Dragline automation - a decade of development : shared autonomy for improving mining equipment productivity

Draglines are massive machines commonly used in surface mining to strip overburden, revealing the targeted minerals for extraction. Automating some or all of the phases of operation of these machines offers the potential for significant productivity and maintenance benefits. The mining industry has a history of slow uptake of automation systems due to the challenges contained in the harsh, complex, three-dimensional (3D), dynamically changing mine operating environment. Robotics as a discipline is finally starting to gain acceptance as a technology with the potential to assist mining operations. This article examines the evolution of robotic technologies applied to draglines in the form of machine embedded intelligent systems. Results from this work include a production trial in which 250,000 tons of material was moved autonomously, experiments demonstrating steps towards full autonomy, and teleexcavation experiments in which a dragline in Australia was tasked by an operator in the United States.

Modelling of inflatable rescue boats (IRBs) in surf conditions to reduce injuries

The Inflatable Rescue Boat (IRB) is arguably the most effective rescue tool used by the Australian surf lifesavers. The exceptional features of high mobility and rapid response have enabled it to become an icon on Australia's popular beaches. However, the IRB's extensive use within an environment that is as rugged as it is spectacular, has led it to become a danger to those who risk their lives to save others. Epidemiological research revealed lower limb injuries to be predominant, particularly the right leg. The common types of injuries were fractures and dislocations, as well as muscle or ligament strains and tears. The concern expressed by Surf Life Saving Queensland (SLSQ) and Surf Life Saving Australia (SLSA) led to a biomechanical investigation into this unique and relatively unresearched field. The aim of the research was to identify the causes of injury and propose processes that may reduce the instances and severity of injury to surf lifesavers during IRB operation. Following a review of related research, a design analysis of the craft was undertaken as an introduction to the craft, its design and uses. The mechanical characteristics of the vessel were then evaluated and the accelerations applied to the crew in the IRB were established through field tests. The data were then combined and modelled in the 3-D mathematical modelling and simulation package, MADYMO. A tool was created to compare various scenarios of boat design and methods of operation to determine possible mechanisms to reduce injuries. The results of this study showed that under simulated wave loading the boats flex around a pivot point determined by the position of the hinge in the floorboard. It was also found that the accelerations experienced by the crew exhibited similar characteristics to road vehicle accidents. Staged simulations indicated the attributes of an optimum foam in terms of thickness and density. Likewise, modelling of the boat and crew produced simulations that predicted realistic crew response to tested variables. Unfortunately, the observed lack of adherence to the SLSA footstrap Standard has impeded successful epidemiological and modelling outcomes. If uniformity of boat setup can be assured then epidemiological studies will be able to highlight the influence of implementing changes to the boat design. In conclusion, the research provided a tool to successfully link the epidemiology and injury diagnosis to the mechanical engineering design through the use of biomechanics. This was a novel application of the mathematical modelling software MADYMO. Other craft can also be investigated in this manner to provide solutions to the problem identified and therefore reduce risk of injury for the operators.

The role of the health surveillance program in the Queensland coal mining industry

The Queensland Coal Industry Employees Health Scheme was implemented in 1993 to provide health surveillance for all Queensland coal industry workers. Tt1e government, mining employers and mining unions agreed that the scheme should operate for seven years. At the expiry of the scheme, an assessment of the contribution of health surveillance to meet coal industry needs would be an essential part of determining a future health surveillance program. This research project has analysed the data made available between 1993 and 1998. All current coal industry employees have had at least one health assessment. The project examined how the centralised nature of the Health Scheme benefits industry by identi~)jng key health issues and exploring their dimensions on a scale not possible by corporate based health surveillance programs. There is a body of evidence that indicates that health awareness - on the scale of the individual, the work group and the industry is not a part of the mining industry culture. There is also growing evidence that there is a need for this culture to change and that some change is in progress. One element of this changing culture is a growth in the interest by the individual and the community in information on health status and benchmarks that are reasonably attainable. This interest opens the way for health education which contains personal, community and occupational elements. An important element of such education is the data on mine site health status. This project examined the role of health surveillance in the coal mining industry as a tool for generating the necessary information to promote an interest in health awareness. The Health Scheme Database provides the material for the bulk of the analysis of this project. After a preliminary scan of the data set, more detailed analysis was undertaken on key health and related safety issues that include respiratory disorders, hearing loss and high blood pressure. The data set facilitates control for confounding factors such as age and smoking status. Mines can be benchmarked to identify those mines with effective health management and those with particular challenges. While the study has confirmed the very low prevalence of restrictive airway disease such as pneu"moconiosis, it has demonstrated a need to examine in detail the emergence of obstructive airway disease such as bronchitis and emphysema which may be a consequence of the increasing use of high dust longwall technology. The power of the Health Database's electronic data management is demonstrated by linking the health data to other data sets such as injury data that is collected by the Department of l\1mes and Energy. The analysis examines serious strain -sprain injuries and has identified a marked difference between the underground and open cut sectors of the industry. The analysis also considers productivity and OHS data to examine the extent to which there is correlation between any pairs ofJpese and previously analysed health parameters. This project has demonstrated that the current structure of the Coal Industry Employees Health Scheme has largely delivered to mines and effective health screening process. At the same time, the centralised nature of data collection and analysis has provided to the mines, the unions and the government substantial statistical cross-sectional data upon which strategies to more effectively manage health and relates safety issues can be based.

Safety impacts of alcohol and other drugs in construction : a mid term report

Estimated 640,700 persons suffered a work-related injury or illness in 2009-2010 and 444 lost their lives as a result in 2008-2009, in Australia Very little is known about what proportion of accidents are directly attributable to the effects of AOD Anecdotal evidence highlights issues of AOD and its association with safety risk on construction sites

Development and synthesis of innovative approaches to the surgical assessment of articular cartilage degeneration

This research has established, through ultrasound, near infrared spectroscopy and biomechanics experiments, parameters and parametric relationships that can form the framework for quantifying the integrity of the articular cartilage-on-bone laminate, and objectively distinguish between normal/healthy and abnormal/degenerated joint tissue, with a focus on articular cartilage. This has been achieved by: 1. using traditional experimental methods to produce new parameters for cartilage assessment; 2. using novel methodologies to develop new parameters; and 3. investigating the interrelationships between mechanical, structural and molec- ular properties to identify and select those parameters and methodologies that can be used in a future arthroscopic probe based on points 1 and 2. By combining the molecular, micro- and macro-structural characteristics of the tissue with its mechanical properties, we arrive at a set of critical benchmarking parameters for viable and early-stage non-viable cartilage. The interrelationships between these characteristics, examined using a multivariate analysis based on principal components analysis, multiple linear regression and general linear modeling, could then to deter- mine those parameters and relationships which have the potential to be developed into a future clinical device. Specifically, this research has found that the ultrasound and near infrared techniques can subsume the mechanical parameters and combine to characterise the tissue at the molecular, structural and mechanical levels over the full depth of the cartilage matrix. It is the opinion in this thesis that by enabling the determination of the precise area of in uence of a focal defect or disease in the joint, demarcating the boundaries of articular cartilage with dierent levels of degeneration around a focal defect, better surgical decisions that will advance the processes of joint management and treatment will be achieved. Providing the basis for a surgical tool, this research will contribute to the enhancement and quanti�cation of arthroscopic procedures, extending to post- treatment monitoring and as a research tool, will enable a robust method for evaluating developing (particularly focalised) treatments.

Systems-based accident analysis in the led outdoor activity domain : application and evaluation of a risk management framework

Safety-compromising accidents occur regularly in the led outdoor activity domain. Formal accident analysis is an accepted means of understanding such events and improving safety. Despite this, there remains no universally accepted framework for collecting and analysing accident data in the led outdoor activity domain. This article presents an application of Rasmussen's risk management framework to the analysis of the Lyme Bay sea canoeing incident. This involved the development of an Accimap, the outputs of which were used to evaluate seven predictions made by the framework. The Accimap output was also compared to an analysis using an existing model from the led outdoor activity domain. In conclusion, the Accimap output was found to be more comprehensive and supported all seven of the risk management framework's predictions, suggesting that it shows promise as a theoretically underpinned approach for analysing, and learning from, accidents in the led outdoor activity domain.

Candidate metastasis suppressor genes uncovered by array comparative genomic hybridization in a mouse allograft model of prostate cancer.

Background The purpose of this study was to identify candidate metastasis suppressor genes from a mouse allograft model of prostate cancer (NE-10). This allograft model originally developed metastases by twelve weeks after implantation in male athymic nude mice, but lost the ability to metastasize after a number of in vivo passages. We performed high resolution array comparative genomic hybridization on the metastasizing and non-metastasizing allografts to identify chromosome imbalances that differed between the two groups of tumors. Results This analysis uncovered a deletion on chromosome 2 that differed between the metastasizing and non-metastasizing tumors. Bioinformatics filters were employed to mine this region of the genome for candidate metastasis suppressor genes. Of the 146 known genes that reside within the region of interest on mouse chromosome 2, four candidate metastasis suppressor genes (Slc27a2, Mall, Snrpb, and Rassf2) were identified. Quantitative expression analysis confirmed decreased expression of these genes in the metastasizing compared to non-metastasizing tumors. Conclusion This study presents combined genomics and bioinformatics approaches for identifying potential metastasis suppressor genes. The genes identified here are candidates for further studies to determine their functional role in inhibiting metastases in the NE-10 allograft model and human prostate cancer.

Empirical investigation of interactive highway safety design model accident prediction algorithm : Rural intersections

One major gap in transportation system safety management is the ability to assess the safety ramifications of design changes for both new road projects and modifications to existing roads. To fulfill this need, FHWA and its many partners are developing a safety forecasting tool, the Interactive Highway Safety Design Model (IHSDM). The tool will be used by roadway design engineers, safety analysts, and planners throughout the United States. As such, the statistical models embedded in IHSDM will need to be able to forecast safety impacts under a wide range of roadway configurations and environmental conditions for a wide range of driver populations and will need to be able to capture elements of driving risk across states. One of the IHSDM algorithms developed by FHWA and its contractors is for forecasting accidents on rural road segments and rural intersections. The methodological approach is to use predictive models for specific base conditions, with traffic volume information as the sole explanatory variable for crashes, and then to apply regional or state calibration factors and accident modification factors (AMFs) to estimate the impact on accidents of geometric characteristics that differ from the base model conditions. In the majority of past approaches, AMFs are derived from parameter estimates associated with the explanatory variables. A recent study for FHWA used a multistate database to examine in detail the use of the algorithm with the base model-AMF approach and explored alternative base model forms as well as the use of full models that included nontraffic-related variables and other approaches to estimate AMFs. That research effort is reported. The results support the IHSDM methodology.

On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts—variation over and above that accounted for by the Poisson density. The extra-variation – or dispersion – is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models—tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31–40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.