Expectations, obligations or promises? A conceptual review and revision of the beliefs comprising the psychological contract

The psychological contract is a frequently deployed construct to examine the dynamics of the employee-employer exchange relationship. While there is consensus that the contract comprises employee and employer beliefs regarding this relationship, the various belief types are not conceptually well-defined and understood. Over time, the contract has been conceptualised as comprising expectations, obligations, promises or some combination therein. While most contemporary researchers focus solely upon promises, the justifications for this position are unpersuasive. This paper theoretically describes the various belief types, identifies their interrelationships and proposes a reconceptualisation of the beliefs constituting the contract. Specifically, it is demonstrated that the extant promise-based belief framework provides too restrictive a theoretical base for a comprehensive understanding of individuals’ psychological contracts.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Community-based public health: an Online Decision Support Systems : for Logan city, Australia

Introduction Among the many requirements of establishing community health, a healthy urban environment stands out as significant one. A healthy urban environment constantly changes and improves community well-being and expands community resources. The promotion efforts for such an environment, therefore, must include the creation of structures and processes that actively work to dismantle existing community inequalities. In general, these processes are hard to manage; therefore, they require reliable planning and decision support systems. Current and previous practices justify that the use of decision support systems in planning for healthy communities have significant impacts on the communities. These impacts include but are not limited to: increasing collaboration between stakeholders and the general public; improving the accuracy and quality of the decision making process; enhancing healthcare services; and improving data and information availability for health decision makers and service planners. Considering the above stated reasons, this study investigates the challenges and opportunities of planning for healthy communities with the specific aim of examining the effectiveness of participatory planning and decision systems in supporting the planning for such communities. Methods This study introduces a recently developed methodology, which is based on an online participatory decision support system. This new decision support system contributes to solve environmental and community health problems, and to plan for healthy communities. The system also provides a powerful and effective platform for stakeholders and interested members of the community to establish an empowered society and a transparent and participatory decision making environment. Results The paper discusses the preliminary findings from the literature review of this decision support system in a case study of Logan City, Queensland. Conclusion The paper concludes with future research directions and applicability of this decision support system in health service planning elsewhere.

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

Evaluation of accessibility for knowledge-based cities

Purpose: The purpose of the paper is to develop a framework for evaluation of accessibility for knowledge based cities. ----- ----- Design/methodology/approach: This approach notifies common mistakes and problems in accessibility assessment for knowledge cities. ----- ----- Originality/value: Accessibility plays a key role in transport sustainability and recognizes the crucial links between transport and sustainable goals like air quality, environmental resource consumption & social equity. In knowledge cities, accessibility has significant effects on quality of life and social equity by improving the mobility of people and goods. Accessibility also influences patterns of growth and economic health by providing access to land. Accessibility is not only one of the components of knowledge cities but also affects other elements of knowledge cities directly or indirectly. ----- ----- Practical implications: The outcomes of the application will be helpful for developing particular methodologies for evaluating knowledge cities. On other words, this methodology attempts to develop an assessment procedure for examining accessibility of knowledge-based cities.

‘You’re a bad driver but I just made a mistake’ : attribution differences between the ‘victims’ and ‘perpetrators’ of scenario-based aggressive driving incidents

Driver aggression is an increasing concern for motorists, with some research suggesting that drivers who behave aggressively perceive their actions as justified by the poor driving of others. Thus attributions may play an important role in understanding driver aggression. A convenience sample of 193 drivers (aged 17-36) randomly assigned to two separate roles (‘perpetrators’ and ‘victims’) responded to eight scenarios of driver aggression. Drivers also completed the Aggression Questionnaire and Driving Anger Scale. Consistent with the actor-observer bias, ‘victims’ (or recipients) in this study were significantly more likely than ‘perpetrators’ (or instigators) to endorse inadequacies in the instigator’s driving skills as the cause of driver aggression. Instigators were significantly more likely attribute the depicted behaviours to external but temporary causes (lapses in judgement or errors) rather than stable causes. This suggests that instigators recognised drivers as responsible for driving aggressively but downplayed this somewhat in comparison to ‘victims’/recipients. Recipients and instigators agreed that the behaviours were examples of aggressive driving but instigators appeared to focus on the degree of intentionality of the driver in making their assessments while recipients appeared to focus on the safety implications. Contrary to expectations, instigators gave mean ratings of the emotional impact of driving aggression on recipients that were higher in all cases than the mean ratings given by the recipients. Drivers appear to perceive aggressive behaviours as modifiable, with the implication that interventions could appeal to drivers’ sense of self-efficacy to suggest strategies for overcoming plausible and modifiable attributions (e.g. lapses in judgement; errors) underpinning behaviours perceived as aggressive.

Marital loss, mental health and the role of perceived social support : findings from six waves of an Australian population based panel study

Objectives: To investigate the impact of transitions out of marriage (separation, widowhood) on the self reported mental health of men and women, and examine whether perceptions of social support play an intervening role. ---------- Methods: The analysis used six waves (2001–06) of an Australian population based panel study, with an analytical sample of 3017 men and 3225 women. Mental health was measured using the MHI-5 scale scored 0–100 (α=0.97), with a higher score indicating better mental health. Perceptions of social support were measured using a 10-item scale ranging from 10 to 70 (α=0.79), with a higher score indicating higher perceived social support. A linear mixed model for longitudinal data was used, with lags for marital status, mental health and social support. ---------- Results: After adjustment for social characteristics there was a decline in mental health for men who separated (−5.79 points) or widowed (−7.63 points), compared to men who remained married. Similar declines in mental health were found for women who separated (−6.65 points) or became widowed (−9.28 points). The inclusion of perceived social support in the models suggested a small mediation effect of social support for mental health with marital loss. Interactions between perceived social support and marital transitions showed a strong moderating effect for men who became widowed. No significant interactions were found for women. ---------- Conclusion: Marital loss significantly decreased mental health. Increasing, or maintaining, high levels of social support has the potential to improve widowed men's mental health immediately after the death of their spouse.

Measurement of affect during simulation-based training

Training designed to support and strengthen higher-order mental abilities now often involves immersion in Virtual Reality where dangerous real world scenarios can be safely replicated. However despite the growing popularity of advanced training simulations, methods for evaluating their use rely heavily on subjective measures or analysis of final outcomes. Without dynamic, objective performance measures the outcome of training in terms of impact on cognitive skills and ability to transfer newly acquired skills to the real world is unknown. The relationship between affective intensity and cognitive learning provides a potential new approach to ensure the processing of cognitions which occur prior to final outcomes, such as problem-solving and decision-making, are adequately evaluated. This paper describes the technical aspects of pilot work recently undertaken to develop a new measurement tool designed to objectively track individual affect levels during simulation-based training.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Project-based IHRM : the emerging approach for the post-GFC world?

This paper reviews some past emphases in IHRM, and recommends that IHR teachers and practitioners consider using project management methodologies to tighten the focus of our diverse activities.

Numerical evaluation of pollutant dispersion at a toll plaza based on system dynamics and Computational Fluid Dynamics models

The health of tollbooth workers is seriously threatened by long-term exposure to polluted air from vehicle exhausts. Using traffic data collected at a toll plaza, vehicle movements were simulated by a system dynamics model with different traffic volumes and toll collection procedures. This allowed the average travel time of vehicles to be calculated. A three-dimension Computational Fluid Dynamics (CFD) model was used with a k–ε turbulence model to simulate pollutant dispersion at the toll plaza for different traffic volumes and toll collection procedures. It was shown that pollutant concentration around tollbooths increases as traffic volume increases. Whether traffic volume is low or high (1500 vehicles/h or 2500 vehicles/h), pollutant concentration decreases if electronic toll collection (ETC) is adopted. In addition, pollutant concentration around tollbooths decreases as the proportion of ETC-equipped vehicles increases. However, if the proportion of ETC-equipped vehicles is very low and the traffic volume is not heavy, then pollutant concentration increases as the number of ETC lanes increases.

Patient education to prevent falls among older hospital inpatients : a randomized controlled trial

Background Falls are a common adverse event during hospitalization of older adults, and few interventions have been shown to prevent then. Methods This study was a 3-group randomized trial to evaluate the efficacy of 2 forms of multimedia patient education compared with usual care for the prevention of in-hospital falls. Older hospital patients (n = 1206) admitted to a mixture of acute (orthopedic, respiratory, and medical) and subacute (geriatric and neurorehabilitation) hospital wards at 2 Australian hospitals were recruited between January 2008 and April 2009. The interventions were a multimedia patient education program based on the health-belief model combined with trained health professional follow-up (complete program), multi-media patient education materials alone (materials only), and usual care (control). Falls data were collected by blinded research assistants by reviewing hospital incident reports, hand searching medical records, and conducting weekly patient interviews. Results Rates of falls per 1000 patient-days did not differ significantly between groups (control, 9.27; materials only, 8.61; and complete program, 7.63). However, there was a significant interaction between the intervention and presence of cognitive impairment. Falls were less frequent among cognitively intact patients in the complete program group (4.01 per 1000 patient-days) than among cognitively intact patients in the materials-only group (8.18 per 1000 patient-days) (adjusted hazard ratio, 0.51; 95% confidence interval, 0.28-0.93]) and control group (8.72 per 1000 patient-days) (adjusted hazard ratio, 0.43; 95% confidence interval, 0.24-0.78). Conclusion Multimedia patient education with trained health professional follow-up reduced falls among patients with intact cognitive function admitted to a range of hospital wards.

Unaided stereo vision based pose estimation

This paper presents the development of a low-cost sensor platform for use in ground-based visual pose estimation and scene mapping tasks. We seek to develop a technical solution using low-cost vision hardware that allows us to accurately estimate robot position for SLAM tasks. We present results from the application of a vision based pose estimation technique to simultaneously determine camera poses and scene structure. The results are generated from a dataset gathered traversing a local road at the St Lucia Campus of the University of Queensland. We show the accuracy of the pose estimation over a 1.6km trajectory in relation to GPS ground truth.