Advancement in automated driving technology has created opportunities for smart urban mobility. Automated vehicles are now a popular topic with the rise of the smart city agenda. However, legislators, urban administrators, policymakers, and planners are unprepared to deal with the possible disruption of autonomous vehicles, which potentially could replace conventional transport. There is a lack of knowledge on how the new capabilities will disrupt and which policy strategies are needed to address such disruption. This paper aims to determine where we are, where we are headed, what the likely impacts of a wider uptake could be, and what needs to be done to generate desired smart urban mobility outcomes. The methodology includes a systematic review of the existing evidence base to understand capability, impact, planning, and policy issues associated with autonomous vehicles. The review reveals the trajectories of technological development, disruptive effects caused by such development, strategies to address the disruptions, and possible gaps in the literature. The paper develops a framework outlining the inter-links among driving forces, uptake factors, impacts and possible interventions. It concludes by advocating the necessity of preparing our cities for autonomous vehicles, although a wider uptake may take quite some time.
During the last several decades, the diversification of economic activities has become a paramount policy for nations and cities with heavy dependence on a single economic driver. Particularly island economies, relying mainly on tourism income, are among the most vulnerable ones to the shocks of global financial crises. In the recent years, some of these tourist islands had attempts to diversify their economic activities by moving towards a knowledge and innovation economy. This paper places one of these islands—Florianópolis, the capital city of the Brazilian state of Santa Catarina—under the microscope to address the question of ‘what it takes to transform a tourist island into an innovation capital’. In order to tackle this question, the study examines economic, social, spatial, and governance conditions and performances, along with the plans and processes of Florianópolis in moving towards an internationally recognized smart innovation island. The methodologic approach includes systematic review of the literature and qualitative analysis of the key development domains of Florianópolis through the lens of knowledge-based urban development. The results of this study provide insights into how to transform a resource-based economy into a knowledge-based one—by disclosing the transition journey of Florianópolis, including progress, challenges, and the new path creation processes. The findings are particularly useful for tourist islands that are aiming for an aspiring knowledge-based urban development and smart city transformation.
Cities all over the world have activated policy support for urban consolidation in recent decades. Rationales for urban consolidation focus on its perceived ability to achieve sustainability goals, including decreased automobile dependence, increased social cohesion and greater walkability. Despite this, there are few international examples of urban consolidation policy implementation that has achieved its stated aims. This paper explores the nature and character of perceptions of urban consolidation held by urban planners, developers, architects and local politicians. The perspectives held by these ‘city shapers’ are integral to urban consolidation debates and delivery, yet the nature and character of their specific views are underexplored in urban studies literature. This paper combines the theoretical lens of Social Representations Theory with the methodological approach of Q-methodology to understand the common sense understandings of urban consolidation held by city shapers in Brisbane, Australia. It identifies, synthesises and critically discusses the social representations employed by city shapers to understand, promote and communicate about urban consolidation. Findings indicate that urban consolidation debates and justifications diverge significantly from stated policy intentions and are based on differing views on ‘good’ urban form, the role of planning and community consultation and the value of higher density housing. We conclude that there is utility and value in identifying how urban consolidation strategies are influenced by the shared beliefs, myths and perceptions held by city shapers. Understanding these narratives and their influence is fundamental to understanding the power-laden manipulation of policy definitions and development outcomes.
Purpose There is wide acknowledgment that training people from all levels of an organization in process management activities and process thinking is a major contributor to the success or failure, and sustainability of business process management (BPM). BPM training is provided in almost all BPM initiatives and involves the investment of valuable financial, human, information and other resources. However, little research has focused on this area. As a result, there is a lack of guidance for organizations in conducting value adding BPM training. The purpose of this paper is to consolidate the current published knowledge on BPM training in the form of a descriptive literature review to paint a picture of the existing work, identify gaps and propose a program of work for the future. Design/methodology/approach A structured descriptive literature review was conducted to understand the current status of literature on training in the domain of BPM. Of an initial search of 90 publications, 64 publications, published between 1994 and 2015, were filtered and reviewed based on their relevance to answer the research question: What has BPM literature mentioned of training people for BPM? This study proposes a research agenda based on this. A grounded theory coding approach was employed, where NVivo 10 was used as a tool to support the analysis. Findings A total of 234 codes (representing emerging themes) were inductively identified from the data. These codes were further analyzed, resulting in eight core themes pertaining to training in the BPM context. Research limitations/implications The paper presents a vivid descriptive overview of the current status of research in BPM training identifying gaps in the literature and presents a research agenda which supports a call for action. Originality/value The paper is the first known of its kind to compile the status of literature focused on BPM training and recommend a research agenda based on such.
The convergence of technology and the city is commonly referred to as the ‘smart city’. It is seen as a possible remedy for the challenges that urbanisation creates in the age of global climate change, and as an enabler of a sustainable and liveable urban future. A review of the abundant but fragmented literature on smart city theories and practices, nevertheless, reveals that there is a limited effort to capture a comprehensive understanding on how the complex and multidimensional nature of the drivers of smart cities are linked to desired outcomes. The paper aims to develop a clearer understanding on this new city model by identifying and linking the key drivers to desired outcomes, and then intertwining them in a multidimensional framework. The methodological approach of this research includes a systematic review of the literature on smart cities, focusing on those aimed at conceptual development and provide empirical evidence base. The review identifies that the literature reveals three types of drivers of smart cities—community, technology, policy—which are linked to five desired outcomes—productivity, sustainability, accessibility, wellbeing, liveability, governance. These drivers and outcomes altogether assemble a smart city framework, where each of them represents a distinctive dimension of the smart cities notion. This paper helps in expanding our understanding beyond a monocentric technology focus of the current common smart city practice.
Cybersecurity studies at undergraduate/postgraduate level are offered at numerous universities in Australia. The level offered varies from a specifically named undergraduate/postgraduate coursework degree to usual IT or relevant degrees offering cybersecurity as a minor or major theme. A minority of universities do not offer any specific cybersecurity specific course while others offer such courses in association with industry organisations. Based upon an extensive analysis of published course/program data from university websites, chosen as the best data repository that would normally be examined by prospective students, this study submits that in Australia available courses are few and are acknowledged as not meeting market demands for skilled cybersecurity professionals. This has been recently recognised by Australia’s Federal Government which has implemented the “Academic Centres of Cyber Security Excellence (ACCSE)” program in its 2016/2017 budget to promote the discipline and university support for it. In summary, courses currently available appear quite limited in scope.
1. Globally the prevalence and impact of invasive non-native plant species is increasing rapidly. Experimentally-based research aimed at supporting management is limited in its ability to keep up with this pace, partly because of the importance of understanding historical abiotic and biotic conditions. Contrastingly, landholders are in unique positions to witness species turnover in grasslands, adapt management practices in response, and learn from successes and failures. 2. This local knowledge could be crucial for identifying feasible solutions to land degradation, and ecological restoration, but local knowledge is rarely explicitly embedded in ecological research. 3. In this study, we use a sequential exploratory strategy where we first interview (semi-directive approach) 15 landholders within the Bega region of New South Wales, Australia concerning the changing ecological characteristics of both extensively and intensively managed grassy woodlands and perceived impacts following arrival of the invasive exotic introduced species, African lovegrass, Eragrostis curvula. 4. Based on the results of these interviews, we then conducted a field study where we tested seven landholder-generated hypotheses at 57 sites. 5. The field study validated many of the landholder management perceptions including: African lovegrass was negatively correlated with species richness, canopy cover and dominant grasses like Themeda trianda. Mechanical slashing increased exotic African lovegrass abundance. The prevalence of African lovegrass in the soil seed bank was positively correlated with its abundance aboveground. Study observations that contradicted landholder perceptions included: African lovegrass was not more palatable nor did its’ abundance decline in response to increasing soil fertility. Spot spraying with herbicides was effective at controlling abundance, despite its reputation as ineffective. Landholder observations also highlighted key hypotheses concerning modes of spread that require long-term studies including the roles of drought and overgrazing. 6. Synthesis and applications. Overall, we found local knowledge coupled with scientific methods can act in tandem as a highly effective approach for developing management recommendations. This approach identifies local perceptions that are not substantiated by scientific data to halt potentially harmful practices, and observations that are insightful predictions about the dynamics and impacts of non-native species that need long-term experiments to corroborate scientifically.
Optimal experimental design is an important methodology for most efficiently allocating resources in an experiment to best achieve some goal. Bayesian experimental design considers the potential impact that various choices of the controllable variables have on the posterior distribution of the unknowns. Optimal Bayesian design involves maximising an expected utility function, which is an analytically intractable integral over the prior predictive distribution. These integrals are typically estimated via standard Monte Carlo methods. In this paper, we demonstrate that the use of randomised quasi-Monte Carlo can bring significant reductions to the variance of the estimated expected utility. This variance reduction can then lead to a more efficient optimisation algorithm for maximising the expected utility.
In November 2012, Queensland University of Technology in Australia launched a giant interactive learning environment known as The Cube. This article reports a phenomenographic investigation into visitors' different experiences of learning in The Cube. At present very little is known about people's learning experience in spaces featuring large interactive screens. We observed many visitors to The Cube and interviewed 26 people. Our analysis identified critical variation across the visitors' experience of learning in The Cube. The findings are discussed as the learning strategy (in terms of absorption, exploration, isolation and collaboration) and the content learned (in terms of technology, skills and topics). Other findings presented here are dimensions of the learning strategy and the content learned, with differing perspectives on each dimension. These outcomes provide early insights into the potential of giant interactive environments to enhance learning approaches and guide the design of innovative learning spaces in higher education.
The Step Up project operated from late 2013 to early 2017 and was funded through the Enhancing the Training of Mathematics and Science Teachers (ETMST) Program. The ETMST Program was a response to the 2012 challenge by the Chief Scientist of Australia for improvements in the preparation of mathematics and science teachers. ETMST identified a complex and multifaceted challenge based around the notion of combining content and pedagogy so that mathematics and science are taught more like they are practised. The project was led by Queensland University of Technology in partnership with Australian Catholic University, Griffith University, James Cook University, The University of Queensland and the Queensland Department of Education and Training. The grant’s focus was on pre-service, secondary mathematics and science teachers in Queensland.
The statistical distribution representing bid values constitutes an essential part of many auction models and has involved a wide range of assumptions, including the Uniform, Normal, Lognormal and Weibull densities. From a modelling point of view, its goodness is defined by how well it enables the probability of a particular bid value to be estimated - a past bid for ex-post analysis and a future bid for ex-ante (forecasting) analysis. However, there is no agreement to date of what is the most appropriate form and empirical work is sparse. Twelve extant construction data-sets from four continents over different time periods are analysed in this paper for their fit to a variety of candidate statistical distributions assuming homogeneity of bidders (ID not known). The results show there is no one single fit-all distribution, but that the 3p Log-Normal, Frechet/2p Log-Normal, Normal, Gamma and Gumbel generally rank the best ex-post and the 2p Log-Normal, Normal, Gamma and Gumbel the best ex-ante - with ex-ante having around three to four times worse fit than ex-post. Final comments focus on the results relating to the third and fourth standardized moments of the bids and a post hoc rationalization of the empirical outcome of the analysis.
This piece serves as the guest editorial of the Special Issue on the 'Open Innovation in Value Chain for Sustainability of Firms'. Firstly, this editorial piece asks whether it is possible for firms to sustain their performance forever. Then, it reviews the popular literature on the value chain. Afterwards, it develops a research framework for open innovation in the value chain, and proposes five ways of open innovation taking place within it. These include user open innovation, customer open innovation, common profit community, together growth community, and inner open innovation. Lastly, this editorial introduces articles from the Special Issue that concentrate on the various open innovation perspectives for firms to achieve sustainability.
Cooperative communication can attain lower error probability in wireless networks by exploiting the inherent broadcast nature and taking advantage of multi-path propagation. In order to leverage performance gains achieved by virtual multiple- input multiple-output (MIMO) systems, we design a novel cooperative protocol, Decode-to-Cooperate (DCOOP). We evaluate its performance on a testbed implemented on Universal Software Radio Peripheral Reconfigurable Input/Output (USRPRIO) platform. The main challenge during the testbed deployment was to consider transmission under tightly synchronized nodes in a slow fading environment. Extensive experiments were performed to evaluate the performance of the testbed and the results show that it can operate at lower transmit power and increase the coverage area for a desired bit error rate (BER).
Mining companies are increasingly being challenged to improve energy efficiency, as a method of reducing both the cost and environmental impact of their operations. The haulage activity at an open-pit mine represents a large proportion of total energy consumption. In many other industries, state-of-the-art operations research techniques, such as advanced scheduling, have been applied to support energy efficiency improvements. Despite this, only a limited amount of research using these techniques has been conducted, to face the challenge of energy efficiency in mining. This research contributes an original mixed integer linear programming formulation that schedules haulage activity to minimise the truck and shovel energy consumption required to meet production targets. Since solving the model is found to be NP-hard and intractable for exact methods, a constructive algorithm and tabu search solution technique is developed to solve the model quickly enough for practical use. An operating mine in South East Queensland is used as a case study, to verify and validate the proposed model and solution technique using sensitivity and scenario analysis where significant potential for improvement is found. Several opportunities for using the model as a decision support tool are discussed, including examples of how it can be used for short, medium and long-term decision making. (C) 2017 ElLsevier B.V. All rights reserved.
Purpose: This study evaluates the radiological properties of different 3D printing materials for a range of photon energies, including kV and MV CT imaging and MV radiotherapy beams.Methods: The CT values of a number of materials were measured on an Aquilion One CT scanner at 80 kVp, 120 kVp and a Tomotherapy Hi Art MVCT imaging beam. Attenuation of the materials in a 6 MV radiotherapy beam was investigated.Results: Plastic filaments printed with various infill densities have CT values of -743 +/- 4, -580 +/- 1 and -113 +/- 3 in 120 kVp CT images which approximate the CT values of low-density lung, high-density lung and soft tissue respectively. Metal-infused plastic filaments printed with a 90% infill density have CT values of 658 +/- 1 and 739 +/- 6 in MVCT images which approximate the attenuation of cortical bone. The effective relative electron density REDeff is used to describe the attenuation of a megavoltage treatment beam, taking into account effects relating to the atomic number and mass density of the material. Plastic filaments printed with a 90% infill density have REDeff values of 1.02 +/- 0.03 and 0.94 +/- 0.02 which approximate the relative electron density RED of soft tissue. Printed resins have REDeff values of 1.11 +/- 0.03 and 1.09 +/- 0.03 which approximate the RED of bone mineral.Conclusions: 3D printers can model a variety of body tissues which can be used to create phantoms useful for both imaging and dosimetric studies. Crown Copyright (C) 2017 Published by Elsevier Ltd on behalf of Associazione Italiana di Fisica Medica. All rights reserved.
Processes that involve moving fronts of populations are prevalent in ecology and cell biology. A common approach to describe these processes is a lattice-based random walk model, which can include mechanisms such as crowding, birth, death, movement and agent-agent adhesion. However, these models are generally analytically intractable and it is computationally expensive to perform sufficiently many realisations of the model to obtain an estimate of average behaviour that is not dominated by random fluctuations. To avoid these issues, both mean-field and corrected mean-field continuum descriptions of random walk models have been proposed. However, both continuum descriptions are inaccurate outside of limited parameter regimes, and corrected mean-field descriptions cannot be employed to describe moving fronts. Here we present an alternative description in terms of the dynamics of groups of contiguous occupied lattice sites and contiguous vacant lattice sites. Our description provides an accurate prediction of the average random walk behaviour in all parameter regimes. Critically, our description accurately predicts the persistence or extinction of the population in situations where previous continuum descriptions predict the opposite outcome. Furthermore, unlike traditional mean-field models, our approach provides information about the spatial clustering within the population and, subsequently, the moving front.
Hydrogen purification from a mixture of gas is a critical step in hydrogen production as an energy source and other clean energy applications. Recently gas purification using membranes with sub-nanometer pores, such as porous graphene has offered an attractive option which purifies the targeted gas from other impurity gases based on size exclusion exploiting the differences in the gases' molecular size. Using a combination of density functional theory (DFT) and molecular dynamic (MD) simulations, we demonstrate that graphitic carbon nitride (g-C3N4), a graphene like 2-dimensional nanomaterial can effectively purify H2 from CO2 and CH4. However, under neutral conditions the H2 flux across the membrane is comparatively weak, and our theoretical analysis shows that the flux can be significantly improved by widening the pore area via applying biaxial strains as low as 2.5% and 5% on the membrane. Interestingly, the strain tuning only improves the membranes H2 permeability, while its excellent H2/CO2 and H2/CH4 selectivity is not compromised.
This article tackles the problem of discovering a process model from an event log recording the execution of tasks in a business process. Previous approaches to this reverse-engineering problem strike different tradeoffs between the accuracy of the discovered models and their structural complexity. With respect to the latter property, empirical studies have demonstrated that block structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several methods for automated process model discovery generate block structured models only. These methods however intertwine the objective of producing accurate models with that of ensuring their structuredness, and often sacrifice the former in favour of the latter. In this paper we propose an alternative approach that separates these concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers accurate but oftentimes unstructured (and even unsound) process models, and then we transform the resulting process model into a structured (and sound) one. An experimental evaluation on synthetic and real-life event logs shows that this discover-and-structure approach consistently outperforms previous approaches with respect to a range of accuracy and complexity measures.
Many biological composite materials such as bone have demonstrated unique mechanical performance, i.e., a combination of superior stiffness and toughness. It has become increasingly clear that the constituents at the nano- and micro-length scales play a critical role in determining the mechanical performance of these biological composites. In this study, the underlying mechanisms governing the mechanical behaviour of the staggered array of mineralised collagen fibrils (MCF) embedded in extra-fibrillar protein matrix were numerically investigated. The evolution of damage zone in protein was estimated using cohesive zone models (CZM). The results indicate that the mechanisms and mechanical behaviour of MCF array are largely dependent on the MCF dimensions and the intrinsic failure energy in extra-fibrillar protein matrix.
