Nation & internation practices in decision support tools in road asset management

This document provides a review of international and national practices in investment decision support tools in road asset management. Efforts were concentrated on identifying analytic frameworks, evaluation methodologies and criteria adopted by current tools. Emphasis was also given to how current approaches support Triple Bottom Line decision-making. Benefit Cost Analysis and Multiple Criteria Analysis are principle methodologies in supporting decision-making in Road Asset Management. The complexity of the applications shows significant differences in international practices. There is continuing discussion amongst practitioners and researchers regarding to which one is more appropriate in supporting decision-making. It is suggested that the two approaches should be regarded as complementary instead of competitive means. Multiple Criteria Analysis may be particularly helpful in early stages of project development, say strategic planning. Benefit Cost Analysis is used most widely for project prioritisation and selecting the final project from amongst a set of alternatives. Benefit Cost Analysis approach is useful tool for investment decision-making from an economic perspective. An extension of the approach, which includes social and environmental externalities, is currently used in supporting Triple Bottom Line decision-making in the road sector. However, efforts should be given to several issues in the applications. First of all, there is a need to reach a degree of commonality on considering social and environmental externalities, which may be achieved by aggregating the best practices. At different decision-making level, the detail of consideration of the externalities should be different. It is intended to develop a generic framework to coordinate the range of existing practices. The standard framework will also be helpful in reducing double counting, which appears in some current practices. Cautions should also be given to the methods of determining the value of social and environmental externalities. A number of methods, such as market price, resource costs and Willingness to Pay, are found in the review. The use of unreasonable monetisation methods in some cases has discredited Benefit Cost Analysis in the eyes of decision makers and the public. Some social externalities, such as employment and regional economic impacts, are generally omitted in current practices. This is due to the lack of information and credible models. It may be appropriate to consider these externalities in qualitative forms in a Multiple Criteria Analysis. Consensus has been reached in considering noise and air pollution in international practices. However, Australia practices generally omitted these externalities. Equity is an important consideration in Road Asset Management. The considerations are either between regions, or social groups, such as income, age, gender, disable, etc. In current practice, there is not a well developed quantitative measure for equity issues. More research is needed to target this issue. Although Multiple Criteria Analysis has been used for decades, there is not a generally accepted framework in the choice of modelling methods and various externalities. The result is that different analysts are unlikely to reach consistent conclusions about a policy measure. In current practices, some favour using methods which are able to prioritise alternatives, such as Goal Programming, Goal Achievement Matrix, Analytic Hierarchy Process. The others just present various impacts to decision-makers to characterise the projects. Weighting and scoring system are critical in most Multiple Criteria Analysis. However, the processes of assessing weights and scores were criticised as highly arbitrary and subjective. It is essential that the process should be as transparent as possible. Obtaining weights and scores by consulting local communities is a common practice, but is likely to result in bias towards local interests. Interactive approach has the advantage in helping decision-makers elaborating their preferences. However, computation burden may result in lose of interests of decision-makers during the solution process of a large-scale problem, say a large state road network. Current practices tend to use cardinal or ordinal scales in measure in non-monetised externalities. Distorted valuations can occur where variables measured in physical units, are converted to scales. For example, decibels of noise converts to a scale of -4 to +4 with a linear transformation, the difference between 3 and 4 represents a far greater increase in discomfort to people than the increase from 0 to 1. It is suggested to assign different weights to individual score. Due to overlapped goals, the problem of double counting also appears in some of Multiple Criteria Analysis. The situation can be improved by carefully selecting and defining investment goals and criteria. Other issues, such as the treatment of time effect, incorporating risk and uncertainty, have been given scant attention in current practices. This report suggested establishing a common analytic framework to deal with these issues.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Decision support for the safe design and operation of unmanned aircraft systems

Unmanned Aircraft Systems (UAS) describe a diverse range of aircraft that are operated without a human pilot on-board. Unmanned aircraft range from small rotorcraft, which can fit in the palm of your hand, through to fixed wing aircraft comparable in size to that of a commercial passenger jet. The absence of a pilot on-board allows these aircraft to be developed with unique performance capabilities facilitating a wide range of applications in surveillance, environmental management, agriculture, defence, and search and rescue. However, regulations relating to the safe design and operation of UAS first need to be developed before the many potential benefits from these applications can be realised. According to the International Civil Aviation Organization (ICAO), a Risk Management Process (RMP) should support all civil aviation policy and rulemaking activities (ICAO 2009). The RMP is described in International standard, ISO 31000:2009 (ISO, 2009a). This standard is intentionally generic and high-level, providing limited guidance on how it can be effectively applied to complex socio-technical decision problems such as the development of regulations for UAS. Through the application of principles and tools drawn from systems philosophy and systems engineering, this thesis explores how the RMP can be effectively applied to support the development of safety regulations for UAS. A sound systems-theoretic foundation for the RMP is presented in this thesis. Using the case-study scenario of a UAS operation over an inhabited area and through the novel application of principles drawn from general systems modelling philosophy, a consolidated framework of the definitions of the concepts of: safe, risk and hazard is made. The framework is novel in that it facilitates the representation of broader subjective factors in an assessment of the safety of a system; describes the issues associated with the specification of a system-boundary; makes explicit the hierarchical nature of the relationship between the concepts and the subsequent constraints that exist between them; and can be evaluated using a range of analytic or deliberative modelling techniques. Following the general sequence of the RMP, the thesis explores the issues associated with the quantified specification of safety criteria for UAS. A novel risk analysis tool is presented. In contrast to existing risk tools, the analysis tool presented in this thesis quantifiably characterises both the societal and individual risk of UAS operations as a function of the flight path of the aircraft. A novel structuring of the risk evaluation and risk treatment decision processes is then proposed. The structuring is achieved through the application of the Decision Support Problem Technique; a modelling approach that has been previously used to effectively model complex engineering design processes and to support decision-making in relation to airspace design. The final contribution made by this thesis is in the development of an airworthiness regulatory framework for civil UAS. A novel "airworthiness certification matrix" is proposed as a basis for the definition of UAS "Part 21" regulations. The outcome airworthiness certification matrix provides a flexible, systematic and justifiable method for promulgating airworthiness regulations for UAS. In addition, an approach for deriving "Part 1309" regulations for UAS is presented. In contrast to existing approaches, the approach presented in this thesis facilitates a traceable and objective tailoring of system-level reliability requirements across the diverse range of UAS operations. The significance of the research contained in this thesis is clearly demonstrated by its practical real world outcomes. Industry regulatory development groups and the Civil Aviation Safety Authority have endorsed the proposed airworthiness certification matrix. The risk models have also been used to support research undertaken by the Australian Department of Defence. Ultimately, it is hoped that the outcomes from this research will play a significant part in the shaping of regulations for civil UAS, here in Australia and around the world.

Matrix metalloproteinases during wound healing - a double edged sword

The extracellular matrix (ECM) provides a framework for cells and gives skin its tensile strength and elasticity. Loss of its integrity necessitates the clearing of damaged components and the deposition of firstly a provisional matrix and later remodelling of the ECM to support a functionally intact tissue. Matrix metalloproteinases (MMPs) are an important family of enzymes that function in the breakdown of the ECM and modulate the function of many biologically active molecules housed in the ECM. Through their enzymatic actions MMPs play a role in fundamental processes such as immune cell infiltration and ECM remodelling during wound repair. Their tight control is necessary for timely wound healing and excessive MMP activity participates in the development and persistence of chronic wounds, while reduced activity contributes to fibrosis. A number of inhibitors have been designed to target this activity and improve wound healing with limited success. Novel strategies are currently being investigated to improve wound healing by targeting MMP modulating molecules.

The mechanical rigidity of the extracellular matrix regulates the structure, motility, and proliferation of glioma cells

Glioblastoma multiforme (GBM) is a malignant astrocytoma of the central nervous system associated with a median survival time of 15 months, even with aggressive therapy. This rapid progression is due in part to diffuse infiltration of single tumor cells into the brain parenchyma, which is thought to involve aberrant interactions between tumor cells and the extracellular matrix (ECM). Here, we test the hypothesis that mechanical cues from the ECM contribute to key tumor cell properties relevant to invasion. We cultured a series of glioma cell lines (U373-MG, U87-MG, U251-MG, SNB19, C6) on fibronectin-coated polymeric ECM substrates of defined mechanical rigidity and investigated the role of ECM rigidity in regulating tumor cell structure, migration, and proliferation. On highly rigid ECMs, tumor cells spread extensively, form prominent stress fibers and mature focal adhesions, and migrate rapidly. As ECM rigidity is lowered to values comparable with normal brain tissue, tumor cells appear rounded and fail to productively migrate. Remarkably, cell proliferation is also strongly regulated by ECM rigidity, with cells dividing much more rapidly on rigid than on compliant ECMs. Pharmacologic inhibition of nonmuscle myosin II–based contractility blunts this rigidity-sensitivity and rescues cell motility on highly compliant substrates. Collectively, our results provide support for a novel model in which ECM rigidity provides a transformative, microenvironmental cue that acts through actomyosin contractility to regulate the invasive properties of GBM tumor cells.

Myogel, a novel, basement membrane-rich, extracellular matrix derived from skeletal muscle, is highly adipogenic in vivo and in vitro

Background/Aims Biological and synthetic scaffolds play important roles in tissue engineering and are being developed towards human clinical applications. Based on previous work from our laboratory, we propose that extracellular matrices from skeletal muscle could be developed for adipose tissue engineering. Methods Extracellular matrices (Myogels) extracted from skeletal muscle of various species were assessed using biochemical assays including ELISA and Western blotting. Biofunctionality was assessed using an in vitro differentiation assay and a tissue engineering construct model in the rat. Results Myogels were successfully extracted from mice, rats, pigs and humans. Myogels contained significant levels of laminin α4- and α2-subunits and collagen I compared to Matrigel™, which contains laminin 1 (α1β1γ1) and collagen IV. Levels of growth factors such as fibroblast growth factor 2 were significantly higher than Matrigel, vascular endothelial growth factor-A levels were significantly lower and all other growth factors were comparable. Myogels reproducibly stimulated adipogenic differentiation of preadipocytes in vitro and the growth of adipose tissue in the rat. Conclusions We found Myogel induces adipocyte differentiation in vitroand shows strong adipogenic potential in vivo, inducing the growth of well-vascularised adipose tissue. Myogel offers an alternative for current support scaffolds in adipose tissue engineering, allowing the scaling up of animal models towards clinical adipose tissue engineering applications.

The role of biological extracellular matrix scaffolds in vascularized three-dimensional tissue growth in vivo

An in vivo murine vascularized chamber model has been shown to generate spontaneous angiogenesis and new tissue formation. This experiment aimed to assess the effects of common biological scaffolds on tissue growth in this model. Either laminin-1, type I collagen, fibrin glue, hyaluronan, or sea sponge was inserted into silicone chambers containing the epigastric artery and vein, one end was sealed with adipose tissue and the other with bone wax, then incubated subcutaneously. After 2, 4, or 6 weeks, tissue from chambers containing collagen I, fibrin glue, hyaluronan, or no added scaffold (control) had small amounts of vascularized connective tissue. Chambers containing sea sponge had moderate connective tissue growth together with a mild "foreign body" inflammatory response. Chambers containing laminin-1, at a concentration 10-fold lower than its concentration in Matrigel™, resulted in a moderate adipogenic response. In summary, (1) biological hydrogels are resorbed and gradually replaced by vascularized connective tissue; (2) sponge-like matrices with large pores support connective tissue growth within the pores and become encapsulated with granulation tissue; (3) laminin-containing scaffolds facilitate adipogenesis. It is concluded that the nature and chemical composition of the scaffold exerts a significant influence on the amount and type of tissue generated in this in vivo chamber model.

Implication of collagen type I-induced membrane-type 1-matrix metalloproteinase expression and matrix metalloproteinase-2 activation in the metastatic progression of breast carcinoma

We have previously demonstrated that fibroblasts and invasive human breast carcinoma (HBC) cells specifically activate matrix metalloproteinase- 2 (MMP-2) when cultured on 3-dimensional gels of type I collagen but not a range of other substrates. We show here the constitutive expression of membrane-type 1 (MT1)-MMP in both fibroblasts, and invasive HBC cell lines, that have fibroblastic attributes presumably acquired through an epithelial- to-mesenchymal transition (EMT). Treatment with collagen type I increased the steady-state MT1-MMP mRNA levels in these cells but did not induce either MT1-MMP expression or MMP-2 activation in noninvasive breast carcinoma cell lines, which retain epithelial features. Basal MT3-MMP mRNA expression had a pattern similar to that of MT1-MMP but was not up-regulated by collagen. MT4- MMP mRNA was seen in both invasive and noninvasive HBC cell lines and was also not collagen-regulated, and MT2-MMP mRNA was not detected in any of the HBC cell lines tested. These data support a role for MT1-MMP in the collagen- induced MMP-2-activation seen in these cells. In situ hybridization analysis of archival breast cancer specimens revealed a close parallel in expression of both collagen type I and MT1-MMP mRNA in peritumoral fibroblasts, which was correlated with aggressiveness of the lesion. Relatively high levels of expression of both mRNA species were seen in fibroblasts close to invasive tumor nests and, although only focally, in certain areas close to preinvasive tumors. These foci may represent hot spots for local degradation and invasive progression. Collectively, these results implicate MT1-MMP in collagen- stimulated MMP-2 activation and suggest that this mechanism may be employed in vivo by both tumor-associated fibroblasts and EMT-derived carcinoma cells to facilitate increased invasion and/or metastasis.