Bayesian network for risk of diarrhea associated with the use of recycled water

Estimating potential health risks associated with recycled (reused) water is highly complex given the multiple factors affecting water quality. We take a conceptual model, which represents the factors and pathways by which recycled water may pose a risk of contracting gastroenteritis, convert the conceptual model to a Bayesian net, and quantify the model using one expert’s opinion. This allows us to make various predictions as to the risks posed under various scenarios. Bayesian nets provide an additional way of modeling the determinants of recycled water quality and elucidating their relative influence on a given disease outcome. The important contribution to Bayesian net methodology is that all model predictions, whether risk or relative risk estimates, are expressed as credible intervals.

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.

HapMap tag-SNP analysis confirms a role for COMT in schizophrenia risk and reveals a novel association

Catechol-O-methyl transferase (COMT) encodes an enzyme involved in the metabolism of dopamine and maps to a commonly deleted region that increases schizophrenia risk. A non-synonymous polymorphism (rs4680) in COMT has been previously found to be associated with schizophrenia and results in altered activity levels of COMT. Using a haplotype block-based gene-tagging approach we conducted an association study of seven COMT single nucleotide polymorphisms (SNPs) in 160 patients with a DSM-IV diagnosis of schizophrenia and 250 controls in an Australian population. Two polymorphisms including rs4680 and rs165774 were found to be significantly associated with schizophrenia. The rs4680 results in a Val/Met substitution but the strongest association was shown by the novel SNP, rs165774, which may still be functional even though it is located in intron five. Individuals with schizophrenia were more than twice as likely to carry the GG genotype compared to the AA genotype for both the rs165774 and rs4680 SNPs. This association was slightly improved when males were analysed separately possibly indicating a degree of sexual dimorphism. Our results confirm that COMT is a good candidate for schizophrenia risk, by replicating the association with rs4680 and identifying a novel SNP association.

Risk taking in Extreme Sports: A phenomenological perspective

Participation in extreme sports is enjoying incredible growth while more traditional recreational activities such as golf are struggling to maintain numbers. Theoretical perspectives on extreme sports and extreme sport participants have assumed that participation is about risk-taking. However, these theory-driven methodologies may reflect judgments that do not necessarily relate to participants' lived experience. In this paper I review current risk-oriented perspectives on extreme sports and present research findings that question this assumed relationship between extreme sports and risk and thus reposition the experience in a hitherto unexplored manner. Risk taking is not the focus. Participants acknowledge that the potential outcome of a mismanaged mistake or accident could be death. However, accepting this potential outcome does not mean that they search for risk. Participants argue that many everyday life events (e.g., driving) are high-risk events. Participants undertake detailed preparation in order to minimise the possibility of negative outcomes because extreme sports trigger a range of positive experiential outcomes. The study is significant as it followed a hermeneutic phenomenological process which did not presuppose a risk-taking orientation. Hermeneutic phenomenology allows for a multitude of data sources including interviews (10 male and 5 female extreme sports participants, ages 30 to 72 years), auto-biographies, videos and other firsthand accounts. This process allowed this unexpected perspective to emerge more clearly.

Practical application of pseudospectral optimization to robot path planning

To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platform’s dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simplified kino-dynamic models to avoid the significant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robot’s dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predefined analytical functions to enable real world application.

Identifying relationship between skid resistance, road characteristics and crashes using probability-based risk approach

Road accidents are of great concerns for road and transport departments around world, which cause tremendous loss and dangers for public. Reducing accident rates and crash severity are imperative goals that governments, road and transport authorities, and researchers are aimed to achieve. In Australia, road crash trauma costs the nation A$ 15 billion annually. Five people are killed, and 550 are injured every day. Each fatality costs the taxpayer A$1.7 million. Serious injury cases can cost the taxpayer many times the cost of a fatality. Crashes are in general uncontrolled events and are dependent on a number of interrelated factors such as driver behaviour, traffic conditions, travel speed, road geometry and condition, and vehicle characteristics (e.g. tyre type pressure and condition, and suspension type and condition). Skid resistance is considered one of the most important surface characteristics as it has a direct impact on traffic safety. Attempts have been made worldwide to study the relationship between skid resistance and road crashes. Most of these studies used the statistical regression and correlation methods in analysing the relationships between skid resistance and road crashes. The outcomes from these studies provided mix results and not conclusive. The objective of this paper is to present a probability-based method of an ongoing study in identifying the relationship between skid resistance and road crashes. Historical skid resistance and crash data of a road network located in the tropical east coast of Queensland were analysed using the probability-based method. Analysis methodology and results of the relationships between skid resistance, road characteristics and crashes are presented.

Waist circumference cut-off values for the prediction of cardiovascular risk factors clustering in Chinese school-aged children : a cross-sectional study

Background: Waist circumference has been identified as a valuable predictor of cardiovascular risk in children. The development of waist circumference percentiles and cut-offs for various ethnic groups are necessary because of differences in body composition. The purpose of this study was to develop waist circumference percentiles for Chinese children and to explore optimal waist circumference cut-off values for predicting cardiovascular risk factors clustering in this population.----- ----- Methods: Height, weight, and waist circumference were measured in 5529 children (2830 boys and 2699 girls) aged 6-12 years randomly selected from southern and northern China. Blood pressure, fasting triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and glucose were obtained in a subsample (n = 1845). Smoothed percentile curves were produced using the LMS method. Receiver-operating characteristic analysis was used to derive the optimal age- and gender-specific waist circumference thresholds for predicting the clustering of cardiovascular risk factors.----- ----- Results: Gender-specific waist circumference percentiles were constructed. The waist circumference thresholds were at the 90th and 84th percentiles for Chinese boys and girls respectively, with sensitivity and specificity ranging from 67% to 83%. The odds ratio of a clustering of cardiovascular risk factors among boys and girls with a higher value than cut-off points was 10.349 (95% confidence interval 4.466 to 23.979) and 8.084 (95% confidence interval 3.147 to 20.767) compared with their counterparts.----- ----- Conclusions: Percentile curves for waist circumference of Chinese children are provided. The cut-off point for waist circumference to predict cardiovascular risk factors clustering is at the 90th and 84th percentiles for Chinese boys and girls, respectively.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

Optimization of container process at seaport terminals

Seaport container terminals are an important part of the logistics systems in international trades. This paper investigates the relationship between quay cranes, yard machines and container storage locations in a multi-berth and multi-ship environment. The aims are to develop a model for improving the operation efficiency of the seaports and to develop an analytical tool for yard operation planning. Due to the fact that the container transfer times are sequence-dependent and with the large number of variables involve, the proposed model cannot be solved in a reasonable time interval for realistically sized problems. For this reason, List Scheduling and Tabu Search algorithms have been developed to solve this formidable and NP-hard scheduling problem. Numerical implementations have been analysed and promising results have been achieved.

Structural reliability prediction of a steel bridge element using Dynamic Object Oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method.

The impact of school connectedness on violent behavior, transport risk taking behavior and associated injuries in adolescence

Adolescents engage in many risk-taking behaviors that have the potential to lead to injury. The school environment has a significant role in shaping adolescent behavior, and this study aimed to provide additional information about the benefits associated with connectedness to school. Early adolescents aged 13 to 15 years (N = 509, 49% boys) were surveyed about school connectedness, engagement in transport and violence risk-taking, and injury experiences. Significant relations were found between school connectedness and reduced engagement in both transport and violence risk-taking, as well as fewer associated injuries. This study has implications for the area of risk-taking and injury prevention, as it suggests the potential for reducing adolescents' injury through school based interventions targeting school connectedness.

Shrinking neighborhood evolution : a novel stochastic algorithm for numerical optimization

Focusing on the conditions that an optimization problem may comply with, the so-called convergence conditions have been proposed and sequentially a stochastic optimization algorithm named as DSZ algorithm is presented in order to deal with both unconstrained and constrained optimizations. The principle is discussed in the theoretical model of DSZ algorithm, from which we present the practical model of DSZ algorithm. Practical model efficiency is demonstrated by the comparison with the similar algorithms such as Enhanced simulated annealing (ESA), Monte Carlo simulated annealing (MCS), Sniffer Global Optimization (SGO), Directed Tabu Search (DTS), and Genetic Algorithm (GA), using a set of well-known unconstrained and constrained optimization test cases. Meanwhile, further attention goes to the strategies how to optimize the high-dimensional unconstrained problem using DSZ algorithm.

Image-based safety assessment : automated spatial safety risk identification of earthmoving and surface mining activities

This paper presents an automated image‐based safety assessment method for earthmoving and surface mining activities. The literature review revealed the possible causes of accidents on earthmoving operations, investigated the spatial risk factors of these types of accident, and identified spatial data needs for automated safety assessment based on current safety regulations. Image‐based data collection devices and algorithms for safety assessment were then evaluated. Analysis methods and rules for monitoring safety violations were also discussed. The experimental results showed that the safety assessment method collected spatial data using stereo vision cameras, applied object identification and tracking algorithms, and finally utilized identified and tracked object information for safety decision making.

History-aware, real-time risk detection in business processes (Extended Version)

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.