were used as positive control, were only capable of promoting osteogenesis after four-weeks. The subcutaneous area of the mouse is most likely inappropriate for assessing new bone formation on collagen scaffolds. This study demonstrated the potential application of CoCl2 pre-treated BMSCs in the tissue engineering not only for periosteum but also bone or other vascularized tissues. In summary, the structure and cell populations in periosteum are age-related, site-specific and closely linked with bone health status. BMSCs as a stem cell source for periosteum engineering are not endothelial cell progenitors but regulators, and CoCl2-treated BMSCs expressed more VEGF and less PEDF. These CoCl2-treated BMSCs enhanced both vascularization and osteogenesis in constructed periosteum transplanted in vivo.

Important omitted spatial variables in safety models: Understanding contributing crash causes at intersections

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causation—rests upon the pursuit of numerous lines of inquiry. The research community has focused on analytical methods development (negative binomial specifications, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might think of different lines of inquiry in terms of ‘low lying fruit’—areas of inquiry that might provide significant improvements in understanding crash causation. It is the contention of this research that omitted variable bias caused by the exclusion of important variables is an important line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant ability to better understand contributing factors to crashes. This study—believed to represent a unique contribution to the safety literature—develops and examines the role of a sizeable set of spatial variables in intersection crash occurrence. In addition to commonly considered traffic and geometric variables, examined spatial factors include local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools. The results indicate that inclusion of these factors results in significant improvement in model explanatory power, and the results also generally agree with expectation. The research illuminates the importance of spatial variables in safety research and also the negative consequences of their omissions.