Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia

This paper reports the application of multicriteria decision making techniques, PROMETHEE and GAIA, and receptor models, PCA/APCS and PMF, to data from an air monitoring site located on the campus of Queensland University of Technology in Brisbane, Australia and operated by Queensland Environmental Protection Agency (QEPA). The data consisted of the concentrations of 21 chemical species and meteorological data collected between 1995 and 2003. PROMETHEE/GAIA separated the samples into those collected when leaded and unleaded petrol were used to power vehicles in the region. The number and source profiles of the factors obtained from PCA/APCS and PMF analyses were compared. There are noticeable differences in the outcomes possibly because of the non-negative constraints imposed on the PMF analysis. While PCA/APCS identified 6 sources, PMF reduced the data to 9 factors. Each factor had distinctive compositions that suggested that motor vehicle emissions, controlled burning of forests, secondary sulphate, sea salt and road dust/soil were the most important sources of fine particulate matter at the site. The most plausible locations of the sources were identified by combining the results obtained from the receptor models with meteorological data. The study demonstrated the potential benefits of combining results from multi-criteria decision making analysis with those from receptor models in order to gain insights into information that could enhance the development of air pollution control measures.

One small difference after another : stories of grassroots leaders

Grassroots groups – autonomous, not-for-profit groups made up of volunteers – and grassroots initiatives play an invaluable, yet often invisible, role in our communities. The informal processes and collective efforts of grassroots associations, social movements, self-help groups and local action collectives are central to civil society and community building. Grassroots leaders are critical to such initiatives, yet little is known about their influences, motivations, successes and challenges. This study aims to address this dearth in the research literature by noting the experiences of a sample of grassroots community leaders to help gain a greater knowledge about community leadership in action. In-depth semi-structured interviews were held with nine grassroots leaders from a broad cross-section of sectors of interest. The criteria for selection were that these leaders were not in a formal non-profit organisation, were not paid for their work yet were leading grassroots groups or initiatives involved in active community building, campaigning or self-help. The paper reflects on findings in regard to the formative experiences that impacted upon the community leaders’ direction in life, their beliefs and ideas about what it means to be a leader, the strategies they use to lead and challenges they continue to face, and the role of learning and support in maintaining and developing their roles. Finally, the key themes relating to grassroots leadership and how these leaders enhance their own effectiveness and resilience are explored.

Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology

1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC), and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80.6% for the AIC, 29.4% for the QIC and 81.6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.

The roles and importance of formal and informal sources of business support in young firms : evidence from the UK’s 2008 Federation of Small Business Survey

Objectives This research explores the relationship between young firms, their growth orientation-intention and a range of relationships which can be seen to provide business support. Prior-work Research indicates that networks impact the firm’s ability to secure resources (Sirmon and Hitt 2003; Liao and Welsch. 2004; Hanlon and Saunders 2007). Networks have been evaluated in a number of ways ranging from simple counts to characteristics of their composition (Davidsson and Honig 2003), strength of relationships (Granovetter 1973) and network diversity (Carter et al 2003). By providing access to resources and knowledge (from start-up assistance and raising capital, (e.g. Smallbone et al, 2003), networks may assist in enabling continued persistence during those times where firms may experience resource constraints owing to firm growth (Baker and Nelson 2005). Approach The data used in this research was generated in the 2008 UK Federation of Small Businesses (FSB) survey. Over 1,000 of the firms responding were found to fall into the category of “young”, ((defined as firms under 4 years old). Firms were considered the unit of analysis with the entrepreneur being the chief spokesperson for the firm. Preliminary data analysis considered key demographic characteristics and industry classifications, comparing the FSB data with that of the UK government’s own (BERR) Small Business Surveys of 2007 and 2008, to establish some degree of representativeness of the respondents. The analysis then examined networks with varying potential ability to provide support for young firms, the networks measured in terms of number, diversity, characteristic and strength in its relationship to young firm growth orientation. The diversity of business-support-related relationships ranged from friends and family, through professional services, customers and suppliers, and government business services, to trade associations and informal business networks. The characteristics of these formal and informal sources of support for new businesses are examined across a range of business support-type activities for new firms. The number of relationships and types of business support are also explored. Finally, the strength of these relationships is examined by analysis of the source of business support, type of business support, and links to the growth orientation-intention of the firm, after controlling for a number of key variables related to firm and industry status and owner characteristics. Results Preliminary analysis of the data by means of univariate analysis showed that average number of sources of advice was around 2.5 (from a potential total of 6). In terms of the diversity of relationships, universities had by far the smallest percentage of firms receiving beneficial advice from them. Government business services were beneficially used by 40% of young firms, the other relationship types being around the 50-55% mark. In terms of characteristics of the advice, the average number of areas in which benefit was achieved was around 5.5 of a maximum of 15. Start-up advice has by far the highest percentage of firms obtaining beneficial advice, with increasing sales, improving contacts and improving confidence being the other categories at or around the 50% mark. Other market-focused areas where benefits were also received were in the areas of new markets, existing product improvements and new product improvements, where around 40% of the young responding firms obtained benefit. Regression techniques evaluating the strength of these relationships in terms of the links between business support (by source of support, type of support, and range of support) and firm growth orientation-intention focus highlighted a number of significant relationships, even after controlling for a range of other explanatory variables identified in the literature. Specifically, there was found to be a positive relationship between receiving business advice generally (regardless of type or source) and growth orientation. This relationship was seen to be stronger, however, when looking at the number of types of beneficial advice received, and stronger again for the number of sources of this advice. In terms of individual sources of advice, customers and suppliers had the strongest relationship with growth, with Government business services also found to be significant. Combining these two sources was also seen to increase the strength of the relationship between these two sources of advice and growth orientation. In considering areas of support, growth was most strongly positively related to advice that benefited the development of new products and services, and also business confidence, but was negatively related to advice linked to business recovery. Finally, amalgamating the 4 key types and sources of advice to examine the impact of combinations of these types and sources of advice also improved the strength of the relationship. Implications The findings will assist in the understanding of young firms in general and growth more specifically, particularly the role and importance of specific sources, types and combinations of business support used more extensively by new young growth-oriented firms. Value This research may assist in processes designed to allow entrepreneurs to make better decisions; educators and support organizations to develop better advice and assistance, and Governments design better conditions for the creation of new growth-oriented businesses.

Attitudes to Formal Business Training and Learning amongst Entrepreneurs in the Cultural Industries: Situated Business Learning through "Doing with Others."

Presents arguments supporting a social model of learning linked to situated learning and cultural capital. Critiques training methods used in cultural industries (arts, publishing, broadcasting, design, fashion, restaurants). Uses case study evidence to demonstrates inadequacies of formal training in this sector. (Contains 49 references.)

Evolutionary algorithms for sustainable building design

This approach to sustainable design explores the possibility of creating an architectural design process which can iteratively produce optimised and sustainable design solutions. Driven by an evolution process based on genetic algorithms, the system allows the designer to “design the building design generator” rather than to “designs the building”. The design concept is abstracted into a digital design schema, which allows transfer of the human creative vision into the rational language of a computer. The schema is then elaborated into the use of genetic algorithms to evolve innovative, performative and sustainable design solutions. The prioritisation of the project’s constraints and the subsequent design solutions synthesised during design generation are expected to resolve most of the major conflicts in the evaluation and optimisation phases. Mosques are used as the example building typology to ground the research activity. The spatial organisations of various mosque typologies are graphically represented by adjacency constraints between spaces. Each configuration is represented by a planar graph which is then translated into a non-orthogonal dual graph and fed into the genetic algorithm system with fixed constraints and expected performance criteria set to govern evolution. The resultant Hierarchical Evolutionary Algorithmic Design System is developed by linking the evaluation process with environmental assessment tools to rank the candidate designs. The proposed system generates the concept, the seed, and the schema, and has environmental performance as one of the main criteria in driving optimisation.

Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

New criteria for evaluating methods of identifying hot spots

Identification of hot spots, also known as the sites with promise, black spots, accident-prone locations, or priority investigation locations, is an important and routine activity for improving the overall safety of roadway networks. Extensive literature focuses on methods for hot spot identification (HSID). A subset of this considerable literature is dedicated to conducting performance assessments of various HSID methods. A central issue in comparing HSID methods is the development and selection of quantitative and qualitative performance measures or criteria. The authors contend that currently employed HSID assessment criteria—namely false positives and false negatives—are necessary but not sufficient, and additional criteria are needed to exploit the ordinal nature of site ranking data. With the intent to equip road safety professionals and researchers with more useful tools to compare the performances of various HSID methods and to improve the level of HSID assessments, this paper proposes four quantitative HSID evaluation tests that are, to the authors’ knowledge, new and unique. These tests evaluate different aspects of HSID method performance, including reliability of results, ranking consistency, and false identification consistency and reliability. It is intended that road safety professionals apply these different evaluation tests in addition to existing tests to compare the performances of various HSID methods, and then select the most appropriate HSID method to screen road networks to identify sites that require further analysis. This work demonstrates four new criteria using 3 years of Arizona road section accident data and four commonly applied HSID methods [accident frequency ranking, accident rate ranking, accident reduction potential, and empirical Bayes (EB)]. The EB HSID method reveals itself as the superior method in most of the evaluation tests. In contrast, identifying hot spots using accident rate rankings performs the least well among the tests. The accident frequency and accident reduction potential methods perform similarly, with slight differences explained. The authors believe that the four new evaluation tests offer insight into HSID performance heretofore unavailable to analysts and researchers.

Universal online interventions might engage psychologically distressed university students who are unlikely to seek formal help

University students are a high risk population for mental health problems, yet few seek professional help when experiencing problems. This study explored the potential role of an online intervention for promoting wellbeing in university students, by investigating students' help-seeking behaviour, intention to use online interventions and student content preference for such interventions; 254 university students responded to an online survey designed for this study. As predicted, students were less likely to seek help as levels of psychological distress increased. Conversely, intention to use an online intervention increased at higher levels of distress, with 39.1%, 49.4% and 57.7% of low, moderate and severely distressed students respectively indicating they would use an online program supporting student well-being. Results suggest that online interventions may be a useful way to provide help to students in need who otherwise may not seek formal help.