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.

Individual, environmental, and meteorological predictors of daily personal ultraviolet radiation exposure measurements in a United States cohort study

Background Individual exposure to ultraviolet radiation (UVR) is challenging to measure, particularly for diseases with substantial latency periods between first exposure and diagnosis of outcome, such as cancer. To guide the choice of surrogates for long-term UVR exposure in epidemiologic studies, we assessed how well stable sun-related individual characteristics and environmental/meteorological factors predicted daily personal UVR exposure measurements. Methods We evaluated 123 United States Radiologic Technologists subjects who wore personal UVR dosimeters for 8 hours daily for up to 7 days (N = 837 days). Potential predictors of personal UVR derived from a self-administered questionnaire, and public databases that provided daily estimates of ambient UVR and weather conditions. Factors potentially related to personal UVR exposure were tested individually and in a model including all significant variables. Results The strongest predictors of daily personal UVR exposure in the full model were ambient UVR, latitude, daily rainfall, and skin reaction to prolonged sunlight (R2 = 0.30). In a model containing only environmental and meteorological variables, ambient UVR, latitude, and daily rainfall were the strongest predictors of daily personal UVR exposure (R2 = 0.25). Conclusions In the absence of feasible measures of individual longitudinal sun exposure history, stable personal characteristics, ambient UVR, and weather parameters may help estimate long-term personal UVR exposure.

Advanced Bioactive Inorganic Materials for Bone Regeneration and Drug Delivery

Bioceramics play an important role in repairing and regenerating bone defects. Annually, more than 500,000 bone graft procedures are performed in the United states and approximately 2.2 million are conducted worldwide. The estimated cost of these procedures approaches $2.5billion per year. Around 60% of the bone graft substitutes available on the market involve bioceramics. It is reported that bioceramics in the world market increase by 9% per year. For this reason, the research of bioceramics has been one of the most active areas during, the past several years. Considering the significant importance of bioceramics, our goal was to compile this book to review the latest research advances in the field of bioceramics. The text also summarizes our work during the past 10 years in an effort to share innovative concepts, design of bioceramisc, and methods for material synthesis and drug delivery. We anticipate that this text will provide some useful information and guidance in the bioceramics field for biomedical engineering researchers and material scientists. Information on novel mesoporous bioactive glasses and silicate-based ceramics for bone regeneration and drug delivery are presented. Mesoporous bioactive glasses have shown multifunctional characteristics of bone regeneration and drug delivery due to their special mesopore structures,whereas silicated-based bioceramics, as typical third-generation biomaterials,possess significant osteostimulation properties. Silica nanospheres with a core-shell structure and specific properties for controllable drug delivery have been carefully reviewed-a variety of advanced synthetic strategies have been developed to construct functional mesoporous silica nanoparticles with a core-shell structure, including hollow, magnetic, or luminescent, and other multifunctional core-shell mesoporous silica nanoparticles. In addition, multifunctional drug delivery systems based on these nanoparticles have been designed and optimized to deliver the drugs into the targeted organs or cells,with a controllable release fashioned by virtue of various internal and external triggers. The novel 3D-printing technique to prepare advanced bioceramic scaffolds for bone tissue engineering applications has been highlighted, including the preparation, mechanical strength, and biological properties of 3D-printed porous scaffolds of calcium phosphate cement and silicate bioceramics. Three-dimensional printing techniques offer improved large-pore structure and mechanical strength. In addition , biomimetic preparation and controllable crystal growth as well as biomineralization of bioceramics are summarized, showing the latest research progress in this area. Finally, inorganic and organic composite materials are reviewed for bone regeneration and gene delivery. Bioactive inorganic and organic composite materials offer unique biological, electrical, and mechanical properties for designing excellent bone regeneration or gene delivery systems. It is our sincere hope that this book will updated the reader as to the research progress of bioceramics and their applications in bone repair and regeneration. It will be the best reward to all the contributors of this book if their efforts herein in some way help reader in any part of their study, research, and career development.

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.

Theoretical study of two states reactivity of methane activation on iron atom and iron dimer

Density functional theory (DFT) calculations have been carried out to explore the catalytic activation of C–H bonds in methane by the iron atom, Fe, and the iron dimer, Fe2. For methane activation on an Fe atom, the calculations suggest that the activation of the first C–H bond is mediated via the triplet excited-state potential energy surface (PES), with initial excitation of Fe to the triplet state being necessary for the reaction to be energetically feasible. Compared with the breaking of the first C–H bond, the cleavage of the second C–H bond is predicted to involve a significantly higher barrier, which could explain experimental observations of the HFeCH3 complex rather than CH2FeH2 in the activation of methane by an Fe atom. For methane activation on an iron dimer, the cleavage of the first C–H bond is quite facile with a barrier only 11.2, 15.8 and 8.4 kcal/mol on the septet state energy surface at the B3LYP/6-311+G(2df,2dp), BPW91/6-311+G(2df,2dp) and M06/B3LYP level, respectively. Cleavage of the second C–H bond from HFe2CH3 involves a barrier calculated respectively as 18.0, 10.7 and 12.4 kcal/mol at the three levels. The results suggest that the elimination of hydrogen from the dihydrogen complex is a rate-determining step. Overall, our results indicate that the iron dimer Fe2 has a stronger catalytic effect on the activation of methane than the iron atom.

Adatom CCV Auger rates via the local density of states

In this letter the core-core-valence Auger transitions of an atomic impurity, both in bulk or adsorbed on a jellium-like surface, are computed within a DFT framework. The Auger rates calculated by the Fermi golden rule are compared with those determined by an approximate and simpler expression. This is based on the local density of states (LDOS) with a core hole present, in a region around the impurity nucleus. Different atoms, Na and Mg, solids, Al and Ag, and several impurity locations are considered. We obtain an excellent agreement between KL1V and KL23V rates worked out with the two approaches. The radius of the sphere in which we calculate the LDOS is the relevant parameter of the simpler approach. Its value only depends on the atomic species regardless of the location of the impurity and the type of substrate. (C) 2003 Elsevier B.V. All rights reserved.

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.

An approach for identifying the limit states of resilience of a water supply system

This paper presents an approach for identifying the limit states of resilience in a water supply system when influenced by different types of pressure (disturbing) forces. Understanding of systemic resilience facilitates identification of the trigger points for early managerial action to avoid further loss of ability to provide satisfactory service availability when the ability to supply water is under pressure. The approach proposed here is to illustrate the usefulness of a surrogate measure of resilience depicted in a three dimensional space encompassing independent pressure factors. That enables visualisation of the transition of the system-state (resilience) between high to low resilience regions and acts as an early warning trigger for decision-making. The necessity of a surrogate measure arises as a means of linking resilience to the identified pressures as resilience cannot be measured directly. The basis for identifying the resilience surrogate and exploring the interconnected relationships within the complete system, is derived from a meta-system model consisting of three nested sub-systems representing the water catchment and reservoir; treatment plant; and the distribution system and end-users. This approach can be used as a framework for assessing levels of resilience in different infrastructure systems by identifying a surrogate measure and its relationship to relevant pressures acting on the system.

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.

An activity and resource advisory system for manufacturing process chains selection at the early stage of product development

Designers need to consider both the functional and production process requirements at the early stage of product development. A variety of the research works found in the literature has been proposed to assist designers in selecting the most viable manufacturing process chain. However, they do not provide any assistance for designers to evaluate the processes according to the particular circumstances of their company. This paper describes a framework of an Activity and Resource Advisory System (ARAS) that generates advice about the required activities and the possible resources for various manufacturing process chains. The system provides more insight, more flexibility, and a more holistic and suitable approach for designers to evaluate and then select the most viable manufacturing process chain at the early stage of product development.