Overcoming barriers in the criminal court system : Examining the challenges faced when interviewing legal stakeholders

This collection contributes to, advances and consolidates discussions of the range of methods and approaches in criminology through the presentation of diverse international case studies in which the authors reflect upon their experiences ...

Increased left hemisphere impairment in high-functioning autism : A tract based spatial statistics study

There is evidence emerging from Diffusion Tensor Imaging (DTI) research that autism spectrum disorders (ASD) are associated with greater impairment in the left hemisphere. Although this has been quantified with volumetric region of interest analyses, it has yet to be tested with white matter integrity analysis. In the present study, tract based spatial statistics was used to contrast white matter integrity of 12 participants with high-functioning autism or Aspergers syndrome (HFA/AS) with 12 typically developing individuals. Fractional Anisotropy (FA) was examined, in addition to axial, radial and mean diffusivity (AD, RD and MD). In the left hemisphere, participants with HFA/AS demonstrated significantly reduced FA in predominantly thalamic and fronto-parietal pathways and increased RD. Symmetry analyses confirmed that in the HFA/AS group, WM disturbance was significantly greater in the left compared to right hemisphere. These findings contribute to a growing body of literature suggestive of reduced FA in ASD, and provide preliminary evidence for RD impairments in the left hemisphere.

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice.

Australian criminal law in the common law jurisdictions : cases and materials

 Australian Criminal Law in the Common Law Jurisdictions is a clear and comprehensive introduction to the fundamentals of criminal law. Updated throughout to reflect recent cases and legislation, the fourth edition combines clear case extracts with incisive author commentary and discussion.

EEG data classification using wavelet features selected by Wilcoxon statistics

This paper introduces a method to classify EEG signals using features extracted by an integration of wavelet transform and the nonparametric Wilcoxon test. Orthogonal Haar wavelet coefficients are ranked based on the Wilcoxon test’s statistics. The most prominent discriminant wavelets are assembled to form a feature set that serves as inputs to the naïve Bayes classifier. Two benchmark datasets, named Ia and Ib, downloaded from the brain–computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed combination of Haar wavelet features and naïve Bayes classifier considerably dominates the competitive classification approaches and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II. Application of naïve Bayes also provides a low computational cost approach that promotes the implementation of a potential real-time BCI system.

Supplier integrity due diligence in public procurement: limiting the criminal risk to Australia

The potential for criminals and terrorism financiers to secure lucrativegovernment contracts poses a risk to Australia’s anti-money laundering,anti-corruption and counter-terrorism financing objectives. This articlecompares the customer due diligence measures that banks are required to implement to prevent money laundering and terrorism financing with the general supplier due diligence practices and processes of key Australian government departments and agencies. It identifies various weaknesses in current procurement practices relating to standard contracts and argues that these render Australian public procurement vulnerable to criminal abuse, threaten compliance with its sanctions regime and potentially undermine the crime combating objectives of its money laundering and terrorism financinglaws. The article recommends that the national interest calls for awhole-of-government approach to improve supplier due diligence in public procurement.

The influence of rare earth mercaptoacetate on the initiation of corrosion on AA2024-T3 Part I: average statistics of each intermetallic composition

De-alloying of S-phase in AA2024-T3 in the presence chlorides, is well-known. However, it is unclear how rare earth mercaptoacetate inhibitors affect this process when immersed in a 0.1. M NaCl solution. This paper analyses data obtained using EPMA on AA2024-T3 surfaces before and after a 16. min immersion period. Cerium and praseodymium mercaptoacetate inhibited the de-alloying process of S-phase particles. Although no significant change in composition was observed for cathodic intermetallics, each appeared to participate in local corrosion reactions as evidenced by the development of surface oxides. Clustering between S-phase and one of the Cu-containing intermetallic domains was also evident.

Improving risk assessment in schizophrenia: epidemiological investigation of criminal history factors

Background : Violence risk assessment in schizophrenia relies heavily on criminal history factors.

Aims : To investigate which criminal history factors are most strongly associated with violent crime in schizophrenia.

Method : A total of 13 806 individuals (8891 men and 4915 women) with two or more hospital admissions for schizophrenia were followed up for violent convictions. Multivariate hazard ratios for 15 criminal history factors included in different risk assessment tools were calculated. The incremental predictive validity of these factors was estimated using tests of discrimination, calibration and reclassification.

Results : Over a mean follow-up of 12.0 years, 17.3% of men (n = 1535) and 5.7% of women (n = 281) were convicted of a violent offence. Criminal history factors most strongly associated with subsequent violence for both men and women were a previous conviction for a violent offence; for assault, illegal threats and/or intimidation; and imprisonment. However, only a previous conviction for a violent offence was associated with incremental predictive validity in both genders following adjustment for young age and comorbid substance use disorder.

Conclusions : Clinical and actuarial approaches to assess violence risk can be improved if included risk factors are tested using multiple measures of performance.

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.