Towards authorisation models for secure information sharing : a survey and research agenda

This article presents a survey of authorisation models and considers their ‘fitness-for-purpose’ in facilitating information sharing. Network-supported information sharing is an important technical capability that underpins collaboration in support of dynamic and unpredictable activities such as emergency response, national security, infrastructure protection, supply chain integration and emerging business models based on the concept of a ‘virtual organisation’. The article argues that present authorisation models are inflexible and poorly scalable in such dynamic environments due to their assumption that the future needs of the system can be predicted, which in turn justifies the use of persistent authorisation policies. The article outlines the motivation and requirement for a new flexible authorisation model that addresses the needs of information sharing. It proposes that a flexible and scalable authorisation model must allow an explicit specification of the objectives of the system and access decisions must be made based on a late trade-off analysis between these explicit objectives. A research agenda for the proposed Objective-based Access Control concept is presented.

Brain-derived neurotrophic factor (BDNF) gene : no major impact on antidepressant treatment response

The brain-derived neurotrophic factor (BDNF) has been suggested to play a pivotal role in the aetiology of affective disorders. In order to further clarify the impact of BDNF gene variation on major depression as well as antidepressant treatment response, association of three BDNF polymorphisms [rs7103411, Val66Met (rs6265) and rs7124442] with major depression and antidepressant treatment response was investigated in an overall sample of 268 German patients with major depression and 424 healthy controls. False discovery rate (FDR) was applied to control for multiple testing. Additionally, ten markers in BDNF were tested for association with citalopram outcome in the STAR*D sample. While BDNF was not associated with major depression as a categorical diagnosis, the BDNF rs7124442 TT genotype was significantly related to worse treatment outcome over 6 wk in major depression (p=0.01) particularly in anxious depression (p=0.003) in the German sample. However, BDNF rs7103411 and rs6265 similarly predicted worse treatment response over 6 wk in clinical subtypes of depression such as melancholic depression only (rs7103411: TTmodels when the remaining SNPs were considered as covariates. The STAR*D analyses did not yield significant results at any of the ten BDNF markers. Our results do not support an association between genetic variation in BDNF and antidepressant treatment response or remission. Post-hoc analyses provide some preliminary support for a potential minor role of genetic variation in BDNF and antidepressant treatment outcome in the context of melancholic depression.

Interrogating statistical models of music perception

We have developed a new experimental method for interrogating statistical theories of music perception by implementing these theories as generative music algorithms. We call this method Generation in Context. This method differs from most experimental techniques in music perception in that it incorporates aesthetic judgments. Generation In Context is designed to measure percepts for which the musical context is suspected to play an important role. In particular the method is suitable for the study of perceptual parameters which are temporally dynamic. We outline a use of this approach to investigate David Temperley’s (2007) probabilistic melody model, and provide some provisional insights as to what is revealed about the model. We suggest that Temperley’s model could be improved by dynamically modulating the probability distributions according to the changing musical context.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

The expression of ADAM-9 and -10 in prostate cancer and their regulation by dihydrotestosterone, insulin-like growth Factor-1 and epidermal growth factor

Prostrate Cancer(PCa)is the most common cause of cancer death amongst Western males. PCa occurs in two distinct stages. In its early stage, growth and development is dependent primarily on male sex hormones (androgens) such as testosterone, although other growth factors have roles maintaining PCa cell survival in this stage. In the later stage of PCa development, growth and.maintenance is independent of androgen stimulation and growth factors including Insulin-like Growth Factor -1 (IGf.:·l) and Epidermal Growth Factor (EGF) are thought to have more crucial roles in cell survival and PCa progression. PCa, in its late stages, is highly aggressive and metastatic, that is, tumorigenic cells migrate from the primary site of the body (prostate) and travel via the systemic and lymphatic circulation, residing and colonising in the bone, lymph node, lung, and in more rare cases, the brain. Metastasis involves both cell migration and tissue degradation activities. The degradation of the extracellular matrix (ECM), the tissue surrounding the organ, is mediated in part by members of a family of 26 proteins called the Matrix Metalloproteases (MMPs), whilst ceil adhesion molecules, of which proteins known as Integrins are included, mediate ce11 migration. A family of proteins known as the ADAMs (A Disintegrin . And Metalloprotease domain) were a recently characterised family at the commencement of this study and now comprise 34 members. Because of their dual nature, possessing an active metaiioprotease domain, homologous to that of the MMPs, and an integrin-binding domain capable of regulating cell-cell and cell-ECM contacts, it was thought likely that members of the ADAMs family may have implications for the progression of aggressive cancers such as those ofthe prostate. This study focussed on two particular ADAMs -9 and -10. ADAM-9 has an active metalloprotease domain, which has been shown to degrade constituents of the ECM, including fibronectin, in vitro. It also has an integrin-binding capacity through association with key integrins involved in PCa progression, such as a6~1. ADAM-10 has no such integrin binding activities, but its bovine orthologue, MADM, is able to degrade coHagen type IV, a major component of basement membranes. It is likely human ADAM-10 has the same activity. It is also known to cleave Ll -a protein involved in cell anchorage activities - and collagen type XVII - which is a principal component of the hemidesmosomes of cellular tight junctions. The cleavage of these proteins enables the cell to be released from the surrounding environment and commence migratory activities, as required in metastasis. Previous studies in this laboratory showed the mRNA expression of the five ADAMs -9,- 10, -11, -15 and -17 in PCa cell lines, characteristic of androgen-dependent and androgen independent disease. These studies were furthered by the characterisation of AD AM-9, -10 and -17 mRNA regulation by Dihydrotestosterone (DHT) in the androgen-responsive cell line (LNCaP). ADAM-9 and -10 mRNA levels were elevated in response to DHT stimulation. Further to these observations, the expression of ADAM-9 and -10 was shown in primary prostate biopsies from patients with PCa. ADAM-1 0 was expressed in the cytoplasm and on the ceH membrane in epithelial and basal cells ofbenign prostate glands, but in high-grade PCa glands, ADAM-I 0 expression was localised to the nucleus and its expression levels appeared to be elevated when compared to low-grade PCa glands. These studies provided a strong background for the hypothesis that ADAM-9 and -10 have key roles in the development ofPCa and provided a basis for further studies.The aims of this study were to: 1) characterise the expression, localisation and levels, of ADAM-9 and -10 mRNA and protein in cell models representing characteristics of normal through androgen-dependent to androgen-independent PCa, as well as to expand the primary PCa biopsy data for ADAM-9 and ADAM-10 to encompass PCa bone metastases 2) establish an in vitro cell system, which could express elevated levels of ADAM-1 0 so that functional cell-based assays such as cell migration, invasion and attachment could be carried out, and 3) to extend the previous hormonal regulation data, to fully characterise the response of ADAM-9 and -10 mRNA and protein levels to DHT, IGF-1, DHT plus IGF-1 and EGF in the hormonal/growth factor responsive cell line LNCaP. For aim 1 (expression of ADAM-9 and -10 mRNA and protein), ADAM-9 and -10 mRNA were characterised by R T -PCR, while their protein products were analysed by Western blot. Both ADAM-9 and -10 mRNA and protein were expressed at readily detectable levels across progressively metastatic PCa cell lines model that represent characteristics of low-grade,. androgen-dependent (LNCaP and C4) to high-grade, androgen-independent (C4-2 and C4-2B) PCa. When the non-tumorigenic prostate cell line RWPE-1 was compared with the metastatic PCa cell line PC-3, differential expression patterns were seen by Western blot analysis. For ADAM-9, the active form was expressed at higher levels in RWPE-1, whilst subcellular fractionation showed that the active form of ADAM-9 was predominantly located in the cell nucleus. For ADAM-I 0, in both of the cell Jines, a nuclear specific isoform of the mature, catalytically active ADAM-I 0 was found. This isoforrn differed by -2 kDa in Mr (smaller) than the cytoplasmic specific isoform. Unprocessed ADAM-I 0 was readily detected in R WPE-1 cell lines but only occasionally detected in PC-3 cell lines. Immunocytochemistry using ADAM-9 and -10 specific antibodies confirmed nuclear, cytoplasmic and membrane expression of both ADAMs in these two cell lines. To examine the possibility of ADAM-9 and -10 being shed into the extracellular environment, membrane vesicles that are constitutively shed from the cell surface and contain membrane-associated proteins were collected from the media of the prostate cell lines RWPE-1, LNCaP and PC-3. ADAM-9 was readily detectable in RWPE- 1 and LNCaP cell membrane vesicles by Western blot analysis, but not in PC-3 cells, whilst the expression of ADAM-I 0 was detected in shed vesicles from each of these prostate cell lines. By Laser Capture Microdissection (LCM), secretory epithelial cells of primary prostate gland biopsies were isolated from benign and malignant glands. These secretory cells, by Western blot analysis, expressed similar Mr bands for ADAM-9 and -10 that were found in PCa cell lines in vitro, indicating that the nuclear specific isoforrn of ADAM-I 0 was present in PCa primary tumours and may represent the predominantly nuclear form of ADAM-I 0 expression, previously shown in high-grade PCa by immunohistochemistry (IHC). ADAM-9 and -10 were also examined by IHC in bone metastases taken from PCa patients at biopsy. Both ADAMs could be detected at levels similar to those shown for Prostate Specific Antigen (PSA) in these biopsies. Furthermore, both ADAM-9 and -10 were predominantly membrane- bound with occasional nuclear expression. For aim 2, to establish a cell system that over-expressed levels of ADAM-10, two fulllength ADAM-I 0 mammalian expression vectors were constructed; ADAM-I 0 was cloned into pcDNA3.1, which contains a CMV promoter, and into pMEP4, containing an inducible metallothionine promoter, whose activity is stimulated by the addition of CdC}z. The efficiency of these two constructs was tested by way of transient transfection in the PCa cell line PC-3, whilst the pcDNA3.1 construct was also tested in the RWPE-1 prostate cell line. Resultant Western blot analysis for all transient transfection assays showed that levels of ADAM-I 0 were not significantly elevated in any case, when compared to levels of the housekeeping gene ~-Tubulin, despite testing various levels of vector DNA, and, for pMEP4, the induction of the transfected cell system with different degrees of stimulation with CdCh to activate the metallothionine promoter post-transfection. Another study in this laboratory found similar results when the same full length ADAM-10 sequence was cloned into a Green Fluorescent Protein (GFP) expressing vector, as no fluorescence was observed by means of transient tran sfection in the same, and other, PCa cell lines. It was hypothesised that the Kozak sequence included in the full-length construct (human ADAMI 0 naturally occurring sequence) is not strong enough to initiate translation in an artificial system, in cells, which, as described in Aim 1, are already expressing readily detectable levels of endogenous ADAM-10. As a result, time constraints prevented any further progress with Aim 2 and functional studies including cell attachment, invasion and migration were unable to be explored. For Aim 3, to characterise the response of ADAM-9 and -10 mRNA and protein levels to DHT, IGF-1, DHT plus IGF-1 and EGF in LNCaP cells, the levels of ADAM-9 and -10 mRNA were not stimulated by DHT or IGF-I alone, despite our previous observations that initially characterised ADAM-9 and -10 mRNA as being responsive to DHT. However, IGF-1 in synergy with DHT did significantly elevate mRNA levels ofboth ADAMs. In the case of ADAM-9 and -10 protein, the same trends of stimulation as found at the rnRNA level were shown by Western blot analysis when ADAM-9 and -10 signal intensity was normalised with the housekeeping protein ~-Tubulin. For EGF treatment, both ADAM-9 and -10 mRNA and protein levels were significantly elevated, and further investigation vm found this to be the case for each of these ADAMs proteins in the nuclear fractions of LNCaP cells. These studies are the first to describe extensively, the expression and hormonal/growth factor regulation of two members of the ADAMs family ( -9 and -1 0) in PCa. These observations imply that the expression of ADAM-9 and -10 have varied roles in PCa whilst it develops from androgen-sensitive (early stage disease), through to an androgeninsensitive (late-stage), metastatic disease. Further studies are now required to investigate the several key areas of focus that this research has revealed, including: • Investigation of the cellular mechanisms that are involved in actively transporting the ADAMs to the cell's nuclear compartment and the ADAMs functional roles in the cell nucleus. • The construction of a full-length human ADAM-10 mammalian expression construct with the introduction of a new Kozak sequence, that elevates ADAM-I 0 expression in an in vitro cell system are required, so that functional assays such as cell invasion, migration and attachment may be carried out to fmd the functional consequences of ADAM expression on cellular behaviour. • The regulation studies also need to be extended by confirming the preliminary observations that the nuclear levels of ADAMs may also be elevated by hormones and growth factors such as DHT, IGF-1 and EGF, as well as the regulation of levels of plasma membrany vesicle associated ADAM expression. Given the data presented in this study, it is likely the ADAMs have differential roles throughout the development of PCa due to their differential cellular localisation and synergistic growth-factor regulation. These observations, along with those further studies outlined above, are necessary in identifying these specific components ofPCa metastasis to which the ADAMs may contribute.

Dynamic visual features for audio-visual speaker verification

The cascading appearance-based (CAB) feature extraction technique has established itself as the state-of-the-art in extracting dynamic visual speech features for speech recognition. In this paper, we will focus on investigating the effectiveness of this technique for the related speaker verification application. By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the visual speech recognition application also provide similar improvements for visual speaker recognition. A further study is conducted comparing synchronous HMM (SHMM) based fusion of CAB visual features and traditional perceptual linear predictive (PLP) acoustic features to show that higher complexity inherit in the SHMM approach does not appear to provide any improvement in the final audio-visual speaker verification system over simpler utterance level score fusion.

Limited information estimation and evaluation of DSGE models

We advance the proposition that dynamic stochastic general equilibrium (DSGE) models should not only be estimated and evaluated with full information methods. These require that the complete system of equations be specified properly. Some limited information analysis, which focuses upon specific equations, is therefore likely to be a useful complement to full system analysis. Two major problems occur when implementing limited information methods. These are the presence of forward-looking expectations in the system as well as unobservable non-stationary variables. We present methods for dealing with both of these difficulties, and illustrate the interaction between full and limited information methods using a well-known model.

Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification

This paper presents an extended study on the implementation of support vector machine(SVM) based speaker verification in systems that employ continuous progressive model adaptation using the weight-based factor analysis model. The weight-based factor analysis model compensates for session variations in unsupervised scenarios by incorporating trial confidence measures in the general statistics used in the inter-session variability modelling process. Employing weight-based factor analysis in Gaussian mixture models (GMM) was recently found to provide significant performance gains to unsupervised classification. Further improvements in performance were found through the integration of SVM-based classification in the system by means of GMM supervectors. This study focuses particularly on the way in which a client is represented in the SVM kernel space using single and multiple target supervectors. Experimental results indicate that training client SVMs using a single target supervector maximises performance while exhibiting a certain robustness to the inclusion of impostor training data in the model. Furthermore, the inclusion of low-scoring target trials in the adaptation process is investigated where they were found to significantly aid performance.

Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. ----- ----- Methods: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. ----- ----- Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. ----- ----- Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.

Luxury fashion: The role of innovation as a key contributing factor in the development of luxury fashion goods and sustainable fashion design

Luxury is a quality that is difficult to define as the historical concept of luxury appears to be both dynamic and culturally specific. The everyday definition explains a ‘luxury’ in relation to a necessity: a luxury (product or service) is defined as something that consumers want rather than need. However, the growth of global markets has seen a boom in what are now referred to as ‘luxury brands’. This branding of products as luxury has resulted in a change in the way consumers understand luxury goods and services. In their attempts to characterize a luxury brand, Fionda & Moore in their article “The anatomy of a Luxury Brand” summarize a range of critical conditions that are in addition to product branding “... including product and design attributes of quality, craftsmanship and innovative, creative and unique products” (Fionda & Moore, 2009). For the purposes of discussing fashion design however, quality and craftsmanship are inseparable while creativity and innovation exist under different conditions. The terms ‘creative’ and ‘innovative’ are often used inter-changeably and are connected with most descriptions of the design process, defining ‘design’ and ‘fashion’ in many cases. Christian Marxt and Fredrik Hacklin identify this condition in their paper “Design, product development, innovation: all the same in the end?”(Marxt & Hacklin, 2005) and suggest that design communities should be aware that the distinction between these terms, whilst once quite definitive, is becoming narrow to a point where they will mean the same thing. In relation to theory building in the discipline this could pose significant problems. Brett Richards (2003) identifies innovation as different from creativity in that innovation aims to transform and implement rather than simply explore and invent. Considering this distinction, in particular relation to luxury branding, may affect the way in which design can contribute to a change in the way luxury fashion goods might be perceived in a polarised fashion market, namely suggesting that ‘luxury’ is what consumers need rather than the ‘pile it high, sell it cheap’ fashion that the current market dynamic would indicate they want. This paper attempts to explore the role of innovation as a key contributing factor in luxury concepts, in particular the relationship between innovation and creativity, the conditions which enable innovation, the role of craftsmanship in innovation and design innovation in relation to luxury fashion products. An argument is presented that technological innovation can be demonstrated as a common factor in the development of luxury fashion product and that the connection between designer and maker will play an important role in the development of luxury fashion goods for a sustainable fashion industry.

Rural activity spaces and transport disadvantage : qualitative analysis of quantitative models integrating time and space

Current knowledge about the relationship between transport disadvantage and activity space size is limited to urban areas, and as a result, very little is known to date about this link in a rural context. In addition, although research has identified transport disadvantaged groups based on their size of activity spaces, these studies have, however, not empirically explained such differences and the result is often a poor identification of the problems facing disadvantaged groups. Research has shown that transport disadvantage varies over time. The static nature of analysis using the activity space concept in previous research studies has lacked the ability to identify transport disadvantage in time. Activity space is a dynamic concept; and therefore possesses a great potential in capturing temporal variations in behaviour and access opportunities. This research derives measures of the size and fullness of activity spaces for 157 individuals for weekdays, weekends, and for a week using weekly activity-travel diary data from three case study areas located in rural Northern Ireland. Four focus groups were also conducted in order to triangulate the quantitative findings and to explain the differences between different socio-spatial groups. The findings of this research show that despite having a smaller sized activity space, individuals were not disadvantaged because they were able to access their required activities locally. Car-ownership was found to be an important life line in rural areas. Temporal disaggregation of the data reveals that this is true only on weekends due to a lack of public transport services. In addition, despite activity spaces being at a similar size, the fullness of activity spaces of low-income individuals was found to be significantly lower compared to their high-income counterparts. Focus group data shows that financial constraint, poor connections both between public transport services and between transport routes and opportunities forced individuals to participate in activities located along the main transport corridors.

A dynamic approach to forecast rental growth

Numerous econometric models have been proposed for forecasting property market performance, but limited success has been achieved in finding a reliable and consistent model to predict property market movements over a five to ten year timeframe. This research focuses on office rental growth forecasts and overviews many of the office rent models that have evolved over the past 20 years. A model by DiPasquale and Wheaton is selected for testing in the Brisbane, Australia office market. The adaptation of this study did not provide explanatory variables that could assist in developing a reliable, predictive model of office rental growth. In light of this result, the paper suggests a system dynamics framework that includes an econometric model based on historical data as well as user input guidance for the primary variables. The rent forecast outputs would be assessed having regard to market expectations and probability profiling undertaken for use in simulation exercises. The paper concludes with ideas for ongoing research.