Modeling the effectiveness of counterterrorism interventions

After the terrorist attacks in the United States on 11 September 2001, terrorism and counter-terrorism efforts moved to the front of popular consciousness and became the focus of national security for governments worldwide. With this increased attention came an urgent interest in understanding and identifying what works in fighting terrorism (Belasco 2010). For Australia, understanding the relative effectiveness of counter-terrorism efforts in nearby neighbours of Indonesia, Thailand and the Philippines is highly relevant for our country's national security. Indonesia, Thailand and the Philippines are all countries that are important to Australia not just because of geographic proximity, but also because of a history of economic ties and the role these countries play as Australia’s regional partners...

A stochastic neighborhood conditional autoregressive model for spatial data

A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.

A discussion of “Estimating the historical and future probabilities of large terrorist events'' by Aaron Clauset and Ryan Woodard

The terrorist attacks in the United States on September 11, 2001 appeared to be a harbinger of increased terrorism and violence in the 21st century, bringing terrorism and political violence to the forefront of public discussion. Questions about these events abound, and “Estimating the Historical and Future Probabilities of Large Scale Terrorist Event” [Clauset and Woodard (2013)] asks specifically, “how rare are large scale terrorist events?” and, in general, encourages discussion on the role of quantitative methods in terrorism research and policy and decision-making. Answering the primary question raises two challenges. The first is identify- ing terrorist events. The second is finding a simple yet robust model for rare events that has good explanatory and predictive capabilities. The challenges of identifying terrorist events is acknowledged and addressed by reviewing and using data from two well-known and reputable sources: the Memorial Institute for the Prevention of Terrorism-RAND database (MIPT-RAND) [Memorial Institute for the Prevention of Terrorism] and the Global Terror- ism Database (GTD) [National Consortium for the Study of Terrorism and Responses to Terrorism (START) (2012), LaFree and Dugan (2007)]. Clauset and Woodard (2013) provide a detailed discussion of the limitations of the data and the models used, in the context of the larger issues surrounding terrorism and policy.

Characterising an ECG signal using statistical modelling : a feasibility study

For clinical use, in electrocardiogram (ECG) signal analysis it is important to detect not only the centre of the P wave, the QRS complex and the T wave, but also the time intervals, such as the ST segment. Much research focused entirely on qrs complex detection, via methods such as wavelet transforms, spline fitting and neural networks. However, drawbacks include the false classification of a severe noise spike as a QRS complex, possibly requiring manual editing, or the omission of information contained in other regions of the ECG signal. While some attempts were made to develop algorithms to detect additional signal characteristics, such as P and T waves, the reported success rates are subject to change from person-to-person and beat-to-beat. To address this variability we propose the use of Markov-chain Monte Carlo statistical modelling to extract the key features of an ECG signal and we report on a feasibility study to investigate the utility of the approach. The modelling approach is examined with reference to a realistic computer generated ECG signal, where details such as wave morphology and noise levels are variable.

Applying a validation framework to a working airport terminal model

Validation is an important issue in the development and application of Bayesian Belief Network (BBN) models, especially when the outcome of the model cannot be directly observed. Despite this, few frameworks for validating BBNs have been proposed and fewer have been applied to substantive real-world problems. In this paper we adopt the approach by Pitchforth and Mengersen (2013), which includes nine validation tests that each focus on the structure, discretisation, parameterisation and behaviour of the BBNs included in the case study. We describe the process and result of implementing a validation framework on a model of a real airport terminal system with particular reference to its effectiveness in producing a valid model that can be used and understood by operational decision makers. In applying the proposed validation framework we demonstrate the overall validity of the Inbound Passenger Facilitation Model as well as the effectiveness of the validity framework itself.

Label-free SERS for natural product provenance

Introduction Natural product provenance is important in the food, beverage and pharmaceutical industries, for consumer confidence and with health implications. Raman spectroscopy has powerful molecular fingerprint abilities. Surface Enhanced Raman Spectroscopy’s (SERS) sharp peaks allow distinction between minimally different molecules, so it should be suitable for this purpose. Methods Naturally caffeinated beverages with Guarana extract, coffee and Red Bull energy drink as a synthetic caffeinated beverage for comparison (20 µL ea.) were reacted 1:1 with Gold nanoparticles functionalised with anti-caffeine antibody (ab15221) (10 minutes), air dried and analysed in a micro-Raman instrument. The spectral data was processed using Principle Component Analysis (PCA). Results The PCA showed Guarana sourced caffeine varied significantly from synthetic caffeine (Red Bull) on component 1 (containing 76.4% of the variance in the data). See figure 1. The coffee containing beverages, and in particular Robert Timms (instant coffee) were very similar on component 1, but the barista espresso showed minor variance on component 1. Both coffee sourced caffeine samples varied with red Bull on component 2, (20% of variance). ************************************************************ Figure 1 PCA comparing a naturally caffeinated beverage containing Guarana with coffee. ************************************************************ Discussion PCA is an unsupervised multivariate statistical method that determines patterns within data. Figure 1 shows Caffeine in Guarana is notably different to synthetic caffeine. Other researchers have revealed that caffeine in Guarana plants is complexed with tannins. Naturally sourced/ lightly processed caffeine (Monster Energy, Espresso) are more inherently different than synthetic (Red Bull) /highly processed (Robert Timms) caffeine, in figure 1, which is consistent with this finding and demonstrates this technique’s applicability. Guarana provenance is important because it is still largely hand produced and its demand is escalating with recognition of its benefits. This could be a powerful technique for Guarana provenance, and may extend to other industries where provenance / authentication are required, e.g. the wine or natural pharmaceuticals industries.

Noisy Bluetooth traffic data?

Traffic state estimation in an urban road network remains a challenge for traffic models and the question of how such a network performs remains a difficult one to answer for traffic operators. Lack of detailed traffic information has long restricted research in this area. The introduction of Bluetooth into the automotive world presented an alternative that has now developed to a stage where large-scale test-beds are becoming available, for traffic monitoring and model validation purposes. But how much confidence should we have in such data? This paper aims to give an overview of the usage of Bluetooth, primarily for the city-scale management of urban transport networks, and to encourage researchers and practitioners to take a more cautious look at what is currently understood as a mature technology for monitoring travellers in urban environments. We argue that the full value of this technology is yet to be realised, for the analytical accuracies peculiar to the data have still to be adequately resolved.

Computation of ECG signal features using MCMC modelling in software and FPGA reconfigurable hardware

Computational optimisation of clinically important electrocardiogram signal features, within a single heart beat, using a Markov-chain Monte Carlo (MCMC) method is undertaken. A detailed, efficient data-driven software implementation of an MCMC algorithm has been shown. Initially software parallelisation is explored and has been shown that despite the large amount of model parameter inter-dependency that parallelisation is possible. Also, an initial reconfigurable hardware approach is explored for future applicability to real-time computation on a portable ECG device, under continuous extended use.

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models.

Combining expert opinions in prior elicitation

We consider the problem of combining opinions from different experts in an explicitly model-based way to construct a valid subjective prior in a Bayesian statistical approach. We propose a generic approach by considering a hierarchical model accounting for various sources of variation as well as accounting for potential dependence between experts. We apply this approach to two problems. The first problem deals with a food risk assessment problem involving modelling dose-response for Listeria monocytogenes contamination of mice. Two hierarchical levels of variation are considered (between and within experts) with a complex mathematical situation due to the use of an indirect probit regression. The second concerns the time taken by PhD students to submit their thesis in a particular school. It illustrates a complex situation where three hierarchical levels of variation are modelled but with a simpler underlying probability distribution (log-Normal).

Ultra sensitive label free surface enhanced Raman spectroscopy method for the detection of biomolecules

We present a proof of concept for a novel nanosensor for the detection of ultra-trace amounts of bio-active molecules in complex matrices. The nanosensor is comprised of gold nanoparticles with an ultra-thin silica shell and antibody surface attachment, which allows for the immobilization and direct detection of bio-active molecules by surface enhanced Raman spectroscopy (SERS) without requiring a Raman label. The ultra-thin passive layer (~1.3 nm thickness) prevents competing molecules from binding non-selectively to the gold surface without compromising the signal enhancement. The antibodies attached on the surface of the nanoparticles selectively bind to the target molecule with high affinity. The interaction between the nanosensor and the target analyte result in conformational rearrangements of the antibody binding sites, leading to significant changes in the surface enhanced Raman spectra of the nanoparticles when compared to the spectra of the un-reacted nanoparticles. Nanosensors of this design targeting the bio-active compounds erythropoietin and caffeine were able to detect ultra-trace amounts the analyte to the lower quantification limits of 3.5×10−13 M and 1×10−9 M, respectively.

Comparison of block and event-related experimental designs in diffusion-weighted functional MRI

PURPOSE To compare diffusion-weighted functional magnetic resonance imaging (DfMRI), a novel alternative to the blood oxygenation level-dependent (BOLD) contrast, in a functional MRI experiment. MATERIALS AND METHODS Nine participants viewed contrast reversing (7.5 Hz) black-and-white checkerboard stimuli using block and event-related paradigms. DfMRI (b = 1800 mm/s2 ) and BOLD sequences were acquired. Four parameters describing the observed signal were assessed: percent signal change, spatial extent of the activation, the Euclidean distance between peak voxel locations, and the time-to-peak of the best fitting impulse response for different paradigms and sequences. RESULTS The BOLD conditions showed a higher percent signal change relative to DfMRI; however, event-related DfMRI showed the strongest group activation (t = 21.23, P < 0.0005). Activation was more diffuse and spatially closer to the BOLD response for DfMRI when the block design was used. DfMRIevent showed the shortest TTP (4.4 +/- 0.88 sec). CONCLUSION The hemodynamic contribution to DfMRI may increase with the use of block designs.

Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions

The issue of using informative priors for estimation of mixtures at multiple time points is examined. Several different informative priors and an independent prior are compared using samples of actual and simulated aerosol particle size distribution (PSD) data. Measurements of aerosol PSDs refer to the concentration of aerosol particles in terms of their size, which is typically multimodal in nature and collected at frequent time intervals. The use of informative priors is found to better identify component parameters at each time point and more clearly establish patterns in the parameters over time. Some caveats to this finding are discussed.