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.

Hydrostratigraphic analysis of the MADE site with full-resolution GPR and direct-push hydraulic profiling

Full-resolution 3D Ground-Penetrating Radar (GPR) data were combined with high-resolution hydraulic conductivity (K) data from vertical Direct-Push (DP) profiles to characterize a portion of the highly heterogeneous MAcro Dispersion Experiment (MADE) site. This is an important first step to better understand the influence of aquifer heterogeneities on observed anomalous transport. Statistical evaluation of DP data indicates non-normal distributions that have much higher similarity within each GPR facies than between facies. The analysis of GPR and DP data provides high-resolution estimates of the 3D geometry of hydrostratigraphic zones, which can then be populated with stochastic K fields. The lack of such estimates has been a significant limitation for testing and parameterizing a range of novel transport theories at sites where the traditional advection-dispersion model has proven inadequate.

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.

Cultivation medium design via elemental balancing for tetraselmis suecica

The primary requirements for high-biomass-concentration microalgal cultivation include a photon source and distribution, efficient gas exchange and suitable growth medium composition. However, for mass outdoor production of microalgae, growth medium composition is a major controlling factor as most of the other factors such as light source and distribution are virtually uncontrollable. This work utilises an elemental balance approach between growth medium and biomass compositions to obtain high-density microalgal cultures in an open system. F medium, commonly used for the cultivation of marine microalgae such as Tetraselmis suecica was redesigned on the basis of increasing the biomass capacity of its major deficient components to support high biomass concentrations (τ ∼ 5.0 % for N, S and τ ∼ 10 % P), and the entire formulation was dissolved in 0.2 um sterile filtered natural seawater. Results show that the new medium (F') displayed a maximum biomass concentration and total lipid concentration of 1.29 g L 1 and 108.7 mg L 1 respectively, which represents over 2-fold increase compared to that of the F medium. Keeping all variables constant except growth medium, and using F medium as the base case of 1 medium cost (MC) unit mg -1 lipid, the F' medium yielded lipid at a cost of only 0.35 MC unit mg -1 lipids. These results show that greater amounts of biomass and lipids can be obtained more economically with minimal extra effort simply by using an optimised growth medium.

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.

Elemental composition of ambient fine particles in urban schools : sources of children’s exposure

Currently, there is a limited understanding of the sources of ambient fine particles that contribute to the exposure of children at urban schools. Since the size and chemical composition of airborne particle are key parameters for determining the source as well as toxicity, PM1 particles (mass concentration of particles with an aerodynamic diameter less than 1 µm) were collected at 24 urban schools in Brisbane, Australia and their elemental composition determined. Based on the elemental composition four main sources were identified; secondary sulphates, biomass burning, vehicle and industrial emissions. The largest contributing source was industrial emissions and this was considered as the main source of trace elements in the PM1 that children were exposed to at school. PM1 concentrations at the schools were compared to the elemental composition of the PM2.5 particles (mass concentration of particles with an aerodynamic diameter less than 2.5 µm) from a previous study conducted at a suburban and roadside site in Brisbane. This comparison revealed that the more toxic heavy metals (V, Cr, Ni, Cu, Zn and Pb), mostly from vehicle and industrial emissions, were predominantly in the PM1 fraction. Thus, the results from this study points to PM1 as a potentially better particle size fraction for investigating the health effects of airborne particles.

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.