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.

Profiling : an application assignment approach for green data centers

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

Performance profiling for predictive train schedules

In this article an alternate sensitivity analysis is proposed for train schedules. It characterises the schedules robustness or lack thereof and provides unique profiles of performance for different sources of delay and for different values of delay. An approach like this is necessary because train schedules are only a prediction of what will actually happen. They can perform poorly with respect to a variety of performance metrics, when deviations and other delays occur, if for instance they can even be implemented, and as originally intended. The information provided by this analytical approach is beneficial because it can be used as part of a proactive scheduling approach to alter a schedule in advance or to identify suitable courses of action for specific “bad behaviour”. Furthermore this information may be used to quantify the cost of delay. The effect of sectional running time (SRT) deviations and additional dwell time in particular were quantified for three railway schedule performance measures. The key features of this approach were demonstrated in a case study.

Computational epigenetic profiling of CpG islets in MTHFR

Computational epigenetics is a new area of research focused on exploring how DNA methylation patterns affect transcription factor binding that affect gene expression patterns. The aim of this study was to produce a new protocol for the detection of DNA methylation patterns using computational analysis which can be further confirmed by bisulfite PCR with serial pyrosequencing. The upstream regulatory element and pre-initiation complex relative to CpG islets within the methylenetetrahydrofolate reductase gene were determined via computational analysis and online databases. The 1,104 bp long CpG island located near to or at the alternative promoter site of methylenetetrahydrofolate reductase gene was identified. The CpG plot indicated that CpG islets A and B, within the island, contained 62 and 75 % GC content CpG ratios of 0.70 and 0.80–0.95, respectively. Further exploration of the CpG islets A and B indicates that the transcription start sites were GGC which were absent from the TATA boxes. In addition, although six PROSITE motifs were identified in CpG B, no motifs were detected in CpG A. A number of cis-regulatory elements were found in different regions within the CpGs A and B. Transcription factors were predicted to bind to CpGs A and B with varying affinities depending on the DNA methylation status. In addition, transcription factor binding may influence the expression patterns of the methylenetetrahydrofolate reductase gene by recruiting chromatin condensation inducing factors. These results have significant implications for the understanding of the architecture of transcription factor binding at CpG islets as well as DNA methylation patterns that affect chromatin structure.

Molecular profiling of exudate from chronic ulcerated wounds

Non-healing wounds represent a significant burden to healthcare systems and societies worldwide. Current best practice treatments of chronic wounds can require patients to undergo extensive periods of therapy without any positive outcome. This consumes substantial healthcare resources and severely impacts patient quality of life. At present, there are no measures to predict a patient's response to best practice care. The hypothesis of this thesis was that biochemical markers could be found within the wound fluid of chronic ulcers and these markers could predict the healing outcome of an ulcer undergoing best practice care. Discovery phase proteomic and mass spectrometry techniques were utilised to determine novel proteins that correlated with the healing outcome of ulcers. These candidate biomarkers could be developed into simple dip-stick tools for use in clinical practice. This would aid clinicians in the choice of effective wound management strategies to address hard-to-heal wounds.

Profiling the nature and context of the Australian prefabricated housing industry

The current state of the prefabricated housing market in Australia is systematically profiled, guided by a theoretical systems model. Particular focus is given to two original data collections. The first identifies manufacturers and builders using prefabrication innovations, and the second compares the context for prefabricated housing in Australia with that of key international jurisdictions. The results indicate a small but growing market for prefabricated housing in Australia, often building upon expertise developed through non-residential building applications. The international comparison highlighted the complexity of the interactions between macro policy decisions and historical influences and the uptake of prefabricated housing. The data suggest factors such as the small scale of the Australian market, and a lack of investment in research, development and training have not encouraged prefabrication. A lack of clear regulatory policy surrounding prefabricated housing is common both in Australia and internationally, with local effects in regards to home warranties and housing finance highlighted. Future research should target the continuing lack of consideration of prefabrication from within the housing construction industry, and build upon the research reported in this paper to further quantify the potential end user market and the continuing development of the industry.

Convergence-guaranteed time-varying RRT path planning for profiling floats in 4-Dimensional flow

This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.

Tensor reduction for user profiling in personalized recommender systems

User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.

Criminal profiling

One of the more widely recognized and practiced subspecialities within forensic criminology is that of criminal profiling. It has a long history, as detailed in Turvey (2008a). It also boasts a small library of distinct literature, with different methods and subspecialities all its own. Criminal profiling is a practice that has seen increasing popular and media attention over the past several decades. It has been depicted in popular fiction such as films like Silence of the Lambs (1991) and television programs like Criminal Minds (2005– present). It has also been applied in a number of high profile cases, including the “Washington Snipers” (see Turvey and McGrath, 2005, for an extended discussion of profiling and the media in the D.C. Sniper case). As a result, students of criminology commonly express an interest in studying criminal profiling with a view to becoming profilers themselves.

Criminal profiling : behavioural consistency, the homology assumption and case linkage

Criminal profiling is an investigative tool used around the world to infer the personality and behavioural characteristics of an offender based on their crime. Case linkage, the process of determining discreet connections between crimes of the same offender, is a practice that falls under the general banner of criminal profiling and has been widely criticized. Two theories, behavioural consistency and the homology assumption, are examined and their impact on profiling in general and case linkage specifically is discussed...

Profiling unlicensed drivers and riders in Queensland. Report to Queensland Department of Transport and Main Roads

Unlicensed driving remains a serious problem for road safety, despite ongoing improvements in traffic law enforcement practices and technology. This report examines de-identified traffic infringement and sanction histories for drivers in Queensland who had lost their licence between 1st January 2003 and 31st December 2008. A total of 546,117 Queensland drivers were identified. Key areas discussed include the prevalence of unlicensed driving and the extent to which particular offences were detected amongst drivers with a licence sanction or disqualified licence.

Who are our repeating students? Profiling students persisting after failure

Research on attrition has focused on the economic significance of low graduation rates in terms of costs to students (fees that do not culminate in a credential) and impact on future income. For a student who fails a unit and repeats the unit multiple times, the financial impact is significant and lasting (Bexley, Daroesman, Arkoudis & James 2013). There are obvious advantages for the timely completion of a degree, both for the student and the institution. Advantages to students include fee minimisation, enhanced engagement opportunities, effectual pathway to employment and a sense of worth, morale and cohort-identity benefits. Work undertaken by the QUT Analytics Project in 2013 and 2014 explored student engagement patterns capturing a variety of data sources and specifically, the use of LMS amongst students in 804 undergraduate units in one semester. Units with high failure rates were given further attention and it was found that students who were repeating a unit were less likely to pass the unit than students attempting it for the first time. In this repeating cohort, academic and behavioural variables were consistently more significant in the modelling than were any demographic variables, indicating that a student’s performance at university is far more impacted by what they do once they arrive than it is by where they come from. The aim of this poster session is to examine the findings and commonalities of a number of case studies that articulated the engagement activities of repeating students (which included collating data from Individual Unit Reports, academic and peer advising programs and engagement with virtual learning resources). Understanding the profile of the repeating student cohort is therefore as important as considering the characteristics of successful students so that the institution might be better placed to target the repeating students and make proactive interventions as early as possible.

Importance of literature profiling: An archival analysis with illustrative examples for IS researchers

The importance of a thorough and systematic literature review has long been recognised across academic domains as critical to the foundation of new knowledge and theory evolution. Driven by an exponentially growing body of knowledge in the IS discipline, there has been a recent influx of guidance on how to conduct a literature review. As literature reviews are emerging as a standalone research method in itself, increasingly these method focused guidelines are of great interest, receiving acceptance at top tier IS publication outlets. Nevertheless, the finer details which offer justification for the selected content, and the effective presentation of supporting data has not been widely discussed in these method papers to date. This paper addresses this gap by exploring the concept of ‘literature profiling’ while arguing that it is a key aspect of a comprehensive literature review. The study establishes the importance of profiling for managing aspects such as quality assurance, transparency and the mitigation of selection bias. And then discusses how profiling can provide a valid basis for data analysis based on the attributes of selected literature. In essence, this study has conducted an archival analysis of literature (predominately from the IS domain) to present its main argument; the value for literature profiling, with supporting exemplary illustrations.

Architecture for service profiling

Service oriented architecture is gaining momentum. However, in order to be successful, the proper and up-to-date description of services is required. Such a description may be provided by service profiling mechanisms, such as one presented in this article. Service profile can be defined as an up-to-date description of a subset of non-functional properties of a service. It allows for service comparison on the basis of non-functional parameters, and choosing the service which is most suited to the needs of a user. In this article the notion of a service profile along with service profiling mechanism is presented as well as the architecture of a profiling system. © 2006 IEEE.

Expression profiling in spondyloarthropathy synovial biopsies highlights changes in expression of inflammatory genes in conjunction with tissue remodelling genes

Background: In the spondyloarthropathies, the underlying molecular and cellular pathways driving disease are poorly understood. By undertaking a study in knee synovial biopsies from spondyloarthropathy (SpA) and ankylosing spondylitis (AS) patients we aimed to elucidate dysregulated genes and pathways. Methods RNA was extracted from six SpA, two AS, three osteoarthritis (OA) and four normal control knee synovial biopsies. Whole genome expression profiling was undertaken using the Illumina DASL system, which assays 24000 cDNA probes. Differentially expressed candidate genes were then validated using quantitative PCR and immunohistochemistry. Results: Four hundred and sixteen differentially expressed genes were identified that clearly delineated between AS/SpA and control groups. Pathway analysis showed altered gene-expression in oxidoreductase activity, B-cell associated, matrix catabolic, and metabolic pathways. Altered «myogene» profiling was also identified. The inflammatory mediator, MMP3, was strongly upregulated (5-fold) in AS/SpA samples and the Wnt pathway inhibitors DKK3 (2.7-fold) and Kremen1 (1.5-fold) were downregulated. Conclusions: Altered expression profiling in SpA and AS samples demonstrates that disease pathogenesis is associated with both systemic inflammation as well as local tissue alterations that may underlie tissue damaging modelling and remodelling outcomes. This supports the hypothesis that initial systemic inflammation in spondyloarthropathies transfers to and persists in the local joint environment, and might subsequently mediate changes in genes directly involved in the destructive tissue remodelling.