Investigating attitudes towards three South American destinations in an emerging long haul market using a model of consumer-based brand equity (CBBE)

Despite the importance of destination image in market competitiveness, and the popularity of the field within tourism literature, there remains a dearth of published research examining travellers’ perceptions of destinations in South America. This manuscript addresses this gap by testing a model of consumer-based brand equity (CBBE) associated with three South American countries; Chile, Brazil and Argentina. The introduction of direct air links and a free trade agreement in 2008 has led destination marketing organisations (DMOs) in these countries to increase promotional efforts in the Australian market. This study shows that the CBBE model is an appropriate tool to explore consumers’ attitudes in the long haul travel context. The findings provide DMOs of the three countries studied, with benchmarks against which to compare the impact of future marketing communications in Australia. The results provide increased transparency and accountability to stakeholders, such as South American tourism businesses and Australian travel intermediaries.

Moderation practices in a faculty of education : the four discourse model

Moderation of student assessment is a critical component of teaching and learning in contemporary universities. In Australia, moderation is mandated through university policies and through the new national university accreditation authority, Tertiary Education Quality and Standards Agency which began operations in late January 2012 (TEQSA, 2012). The TEQSA requirement to declare details of moderation and any other arrangements used to support consistency and reliability of assessment and grading across each subject in the course of study is a radical step intended to move toward heightened accountability and greater transparency in the tertiary sector as well as entrenching evidence-based practice in the management of Australian academic programs. In light of this reform, the purpose of this project was to investigate and analyse current moderation practices operating within a faculty of education at a large urban university in Queensland, Australia. This qualitative study involved interviews with the unit coordinators (n=21) and tutors (n=8) of core undergraduate education units and graduate diploma units within the faculty. Four distinct discourses of moderation that academics drew on to discuss their practices were identified in the study. These were: equity, justification, community building, and accountability. These discourses, together with recommendations for changes to moderation practices are discussed in this paper.

Generalised gamma bidding model

A generalised gamma bidding model is presented, which incorporates many previous models. The log likelihood equations are provided. Using a new method of testing, variants of the model are fitted to some real data for construction contract auctions to find the best fitting models for groupings of bidders. The results are examined for simplifying assumptions, including all those in the main literature. These indicate no one model to be best for all datasets. However, some models do appear to perform significantly better than others and it is suggested that future research would benefit from a closer examination of these.

Improving work and life balance : building a multilevel model of work-life balance focusing on group structure and relational job design

One in five Australian workers believes that work doesn’t fit well with their family and social commitments. Concurrently, organisations are recognising that to stay competitive they need policies and practices that support the multiple aspects of employees’ lives. Many employees work in group environments yet there is currently little group level work-life balance research. This paper proposes a new theoretical framework developed to understand the design of work groups to better facilitate work-life balance. This new framework focuses on task and relational job designs, group structures and processes and workplace culture.

Characterisation of a human skin equivalent model to study the effects of ultraviolet B radiation on keratinocytes

The incidences of skin cancers resulting from chronic ultraviolet radiation (UVR) exposure are on the incline both in Australia and globally. Hence, the cellular and molecular pathways associated with UVR-induced photocarcinogenesis urgently need to be elucidated, in order to develop more robust preventative and treatment strategies against skin cancers. In vitro investigations into the effects of UVR (in particular the highly-mutagenic UVB wavelength) have, to date, mainly involved the use of cell culture and animal models. However, these models possess biological disparities to native skin, which to some extent have limited their relevance to the in vivo situation. To address this, we characterised a 3-dimensional, tissue-engineered human skin equivalent (HSE) model (consisting of primary human keratinocytes cultured on a dermal-derived scaffold) as a representation of a more physiologically-relevant platform to study keratinocyte responses to UVB. Significantly, we demonstrate that this model retains several important epidermal properties of native skin. Moreover, UVB-irradiation of the HSE constructs was shown to induce key markers of photodamage in the HSE keratinocytes, including the formation of cyclobutane pyrimidine dimers, the activation of apoptotic pathways, the accumulation of p53 and the secretion of inflammatory cytokines. Importantly, we also demonstrate that the UVB-exposed HSE constructs retain the capacity for epidermal repair and regeneration following photodamage. Together, our results demonstrate the potential of this skin equivalent model as a tool to study various aspects of the acute responses of human keratinocytes to UVB radiation damage.

The impact of spatial scales and spatial smoothing on the outcome of Bayesian spatial model

Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such as occurrence of a disease), we study the geographical variation of residual disease risk using regular grid cells. The individual disease risk is modelled using a logistic model with the inclusion of spatially unstructured and/or spatially structured random effects. Three spatial smoothness priors for the spatially structured component are employed in modelling, namely an intrinsic Gaussian Markov random field, a second-order random walk on a lattice, and a Gaussian field with Matern correlation function. We investigate how changes in grid cell size affect model outcomes under different spatial structures and different smoothness priors for the spatial component. A realistic example (the Humberside data) is analyzed and a simulation study is described. Bayesian computation is carried out using an integrated nested Laplace approximation. The results suggest that the performance and predictive capacity of the spatial models improve as the grid cell size decreases for certain spatial structures. It also appears that different spatial smoothness priors should be applied for different patterns of point data.

Assessing land-use and transport integration via a spatial composite indexing model

Achieving sustainable urban development is identified as one ultimate goal of many contemporary planning endeavours and has become central to formulation of urban planning policies. Within this concept, land-use and transport integration is highlighted as one of the most important and attainable policy objectives. In many cities, integration is embraced as an integral part of local development plans, and a number of key integration principles are identified. However, the lack of available evaluation methods to measure extent of urban sustainability levels prevents successful implementation of these principles. This paper introduces a new indicator-based spatial composite indexing model developed to measure sustainability performance of urban settings by taking into account land-use and transport integration principles. Model indicators are chosen via a thorough selection process in line with key principles of land-use and transport integration. These indicators are grouped into categories and themes according to their topical relevance. These indicators are then aggregated to form a spatial composite index to portray an overview of the sustainability performance of the pilot study area used for model demonstration. The study results revealed that the model is a practical instrument for evaluating success of local integration policies and visualizing sustainability performance of built environments and useful in both identifying problematic areas as well as formulating policy interventions.

Perceptions of organ donors and willingness to donate organs upon death : a test of the prototype/willingness model

Understanding people's organ donation decisions may narrow the gap between organ supply and demand. In two studies, participants who had not recorded their posthumous organ donation decision (Study 1, N = 210; Study 2, N = 307) completed items assessing prototype/willingness model (PWM; attitude, subjective norm, donor prototype favorability and similarity, willingness) constructs. Attitude, subjective norm, and prototype similarity predicted willingness to donate. Prototype favorability and a Prototype Favorability × Similarity interaction predicted willingness (Study 2). These findings provide support for the PWM in altruistic health contexts, highlighting the importance of people's perceptions about organ donors in their donation decisions.

Developing a model of links between general and workplace belongingness and depressive symptoms

Given the high prevalence of depression in the community there is urgent need to understand the interpersonal predictors of this disorder. Data from large community samples indicates that a diminished sense of belonging appears to be the most salient and immediate antecedent of a rapid depressive response. Belongingness in the workplace is also very important and associated with depressive symptoms over and above associations attributable to general or community belongingness. Finally it appears that the personality factor of interpersonal sensitivity moderates the relationship between belongingness and depressive symptoms. Results have extensive future implications for the prevention and treatment of depression.

Modelling survival data to account for model uncertainty: a single model or model averaging?

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution

Locally weighted learning model predictive control for nonlinear and time varying dynamics

This paper proposes an online learning control system that uses the strategy of Model Predictive Control (MPC) in a model based locally weighted learning framework. The new approach, named Locally Weighted Learning Model Predictive Control (LWL-MPC), is proposed as a solution to learn to control robotic systems with nonlinear and time varying dynamics. This paper demonstrates the capability of LWL-MPC to perform online learning while controlling the joint trajectories of a low cost, three degree of freedom elastic joint robot. The learning performance is investigated in both an initial learning phase, and when the system dynamics change due to a heavy object added to the tool point. The experiment on the real elastic joint robot is presented and LWL-MPC is shown to successfully learn to control the system with and without the object. The results highlight the capability of the learning control system to accommodate the lack of mechanical consistency and linearity in a low cost robot arm.

A bioengineered 3D ovarian cancer model for the assessment of peptidase-mediated enhancement of spheroid growth and intraperitoneal spread

Cancer-associated proteases promote peritoneal dissemination and chemoresistance in malignant progression. In this study, kallikrein-related peptidases 4, 5, 6, and 7 (KLK4-7)-cotransfected OV-MZ-6 ovarian cancer cells were embedded in a bioengineered three-dimensional (3D) microenvironment that contains RGD motifs for integrin engagement to analyze their spheroid growth and survival after chemotreatment. KLK4-7-cotransfected cells formed larger spheroids and proliferated more than controls in 3D, particularly within RGD-functionalized matrices, which was reduced upon integrin inhibition. In contrast, KLK4-7-expressing cell monolayers proliferated less than controls, emphasizing the relevance of the 3D microenvironment and integrin engagement. In a spheroid-based animal model, KLK4-7-overexpression induced tumor growth after 4 weeks and intraperitoneal spread after 8 weeks. Upon paclitaxel administration, KLK4-7-expressing tumors declined in size by 91% (controls: 87%) and showed 90% less metastatic outgrowth (controls: 33%, P<0.001). KLK4-7-expressing spheroids showed 53% survival upon paclitaxel treatment (controls: 51%), accompanied by enhanced chemoresistance-related factors, and their survival was further reduced by combination treatment of paclitaxel with KLK4/5/7 (22%, P=0.007) or MAPK (6%, P=0.006) inhibition. The concomitant presence of KLK4-7 in ovarian cancer cells together with integrin activation drives spheroid formation and proliferation. Combinatorial approaches of paclitaxel and KLK/MAPK inhibition may be more efficient for late-stage disease than chemotherapeutics alone as these inhibitory regimens reduced cancer spheroid growth to a greater extent than paclitaxel alone.

Process model comprehension : the effects of cognitive abilities, learning style, and strategy

Process models are used to convey semantics about business operations that are to be supported by an information system. A wide variety of professionals is targeted to use such models, including people who have little modeling or domain expertise. We identify important user characteristics that influence the comprehension of process models. Through a free simulation experiment, we provide evidence that selected cognitive abilities, learning style, and learning strategy influence the development of process model comprehension. These insights draw attention to the importance of research that views process model comprehension as an emergent learning process rather than as an attribute of the models as objects. Based on our findings, we identify a set of organizational intervention strategies that can lead to more successful process modeling workshops.

Development of a computer based 1D-2D dynamic flood model. Case study of the Pagsangaan river basin in Leyte, the Philippines

Floods are among the most devastating events that affect primarily tropical, archipelagic countries such as the Philippines. With the current predictions of climate change set to include rising sea levels, intensification of typhoon strength and a general increase in the mean annual precipitation throughout the Philippines, it has become paramount to prepare for the future so that the increased risk of floods on the country does not translate into more economic and human loss. Field work and data gathering was done within the framework of an internship at the former German Technical Cooperation (GTZ) in cooperation with the Local Government Unit of Ormoc City, Leyte, The Philippines, in order to develop a dynamic computer based flood model for the basin of the Pagsangaan River. To this end, different geo-spatial analysis tools such as PCRaster and ArcGIS, hydrological analysis packages and basic engineering techniques were assessed and implemented. The aim was to develop a dynamic flood model and use the development process to determine the required data, availability and impact on the results as case study for flood early warning systems in the Philippines. The hope is that such projects can help to reduce flood risk by including the results of worst case scenario analyses and current climate change predictions into city planning for municipal development, monitoring strategies and early warning systems. The project was developed using a 1D-2D coupled model in SOBEK (Deltares Hydrological modelling software package) and was also used as a case study to analyze and understand the influence of different factors such as land use, schematization, time step size and tidal variation on the flood characteristics. Several sources of relevant satellite data were compared, such as Digital Elevation Models (DEMs) from ASTER and SRTM data, as well as satellite rainfall data from the GIOVANNI server (NASA) and field gauge data. Different methods were used in the attempt to partially calibrate and validate the model to finally simulate and study two Climate Change scenarios based on scenario A1B predictions. It was observed that large areas currently considered not prone to floods will become low flood risk (0.1-1 m water depth). Furthermore, larger sections of the floodplains upstream of the Lilo- an’s Bridge will become moderate flood risk areas (1 - 2 m water depth). The flood hazard maps created for the development of the present project will be presented to the LGU and the model will be used to create a larger set of possible flood prone areas related to rainfall intensity by GTZ’s Local Disaster Risk Management Department and to study possible improvements to the current early warning system and monitoring of the basin section belonging to Ormoc City; recommendations about further enhancement of the geo-hydro-meteorological data to improve the model’s accuracy mainly on areas of interest will also be presented at the LGU.