Consumer product-related injuries in Queensland children : A snapshot of current problem areas for potential action

This report presents a snapshot from work which was funded by the Queensland Injury Prevention Council in 2010-11 titled “Feasibility of Using Health Data Sources to Inform Product Safety Surveillance in Queensland children”. The project provided an evaluation of the current available evidence-base for identification and surveillance of product-related injuries in children in Queensland and Australia. A comprehensive 300 page report was produced (available at: http://eprints.qut.edu.au/46518/) and a series of recommendations were made which proposed: improvements in the product safety data system, increased utilisation of health data for proactive and reactive surveillance, enhanced collaboration between the health sector and the product safety sector, and improved ability of health data to meet the needs of product safety surveillance. At the conclusion of the project, a Consumer Product Injury Research Advisory group (CPIRAG) was established as a working party to the Queensland Injury Prevention Council (QIPC), to prioritise and advance these recommendations and to work collaboratively with key stakeholders to promote the role of injury data to support product safety policy decisions at the Queensland and national level. This group continues to meet monthly and is comprised of the organisations represented on the second page of this report. One of the key priorities of the CPIRAG group for 2012 was to produce a snapshot report to highlight problem areas for potential action arising out of the larger report. Subsequent funding to write this snapshot report was provided by the Institute for Health and Biomedical Innovation, Injury Prevention and Rehabilitation Domain at QUT in 2012. This work was undertaken by Dr Kirsten McKenzie and researchers from QUT's Centre for Accident Research and Road Safety - Queensland. This snapshot report provides an evidence base for potential further action on a range of children’s products that are significantly represented in injury data. Further information regarding injury hazards, safety advice and regulatory responses are available on the Office of Fair Trading (OFT) Queensland website and the Product Safety Australia websites. Links to these resources are provided for each product reviewed.

Reasoning about component similarity in building product models from the construction perspective

Identifying the design features that impact construction is essential to developing cost effective and constructible designs. The similarity of building components is a critical design feature that affects method selection, productivity, and ultimately construction cost and schedule performance. However, there is limited understanding of what constitutes similarity in the design of building components and limited computer-based support to identify this feature in a building product model. This paper contributes a feature-based framework for representing and reasoning about component similarity that builds on ontological modelling, model-based reasoning and cluster analysis techniques. It describes the ontology we developed to characterize component similarity in terms of the component attributes, the direction, and the degree of variation. It also describes the generic reasoning process we formalized to identify component similarity in a standard product model based on practitioners' varied preferences. The generic reasoning process evaluates the geometric, topological, and symbolic similarities between components, creates groupings of similar components, and quantifies the degree of similarity. We implemented this reasoning process in a prototype cost estimating application, which creates and maintains cost estimates based on a building product model. Validation studies of the prototype system provide evidence that the framework is general and enables a more accurate and efficient cost estimating process.

Optimisation of bio-oil extraction process from Beauty Leaf (Calophyllum inophyllum) oil seed as a second generation biodiesel source

The Beauty Leaf tree (Calophyllum inophyllum) is a potential source of non-edible vegetable oil for producing future generation biodiesel because of its ability to grow in a wide range of climate conditions, easy cultivation, high fruit production rate, and the high oil content in the seed. This plant naturally occurs in the coastal areas of Queensland and the Northern Territory in Australia, and is also widespread in south-east Asia, India and Sri Lanka. Although Beauty Leaf is traditionally used as a source of timber and orientation plant, its potential as a source of second generation biodiesel is yet to be exploited. In this study, the extraction process from the Beauty Leaf oil seed has been optimised in terms of seed preparation, moisture content and oil extraction methods. The two methods that have been considered to extract oil from the seed kernel are mechanical oil extraction using an electric powered screw press, and chemical oil extraction using n-hexane as an oil solvent. The study found that seed preparation has a significant impact on oil yields, especially in the screw press extraction method. Kernels prepared to 15% moisture content provided the highest oil yields for both extraction methods. Mechanical extraction using the screw press can produce oil from correctly prepared product at a low cost, however overall this method is ineffective with relatively low oil yields. Chemical extraction was found to be a very effective method for oil extraction for its consistence performance and high oil yield, but cost of production was relatively higher due to the high cost of solvent. However, a solvent recycle system can be implemented to reduce the production cost of Beauty Leaf biodiesel. The findings of this study are expected to serve as the basis from which industrial scale biodiesel production from Beauty Leaf can be made.

The product of text and “Other” statements : discourse analysis and the critical use of Foucault

Much has been written on Michel Foucault’s reluctance to clearly delineate a research method, particularly with respect to genealogy (Harwood 2000; Meadmore, Hatcher, & McWilliam 2000; Tamboukou 1999). Foucault (1994, p. 288) himself disliked prescription stating, “I take care not to dictate how things should be” and wrote provocatively to disrupt equilibrium and certainty, so that “all those who speak for others or to others” no longer know what to do. It is doubtful, however, that Foucault ever intended for researchers to be stricken by that malaise to the point of being unwilling to make an intellectual commitment to methodological possibilities. Taking criticism of “Foucauldian” discourse analysis as a convenient point of departure to discuss the objectives of poststructural analyses of language, this paper develops what might be called a discursive analytic; a methodological plan to approach the analysis of discourses through the location of statements that function with constitutive effects.

Comparing child product safety concerns with injury incidents : does the evidence support the response?

Background Efficient effective child product safety (PS) responses require data on hazards, injury severity and injury probability. PS responses in Australia largely rely on reports from manufacturers/retailers, other jurisdictions/regulators, or consumers. The extent to which reactive responses reflect actual child injury priorities is unknown. Aims/Objectives/Purpose This research compared PS issues for children identified using data compiled from PS regulatory data and data compiled from health data sources in Queensland, Australia. Methods PS regulatory documents describing issues affecting children in Queensland in 2008–2009 were compiled and analysed to identify frequent products and hazards. Three health data sources (ED, injury surveillance and hospital data) were analysed to identify frequent products and hazards. Results/Outcomes Projectile toys/squeeze toys were the priority products for PS regulators with these toys having the potential to release small parts presenting choking hazards. However, across all health datasets, falls were the most common mechanism of injury, and several of the products identified were not subject to a PS system response. While some incidents may not require a response, a manual review of injury description text identified child poisonings and burns as common mechanisms of injuries in the health data where there was substantial documentation of product-involvement, yet only 10% of PS system responses focused on these two mechanisms combined. Significance/contribution to the field Regulatory data focused on products that fail compliance checks with ‘potential’ to cause harm, and health data identified actual harm, resulting in different prioritisation of products/mechanisms. Work is needed to better integrate health data into PS responses in Australia.

Integrating collaborative filtering and matching-based search for product recommendation

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and thus help them in making good decisions about which product to buy from the vast number of product choices available to them. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based recommender system approaches. These approaches are not suitable for recommending luxurious and infrequently purchased products as they rely on a large amount of ratings data that is not usually available for such products. This research aims to explore novel approaches for recommending infrequently purchased products by exploiting user generated content such as user reviews and product click streams data. From reviews on products given by the previous users, association rules between product attributes are extracted using an association rule mining technique. Furthermore, from product click streams data, user profiles are generated using the proposed user profiling approach. Two recommendation approaches are proposed based on the knowledge extracted from these resources. The first approach is developed by formulating a new query from the initial query given by the target user, by expanding the query with the suitable association rules. In the second approach, a collaborative-filtering recommender system and search-based approaches are integrated within a hybrid system. In this hybrid system, user profiles are used to find the target user’s neighbour and the subsequent products viewed by them are then used to search for other relevant products. Experiments have been conducted on a real world dataset collected from one of the online car sale companies in Australia to evaluate the effectiveness of the proposed recommendation approaches. The experiment results show that user profiles generated from user click stream data and association rules generated from user reviews can improve recommendation accuracy. In addition, the experiment results also prove that the proposed query expansion and the hybrid collaborative filtering and search-based approaches perform better than the baseline approaches. Integrating the collaborative-filtering and search-based approaches has been challenging as this strategy has not been widely explored so far especially for recommending infrequently purchased products. Therefore, this research will provide a theoretical contribution to the recommender system field as a new technique of combining collaborative-filtering and search-based approaches will be developed. This research also contributes to a development of a new query expansion technique for infrequently purchased products recommendation. This research will also provide a practical contribution to the development of a prototype system for recommending cars.

Developing a method for assessing product inclusivity

In order to develop more inclusive products and services, designers need a means of assessing the inclusivity of existing products and new concepts. Following previous research on the development of scales for inclusive design at University of Cambridge, Engineering Design Centre (EDC) [1], this paper presents the latest version of the exclusion audit method. For a specific product interaction, this estimates the proportion of the Great British population who would be excluded from using a product or service, due to the demands the product places on key user capabilities. A critical part of the method involves rating of the level of demand placed by a task on a range of key user capabilities, so the procedure to perform this assessment was operationalised and then its reliability was tested with 31 participants. There was no evidence that participants rated the same demands consistently. The qualitative results from the experiment suggest that the consistency of participants’ demand level ratings could be significantly improved if the audit materials and their instructions better guided the participant through the judgement process.

Towards a capabilities database to inform inclusive design : Experimental investigation of effective survey-based predictors of human-product interaction

A key issue in the field of inclusive design is the ability to provide designers with an understanding of people's range of capabilities. Since it is not feasible to assess product interactions with a large sample, this paper assesses a range of proxy measures of design-relevant capabilities. It describes a study that was conducted to identify which measures provide the best prediction of people's abilities to use a range of products. A detailed investigation with 100 respondents aged 50-80 years was undertaken to examine how they manage typical household products. Predictor variables included self-report and performance measures across a variety of capabilities (vision, hearing, dexterity and cognitive function), component activities used in product interactions (e.g. using a remote control, touch screen) and psychological characteristics (e.g. self-efficacy, confidence with using electronic devices). Results showed, as expected, a higher prevalence of visual, hearing, dexterity, cognitive and product interaction difficulties in the 65-80 age group. Regression analyses showed that, in addition to age, performance measures of vision (acuity, contrast sensitivity) and hearing (hearing threshold) and self-report and performance measures of component activities are strong predictors of successful product interactions. These findings will guide the choice of measures to be used in a subsequent national survey of design-relevant capabilities, which will lead to the creation of a capability database. This will be converted into a tool for designers to understand the implications of their design decisions, so that they can design products in a more inclusive way.

Calculation of dose kernels for radiotherapy applications using the GEANT 4 Monte Carlo toolkit

Dose kernels may be used to calculate dose distributions in radiotherapy (as described by Ahnesjo et al., 1999). Their calculation requires use of Monte Carlo methods, usually by forcing interactions to occur at a point. The Geant4 Monte Carlo toolkit provides a capability to force interactions to occur in a particular volume. We have modified this capability and created a Geant4 application to calculate dose kernels in cartesian, cylindrical, and spherical scoring systems. The simulation considers monoenergetic photons incident at the origin of a 3 m x 3 x 9 3 m water volume. Photons interact via compton, photo-electric, pair production, and rayleigh scattering. By default, Geant4 models photon interactions by sampling a physical interaction length (PIL) for each process. The process returning the smallest PIL is then considered to occur. In order to force the interaction to occur within a given length, L_FIL, we scale each PIL according to the formula: PIL_forced = L_FIL 9 (1 - exp(-PIL/PILo)) where PILo is a constant. This ensures that the process occurs within L_FIL, whilst correctly modelling the relative probability of each process. Dose kernels were produced for an incident photon energy of 0.1, 1.0, and 10.0 MeV. In order to benchmark the code, dose kernels were also calculated using the EGSnrc Edknrc user code. Identical scoring systems were used; namely, the collapsed cone approach of the Edknrc code. Relative dose difference images were then produced. Preliminary results demonstrate the ability of the Geant4 application to reproduce the shape of the dose kernels; median relative dose differences of 12.6, 5.75, and 12.6 % were found for an incident photon energy of 0.1, 1.0, and 10.0 MeV respectively.

Connected learning in the library as a product of hacking, making, social diversity and messiness

Learning is most effective when intrinsically motivated through personal interest, and situated in a supportive socio-cultural context. This paper reports on findings from a study that explored implications for design of interactive learning environments through 18 months of ethnographic observations of people’s interactions at “Hack The Evening” (HTE). HTE is a meetup group initiated at the State Library of Queensland in Brisbane, Australia, and dedicated to provide visitors with opportunities for connected learning in relation to hacking, making and do-it-yourself technology. The results provide insights into factors that contributed to HTE as a social, interactive and participatory environment for learning – knowledge is created and co-created through uncoordinated interactions among participants that come from a diversity of backgrounds, skills and areas of expertise. The insights also reveal challenges and barriers that the HTE group faced in regards to connected learning. Four dimensions of design opportunities are presented to overcome those challenges and barriers towards improving connected learning in library buildings and other free-choice learning environments that seek to embody a more interactive and participatory culture among their users. The insights are relevant for librarians as well as designers, managers and decision makers of other interactive and free-choice learning environments.

International tax and the Australian wine industry: Is your client’s product destined to sail the seven seas?

The export market for Australian wine continues to grow at a rapid rate, with imported wines also playing a role in market share in sales in Australia. It is estimated that over 60 per cent of all Australian wine is exported, while 12 per cent of wine consumed in Australia has overseas origins. In addition to understanding the size and direction (import or export) of wines, the foreign locales also play an important role in any tax considerations. While the export market for Australian produced alcohol continues to grow, it is into the Asian market that the most significant inroads are occurring. Sales into China of bottled wine over $7.50 per litre recently overtook the volume sold our traditional partners of the United States and Canada. It is becoming easier for even small to medium sized businesses to export their services or products overseas. However, it is vital for those businesses to understand the tax rules applying to any international transactions. Specifically, one of the first tax regimes that importers and exporters need to understand once they decide to establish a presence overseas is transfer pricing. These are the rules that govern the cross-border prices of goods, services and other transactions entered into between related parties. This paper is Part 2 of the seminar presented on transfer pricing and international tax issues which are particularly relevant to the wine industry. The predominant focus of Part 2 is to discuss four key areas likely to affect international expansion. First, the use of the available transfer pricing methodologies for international related party transactions is discussed. Second, the affects that double tax agreements will have on taking a business offshore are considered. Third, the risks associated with aggressive tax planning through tax information exchange agreements is reviewed. Finally, the paper predicts future ‘trip-wires’ and areas to ‘watch out for’ for practitioners dealing with clients operating in the international arena.

Sampling stored-product insect pests : a comparison of four statistical sampling models for probability of pest detection

Background: Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult owing to species biology and behavioural characteristics. The design of robust sampling programmes should be based on an underlying statistical distribution that is sufficiently flexible to capture variations in the spatial distribution of the target species. Results: Comparisons are made of the accuracy of four probability-of-detection sampling models - the negative binomial model,1 the Poisson model,1 the double logarithmic model2 and the compound model3 - for detection of insects over a broad range of insect densities. Although the double log and negative binomial models performed well under specific conditions, it is shown that, of the four models examined, the compound model performed the best over a broad range of insect spatial distributions and densities. In particular, this model predicted well the number of samples required when insect density was high and clumped within experimental storages. Conclusions: This paper reinforces the need for effective sampling programs designed to detect insects over a broad range of spatial distributions. The compound model is robust over a broad range of insect densities and leads to substantial improvement in detection probabilities within highly variable systems such as grain storage.

Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.