Profiling high-range speeding offenders : investigating criminal history, personal characteristics, traffic offences, and crash history

This paper reports profiling information for speeding offenders and is part of a larger project that assessed the deterrent effects of increased speeding penalties in Queensland, Australia, using a total of 84,456 speeding offences. The speeding offenders were classified into three groups based on the extent and severity of an index offence: once-only low-rang offenders; repeat high-range offenders; and other offenders. The three groups were then compared in terms of personal characteristics, traffic offences, crash history and criminal history. Results revealed a number of significant differences between repeat high-range offenders and those in the other two offender groups. Repeat high-range speeding offenders were more likely to be male, younger, hold a provisional and a motorcycle licence, to have committed a range of previous traffic offences, to have a significantly greater likelihood of crash involvement, and to have been involved in multiple-vehicle crashes than drivers in the other two offender types. Additionally, when a subset of offenders’ criminal histories were examined, results revealed that repeat high-range speeding offenders were also more likely to have committed a previous criminal offence compared to once only low-range and other offenders and that 55.2% of the repeat high-range offenders had a criminal history. They were also significantly more likely to have committed drug offences and offences against order than the once only low-range speeding offenders, and significantly more likely to have committed regulation offences than those in the other offenders group. Overall, the results indicate that speeding offenders are not an homogeneous group and that, therefore, more tailored and innovative sanctions should be considered and evaluated for high-range recidivist speeders because they are a high-risk road user group.

Strategic supply chain management factors influencing agribusiness innovation utilization

Purpose – The purpose of this paper is to examine empirically, an industry development paradox, using embryonic literature in the area of strategic supply chain management, together with innovation management literature. This study seeks to understand how, forming strategic supply chain relationships, and developing strategic supply chain capability, influences beneficial supply chain outcomes expected from utilizing industry-led innovation, in the form of electronic business solutions using the internet, in the Australian beef industry. Findings should add valuable insights to both academics and practitioners in the fields of supply chain innovation management and strategic supply chain management, and expand knowledge to current literature. Design/methodology/approach – This is a quantitative study comparing innovative and non-innovative supply chain operatives in the Australian beef industry, through factor analysis and structural equation modeling using PAWS Statistical V18 and AMOS V18 to analyze survey data from 412 respondents from the Australian beef supply chain. Findings – Key findings are that both innovative and non-innovative supply chain operators attribute supply chain synchronization as only a minor indicator of strategic supply chain capability, contrary to the literature; and they also indicate strategic supply chain capability has a minor influence in achieving beneficial outcomes from utilizing industry-led innovation. These results suggest a lack of coordination between supply chain operatives in the industry. They also suggest a lack of understanding of the benefits of developing a strategic supply chain management competence, particularly in relation to innovation agendas, and provides valuable insights as to why an industry paradox exists in terms of the level of investment in industry-led innovation, vs the level of corresponding benefit achieved. Research limitations/implications – Results are not generalized due to the single agribusiness industry studied and the single research method employed. However, this provides opportunity for further agribusiness studies in this area and also studies using alternate methods, such as qualitative, in-depth analysis of these factors and their relationships, which may confirm results or produce different results. Further, this study empirically extends existing theoretical contributions and insights into the roles of strategic supply chain management and innovation management in improving supply chain and ultimately industry performance while providing practical insights to supply chain practitioners in this and other similar agribusiness industries. Practical implications – These findings confirm results from a 2007 research (Ketchen et al., 2007) which suggests supply chain practice and teachings need to take a strategic direction in the twenty-first century. To date, competence in supply chain management has built up from functional and process orientations rather than from a strategic perspective. This study confirms that there is a need for more generalists that can integrate with various disciplines, particularly those who can understand and implement strategic supply chain management. Social implications – Possible social implications accrue through the development of responsible government policy in terms of industry supply chains. Strategic supply chain management and supply chain innovation management have impacts to the social fabric of nations through the sustainability of their industries, especially agribusiness industries which deal with food safety and security. If supply chains are now the competitive weapon of nations then funding innovation and managing their supply chain competitiveness in global markets requires a strategic approach from everyone, not just the industry participants. Originality/value – This is original empirical research, seeking to add value to embryonic and important developing literature concerned with adopting a strategic approach to supply chain management. It also seeks to add to existing literature in the area of innovation management, particularly through greater understanding of the implications of nations developing industry-wide, industry-led innovation agendas, and their ramifications to industry supply chains.

Factors influencing bike share membership : an analysis of Melbourne and Brisbane

The number of bike share programs has increased rapidly in recent years and there are currently over 700 programs in operation globally. Australia’s two bike share programs have been in operation since 2010 and have significantly lower usage rates compared to Europe, North America and China. This study sets out to understand and quantify the factors influencing bike share membership in Australia’s two bike share programs located in Melbourne and Brisbane. An online survey was administered to members of both programs as well as a group with no known association with bike share. A logistic regression model revealed several significant predictors of membership including reactions to mandatory helmet legislation, riding activity over the previous month, and the degree to which convenience motivated private bike riding. In addition, respondents aged 18 - 34 and having docking station within 250m of their workplace were found to be statistically significant predictors of bike share membership. Finally, those with relatively high incomes increased the odds of membership. These results provide insight as to the relative influence of various factors impacting on bike share membership in Australia. The findings may assist bike share operators to maximize membership potential and help achieve the primary goal of bike share – to increase the sustainability of the transport system.

Passenger segmentation using smart card data

Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.

Experiential brand deployment : improving tourism brand evaluations

This article examines the use of an experiential branding process to help leisure resort businesses evaluate their brand. We integrate experiential marketing and the quality function development approach in combination to help understand the brand from the perspectives of both the consumer and firm, to help resort service businesses build their experience-oriented competitive brands. The value of this study is that it provides a real-world brand framework, especially those resorts with limited resources. Much is spoken about the influence of the brand and why it is important, but little is known about decisions related to developing a brand, especially for firms that have limited resources such as resort tourism operators. Tourism operators tend to be small-to-medium enterprises that do not necessarily have the capacity to do everything suggested. Therefore, we explore how firms assess the critical elements of their brand by using an integrated approach. For example, the study finds that, first, by using the quality function development method resorts can identify the most critical brand elements, and second, we identify the associated strengths of each brand element and confirm the identified resort’s critical brand elements for investment. Results show the potential strategies to create a more holistic set of experiences.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Texture enhancement is an important component of image processing that finds extensive application in science and engineering. The quality of medical images, quantified using the imaging texture, plays a significant role in the routine diagnosis performed by medical practitioners. Most image texture enhancement is performed using classical integral order differential mask operators. Recently, first order fractional differential operators were used to enhance images. Experimentation with these methods led to the conclusion that fractional differential operators not only maintain the low frequency contour features in the smooth areas of the image, but they also nonlinearly enhance edges and textures corresponding to high frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we apply the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other first order fractional differential operators, we find that our new algorithms provide higher signal to noise values and superior image quality.

Tools, building blocks, and rewards : traditional media organizations learn to engage with productive audiences

This paper explores how traditional media organizations (such as magazines, music, film, books, and newspapers) develop routines for coping with an increasingly productive audience. While previous studies have reported on how such organizations have been affected by digital technologies, this study makes a contribution to this literature by being one of the first to show how organizational routines for engaging with an increasingly productive audience actually emerge and diffuse between industries. The paper explores to what extent routines employed by two traditional media organizations have been brought in from other organizational settings, specifically from so-called ‘software platform operators’. Data on routines for engaging with productive audiences have been collected from two information-rich cases in the music and the magazine industries, and from eight high-profile software platform operators. The paper concludes that the routines employed by the two traditional media organizations and by the software platform operators are based on the same set of principles: Provide the audience with (a) tools that allow them to easily generate cultural content; (b) building blocks which facilitate their creative activities; and (c) recognition and rewards based on both rationality and emotion.

Transit route occupancy load factor and passenger average travel time for quality of service assessment

This paper investigates quality of service (QoS) and resource productivity implications of transit route passenger loading and travel time. It highlights the value of occupancy load factor as a direct passenger comfort QoS measure. Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia, is used to investigate time series correlation between occupancy load factor and passenger average travel time. Correlation is strong across the entire span of service in both directions. Passengers tend to be making longer, peak direction commuter trips under significantly less comfortable conditions than off-peak. The Transit Capacity and Quality of Service Manual uses segment based load factor as a measure of onboard loading comfort QoS. This paper provides additional insight into QoS by relating the two route based dimensions of occupancy load factor and passenger average travel time together in a two dimensional format, both from the passenger’s and operator’s perspectives. Future research will apply Value of Time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent onboard. This would also assist in transit service quality econometric modeling. The methodology can be readily applied in a practical setting where AFC data for fixed scheduled routes is available. The study outcomes also provide valuable research and development directions.

Transit route occupancy load factor and passenger average travel time for quality of service assessment

This presentation investigates quality of service (QoS) and resource productivity implications of transit route passenger loading and travel time. It highlights the value of occupancy load factor as a direct passenger comfort QoS measure. Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia, is used to investigate time series correlation between occupancy load factor and passenger average travel time. Correlation is strong across the entire span of service in both directions. Passengers tend to be making longer, peak direction commuter trips under significantly less comfortable conditions than off-peak. The Transit Capacity and Quality of Service Manual uses segment based load factor as a measure of onboard loading comfort QoS. This paper provides additional insight into QoS by relating the two route based dimensions of occupancy load factor and passenger average travel time together in a two dimensional format, both from the passenger’s and operator’s perspectives. Future research will apply Value of Time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent onboard. This would also assist in transit service quality econometric modeling. The methodology can be readily applied in a practical setting where AFC data for fixed scheduled routes is available. The study outcomes also provide valuable research and development directions.

LacO-LacI interaction in affinity adsorption of plasmid DNA

Current approaches for purifying plasmids from bacterial production systems exploit the physiochemical properties of nucleic acids in non-specific capture systems. In this study, an affinity system for plasmid DNA (pDNA) purification has been developed utilizing the interaction between the lac operon (lacO) sequence contained in the pDNA and a 64mer synthetic peptide representing the DNA-binding domain of the lac repressor protein, LacI. Two plasmids were evaluated, the native pUC19 and pUC19 with dual lacO3/lacOs operators (pUC19lacO3/lacOs), where the lacOs operator is perfectly symmetrical. The DNA-protein affinity interaction was evaluated by surface plasmon resonance using a Biacore system. The affinity capture of DNA in a chromatography system was evaluated using LacI peptide that had been immobilized to Streamline™ adsorbent. The KD-values for double stranded DNA (dsDNA) fragments containing lacO1 and lacO3 and lacOs and lacO3 were 5.7 ± 0.3 × 10 -11 M and 4.1 ± 0.2 × 10-11 M respectively, which compare favorably with literature reports of 5 × 10-10 - 1 × 10-9 M for native laCO1 and 1-1.2 × 10-10 M for lacO1 in a saline buffer. Densitometric analysis of the gel bands from the affinity chromatography run clearly showed a significant preference for capture of the supercoiled fraction from the feed pDNA sample. The results indicate the feasibility of the affinity approach for pDNA capture and purification using native protein-DNA interaction.

Rough Fuzzy Control of SVC for Power System Stability Enhancement

Modern power systems have become more complex due to the growth in load demand, the installation of Flexible AC Transmission Systems (FACTS) devices and the integration of new HVDC links into existing AC grids. On the other hand, the introduction of the deregulated and unbundled power market operational mechanism, together with present changes in generation sources including connections of large renewable energy generation with intermittent feature in nature, have further increased the complexity and uncertainty for power system operation and control. System operators and engineers have to confront a series of technical challenges from the operation of currently interconnected power systems. Among the many challenges, how to evaluate the steady state and dynamic behaviors of existing interconnected power systems effectively and accurately using more powerful computational analysis models and approaches becomes one of the key issues in power engineering. The traditional computing techniques have been widely used in various fields for power system analysis with varying degrees of success. The rapid development of computational intelligence, such as neural networks, fuzzy systems and evolutionary computation, provides tools and opportunities to solve the complex technical problems in power system planning, operation and control.

Bayesian networks for information synthesis in complex systems

This thesis introduces a method of applying Bayesian Networks to combine information from a range of data sources for effective decision support systems. It develops a set of techniques in development, validation, visualisation, and application of Complex Systems models, with a working demonstration in an Australian airport environment. The methods presented here have provided a modelling approach that produces highly flexible, informative and applicable interpretations of a system's behaviour under uncertain conditions. These end-to-end techniques are applied to the development of model based dashboards to support operators and decision makers in the multi-stakeholder airport environment. They provide highly flexible and informative interpretations and confidence in these interpretations of a system's behaviour under uncertain conditions.

The effect of autonomy transparency in human-robot interactions : a preliminary study on operator cognitive workload and situation awareness in multiple heterogeneous UAV management

The autonomous capabilities in collaborative unmanned aircraft systems are growing rapidly. Without appropriate transparency, the effectiveness of the future multiple Unmanned Aerial Vehicle (UAV) management paradigm will be significantly limited by the human agent’s cognitive abilities; where the operator’s CognitiveWorkload (CW) and Situation Awareness (SA) will present as disproportionate. This proposes a challenge in evaluating the impact of robot autonomous capability feedback, allowing the human agent greater transparency into the robot’s autonomous status - in a supervisory role. This paper presents; the motivation, aim, related works, experiment theory, methodology, results and discussions, and the future work succeeding this preliminary study. The results in this paper illustrates that, with a greater transparency of a UAV’s autonomous capability, an overall improvement in the subjects’ cognitive abilities was evident, that is, with a confidence of 95%, the test subjects’ mean CW was demonstrated to have a statistically significant reduction, while their mean SA was demonstrated to have a significant increase.

Empirical modelling of the relationship between bus and car speeds on signalised urban networks

Vehicle speed is an important attribute for analysing the utility of a transport mode. The speed relationship between multiple modes of transport is of interest to traffic planners and operators. This paper quantifies the relationship between bus speed and average car speed by integrating Bluetooth data and Transit Signal Priority data from the urban network in Brisbane, Australia. The method proposed in this paper is the first of its kind to relate bus speed and average car speed by integrating multi-source traffic data in a corridor-based method. Three transferable regression models relating not-in-service bus, in-service bus during peak periods, and in-service bus during off-peak periods with average car speed are proposed. The models are cross-validated and the interrelationships are significant.

Teleoperation of a 7 DOF humanoid robot arm using human arm accelerations and EMG signals

We present our work on tele-operating a complex humanoid robot with the help of bio-signals collected from the operator. The frameworks (for robot vision, collision avoidance and machine learning), developed in our lab, allow for a safe interaction with the environment, when combined. This even works with noisy control signals, such as, the operator’s hand acceleration and their electromyography (EMG) signals. These bio-signals are used to execute equivalent actions (such as, reaching and grasping of objects) on the 7 DOF arm.