Fusion of worlds?: Consumers' self-narrative in a hybrid digital/physical world

As the Internet becomes deeply embedded in consumers' life and continuously accessed through mobile devices, the boundary between the digital and the physical becomes less defined. This thesis investigates how this blurring boundary impacts on consumers' construction of their self-narrative and found that consumers' narrative is paradoxically coherent and fragmented and depicts a heroic story of the self. Prior studies show consumers achieve their desired life story through meaningful consumption, however, the notion of fragmented lives are challenged. Further, extensive digital leisure consumption is often viewed in a less positive light. Nevertheless, consumers, who significantly consume digital leisure, do not disregard their physical world or favour one space over the other. Rather, they negotiate key aspects from their digital and physical lives and fluidly move between these two worlds, creating a hybrid narrative that saves their self and others.

Product-library-derived specification parameters in building information models augmentation

This paper conceptualizes a framework for bridging the BIM (building information modelling)-specifications divide through augmenting objects within BIM with specification parameters derived from a product library. We demonstrate how model information, enriched with data at various LODs (levels of development), can evolve simultaneously with design and construction using different representation of a window object embedded in a wall as lifecycle phase exemplars at different levels of granularity. The conceptual standpoint is informed by the need for exploring a methodological approach which extends beyond current limitations of current modelling platforms in enhancing the information content of BIM models. Therefore, this work demonstrates that BIM objects can be augmented with construction specification parameters leveraging product libraries.

Surgical fusion of early onset severe scoliosis increases survival in Rett syndrome: A cohort study

Aim Scoliosis is a common co-morbidity in Rett syndrome and spinal fusion may be recommended if severe. We investigated the impact of spinal fusion on survival and risk of severe lower respiratory tract infection in Rett syndrome. Method Data were ascertained from hospital medical records, the Australian Rett Syndrome Database, a longitudinal and population-based registry, and from the Australian Institute of Health and Welfare National Death Index database. Cox regression and generalized estimating equation models were used to estimate the effects of spinal surgery on survival and severe respiratory infection respectively in 140 females who developed severe scoliosis (Cobb angle ≥45°) before adulthood. Results After adjusting for mutation type and age of scoliosis onset, the rate of death was lower in the surgery group (hazard ratio [HR] 0.30, 95% confidence interval [CI] 0.12–0.74; p=0.009) compared to those without surgery. Rate of death was particularly reduced for those with early onset scoliosis (HR 0.17, 95% CI 0.06–0.52; p=0.002). There was some evidence to suggest that spinal fusion was associated with a reduction in risk of severe respiratory infection among those with early onset scoliosis (risk ratio 0.41, 95% CI 0.16–1.03; p=0.06). Interpretation With appropriate cautions, spinal fusion confers an advantage to life expectancy in Rett syndrome.

Generating product feature hierarchy from product reviews

User generated information such as product reviews have been booming due to the advent of web 2.0. In particular, rich information associated with reviewed products has been buried in such big data. In order to facilitate identifying useful information from product (e.g., cameras) reviews, opinion mining has been proposed and widely used in recent years. In detail, as the most critical step of opinion mining, feature extraction aims to extract significant product features from review texts. However, most existing approaches only find individual features rather than identifying the hierarchical relationships between the product features. In this paper, we propose an approach which finds both features and feature relationships, structured as a feature hierarchy which is referred to as feature taxonomy in the remainder of the paper. Specifically, by making use of frequent patterns and association rules, we construct the feature taxonomy to profile the product at multiple levels instead of single level, which provides more detailed information about the product. The experiment which has been conducted based upon some real world review datasets shows that our proposed method is capable of identifying product features and relations effectively.

A data fusion approach of multiple maintenance data sources for real-world reliability modelling

A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.

Molecular modeling of Bt Cry1Ac (DI–DII)–ASAL (Allium sativum lectin)–fusion protein and its interaction with aminopeptidase N (APN) receptor of Manduca sexta

Genetic engineering of Bacillus thuringiensis (Bt) Cry proteins has resulted in the synthesis of various novel toxin proteins with enhanced insecticidal activity and specificity towards different insect pests. In this study, a fusion protein consisting of the DI–DII domains of Cry1Ac and garlic lectin (ASAL) has been designed in silico by replacing the DIII domain of Cry1Ac with ASAL. The binding interface between the DI–DII domains of Cry1Ac and lectin has been identified using protein–protein docking studies. Free energy of binding calculations and interaction profiles between the Cry1Ac and lectin domains confirmed the stability of fusion protein. A total of 18 hydrogen bonds was observed in the DI–DII–lectin fusion protein compared to 11 hydrogen bonds in the Cry1Ac (DI–DII–DIII) protein. Molecular mechanics/Poisson–Boltzmann (generalized-Born) surface area [MM/PB (GB) SA] methods were used for predicting free energy of interactions of the fusion proteins. Protein–protein docking studies based on the number of hydrogen bonds, hydrophobic interactions, aromatic–aromatic, aromatic–sulphur, cation–pi interactions and binding energy of Cry1Ac/fusion proteins with the aminopeptidase N (APN) of Manduca sexta rationalised the higher binding affinity of the fusion protein with the APN receptor compared to that of the Cry1Ac–APN complex, as predicted by ZDOCK, Rosetta and ClusPro analysis. The molecular binding interface between the fusion protein and the APN receptor is well packed, analogously to that of the Cry1Ac–APN complex. These findings offer scope for the design and development of customized fusion molecules for improved pest management in crop plants.

Empowerment climate, supervisory support for creativity and work role performance in new product development settings: A team level investigation

This thesis is an examination of how organisational context variables affect the performance of new product development (NPD) teams. Specifically, the extent to how team empowerment climate and supervisory support for creativity impact NPD team performance. Moreover, this thesis is a step forward in the ongoing development of work role performance theory by examining Griffin et al.'s (2007) work role performance model in the context of NPD teams. This thesis addresses the lack of research exploring work role performance dimensions in NPD teams and the extent to which a team empowerment climate and supervisory support for creativity impact NPDs performance.

Time-driven life cycle cost estimation system for product family design

This research develops a design support system, which is able to estimate the life cycle cost of different product families at the early stage of product development. By implementing the system, a designer is able to develop various cost effective product families in a shorter lead-time and minimise the destructive impact of the product family on the environment.

Malaria rapid diagnostic test performance: Results of WHO product testing of malaria RDTs: Round 6 (2014-2015)

Overview This report, published in conjunction with a summary overview of results of rounds 1–6, is the sixth in a series of laboratory-based evaluations of rapid diagnostic tests (RDTs) for malaria. It provides a comparative measure of their performance in a standardized way to distinguish between well and poorly performing tests. It can be used by malaria control programmes and guide WHO procurement recommendations for these diagnostic tools. The evaluation reported here was a joint project of the WHO Global Malaria Programme, the Foundation for Innovative New Diagnostics (FIND) and the United States Centers for Disease Control and Prevention (CDC) within the WHO-FIND Malaria RDT Evaluation Programme. The project was financed by FIND through a grant from UNITAID.

Bio-inspired multifunctional metallic foams through the fusion of different biological solutions

Nature is a school for scientists and engineers. Inherent multiscale structures of biological materials exhibit multifunctional integration. In nature, the lotus, the water strider, and the flying bird evolved different and optimized biological solutions to survive. In this contribution, inspired by the optimized solutions from the lotus leaf with superhydrophobic self-cleaning, the water strider leg with durable and robust superhydrophobicity, and the lightweight bird bone with hollow structures, multifunctional metallic foams with multiscale structures are fabricated, demonstrating low adhesive superhydrophobic self-cleaning, striking loading capacity, and superior repellency towards different corrosive solutions. This approach provides an effective avenue to the development of water strider robots and other aquatic smart devices floating on water. Furthermore, the resultant multifunctional metallic foam can be used to construct an oil/water separation apparatus, exhibiting a high separation efficiency and long-term repeatability. The presented approach should provide a promising solution for the design and construction of other multifunctional metallic foams in a large scale for practical applications in the petro-chemical field. Optimized biological solutions continue to inspire and to provide design idea for the construction of multiscale structures with multifunctional integration. Inspired by the optimized biological solutions from the lotus leaf with superhydrophobic self-cleaning, the water strider leg with durable and robust superhydrophobicity, and the lightweight bird bone with hollow structures, multifunctional metallic foams with multiscale structures are fabricated, demonstrating low adhesive superhydrophobic self-cleaning, striking loading capacity, stable corrosion resistance, and oil/water separation.

Decision fusion for reliable flood mapping using remote sensing images

Flood extent mapping is a basic tool for flood damage assessment, which can be done by digital classification techniques using satellite imageries, including the data recorded by radar and optical sensors. However, converting the data into the information we need is not a straightforward task. One of the great challenges involved in the data interpretation is to separate the permanent water bodies and flooding regions, including both the fully inundated areas and the wet areas where trees and houses are partly covered with water. This paper adopts the decision fusion technique to combine the mapping results from radar data and the NDVI data derived from optical data. An improved capacity in terms of identifying the permanent or semi-permanent water bodies from flood inundated areas has been achieved. Computer software tools Multispec and Matlab were used.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.