An analysis of cyclists crashes to identify ITS-based intervention

Cycling provides a number of health and environmental benefits. However, cyclists are more likely to suffer serious injury or be killed in traffic accidents than car drivers and the estimated cost of crashes in Australia is $1.25AU billion per year. Current interventions to reduce bicycle crashes include compulsory helmet use, media campaigns, and the provision of cycling lanes, as well as road user education and training. It is difficult to assess the effectiveness of current interventions as there is no accurate measure of cyclist exposure in South East Queensland (SEQ). This paper analyses cyclist crash characteristics in Queensland with the view to identifying appropriate Intelligent Transport Systems (ITS) based intervention to reduce cyclist injury and death. The inappropriateness of some ITS interventions to improve cyclist safety is highlighted and a set of ITS interventions are identified, based on Queensland crash data 2002-2006.

Power system stability analysis based on descriptive study of electrical indices

In a power network, when a propagation energy wave caused by a disturbance hits a weak link, a reflection is appeared and some of energy is transferred across the link. In this work, an analytical descriptive methodology is proposed to study the dynamical stability of a large scale power system. For this purpose, the measured electrical indices (angle, or voltage/frequency) following a fault in different points among the network are used, and the behaviors of the propagated waves through the lines, nodes and buses are studied. This work addresses a new tool for power system stability analysis based on a descriptive study of electrical measurements. The proposed methodology is also useful to detect the contingency condition and synthesis of an effective emergency control scheme.

Impact of cognitive load and frustration on drivers’ speech [Abstract]

Secondary tasks such as cell phone calls or interaction with automated speech dialog systems (SDSs) increase the driver’s cognitive load as well as the probability of driving errors. This study analyzes speech production variations due to cognitive load and emotional state of drivers in real driving conditions. Speech samples were acquired from 24 female and 17 male subjects (approximately 8.5 h of data) while talking to a co-driver and communicating with two automated call centers, with emotional states (neutral, negative) and the number of necessary SDS query repetitions also labeled. A consistent shift in a number of speech production parameters (pitch, first format center frequency, spectral center of gravity, spectral energy spread, and duration of voiced segments) was observed when comparing SDS interaction against co-driver interaction; further increases were observed when considering negative emotion segments and the number of requested SDS query repetitions. A mel frequency cepstral coefficient based Gaussian mixture classifier trained on 10 male and 10 female sessions provided 91% accuracy in the open test set task of distinguishing co-driver interactions from SDS interactions, suggesting—together with the acoustic analysis—that it is possible to monitor the level of driver distraction directly from their speech.

A model for computer profiling

This paper discusses the use of models in automatic computer forensic analysis, and proposes and elaborates on a novel model for use in computer profiling, the computer profiling object model. The computer profiling object model is an information model which models a computer as objects with various attributes and inter-relationships. These together provide the information necessary for a human investigator or an automated reasoning engine to make judgements as to the probable usage and evidentiary value of a computer system. The computer profiling object model can be implemented so as to support automated analysis to provide an investigator with the information needed to decide whether manual analysis is required.

A regulatory analysis of international climate change regulation

The political challenges impeding the negotiation of a comprehensive multilateral agreement on international climate change have received a great deal of attention. A question that has gone somewhat overlooked is what essential components an effective regulatory scheme to reduce greenhouse gas emissions should contain. The objective of this article is to examine the regulatory architecture of current international arrangements relating to global climate change regulation. A systematic analysis of the structure, substantive composition, and administrative characteristics of the UNFCCC and Kyoto Protocol is undertaken. The analytical standard against which the agreements are examined is whether current international regulatory arrangements satisfy the basic requirements of regulatory coherence. The analysis identifies how the present scheme consists of a complex institutional structure that lacks a substantive regulatory core. The implications of the absence of functional and effective mechanisms to govern greenhouse gas emission reductions are considered in relation to the principles of good regulatory design. This, in turn, provides useful insights into how a better regulatory scheme might be designed.

Strategic information systems research : an archival analysis

The Journal of Strategic Information Systems (JSIS) has been an international outlet for Information Systems research that focuses on strategic issues since 1991. This paper reports on an analysis of the research published in JSIS to date. The paper presents a preliminary classification system for research topics related to Strategic Information Systems into which all 316 JSIS research papers as at end 2009 are classified. Discussion on changing emphases in topics over time is provided, in the context of the editorial philosophy of the journal. The paper seeks to stimulate discussion on future directions for research in Strategic Information Systems.

Demographic category membership and leadership in small groups : a social identity analysis

Developing the social identity theory of leadership (e.g., [Hogg, M. A. (2001). A social identity theory of leadership. Personality and Social Psychology Review, 5, 184–200]), an experiment (N=257) tested the hypothesis that as group members identify more strongly with their group (salience) their evaluations of leadership effectiveness become more strongly influenced by the extent to which their demographic stereotype-based impressions of their leader match the norm of the group (prototypicality). Participants, with more or less traditional gender attitudes (orientation), were members, under high or low group salience conditions (salience), of non-interactive laboratory groups that had “instrumental” or “expressive” group norms (norm), and a male or female leader (leader gender). As predicted, these four variables interacted significantly to affect perceptions of leadership effectiveness. Reconfiguration of the eight conditions formed by orientation, norm and leader gender produced a single prototypicality variable. Irrespective of participant gender, prototypical leaders were considered more effective in high then low salience groups, and in high salience groups prototypical leaders were more effective than less prototypical leaders. Alternative explanations based on status characteristics and role incongruity theory do not account well for the findings. Implications of these results for the glass ceiling effect and for a wider social identity analysis of the impact of demographic group membership on leadership in small groups are discussed.

Critical discourse analysis and political economy of communication : understanding the new corporate order

This article uses critical discourse analysis to analyse material shifts in the political economy of communications. It examines texts of major corporations to describe four key changes in political economy: (1) the separation of ownership from control; (2) the separation of business from industry; (3) the separation of accountability from responsibility; and (4) the subjugation of ‘going concerns’ by overriding concerns. The authors argue that this amounts to a political economic shift from traditional concepts of ‘capitalism’ to a new ‘corporatism’ in which the relationships between public and private, state and individual interests have become redefined and obscured through new discourse strategies. They conclude that the present financial and regulatory ‘crisis’ cannot be adequately resolved without a new analytic framework for examining the relationships between corporation, discourse and political economy.

What shapes player performance in soccer? Empirical findings from a panel analysis

In this article, we investigate the pay-performance relationship of soccer players using individual data from eight seasons of the German soccer league Bundesliga. We find a nonlinear pay-performance relationship, indicating that salary does indeed affect individual performance. The results further show that player performance is affected not only by absolute income level but also by relative income position. An additional analysis of the performance impact of team effects provides evidence of a direct impact of team-mate attributes on individual player performance.

An analysis of the determinants of job satisfaction when individuals' baseline satisfaction levels may differ.

A growing literature seeks to explain differences in individuals' self-reported satisfaction with their jobs. The evidence so far has mainly been based on cross-sectional data and when panel data have been used, individual unobserved heterogeneity has been modelled as an ordered probit model with random effects. This article makes use of longitudinal data for Denmark, taken from the waves 1995-1999 of the European Community Household Panel, and estimates fixed effects ordered logit models using the estimation methods proposed by Ferrer-i-Carbonel and Frijters (2004) and Das and van Soest (1999). For comparison and testing purposes a random effects ordered probit is also estimated. Estimations are carried out separately on the samples of men and women for individuals' overall satisfaction with the jobs they hold. We find that using the fixed effects approach (that clearly rejects the random effects specification), considerably reduces the number of key explanatory variables. The impact of central economic factors is the same as in previous studies, though. Moreover, the determinants of job satisfaction differ considerably between the genders, in particular once individual fixed effects are allowed for.

Using honeypots to analyse anomalous Internet activities

Monitoring Internet traffic is critical in order to acquire a good understanding of threats to computer and network security and in designing efficient computer security systems. Researchers and network administrators have applied several approaches to monitoring traffic for malicious content. These techniques include monitoring network components, aggregating IDS alerts, and monitoring unused IP address spaces. Another method for monitoring and analyzing malicious traffic, which has been widely tried and accepted, is the use of honeypots. Honeypots are very valuable security resources for gathering artefacts associated with a variety of Internet attack activities. As honeypots run no production services, any contact with them is considered potentially malicious or suspicious by definition. This unique characteristic of the honeypot reduces the amount of collected traffic and makes it a more valuable source of information than other existing techniques. Currently, there is insufficient research in the honeypot data analysis field. To date, most of the work on honeypots has been devoted to the design of new honeypots or optimizing the current ones. Approaches for analyzing data collected from honeypots, especially low-interaction honeypots, are presently immature, while analysis techniques are manual and focus mainly on identifying existing attacks. This research addresses the need for developing more advanced techniques for analyzing Internet traffic data collected from low-interaction honeypots. We believe that characterizing honeypot traffic will improve the security of networks and, if the honeypot data is handled in time, give early signs of new vulnerabilities or breakouts of new automated malicious codes, such as worms. The outcomes of this research include: • Identification of repeated use of attack tools and attack processes through grouping activities that exhibit similar packet inter-arrival time distributions using the cliquing algorithm; • Application of principal component analysis to detect the structure of attackers’ activities present in low-interaction honeypots and to visualize attackers’ behaviors; • Detection of new attacks in low-interaction honeypot traffic through the use of the principal component’s residual space and the square prediction error statistic; • Real-time detection of new attacks using recursive principal component analysis; • A proof of concept implementation for honeypot traffic analysis and real time monitoring.

Thermal analysis and hot stage Raman spectroscopy of the basic copper arsenate mineral : euchroite

The thermal analysis of euchroite shows two mass loss steps in the temperature range 100 to 105°C and 185 to 205°C. These mass loss steps are attributed to dehydration and dehydroxylation of the mineral. Hot stage Raman spectroscopy (HSRS) has been used to study the thermal stability of the mineral euchroite, a mineral involved in a complex set of equilibria between the copper hydroxy arsenates: euchroite Cu2(AsO4)(OH).3H2O → olivenite Cu2(AsO4)(OH) → strashimirite Cu8(AsO4)4(OH)4.5H2O → arhbarite Cu2Mg(AsO4)(OH)3. Hot stage Raman spectroscopy inolves the collection of Raman spectra as a function of the temperature. HSRS shows that the mineral euchroite decomposes between 125 and 175 °C with the loss of water. At 125 °C, Raman bands are observed at 858 cm-1 assigned to the ν1 AsO43- symmetric stretching vibration and 801, 822 and 871 cm-1 assigned to the ν3 AsO43- (A1) antisymmetric stretching vibration. A distinct band shift is observed upon heating to 275 °C. At 275 °C the four Raman bands are resolved at 762, 810, 837 and 862 cm-1. Further heating results in the diminution of the intensity in the Raman spectra and this is attributed to sublimation of the arsenate mineral. Hot stage Raman spectroscopy is most useful technique for studying the thermal stability of minerals especially when only very small amounts of mineral are available.

Thermogravimetric analysis and hot stage Raman spectroscopy of cubic indium hydroxide

The transition of cubic indium hydroxide to cubic indium oxide has been studied by thermogravimetric analysis complimented with hot stage Raman spectroscopy. Thermal analysis shows the transition of In(OH)3 to In2O3 occurs at 219°C. The structure and morphology of In(OH)3 synthesised using a soft chemical route at low temperatures was confirmed by X-ray diffraction and scanning electron microscopy. A topotactical relationship exists between the micro/nano-cubes of In(OH)3 and In2O3. The Raman spectrum of In(OH)3 is characterised by an intense sharp band at 309 cm-1 attributed to ν1 In-O symmetric stretching mode, bands at 1137 and 1155 cm-1 attributed to In-OH δ deformation modes, bands at 3083, 3215, 3123 and 3262 cm-1 assigned to the OH stretching vibrations. Upon thermal treatment of In(OH)3 new Raman bands are observed at 125, 295, 488 and 615 cm-1 attributed to In2O3. Changes in the structure of In(OH)3 with thermal treatment is readily followed by hot stage Raman spectroscopy.

Thermal analysis of synthetic reevesite and cobalt substituted reevesite (Ni,Co)6Fe2(OH)16(CO3)•4H2O

The mineral reevesite and the cobalt substituted reevesite have been synthesised. The d(003) spacings of the minerals ranged from 7.54 to 7.95 Å. The maximum d(003) value occurred at around Ni:Co 0.4:0.6. This maximum in interlayer distance is proposed to be due to a greater number of carbonate anions and water molecules intercalated into the structure. The stability of the reevesite and cobalt doped reevesite was determined by thermogravimetric analysis. The maximum temperature of the reevesite occurs for the unsubstituted reevesite and is around 220°C. The effect of cobalt substitution results in a decrease in thermal stability of the reevesites. Four thermal decomposition steps are observed and are attributed to dehydration, dehydroxylation and decarbonation, decomposition of the formed carbonate and oxygen loss at ~807 °C. A mechanism for the thermal decomposition of the reevesite and the cobalt substituted reevesite is proposed.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.