Incorporating parameter uncertainty into quantitative microbial risk assessment (QMRA)

Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

Modelling bus lost time : an additional parameter influencing bus dwell time and station platform capacity at a BRT station platform

The common approach to estimate bus dwell time at a BRT station is to apply the traditional dwell time methodology derived for suburban bus stops. In spite of being sensitive to boarding and alighting passenger numbers and to some extent towards fare collection media, these traditional dwell time models do not account for the platform crowding. Moreover, they fall short in accounting for the effects of passenger/s walking along a relatively longer BRT platform. Using the experience from Brisbane busway (BRT) stations, a new variable, Bus Lost Time (LT), is introduced in traditional dwell time model. The bus lost time variable captures the impact of passenger walking and platform crowding on bus dwell time. These are two characteristics which differentiate a BRT station from a bus stop. This paper reports the development of a methodology to estimate bus lost time experienced by buses at a BRT platform. Results were compared with the Transit Capacity and Quality of Servce Manual (TCQSM) approach of dwell time and station capacity estimation. When the bus lost time was used in dwell time calculations it was found that the BRT station platform capacity reduced by 10.1%.

Hysteresis modeling of Magneto-Rheological (MR) fluid damper by self tuning fuzzy control

Magneto-rheological (MR) fluid damper is a semi-active control device that has recently received more attention by the vibration control community. But inherent nonlinear hysteresis character of magneto-rheological fluid dampers is one of the challenging aspects for utilizing this device to achieve high system performance. So the development of accurate model is necessary to take the advantage their unique characteristics. Research by others [3] has shown that a system of nonlinear differential equations can successfully be used to describe the hysteresis behavior of the MR damper. The focus of this paper is to develop an alternative method for modeling a damper in the form of centre average fuzzy interference system, where back propagation learning rules are used to adjust the weight of network. The inputs for the model are used from the experimental data. The resulting fuzzy interference system is satisfactorily represents the behavior of the MR fluid damper with reduced computational requirements. Use of the neuro-fuzzy model increases the feasibility of real time simulation.

An examination of the relationship between emotional intelligence, leadership style and perceived leadership outcomes in Australian educational institutions

In the field of leadership studies transformational leadership theory (e.g., Bass, 1985; Avolio, Bass, & Jung, 1995) has received much attention from researchers in recent years (Hughes, Ginnet, & Curphy, 2009; Hunt, 1999). Many previous studies have found that transformational leadership is related to positive outcomes such as the satisfaction, motivation and performance of followers in organisations (Judge & Piccolo, 2004; Lowe, Kroeck, & Sivasubramaniam, 1996), including in educational institutions (Chin, 2007; Leithwoood & Jantzi, 2005). Hence, it is important to explore constructs that may predict leadership style in order to identify potential transformational leaders in leadership assessment and selection procedures. Several researchers have proposed that emotional intelligence (EI) is one construct that may account for hitherto unexplained variance in transformational leadership (Mayer, 2001; Watkin, 2000). Different models of EI exist (e.g., Goleman, 1995, 2001; Bar-On, 1997; Mayer & Salovey, 1997) but momentum is growing for the Mayer and Salovey (1997) model to be considered the most useful (Ashkanasy & Daus, 2005; Daus & Ashkanasy, 2005). Studies in non-educational settings claim to have found that EI is a useful predictor of leadership style and leader effectiveness (Harms & Crede, 2010; Mills, 2009) but there is a paucity of studies which have examined the Mayer and Salovey (1997) model of EI in educational settings. Furthermore, other predictor variables have rarely been controlled in previous studies and only self-ratings of leadership behaviours, rather than multiple ratings, have usually been obtained. Therefore, more research is required in educational settings to answer the question: to what extent is the Mayer and Salovey (1997) model of EI a useful predictor of leadership style and leadership outcomes? This project, set in Australian educational institutions, was designed to move research in the field forward by: using valid and reliable instruments, controlling for other predictors, obtaining an adequately sized sample of real leaders as participants and obtaining multiple ratings of leadership behaviours. Other variables commonly used to predict leadership behaviours (personality factors and general mental ability) were assessed and controlled in the project. Additionally, integrity was included as another potential predictor of leadership behaviours as it has previously been found to be related to transformational leadership (Parry & Proctor-Thomson, 2002). Multiple ratings of leadership behaviours were obtained from each leader and their supervisors, peers and followers. The following valid and reliable psychological tests were used to operationalise the variables of interest: leadership styles and perceived leadership outcomes (Multifactor Leadership Questionnaire, Avolio et al., 1995), EI (Mayer–Salovey–Caruso Emotional Intelligence Test, Mayer, Salovey, & Caruso, 2002), personality factors (The Big Five Inventory, John, Donahue, & Kentle, 1991), general mental ability (Wonderlic Personnel Test-Quicktest, Wonderlic, 2003) and integrity (Integrity Express, Vangent, 2002). A Pilot Study (N = 25 leaders and 75 raters) made a preliminary examination of the relationship between the variables included in the project. Total EI, the experiential area, and the managing emotions and perceiving emotions branches of EI, were found to be related to transformational leadership which indicated that further research was warranted. In the Main Study, 144 leaders and 432 raters were recruited as participants to assess the discriminant validity of the instruments and examine the usefulness of EI as a predictor of leadership style and perceived leadership outcomes. Scores for each leadership scale across the four rating levels (leaders, supervisors, peers and followers) were aggregated with the exception of the management-by-exception active scale of transactional leadership which had an inadequate level of interrater agreement. In the descriptive and measurement component of the Main Study, the instruments were found to demonstrate adequate discriminant validity. The impact of role and gender on leadership style and EI were also examined, and females were found to be more transformational as leaders than males. Females also engaged in more contingent reward (transactional leadership) behaviours than males, whilst males engaged in more passive/avoidant leadership behaviours than females. In the inferential component of the Main Study, multiple regression procedures were used to examine the usefulness of EI as a predictor of leadership style and perceived leadership outcomes. None of the EI branches were found to be related to transformational leadership or the perceived leadership outcomes variables included in the study. Openness, emotional stability (the inverse of neuroticism) and general mental ability (inversely) each predicted a small amount of variance in transformational leadership. Passive/avoidant leadership was inversely predicted by the understanding emotions branch of EI. Overall, EI was not found to be a useful predictor of leadership style and leadership outcomes in the Main Study of this project. Implications for researchers and human resource practitioners are discussed.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

Queue protection parameters’ fine-tuning for variable speed limits

Any incident on motorways potentially can be followed by secondary crashes. Rear-end crashes also could happen as a result of queue formation downstream of high speed platoons. To decrease the occurrence of secondary crashes and rear-end crashes, Variable Speed Limits (VSL) can be applied to protect queue formed downstream. This paper focuses on fine tuning the Queue Protection algorithm of VSL. Three performance indicators: activation time, deactivation time and number of false alarms are selected to optimise the Queue Protection algorithm. A calibrated microscopic traffic simulation model of Pacific Motorway in Brisbane is used for the optimisation. Performance of VSL during an incident and heavy congestion and the benefit of VSL will be presented in the paper.

Infinite-horizon policy-gradient estimation

Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a biased estimate of the gradient of the average reward in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter β ∈ [0,1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter β is related to the mixing time of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward. ©2001 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Active transonic aerofoil design optimization using robust multiobjective evolutionary algorithms

The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary-layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. Two test cases are conducted: the first test assumes the boundary-layer transition position is at 45% of chord from the leading edge, and the second test considers robust design optimization for the shock control bump at the variability of boundary-layer transition positions. The numerical result shows that the optimization method coupled to uncertainty design techniques produces Pareto optimal shock-control-bump shapes, which have low sensitivity and high aerodynamic performance while having significant total drag reduction.

Bounded parameter Markov Decision Processes with average reward criterion

Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, the notion of an optimal policy for a BMDP is not entirely straightforward. We consider two notions of optimality based on optimistic and pessimistic criteria. These have been analyzed for discounted BMDPs. Here we provide results for average reward BMDPs. We establish a fundamental relationship between the discounted and the average reward problems, prove the existence of Blackwell optimal policies and, for both notions of optimality, derive algorithms that converge to the optimal value function.