Evaluation of green public road procurement in Australia : current practices and gaps to fill

Public road authorities have a key responsibility in driving initiatives for reducing greenhouse gas (GHG) emissions in the road construction project lifecycle. A coherent and efficient chain of procurement processes and methods is needed to convert green policies into tangible actions that capture the potential for GHG reduction. Yet, many infrastructure clients lack developed methodologies regarding green procurement practices. Designing more efficient solutions for green procurement requires an evaluation of the current initiatives and stages of development. A mapping of the current GHG reduction initiatives in Australian public road procurement is presented in this paper. The study includes the five largest Australian state road authorities, which cover 94% of the total 817,089 km of Australian main roads (not local) and account for 96% of the total A$13 billion annual major road construction and maintenance expenditure. The state road authorities’ green procurement processes and tools are evaluated based on interviews and a review of documents. Altogether 12 people, comprising 1-3 people of each organisation, participated in the interviews and provided documents. An evaluation matrix was developed for mapping the findings across the lifecycle of road construction project delivery. The results show how Australian state road authorities drive decisions with an impact on GHG emissions on the strategic planning phase, project development phase, and project implementation phase. The road authorities demonstrate varying levels of advancement in their green procurement methodologies. Six major gaps in the current green procurement processes are identified and, respectively, six recommendations for future research and development are suggested. The greatest gaps remain in the project development phase, which has a critical role in fixing the project (GHG reduction) goals, identifying risks and opportunities, and selecting the contractor to deliver the project. Specifically, the role of mass-haul optimisation as a part of GHG minimisation was reviewed, and mass-haul management was found to be an underutilised element with GHG reduction potential.

Fast fourier analysis of structural organization in decellularized cartilage-on-bone laminates

Denaturation of tissues can provide a unique biological environment for regenerative medicine application only if minimal disruption of their microarchitecture is achieved during the decellularization process. The goal is to keep the structural integrity of such a construct as functional as the tissues from which they were derived. In this work, cartilage-on-bone laminates were decellularized through enzymatic, non-ionic and ionic protocols. This work investigated the effects of decellularization process on the microarchitecture of cartiligous extracellular matrix; determining the extent of how each process deteriorated the structural organization of the network. High resolution microscopy was used to capture cross-sectional images of samples prior to and after treatment. The variation of the microarchitecture was then analysed using a well defined fast Fourier image processing algorithm. Statistical analysis of the results revealed how significant the alternations among aforementioned protocols were (p < 0.05). Ranking the treatments by their effectiveness in disrupting the ECM integrity, they were ordered as: Trypsin> SDS> Triton X-100.

Viruses of banana in East Africa

Bananas are one of the world's most important food crops, providing sustenance and income for millions of people in developing countries and supporting large export industries. Viruses are considered major constraints to banana production, germplasm multiplication and exchange, and to genetic improvement of banana through traditional breeding. In Africa, the two most important virus diseases are bunchy top, caused by Banana bunchy top virus (BBTV), and banana streak disease, caused by Banana streak virus (BSV). BBTV is a serious production constraint in a number of countries within/bordering East Africa, such as Burundi, Democratic Republic of Congo, Malawi, Mozambique, Rwanda and Zambia, but is not present in Kenya, Tanzania and Uganda. Additionally, epidemics of banana streak disease are occurring in Kenya and Uganda. The rapidly growing tissue culture (TC) industry within East Africa, aiming to provide planting material to banana farmers, has stimulated discussion about the need for virus indexing to certify planting material as virus-free. Diagnostic methods for BBTV and BSV have been reported and, for BBTV, PCR-based assays are reliable and relatively straightforward. However for BSV, high levels of serological and genetic variability and the presence of endogenous virus sequences within the banana genome complicate diagnosis. Uganda has been shown to contain the greatest diversity in BSV isolates found anywhere in the world. A broad-spectrum diagnostic test for BSV detection, which can discriminate between endogenous and episomal BSV sequences, is a priority. This PhD project aimed to establish diagnostic methods for banana viruses, with a particular focus on the development of novel methods for BSV detection, and to use these diagnostic methods for the detection and characterisation of banana viruses in East Africa. A novel rolling-circle amplification (RCA) method was developed for the detection of BSV. Using samples of Banana streak MY virus (BSMYV) and Banana streak OL virus (BSOLV) from Australia, this method was shown to distinguish between endogenous and episomal BSV sequences in banana plants. The RCA assay was used to screen a collection of 56 banana samples from south-west Uganda for BSV. RCA detected at least five distinct BSV isolates in these samples, including BSOLV and Banana streak GF virus (BSGFV) as well as three BSV isolates (Banana streak Uganda-I, -L and -M virus) for which only partial sequences had been previously reported. These latter three BSV had only been detected using immuno-capture (IC)-PCR and thus were possible endogenous sequences. In addition to its ability to detect BSV, the RCA protocol was also demonstrated to detect other viruses within the family Caulimoviridae, including Sugar cane bacilliform virus, and Cauliflower mosaic virus. Using the novel RCA method, three distinct BSV isolates from both Kenya and Uganda were identified and characterised. The complete genome of these isolates was sequenced and annotated. All six isolates were shown to have a characteristic badnavirus genome organisation with three open reading frames (ORFs) and the large polyprotein encoded by ORF 3 was shown to contain conserved amino acid motifs for movement, aspartic protease, reverse transcriptase and ribonuclease H activities. As well, several sequences important for expression and replication of the virus genome were identified including the conserved tRNAmet primer binding site present in the intergenic region of all badnaviruses. Based on the International Committee on Taxonomy of Viruses (ICTV) guidelines for species demarcation in the genus Badnavirus, these six isolates were proposed as distinct species, and named Banana streak UA virus (BSUAV), Banana streak UI virus (BSUIV), Banana streak UL virus (BSULV), Banana streak UM virus (BSUMV), Banana streak CA virus (BSCAV) and Banana streak IM virus (BSIMV). Using PCR with species-specific primers designed to each isolate, a genotypically diverse collection of 12 virus-free banana cultivars were tested for the presence of endogenous sequences. For five of the BSV no amplification was observed in any cultivar tested, while for BSIMV, four positive samples were identified in cultivars with a B-genome component. During field visits to Kenya, Tanzania and Uganda, 143 samples were collected and assayed for BSV. PCR using nine sets of species-specific primers, and RCA, were compared for BSV detection. For five BSV species with no known endogenous counterpart (namely BSCAV, BSUAV, BSUIV, BSULV and BSUMV), PCR was used to detect 30 infections from the 143 samples. Using RCA, 96.4% of these samples were considered positive, with one additional sample detected using RCA which was not positive using PCR. For these five BSV, PCR and RCA were both useful for identifying infected samples, irrespective of the host cultivar genotype (Musa A- or B-genome components). For four additional BSV with known endogenous counterparts in the M. balbisiana genome (BSOLV, BSGFV, BSMYV and BSIMV), PCR was shown to detect 75 infections from the 143 samples. In 30 samples from cultivars with an A-only genome component there was 96.3% agreement between PCR positive samples and detection using RCA, again demonstrating either PCR or RCA are suitable methods for detection. However, in 45 samples from cultivars with some B-genome component, the level of agreement between PCR positive samples and RCA positive samples was 70.5%. This suggests that, in cultivars with some B-genome component, many infections were detected using PCR which were the result of amplification of endogenous sequences. In these latter cases, RCA or another method which discriminates between endogenous and episomal sequences, such as immuno-capture PCR, is needed to diagnose episomal BSV infection. Field visits were made to Malawi and Rwanda to collect local isolates of BBTV for validation of a PCR-based diagnostic assay. The presence of BBTV in samples of bananas with bunchy top disease was confirmed in 28 out of 39 samples from Malawi and all nine samples collected in Rwanda, using PCR and RCA. For three isolates, one from Malawi and two from Rwanda, the complete nucleotide sequences were determined and shown to have a similar genome organisation to previously published BBTV isolates. The two isolates from Rwanda had at least 98.1% nucleotide sequence identity between each of the six DNA components, while the similarity between isolates from Rwanda and Malawi was between 96.2% and 99.4% depending on the DNA component. At the amino acid level, similarities in the putative proteins encoded by DNA-R, -S, -M, - C and -N were found to range between 98.8% to 100%. In a phylogenetic analysis, the three East African isolates clustered together within the South Pacific subgroup of BBTV isolates. Nucleotide sequence comparison to isolates of BBTV from outside Africa identified India as the possible origin of East African isolates of BBTV.

Knowledge-Intensive Business Processes: Proceedings of the 1st International Workshop on Knowledge-intensive Business Processes [CEUR Workshop Proceedings, Volume 861]

Nowadays, Workflow Management Systems (WfMSs) and, more generally, Process Management Systems (PMPs) are process-aware Information Systems (PAISs), are widely used to support many human organizational activities, ranging from well-understood, relatively stable and structures processes (supply chain management, postal delivery tracking, etc.) to processes that are more complicated, less structured and may exhibit a high degree of variation (health-care, emergency management, etc.). Every aspect of a business process involves a certain amount of knowledge which may be complex depending on the domain of interest. The adequate representation of this knowledge is determined by the modeling language used. Some processes behave in a way that is well understood, predictable and repeatable: the tasks are clearly delineated and the control flow is straightforward. Recent discussions, however, illustrate the increasing demand for solutions for knowledge-intensive processes, where these characteristics are less applicable. The actors involved in the conduct of a knowledge-intensive process have to deal with a high degree of uncertainty. Tasks may be hard to perform and the order in which they need to be performed may be highly variable. Modeling knowledge-intensive processes can be complex as it may be hard to capture at design-time what knowledge is available at run-time. In realistic environments, for example, actors lack important knowledge at execution time or this knowledge can become obsolete as the process progresses. Even if each actor (at some point) has perfect knowledge of the world, it may not be certain of its beliefs at later points in time, since tasks by other actors may change the world without those changes being perceived. Typically, a knowledge-intensive process cannot be adequately modeled by classical, state of the art process/workflow modeling approaches. In some respect there is a lack of maturity when it comes to capturing the semantic aspects involved, both in terms of reasoning about them. The main focus of the 1st International Workshop on Knowledge-intensive Business processes (KiBP 2012) was investigating how techniques from different fields, such as Artificial Intelligence (AI), Knowledge Representation (KR), Business Process Management (BPM), Service Oriented Computing (SOC), etc., can be combined with the aim of improving the modeling and the enactment phases of a knowledge-intensive process. The 1st International Workshop on Knowledge-intensive Business process (KiBP 2012) was held as part of the program of the 2012 Knowledge Representation & Reasoning International Conference (KR 2012) in Rome, Italy, in June 2012. The workshop was hosted by the Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio Ruberti of Sapienza Universita di Roma, with financial support of the University, through grant 2010-C26A107CN9 TESTMED, and the EU Commission through the projects FP7-25888 Greener Buildings and FP7-257899 Smart Vortex. This volume contains the 5 papers accepted and presented at the workshop. Each paper was reviewed by three members of the internationally renowned Program Committee. In addition, a further paper was invted for inclusion in the workshop proceedings and for presentation at the workshop. There were two keynote talks, one by Marlon Dumas (Institute of Computer Science, University of Tartu, Estonia) on "Integrated Data and Process Management: Finally?" and the other by Yves Lesperance (Department of Computer Science and Engineering, York University, Canada) on "A Logic-Based Approach to Business Processes Customization" completed the scientific program. We would like to thank all the Program Committee members for the valuable work in selecting the papers, Andrea Marrella for his valuable work as publication and publicity chair of the workshop, and Carola Aiello and the consulting agency Consulta Umbria for the organization of this successful event.

System identification, estimation and control for a cost effective open-source quadcopter

This paper describes system identification, estimation and control of translational motion and heading angle for a cost effective open-source quadcopter — the MikroKopter. The dynamics of its built-in sensors, roll and pitch attitude controller, and system latencies are determined and used to design a computationally inexpensive multi-rate velocity estimator that fuses data from the built-in inertial sensors and a low-rate onboard laser range finder. Control is performed using a nested loop structure that is also computationally inexpensive and incorporates different sensors. Experimental results for the estimator and closed-loop positioning are presented and compared with ground truth from a motion capture system.

Influence tactics and leader effectiveness : how effective, contemporary leaders influence subordinates

Defining the difference between successful and mediocre leaders is a quest that has attracted many renowned scholars, drawing vast amounts of research effort. Yet while there are excellent theoretical explanations of what leaders should do: exhibit transformational behaviours, demonstrate authenticity, build productive relationships with followers and so on; there is still a scarcity of empirically-based research advising practicing leaders how to do these things. This study seeks to provide guidance about the fine-grained processes that effective leaders use on a daily basis to undertake the core process of all leadership activity; influencing followers. Using a grounded research approach, this study employs qualitative methods to capture the detail of effective leader behaviour and the micro-level influence processes that leaders use to create effective follower outcomes. Conducted in the health services industry with medical and allied health leaders, the study sought to answer the question: What influence methods might effective, contemporary leaders be using? The study builds on existing influence research, seeking to extend and update the typology of 11 influence tactics originally developed by Yukl and others, and which has been static since the late 1990s. Eight new influence tactics were identified, offering practicing leaders a powerful suite of potential strategies and representing a significant contribution to the field. Further research is recommended to confirm the identified influence constructs and test the generalisability of these findings to broader leader populations in health organisations and other knowledge-based organisations.

Measuring reef complexity and rugosity from monocular video bathymetric reconstruction

Topographic structural complexity of a reef is highly correlated to coral growth rates, coral cover and overall levels of biodiversity, and is therefore integral in determining ecological processes. Modeling these processes commonly includes measures of rugosity obtained from a wide range of different survey techniques that often fail to capture rugosity at different spatial scales. Here we show that accurate estimates of rugosity can be obtained from video footage captured using underwater video cameras (i.e., monocular video). To demonstrate the accuracy of our method, we compared the results to in situ measurements of a 2m x 20m area of forereef from Glovers Reef atoll in Belize. Sequential pairs of images were used to compute fine scale bathymetric reconstructions of the reef substrate from which precise measurements of rugosity and reef topographic structural complexity can be derived across multiple spatial scales. To achieve accurate bathymetric reconstructions from uncalibrated monocular video, the position of the camera for each image in the video sequence and the intrinsic parameters (e.g., focal length) must be computed simultaneously. We show that these parameters can be often determined when the data exhibits parallax-type motion, and that rugosity and reef complexity can be accurately computed from existing video sequences taken from any type of underwater camera from any reef habitat or location. This technique provides an infinite array of possibilities for future coral reef research by providing a cost-effective and automated method of determining structural complexity and rugosity in both new and historical video surveys of coral reefs.

Humour and charm in a story of race, headwear and healing

“There are always stories in the wearing” of the iconic beanie just as there are head and heart-warming stories in the making of Alana Valentine’s beautifully crafted Head Full of Love. Nessa and Tilly’s lives entangle at the annual Alice Springs Beanie Festival. The tourist gazes, the craftswoman crochets. Both women have broken pieces of their past that relationship can heal. Collete Mann and Roxanne McDonald capture the rhythm and nuance of difficult relationships perfectly. The humour, pathos and unsentimental depiction of black/white relations in Wesley Enoch’s production promise more than a story, they are a community conversation.

Embedding human expert cognition and real-time trajectory planning in autonomous UAS

This thesis presents a new approach to compute and optimize feasible three dimensional (3D) flight trajectories using aspects of Human Decision Making (HDM) strategies, for fixed wing Unmanned Aircraft (UA) operating in low altitude environments in the presence of real time planning deadlines. The underlying trajectory generation strategy involves the application of Manoeuvre Automaton (MA) theory to create sets of candidate flight manoeuvres which implicitly incorporate platform dynamic constraints. Feasible trajectories are formed through the concatenation of predefined flight manoeuvres in an optimized manner. During typical UAS operations, multiple objectives may exist, therefore the use of multi-objective optimization can potentially allow for convergence to a solution which better reflects overall mission requirements and HDM preferences. A GUI interface was developed to allow for knowledge capture from a human expert during simulated mission scenarios. The expert decision data captured is converted into value functions and corresponding criteria weightings using UTilite Additive (UTA) theory. The inclusion of preferences elicited from HDM decision data within an Automated Decision System (ADS) allows for the generation of trajectories which more closely represent the candidate HDM’s decision strategies. A novel Computationally Adaptive Trajectory Decision optimization System (CATDS) has been developed and implemented in simulation to dynamically manage, calculate and schedule system execution parameters to ensure that the trajectory solution search can generate a feasible solution, if one exists, within a given length of time. The inclusion of the CATDS potentially increases overall mission efficiency and may allow for the implementation of the system on different UAS platforms with varying onboard computational capabilities. These approaches have been demonstrated in simulation using a fixed wing UAS operating in low altitude environments with obstacles present.

Reinvigorating the domestic violence sector : systemically addressing conflict, power and practitioner turnover

The purpose of this exploratory Australian study was to consider methods of retaining skilled and experienced staff within the domestic violence sector. The antecedents that might influence turnover of practitioners were investigated and analysed. Antecedents broadly included the work-related factors, organisational factors and professional factors. The changing nature of the domestic violence sector was also examined, in particular, feminist identity and feminist practice frameworks. It became evident, however, that the primary reasons for the turnover of study participants can be described as parallel power processes. The concept of parallel power processes as developed through this research aims to capture how workplace behaviours can strongly mirror, or parallel, behaviours used by domestic violence perpetrators. As such, it appears that some domestic violence practitioners are experiencing their own abusive relationship, not within the confines of their home, but within their workplace. Additionally, parallel power processes are compounded by ineffective conflict management processes within the workplace. These concepts directly contribute to practitioners leaving their workplace and, sometimes, the sector. This qualitative study utilised a feminist research epistemology and focused strongly on practitioners' stories. Interviews were undertaken with fifteen domestic violence practitioners from three services within South-East Queensland, Australia. Two sets of semi-structured interviews provided in-depth information based on practitioners‘ experiences of working within this specialised sector. Analysis was conducted using a thematic analytical frame, drawing attention to the key themes as mentioned above. From these findings, it is suggested that in order to retain practitioners, domestic violence services must identify and address parallel power processes through effective conflict management processes. In an operational sense, it is recommended that education and training be undertaken within all staffing levels, in particular management committees. Lastly, it is recommended that the sector itself places greater attention on the re-invigoration of the feminist principles and philosophy that has traditionally guided the sector.

Investigating the dynamics of the psychological contract : how and why individuals' contract beliefs change

The overall objective of this thesis is to explore how and why the content of individuals' psychological contracts changes over time. The contract is generally understood as "individual beliefs, shaped by the organisation, regarding the terms of an exchange agreement between individuals and their organisation" (Rousseau, 1995, p. 9). With an overall study sampling frame of 320 graduate organisational newcomers, a mixed method longitudinal research design comprised of three sequential, inter-related studies is employed in order to capture the change process. From the 15 semi-structured interviews conducted in Study 1, the key findings included identifying a relatively high degree of mutuality between employees' and their managers' reciprocal contract beliefs around the time of organisational entry. Also, at this time, individuals had developed specific components of their contract content through a mix of social network information (regarding broader employment expectations) and perceptions of various elements of their particular organisation's reputation (for more firm-specific expectations). Study 2 utilised a four-wave survey approach (available to the full sampling frame) over the 14 months following organisational entry to explore the 'shape' of individuals' contract change trajectories and the role of four theorised change predictors in driving these trajectories. The predictors represented an organisational-level informational cue (perceptions of corporate reputation), a dyadic-level informational cue (perceptions of manager-employee relationship quality) and two individual difference variables (affect and hardiness). Through the use of individual growth modelling, the findings showed differences in the general change patterns across contract content components of perceived employer (exhibiting generally quadratic change patterns) and employee (exhibiting generally no-change patterns) obligations. Further, individuals differentially used the predictor variables to construct beliefs about specific contract content. While both organisational- and dyadic-level cues were focused upon to construct employer obligation beliefs, organisational-level cues and individual difference variables were focused upon to construct employee obligation beliefs. Through undertaking 26 semi-structured interviews, Study 3 focused upon gaining a richer understanding of why participants' contracts changed, or otherwise, over the study period, with a particular focus upon the roles of breach and violation. Breach refers to an employee's perception that an employer obligation has not been met and violation refers to the negative and affective employee reactions which may ensue following a breach. The main contribution of these findings was identifying that subsequent to a breach or violation event a range of 'remediation effects' could be activated by employees which, depending upon their effectiveness, served to instigate either breach or contract repair or both. These effects mostly instigated broader contract repair and were generally cognitive strategies enacted by an individual to re-evaluate the breach situation and re-focus upon other positive aspects of the employment relationship. As such, the findings offered new evidence for a clear distinction between remedial effects which serve to only repair the breach (and thus the contract) and effects which only repair the contract more broadly; however, when effective, both resulted in individuals again viewing their employment relationships positively. Overall, in response to the overarching research question of this thesis, how and why individuals' psychological contract beliefs change, individuals do indeed draw upon various information sources, particularly at the organisational-level, as cues or guides in shaping their contract content. Further, the 'shapes' of the changes in beliefs about employer and employee obligations generally follow different, and not necessarily linear, trajectories over time. Finally, both breach and violation and also remedial actions, which address these occurrences either by remedying the breach itself (and thus the contract) or the contract only, play central roles in guiding individuals' contract changes to greater or lesser degrees. The findings from the thesis provide both academics and practitioners with greater insights into how employees construct their contract beliefs over time, the salient informational cues used to do this and how the effects of breach and violation can be mitigated through creating an environment which facilitates the use of effective remediation strategies.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Selective essays in competitive environments

This thesis is a collection of essays that utilises descriptive and empirical tools to examine competitive environments such as in academia, superrich and sport. The essays capture different aspects of the winner-take-all phenomenon by looking at citation and publication inequality in a top tier economics journal namely the American Economic Review. How globalisation and corruption influence the accumulation of extraordinary wealth and finally, how in a fairly equal competition, that is in the National Rugby League in Australia, wearing red shirts could lead to a comparative advantage and hence, tip the balance between winning and losing. The results within academia indicate that a highly unequal distribution exist, in which only a few top authors or institutions produce the majority of output. Furthermore, the results obtained in the superrich environment indicate that corruption and globalisation enhances the accumulation of extraordinary wealth. Finally, the results in the sport environment are mixed. While we find support for a positive effect of wearing red jerseys in our descriptive analysis, we find a negative effect when we control at the team level. However, when we investigate the relative difference in the degree of redness between home and away team, we find a quite strong positive effect of wearing red shirts even after controlling at the team level.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

A database for person re-identification in multi-camera surveillance networks

Person re-identification involves recognising individuals in different locations across a network of cameras and is a challenging task due to a large number of varying factors such as pose (both subject and camera) and ambient lighting conditions. Existing databases do not adequately capture these variations, making evaluations of proposed techniques difficult. In this paper, we present a new challenging multi-camera surveillance database designed for the task of person re-identification. This database consists of 150 unscripted sequences of subjects travelling in a building environment though up to eight camera views, appearing from various angles and in varying illumination conditions. A flexible XML-based evaluation protocol is provided to allow a highly configurable evaluation setup, enabling a variety of scenarios relating to pose and lighting conditions to be evaluated. A baseline person re-identification system consisting of colour, height and texture models is demonstrated on this database.