Application of lean production to reducing operational waste in a tile manufacturing process

Roofing tile manufacturing is a mass production process with high operational and inventory wastes and costs. Due to huge operational costs, excessive inventory and wastes, and quality problems, roofing tile manufacturers are trying to implement lean manufacturing practice in their operations in order to remain competitive in an ncreasingly competitive global market. The aim of this research is to evaluate the possibility of reducing the operational and inventory costs of the tile manufacturing process through waste minimization. This paper analyses the current waste situation in a tile manufacturing process and develops current and future value stream mapping for such a process with a view to implementing lean principles in manufacturing. The focus of the approach is on cost reduction by eliminating non-value-added activities.

Offer to settle in an originating application : is it an offer under the UCPR?

Sheryl Jackson looks at the decision of Justice McMeekin in Northbound Property Group Pty Ltd v Carosi (No.2) [2013] QSC 189.

Quality based frame selection for face clustering in news video

Clustering identities in a broadcast video is a useful task to aid in video annotation and retrieval. Quality based frame selection is a crucial task in video face clustering, to both improve the clustering performance and reduce the computational cost. We present a frame work that selects the highest quality frames available in a video to cluster the face. This frame selection technique is based on low level and high level features (face symmetry, sharpness, contrast and brightness) to select the highest quality facial images available in a face sequence for clustering. We also consider the temporal distribution of the faces to ensure that selected faces are taken at times distributed throughout the sequence. Normalized feature scores are fused and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face clustering system. We present a news video database to evaluate the clustering system performance. Experiments on the newly created news database show that the proposed method selects the best quality face images in the video sequence, resulting in improved clustering performance.

Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions

Diagnostics of rolling element bearings involves a combination of different techniques of signal enhancing and analysis. The most common procedure presents a first step of order tracking and synchronous averaging, able to remove the undesired components, synchronous with the shaft harmonics, from the signal, and a final step of envelope analysis to obtain the squared envelope spectrum. This indicator has been studied thoroughly, and statistically based criteria have been obtained, in order to identify damaged bearings. The statistical thresholds are valid only if all the deterministic components in the signal have been removed. Unfortunately, in various industrial applications, characterized by heterogeneous vibration sources, the first step of synchronous averaging is not sufficient to eliminate completely the deterministic components and an additional step of pre-whitening is needed before the envelope analysis. Different techniques have been proposed in the past with this aim: The most widely spread are linear prediction filters and spectral kurtosis. Recently, a new technique for pre-whitening has been proposed, based on cepstral analysis: the so-called cepstrum pre-whitening. Owing to its low computational requirements and its simplicity, it seems a good candidate to perform the intermediate pre-whitening step in an automatic damage recognition algorithm. In this paper, the effectiveness of the new technique will be tested on the data measured on a full-scale industrial bearing test-rig, able to reproduce the harsh conditions of operation. A benchmark comparison with the traditional pre-whitening techniques will be made, as a final step for the verification of the potentiality of the cepstrum pre-whitening.

Nutrition prescription to achieve positive outcomes in Chronic Kidney Disease : a systematic review

In Chronic Kidney Disease (CKD), management of diet is important in prevention of disease progression and symptom management, however evidence on nutrition prescription is limited. Recent international CKD guidelines and literature was reviewed to address the following question “What is the appropriate nutrition prescription to achieve positive outcomes in adult patients with chronic kidney disease?” Databases included in the search were Medline and CINAHL using EBSCOhost search engine, Embase and the Cochrane Database of Systematic Reviews published from 2000 to 2009. International guidelines pertaining to nutrition prescription in CKD were also reviewed from 2000 to 2013. Three hundred and eleven papers and eight guidelines were reviewed by three reviewers. Evidence was graded as per the National Health and Medical Research Council of Australia criteria. The evidence from thirty six papers was tabulated under the following headings: protein, weight loss, enteral support, vitamin D, sodium, fat, fibre, oral nutrition supplements, nutrition counselling, including protein and phosphate, nutrients in peritoneal dialysis solution and intradialytic parenteral nutrition, and was compared to international guidelines. While more evidence based studies are warranted, the customary nutrition prescription remains satisfactory with the exception of Vitamin D and phosphate. In these two areas, additional research is urgently needed given the potential of adverse outcomes for the CKD patient.

How accurate is the identification of serious traffic injuries by Police? The concordance between Police and hospital reported traffic injuries

Police reported crash data are the primary source of crash information in most jurisdictions. However, the definition of serious injury within police-reported data is not consistent across jurisdictions and may not be accurate. With the Australian National Road Safety Strategy targeting the reduction of serious injuries, there is a greater need to assess the accuracy of the methods used to identify these injuries. A possible source of more accurate information relating to injury severity is hospital data. While other studies have compared police and hospital data to highlight the under-reporting in police-reported data, little attention has been given to the accuracy of the methods used by police to identify serious injuries. The current study aimed to assess how accurate the identification of serious injuries is in police-reported crash data, by comparing the profiles of transport-related injuries in the Queensland Road Crash Database with an aligned sample of data from the Queensland Hospital Admitted Patients Data Collection. Results showed that, while a similar number of traffic injuries were recorded in both data sets, the profile of these injuries was different based on gender, age, location, and road user. The results suggest that the ‘hospitalisation’ severity category used by police may not reflect true hospitalisations in all cases. Further, it highlights the wide variety of severity levels within hospitalised cases that are not captured by the current police-reported definitions. While a data linkage study is required to confirm these results, they highlight that a reliance on police-reported serious traffic injury data alone could result in inaccurate estimates of the impact and cost of crashes and lead to a misallocation of valuable resources.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

Evaluation of two-view geometry methods with automatic ground-truth generation

A large number of methods have been published that aim to evaluate various components of multi-view geometry systems. Most of these have focused on the feature extraction, description and matching stages (the visual front end), since geometry computation can be evaluated through simulation. Many data sets are constrained to small scale scenes or planar scenes that are not challenging to new algorithms, or require special equipment. This paper presents a method for automatically generating geometry ground truth and challenging test cases from high spatio-temporal resolution video. The objective of the system is to enable data collection at any physical scale, in any location and in various parts of the electromagnetic spectrum. The data generation process consists of collecting high resolution video, computing accurate sparse 3D reconstruction, video frame culling and down sampling, and test case selection. The evaluation process consists of applying a test 2-view geometry method to every test case and comparing the results to the ground truth. This system facilitates the evaluation of the whole geometry computation process or any part thereof against data compatible with a realistic application. A collection of example data sets and evaluations is included to demonstrate the range of applications of the proposed system.

A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters : theory and application to microfilaments

As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grained level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.

A needle in a haystack : the use of routinely collected emergency department injury surveillance data to help identify physical child abuse

A retrospective, descriptive analysis of a sample of children under 18 years presenting to a hospital emergency department (ED) for treatment of an injury was conducted. The aim was to explore characteristics and identify differences between children assigned abuse codes and children assigned unintentional injury codes using an injury surveillance database. Only 0.1% of children had been assigned the abuse code and 3.9% a code indicating possible abuse. Children between 2-5 years formed the largest proportion of those coded to abuse. Superficial injury and bruising were the most common types of injury seen in children in the abuse group and the possible abuse group (26.9% and 18.8% respectively), whereas those with unintentional injury were most likely to present with open wounds (18.4%). This study demonstrates that routinely collected injury surveillance data can be a useful source of information for describing injury characteristics in children assigned abuse codes compared to those assigned no abuse codes.

The impact of the built environment on health across the life course : design of a cross-sectional data linkage study

Introduction: The built environment is increasingly recognised as being associated with health outcomes. Relationships between the built environment and health differ among age groups, especially between children and adults, but also between younger, mid-age and older adults. Yet few address differences across life stage groups within a single population study. Moreover, existing research mostly focuses on physical activity behaviours, with few studying objective clinical and mental health outcomes. The Life Course Built Environment and Health (LCBEH) project explores the impact of the built environment on self-reported and objectively measured health outcomes in a random sample of people across the life course. Methods and analysis: This cross-sectional data linkage study involves 15 954 children (0–15 years), young adults (16–24 years), adults (25–64 years) and older adults (65+years) from the Perth metropolitan region who completed the Health and Wellbeing Surveillance System survey administered by the Department of Health of Western Australia from 2003 to 2009. Survey data were linked to Western Australia's (WA) Hospital Morbidity Database System (hospital admission) and Mental Health Information System (mental health system outpatient) data. Participants’ residential address was geocoded and features of their ‘neighbourhood’ were measured using Geographic Information Systems software. Associations between the built environment and self-reported and clinical health outcomes will be explored across varying geographic scales and life stages. Ethics and dissemination: The University of Western Australia's Human Research Ethics Committee and the Department of Health of Western Australia approved the study protocol (#2010/1). Findings will be published in peer-reviewed journals and presented at local, national and international conferences, thus contributing to the evidence base informing the design of healthy neighbourhoods for all residents.

Silver deposits in cutaneous burn scar tissue is a common phenomenon following application of a silver dressing

BACKGROUND Silver dressings have been widely and successfully used to prevent cutaneous wounds, including burns, chronic ulcers, dermatitis and other cutaneous conditions, from infection. However, in a few cases, skin discolouration or argyria-like appearances have been reported. This study investigated the level of silver in scar tissue post-burn injury following application of Acticoat, a silver dressing. METHODS A porcine deep dermal partial thickness burn model was used. Burn wounds were treated with this silver dressing until completion of re-epithelialization, and silver levels were measured in a total of 160 scars and normal tissues. RESULTS The mean level of silver in scar tissue covered with silver dressings was 136 microg/g, while the silver level in normal skin was less than 0.747 microg/g. A number of wounds had a slate-grey appearance, and dissection of the scars revealed brown-black pigment mostly in the middle and deep dermis within the scar. The level of silver and the severity of the slate-grey discolouration were correlated with the length of time of the silver dressing application. CONCLUSIONS These results show that silver deposition in cutaneous scar tissue is a common phenomenon, and higher levels of silver deposits and severe skin discolouration are correlated with an increase in the duration of this silver dressing application.

Silver absorption on burns after the application of Acticoat : data from pediatric patients and a porcine burn model

Silver dressings have been widely used to successfully prevent burn wound infection and sepsis. However, a few case studies have reported the functional abnormality and failure of vital organs, possibly caused by silver deposits. The aim of this study was to investigate the serum silver level in the pediatric burn population and also in several internal organs in a porcine burn model after the application of Acticoat. A total of 125 blood samples were collected from 46 pediatric burn patients. Thirty-six patients with a mean of 13.4% TBSA burns had a mean peak serum silver level of 114 microg/L, whereas 10 patients with a mean of 1.85% TBSA burns had an undetectable level of silver (<5.4 microg/L). Overall, serum silver levels were closely related to burn sizes. However, the highest serum silver was 735 microg/L in a 15-month-old toddler with 10% TBSA burns and the second highest was 367 microg/L in a 3-year old with 28% TBSA burns. In a porcine model with 2% TBSA burns, the mean peak silver level was 38 microg/L at 2 to 3 weeks after application of Acticoat and was then significantly reduced to an almost undetectable level at 6 weeks. Of a total of four pigs, silver was detected in all four livers (1.413 microg/g) and all four hearts (0.342 microg/g), three of four kidneys (1.113 microg/g), and two of four brains (0.402 microg/g). This result demonstrated that although variable, the level of serum silver was positively associated with the size of burns, and significant amounts of silver were deposited in internal organs in pigs with only 2% TBSA burns, after application of Acticoat.

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Local inter-session variability modelling for object classification

Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset.