Multiple perspectives framework to model complex processes

The growing complexity of organisations has resulted in collaboration between multiple stakeholders becoming a challenging and critical issue that organisations must address in order to ensure their practices are sustainable. A multiple-case field study was conducted in order to demonstrate the proposed methodology of analysis and examination for knowledge-based systems in an actual organisational setting. The use of a multiple-perspective framework to improve understanding of the complex relationships in such systems was examined. In particular, the case study focused on the Australian Government’s Nation Building Economic Stimulus Plan (NBESP) which provided $1.9 billion to construct social housing across the State over two years. The results suggest that the use of a multi-perspective framework is appropriate and that there is a need for attention to be paid to the economic perspective.

The "HEALTH" model- part 1 : treatment program guidelines for complex PTSD

This paper outlines the development and piloting of the “HEALTH” model for treatment of Complex PTSD in clients who have experienced multiple traumas across childhood and adulthood - particularly child sexual abuse and sexual assault in adulthood. As a guideline-based treatment model, HEALTH outlines six stages of intervention: (1) having a supportive therapist; (2) ensuring personal safety; (3) assisting with daily functioning; (4) self-regulation - learning to manage core PTSD symptoms; (5) treating Complex PTSD symptoms; and, finally, (6) having patience and persistence to enable “ego strengthening”. Using a case study approach, we provide both qualitative and quantitative assessment data for the individuals in the study, all of whom displayed numerous pre-treatment symptoms of Complex PTSD. Such programs are different to standard PTSD treatment programs that focus predominantly on core PTSD symptoms of re-experiencing, avoidance and arousal. The results of this study provided support for the use of guideline-based treatment programs that cater specifically for the needs of those who have suffered long-term/multiple trauma experiences by targeting Complex PTSD symptoms.

3D particle-based cell modelling for haptic microrobotic cell injection

Introducing haptic interface to conduct microrobotic intracellular injection has many beneficial implications. In particular, the haptic device provides force feedback to the bio-operator's hand. This paper introduces a 3D particle-based model to simulate the deformation of the cell membrane and corresponding cellular forces during microrobotic cell injection. The model is based on the kinematic and dynamic of spring – damper multi particle joints considering visco-elastic fluidic properties. It simulates the indentation force feedback as well as cell visual deformation during the microinjection. The model is verified using experimental data of zebrafish embryo microinjection. The results demonstrate that the developed cell model is capable of estimating zebrafish embryo deformation and force feedback accurately.

Formulation and simulation of a 3D mechanical model of embryos for microinjection

The understanding of cell manipulation, for example in microinjection, requires an accurate model of the cells. Motivated by this important requirement, a 3D particlebased mechanical model is derived for simulating the deformation of the fish egg membrane and the corresponding cellular forces during microrobotic cell injection. The model is formulated based on the kinematic and dynamic of spring- damper configuration with multi-particle joints considering the visco-elastic fluidic properties. It simulates the indentation force feedback as well as cell visual deformation during microinjection. A preliminary simulation study is conducted with different parameter configurations. The results indicate that the proposed particle-based model is able to provide similar deformation profiles as observed from a real microinjection experiment of the zebrafish embryo published in the literature. As a generic modelling approach is adopted, the proposed model also has the potential in applications with different types of manipulation such as micropipette cell aspiration.

Understanding the link between body dissatisfaction and binge eating: a model comparison approach

 This thesis explored the association between body dissatisfaction and binge eating by comparing three competing theoretical frameworks. Study I utilised a cross-sectional design and collectively these findings suggest the superiority of the dual pathway model (dietary restraint and negative affect) over the objectification theory and the escape model. The purpose of Study II was then to extend on the findings from Study I by further examining in real-time the model/theory that most strongly explained the body dissatisfaction-binge eating relationship. Participants were prompted at random intervals seven times daily across the course of a week to self-report their state body dissatisfaction, current mood experiences, and eating practices. Results revealed that negative mood, but not dietary restraint, significantly mediated the state body dissatisfaction-binge eating relationship. These results highlight that the dual pathway model is robust, but raise the possibility that the dietary restraint path in the model is not well operationalized. In light of the non-significant mediating effect of dietary restraint, this led the researcher to identify various modeling alternatives to further understand the mediating influences of the pathways of negative affect and dietary restraint.

Rapid Physarum Algorithm for shortest path problem

As shortest path (SP) problem has been one of the most fundamental network optimization problems for a long time, technologies for this problem are still being studied. In this paper, a new method by integrating a path finding mathematical model, inspired by Physarum polycephalum, with extracted one heuristic rule to solve SP problem has been proposed, which is called Rapid Physarum Algorithm (RPA). Simulation experiments have been carried out on three different network topologies with varying number of nodes. It is noted that the proposed RPA can find the optimal path as the path finding model does for most networks. What is more, experimental results show that the performance of RPA surpasses the path finding model on both iterations and solution time. © 2014 Elsevier B.V.

Parametric conditional frailty models for recurrent cardiovascular events in the lipid study

Background Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals.Methods We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females. Results A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR¼0.71, 95% CI0.60–0.83). However, the treatment effect was not significant in women due to smaller sample size (HR¼0.75, 95% CI 0.51–1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p¼0.24 for men and p¼0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6–4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4–13.6). Limitations The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors.Conclusions The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.

Consumers who don't want to look for information

A model to identify and classify consumers with high resistance to searching for information (HRSI) was developed and tested. We found that individuals with high levels of confidence about a purchase but who also ascribed low levels of personal importance to the transaction were significantly (p=.004) more likely to be HRSI. Using Multiple Discriminant Analysis, our model classified and predicted HRSI consumers well (p=.004, 57% above chance) but not low-resistance consumers (p=.6, 26.4% below chance).

Browsing behavior mimicking attacks on popular web sites for large botnets

With the significant growth of botnets, application layer DDoS attacks are much easier to launch using large botnet, and false negative is always a problem for intrusion detection systems in real practice. In this paper, we propose a novel application layer DDoS attack tool, which mimics human browsing behavior following three statistical distributions, the Zipf-like distribution for web page popularity, the Pareto distribution for page request time interval for an individual browser, and the inverse Gaussian distribution for length of browsing path. A Markov model is established for individual bot to generate attack request traffic. Our experiments indicated that the attack traffic that generated by the proposed tool is pretty similar to the real traffic. As a result, the current statistics based detection algorithms will result high false negative rate in general. In order to counter this kind of attacks, we discussed a few preliminary solutions at the end of this paper.

Higher social distress and lower psycho-social wellbeing:examining the coping capacity and health of people with hearing impairment

Abstract
Purpose: The objectives of this paper are as follows: (1) propose an explanatory model as to how hearing disability may impact on health and (2) examine the model’s utility.
Methods: Data were collected on the psycho-social wellbeing, disability and physical health of farmers (n=56) participating in an intervention to manage the social impacts of hearing disability. Two models were proposed and examined using multiple hierarchical linear regression. Model 1 used self-rated quality of life and model 2 used capacity to manage hearing and listening impairments, as dependent variables.
Results: The analyses found that physical measures of hearing impairment (audiograms) were not correlated with physical or mental health outcomes. However, in model 1, self-confidence and self-rated ability to manage hearing impairment were most closely associated with reduced quality of life (anxiety and diastolic blood pressure were positively associated with quality of life). In model 2, higher anxiety and reduced self-confidence were associated with decreasing ability to successfully manage one’s hearing impairment.
Conclusions: The findings support the explanatory model that stress is higher and wellbeing lower when the fit between the person’s coping capacity and environmental demands is poor.

Bayesian nonparametric multilevel clustering with group-level contexts

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group- level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dinchiet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polyaurn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.

Aligning business and IT strategies in multi-business organizations

The alignment of business and information technology (IT) strategies is an important and enduring theoretical challenge for the information systems discipline, remaining a top issue in practice over the past 20 years. Multi-business organizations (MBOs) present a particular alignment challenge because business strategies are developed at the corporate level, within individual strategic business units and across the corporate investment cycle. In contrast, the extant literature implicitly assumes that IT strategy is aligned with a single business strategy at a single point in time. This paper draws on resource-based theory and path dependence to model functional, structural, and temporal IT strategic alignment in MBOs. Drawing on Makadok's theory of profit, we show how each form of alignment creates value through the three strategic drivers of competence, governance, and flexibility, respectively. We illustrate the model with examples from a case study on the Commonwealth Bank of Australia. We also explore the model's implications for existing IT alignment models, providing alternative theoretical explanations for how IT alignment creates value.

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes. © 2014 Elsevier Ltd.

Towards powerful experimental and statistical approaches to study intraindividual variability in labile traits

There is a long-standing interest in behavioural ecology, exploring the causes and correlates of consistent individual differences in mean behavioural traits ('personality') and the response to the environment ('plasticity'). Recently, it has been observed that individuals also consistently differ in their residual intraindividual variability (rIIV). This variation will probably have broad biological and methodological implications to the study of trait variation in labile traits, such as behaviour and physiology, though we currently need studies to quantify variation in rIIV, using more standardized and powerful methodology. Focusing on activity rates in guppies (Poecilia reticulata), we provide a model example, from sampling design to data analysis, in how to quantify rIIV in labile traits. Building on the doubly hierarchical generalized linear model recently used to quantify individual differences in rIIV, we extend the model to evaluate the covariance between individual mean values and their rIIV. After accounting for time-related change in behaviour, our guppies substantially differed in rIIV, and it was the active individuals that tended to be more consistent (lower rIIV). We provide annotated data analysis code to implement these complex models, and discuss how to further generalize the model to evaluate covariances with other aspects of phenotypic variation.