Determinants of listed property trust bond ratings : Australian evidence

Using artificial neural networks (ANN) and ordinal regression (OR) as alternative methods to predict LPT bond ratings, we examine the role that various financial and industry variables have on Listed Property Trust (LPT) bond ratings issued by Standard and Poor’s from 1999-2006. Our study shows that both OR and ANN provide robust alternatives to rating LPT bonds and that there are no significant differences in results between the two full models. OR results show that of the financial variables used in our models, debt coverage and financial leverage ratios have the most profound effect on LPT bond ratings. Further, ANN results show that 73.0% of LPT bond rating is attributable to financial variables and 23.0% to industry-based variables with office LPT sector accounting for 2.6%, retail LPT 10.9% and stapled management structure 13.5%.

The importance of school connectedness in adolescent risk taking and injury prevention

Background: Injury is the leading cause of mortality for young people in Australia (AIHW, 2008). Adolescent injury mortality is consistently associated with risk taking behaviour, including transport and interpersonal violence (AIHW, 2003), which often occurs in the context of alcohol and other substance use. A rapid increase in risk taking and injury through early to late adolescence highlights the need for effective school based interventions. Aim: The aim of the current research was to examine the relationship between school connectedness and adolescent risk and injury, in order to inform effective prevention approaches. School connectedness, or students’ feelings of belongingness to school, has been shown to be a critical protective factor in adolescence which can be targeted effectively through teacher interventions. Despite evidence linking low school connectedness with increased health risk behaviour, including substance use and violence, research has not yet addressed possible links between connectedness and a broader range of risk taking behaviours (e.g. transport risks) or injury. Method: This study involved background data collection to inform the development of an intervention. A total of 595 Year 9 students (aged 13-14 years) from 5 Southeast Queensland high schools completed questionnaires that included measures of school connectedness, risk taking behaviour, alcohol and other substance use, and injuries. Results: Increased school connectedness was found to be associated with fewer transport risk behaviours and with decreased alcohol and other substance use for both males and females. Similarly, increased school connectedness was associated with fewer passenger and motorcycle injuries for male participants. Both males and females with increased school connectedness reported fewer alcohol related injuries. Implications: These results indicate that school connectedness appears to have protective effects for early adolescence. These findings may also hold for older adolescents and indicate that it may be an important factor to target in school based risk and injury prevention programs. A school connectedness intervention is currently being designed, focusing on teacher professional development. The intervention will be implemented in conjunction with a curriculum based injury prevention program for Year 9 students and will be evaluated through a large scale cluster randomised trial involving 26 schools.

Building an ontology and process architecture for engineering asset management

Historically, asset management focused primarily on the reliability and maintainability of assets; organisations have since then accepted the notion that a much larger array of processes govern the life and use of an asset. With this, asset management’s new paradigm seeks a holistic, multi-disciplinary approach to the management of physical assets. A growing number of organisations now seek to develop integrated asset management frameworks and bodies of knowledge. This research seeks to complement existing outputs of the mentioned organisations through the development of an asset management ontology. Ontologies define a common vocabulary for both researchers and practitioners who need to share information in a chosen domain. A by-product of ontology development is the realisation of a process architecture, of which there is also no evidence in published literature. To develop the ontology and subsequent asset management process architecture, a standard knowledge-engineering methodology is followed. This involves text analysis, definition and classification of terms and visualisation through an appropriate tool (in this case, the Protégé application was used). The result of this research is the first attempt at developing an asset management ontology and process architecture.

A review on degradation models in reliability analysis

With increasingly complex engineering assets and tight economic requirements, asset reliability becomes more crucial in Engineering Asset Management (EAM). Improving the reliability of systems has always been a major aim of EAM. Reliability assessment using degradation data has become a significant approach to evaluate the reliability and safety of critical systems. Degradation data often provide more information than failure time data for assessing reliability and predicting the remnant life of systems. In general, degradation is the reduction in performance, reliability, and life span of assets. Many failure mechanisms can be traced to an underlying degradation process. Degradation phenomenon is a kind of stochastic process; therefore, it could be modelled in several approaches. Degradation modelling techniques have generated a great amount of research in reliability field. While degradation models play a significant role in reliability analysis, there are few review papers on that. This paper presents a review of the existing literature on commonly used degradation models in reliability analysis. The current research and developments in degradation models are reviewed and summarised in this paper. This study synthesises these models and classifies them in certain groups. Additionally, it attempts to identify the merits, limitations, and applications of each model. It provides potential applications of these degradation models in asset health and reliability prediction.

A review on reliability models with covariates

Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting Time-to-Failure (TTF) and the probability of failure in future time are essential. In traditional reliability models, the lifetime of assets is estimated using failure time data. However, in most real-life situations and industry applications, the lifetime of assets is influenced by different risk factors, which are called covariates. The fundamental notion in reliability theory is the failure time of a system and its covariates. These covariates change stochastically and may influence and/or indicate the failure time. Research shows that many statistical models have been developed to estimate the hazard of assets or individuals with covariates. An extensive amount of literature on hazard models with covariates (also termed covariate models), including theory and practical applications, has emerged. This paper is a state-of-the-art review of the existing literature on these covariate models in both the reliability and biomedical fields. One of the major purposes of this expository paper is to synthesise these models from both industrial reliability and biomedical fields and then contextually group them into non-parametric and semi-parametric models. Comments on their merits and limitations are also presented. Another main purpose of this paper is to comprehensively review and summarise the current research on the development of the covariate models so as to facilitate the application of more covariate modelling techniques into prognostics and asset health management.

Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation

To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.

Prognosis of bearing failure based on health state estimation

This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.

E-mail marketing in the context of cosmetics

This study investigates e-mail marketing using data from a survey of 839 Finnish customers of an international cosmetics brand. E-mail marketing involves the use of e-mail to send promotions and information to customers. In this context we address two research questions: (1) What e-mail advertising factors drive visits to a physical (i.e., bricks-and-mortar) company sales outlet? and, (2) Does e-mail advertising influence brand satisfaction? Our findings indicate that useful e-mails can influence customers to visit the store. Further, brand satisfaction is positively influenced if e-mails are interesting and also by the amount of e-mail received by customers.

EMI issues in high power and high level diode-clamped converters

Planar busbar is a good candidate to reduce interconnection inductance in high power inverters compared with cables. However, power switching components with fast switching combined with hard switched-converters produce high di/dt during turn off time and busbar stray inductance then becomes an important issue which creates overvoltage. It is necessary to keep the busbar stray inductance as low as possible to decrease overvoltage and Electromagnetic Interference (EMI) noise. In this paper, the effect of different transient current loops on busbar physical structure of the high-voltage high-level diode-clamped converters will be highlighted. Design considerations of proper planar busbar will also be presented to optimise the overall design of diode-clamped converters.

Does occupational stress really matter? A study of psychological climate and stress on job satisfaction in Thailand

This study explored the prediction of psychological climate and stresses on job satisfaction in non U.S. setting. A total of 450 surveys were sent to 11 organisations in Thailand and employees were asked to fill out the survey. The first hypothesis that positive psychological climate dimensions predicted lower level of stresses among Thai employees was partially accepted. Further regression analysis tested second hypothesis that positive psychological climate dimensions and low level of stresses predict job satisfaction among Thai employees. Contrary to expectation, only stress variables predicted job satisfaction. Thai culture influence was discussed.

Assessment of Speech Dialog Systems using Multi-Modal Cognitive Load Analysis and Driving Performance Metrics

In this paper, cognitive load analysis via acoustic- and CAN-Bus-based driver performance metrics is employed to assess two different commercial speech dialog systems (SDS) during in-vehicle use. Several metrics are proposed to measure increases in stress, distraction and cognitive load and we compare these measures with statistical analysis of the speech recognition component of each SDS. It is found that care must be taken when designing an SDS as it may increase cognitive load which can be observed through increased speech response delay (SRD), changes in speech production due to negative emotion towards the SDS, and decreased driving performance on lateral control tasks. From this study, guidelines are presented for designing systems which are to be used in vehicular environments.

Multi-vehicles interaction graph model for cooperative collision warning system

Cooperative collision warning system for road vehicles, enabled by recent advances in positioning systems and wireless communication technologies, can potentially reduce traffic accident significantly. To improve the system, we propose a graph model to represent interactions between multiple road vehicles in a specific region and at a specific time. Given a list of vehicles in vicinity, we can generate the interaction graph using several rules that consider vehicle's properties such as position, speed, heading, etc. Safety applications can use the model to improve emergency warning accuracy and optimize wireless channel usage. The model allows us to develop some congestion control strategies for an efficient multi-hop broadcast protocol.

Efficient Voltage/Current Spike Reduction by Active Gate Signaling

This paper presents an Active Gate Signaling scheme to reduce voltage/current spikes across insulated gate power switches in hard switching power electronic circuits. Voltage and/or current spikes may cause EMI noise. In addition, they increase voltage/current stress on the switch. Traditionally, a higher gate resistance is chosen to reduce voltage/current spikes. Since the switching loss will increase remarkably, an active gate voltage control scheme is developed to improve efficiency of hard switching circuits while the undesirable voltage and/or current spikes are minimized.

Comparative assessment of characteristics influencing the industrial property markets in Sydney and Hong Kong

Industrial property is commonly located in a designated ‘industrial’ precinct. An industrial property has a specific design and a number of services to support industrial activities including manufacture, distribution and transportation. Although it has a unique characteristic, certain industrial factor might operate differently in different countries. The aim of this paper is to provide a comparison between the Sydney and Hong Kong industrial property characteristics and to highlight their similarities and differences. This exploratory research used secondary data to provide background information of government policy and market conditions. Two case studies were use to illustrate similarities, trends, differences and to explore town planning, specific property characteristics including location, design and layout. Then, analyse whether these factors influence the performance and value of an industrial asset. The location of industrial properties varies between each country and depends heavily on infrastructure. It was noted that the town planning restrictions not only vary between markets and cities but also between property lots. The market conditions of both industrial markets were investigated and the supply and demand and rental levels in both cities were distinctly opposite.

QoS-Based Web Service Scheduling Accommodating Inter-Service Dependencies Using Minimal-Conflict Hill-Climbing Repair Genetic Algorithm

In the filed of semantic grid, QoS-based Web service scheduling for workflow optimization is an important problem.However, in semantic and service rich environment like semantic grid, the emergence of context constraints on Web services is very common making the scheduling consider not only quality properties of Web services, but also inter service dependencies which are formed due to the context constraints imposed on Web services. In this paper, we present a repair genetic algorithm, namely minimal-conflict hill-climbing repair genetic algorithm, to address scheduling optimization problems in workflow applications in the presence of domain constraints and inter service dependencies. Experimental results demonstrate the scalability and effectiveness of the genetic algorithm.