The Effects of Competition Policy on TFP Growth: Some Evidence from the Malaysian Electricity Supply Industry

The main objectives of this paper are to measure total factor productivity (TFP) growth in the electricity supply industry in Peninsular Malaysia from 1975 to 2005 and to assess the impact of private entry reforms upon TFP in this industry. Prior to 1995, a government-linked, vertically-integrated electricity utility, Tenaga Nasional Berhad (TNB), was essentially the sole operator. However, since 1995 privately-owned Independent Power Producers (IPPs) have also begun generating electricity, all of which is purchased by TNB under fixed Power Purchase Agreements (PPAs). The introduction of IPPs has reduced the need for TNB to find finance for new power plants. It has been argued that the participation of IPPs in the electricity generation industry should also facilitate improvements in industry productivity; however this proposition is yet to be tested. In this study we calculate TFP growth using Törnqvist index methods, finding that there is no direct evidence of productivity improvements attributable to the privatization. Furthermore, it is not clear that consumers have benefited from this, since the PPAs have generally been quite generous to the IPPs in terms of risk sharing and prices paid.

Augmented reality for nDimensional building information modelling contextualization, customization and curation

This paper presents an experimental method and apparatus of augmented reality (AR) for nDimensional (nD) building information modeling (BIM). BIM allows nD information to be visualized simultaneously by architects, engineers and constructors to gain a synchronized understanding viewing from different perspectives. However, BIM is conventionally being operated on a desktop-based computer which makes collaboration less flexible, and it also creates an isolation gap between the model and reality. This isolation gap does not severely affect experienced and skilled professionals, as they can bridge the isolation gap with their intuition developed over the years. Nevertheless, users who are lack of such experience will feel the isolation gap between the digital realm and practicable reality, which could be the hurdle in project participation and decision making. AR allows virtual content to be mixed with real environment for user experience. In the context of our study, AR is functional to present the nD information of BIM, at the same time retaining users’ connection with the reality. It is not just being utilized solely for presentation, but also to maximize the potential for communication, interaction and experience. This pilot study investigates effective technological approach of using AR as an effective collaboration technology combining with BIM through proposed key aspects of contextualization, customization and curation. Contextualization is significant to enable users to understand the AR content by making the presented information meaningful to the target audience, implemented thru the means of 2D annotations, animations and options comparison. This study compares both AR BIM with and without contextualization. Customization can generate unique virtual environment and content for different level of users tailored to their needs and preference to create intuitive interaction with AR BIM. Curation is crucial to provide users with a reliable experience, and to formulate a continually improving AR BIM thru log data and users’ feedback. All in all, this paper explores the major aspects of contextualization, customization and curation, to distinguish effective approach in the currently “free for all” AR BIM development. Finally, an implication is provided for future study in terms of balance in information sufficiency and complexity for AR BIM

Making the journey with me : a qualitative study of experiences of a bespoke mental health smoking cessation intervention for service users with serious mental illness

BACKGROUND: Smoking is one of the major modifiable risk factors contributing to early mortality for people with serious mental illness. However, only a minority of service users access smoking cessation interventions and there are concerns about the appropriateness of generic stop-smoking services for this group. The SCIMITAR (Smoking Cessation Intervention for Severe Mental Ill-Health Trial) feasibility study explored the effectiveness of a bespoke smoking cessation intervention delivered by mental health workers. This paper reports on the nested qualitative study within the trial. METHODS: Qualitative semi-structured interviews were conducted with 13 service users receiving the intervention and 3 of the MHSCPs (mental health smoking cessation practitioners) delivering the intervention. Topic guides explored the perceived acceptability of the intervention particularly in contrast to generic stop-smoking services, and perceptions of the implementation of the intervention in practice. Transcripts were analysed using the Constant Comparative Method. RESULTS: Generic services were reported to be inappropriate for this group, due to concerns over stigma and a lack of support from health professionals. The bespoke intervention was perceived positively, with both practitioners and service users emphasising the benefits of flexibility and personalisation in delivery. The mental health background of the practitioners was considered valuable not only due to their increased understanding of the service users' illness but also due to the more collaborative relationship style they employed. Challenges involved delays in liaising with general practitioners and patient struggles with organisation and motivation, however the MHSCP was considered to be well placed to address these problems. CONCLUSION: The bespoke smoking cessation intervention was acceptable to service users and the both service users and practitioners reported the value of a protected mental health worker role for delivering smoking cessation to this group. The results have wider implications for understanding how to achieve integrated and personalised care for this high-risk population and further underscore the need for sensitised smoking cessation support for people with serious mental illness. TRIAL REGISTRATION: Current Controlled Trials ISRCTN79497236 . Registered 3(rd) July 2009

Detection and tracking of ocean layers using an AUV with UKF based extremum seeking control in the Baltic Sea

Adaptive sampling and situational awareness are key features of modern autonomous underwater vehicles (AUVs) since data quality can be improved while operation time and cost can be reduced. An example for adaptive sampling in the marine environmental context is thermocline detection and tracking. The thermocline as horizontal ocean layer separates warm and cold water and is a key feature in many marine disciplines. For example, it influences the distribution and exchange of nutrients and is a habitat for many organisms. In this paper we use an unscented Kalman Filter (UKF) based extremum seeking control (ESC) to find and follow ocean layers such as the thermocline. Computer simulations and real-world tests show that the method is able to find and track non-trivial real-world ocean layers with sensors subject to hysteresis and delay effects

Multi-AUV sediment plume estimation using Bayesian optimization

Sediment plumes created by dredging or mining activities have an impact on the ecosystem in a much larger area than the mining or dredging area itself. It is therefore important and sometimes mandatory to monitor the developing plume to quantify the impact on the ecosystem including its spatial-temporal evolution. To this end, a Bayesian Optimization (BO)-based approach is proposed for plume monitoring using autonomous underwater vehicles (AUVs), which are used as a sensor network. Their paths are updated based on the BO, and additionally, a split-path method and the traveling salesman problem are utilized to account for the distances the AUVs have to travel and to increase the efficiency. To address the time variance of the plume, a sliding-window approach is used in the BO and the dynamics of the plume are modeled by a drift and decay rate of the suspended particulate matter (SPM) concentration measurements. Simulation results with SPM data from a simulation of a dredge experiment in the Pacific Ocean show that the method is able to monitor the plume over space and time with good overall estimation error

Diverse assessment methods in group work settings

The assessment scheme and mid-course feedback play a central role in the student's learning experience. However, within the student population there are many different perceptions of teaching and learning, and to accommodate these a diverse range of assessment and feedback activites are required. This issue is particularly important when group-orientated problem-based learning is employed, since much of the learning occurs within the groups and away from the direct supervision of the unit coordinators. We have explored a range of assessment styles in a suite of units of study in second year chemical engineering, centred around group-based project work. Group written project reports, interviews, confidential self and peer-assessments, individual laboratory reports, quizzes and a final examination have been used so far. Alignment of these assessments and teaching & learning activities with the learning outcomes guided our development of this framework, and this alignment has been verified by the students' results. The projects themselves are open-ended and present realistic engineering scenarios, including recommending the best type of artificial heart, the overall design of a desalination plant, and the design of a soap and cosmetics factory. A high level of student engagement and enthusiasm for the project work has been observed, arising mainly from the real-world nature of the projects, coupled with the stimuli provided by the range of assessment activities used

Multi-AUV sediment plume estimation using Bayesian optimization

Sediment plumes created by dredging or mining activities have an impact on the ecosystem in a much larger area than the mining or dredging area itself. It is therefore important and sometimes mandatory to monitor the developing plume to quantify the impact on the ecosystem including its spatial-temporal evolution. To this end, a Bayesian Optimization (BO)-based approach is proposed for plume monitoring using autonomous underwater vehicles (AUVs), which are used as a sensor network. Their paths are updated based on the BO, and additionally, a split-path method and the traveling salesman problem are utilized to account for the distances the AUVs have to travel and to increase the efficiency. To address the time variance of the plume, a sliding-window approach is used in the BO and the dynamics of the plume are modeled by a drift and decay rate of the suspended particulate matter (SPM) concentration measurements. Simulation results with SPM data from a simulation of a dredge experiment in the Pacific Ocean show that the method is able to monitor the plume over space and time with good overall estimation error