A review of efficiency measures for REITs and their specific application for Malaysian Islamic REITs

Purpose This paper aims to present a conceptual model on the efficiency of Islamic Real Estate Trusts (I-REITs) available in Malaysia. The key difference between the Islamic and their conventional investment vehicle part is mainly its own Shariah framework. Design/methodology/approach The paper reviews and synthesises the relevant literature on the performance analysis and efficiency measurements of Real Estate Investment Trusts. The paper then develops and proposes a conceptual model to measure the efficiency of Malaysian Islamic REITs. Findings The paper identifies and examines the appropriate methods and instruments to measure the efficiency in relation to the risk and profitability of Islamic REITs. The efficiency measure is important for the fund managers in order to maximise the shareholders’ return in an investment of property portfolio as well as proposing the best way to allocate resources efficiently. Research limitation/implications This is a preliminary review of current work that identifies the issues that will be addressed in future empirical research. The authors will be undertaking this future empirical research in measuring the efficiency of Malaysian REITs particularly the Islamic REITs using the non-parametric approach of Data Envelopment Analysis. Originality/value To date, there has been very limited research on the efficiency measurement of Islamic REITs. The current analysis of REIT has been focused on traditional non-Islamic funds. This paper will review and discuss the current literature on efficiency measurement to determine the most appropriate approaches and methodologies for future application in performance analysis of efficiency measure for Malaysian Islamic REITs.

Biological application of carbon nanotubes and graphene

Carbon nanotubes (CNTs) and graphene are two representative nanomaterials comprised of purely element carbon [1,2]. Graphene is the two-dimensional, hexagonal sp2-carbon ring networks with one atomic layer thickness, while CNTs can be envisaged as one or several graphene sheets concentrically rolled up into a one-dimensional cylindrical structure, so-called singlewalled (SW) or multi-walled (MW) CNTs, respectively. Figure 12.1 shows the schematic diagram of structures of graphene, SWCNT and MWCNT. Owing to their exceptional mechanical, electrical, optical and thermal properties, CNTs and graphene have been widely considered as a new type of materials with great potentials to revolutionalize many of the biological and medical fields [3–5].

Preparation of magnetic porous ceramsite and its application in biological aerated filters

Ceramsite plays a significant role as a biological aerated filter (BAF) in the treatment of wastewater. In this study, a mixture of goethite, sawdust and palygorskite clay was thermally treated to form magnetic porous ceramsite (MPC). An optimization experiment was conducted to measure the compressive strength of the MPC. X-ray diffraction (XRD), scanning electron microscopy (SEM), and polarizing microscopy (PM) characterized the pore structure of the MPC. The results show that a combination of goethite, sawdust and palygorskite clay with a mass ratio of 10:2:5 is suitable for the formation of MPC. The compressive strength of MPC conforms to the Chinese national industrial standard (CJ/T 299-2008) for wastewater treatment. The SEM and PM results also show that the uniform and interconnected pores in MPC were well suited for microbial growth. The MPC produced in this study can serve as a biomedium for advanced wastewater treatment.

The root locus method: application to linear stability analysis and design of cooperative car-following models

A global framework for linear stability analyses of traffic models, based on the dispersion relation root locus method, is presented and is applied taking the example of a broad class of car-following (CF) models. This approach is able to analyse all aspects of the dynamics: long waves and short wave behaviours, phase velocities and stability features. The methodology is applied to investigate the potential benefits of connected vehicles, i.e. V2V communication enabling a vehicle to send and receive information to and from surrounding vehicles. We choose to focus on the design of the coefficients of cooperation which weights the information from downstream vehicles. The coefficients tuning is performed and different ways of implementing an efficient cooperative strategy are discussed. Hence, this paper brings design methods in order to obtain robust stability of traffic models, with application on cooperative CF models

Study of robust H(infinity) filtering application in loosely coupled INS/GPS system

Since a celebrate linear minimum mean square (MMS) Kalman filter in integration GPS/INS system cannot guarantee the robustness performance, a H(infinity) filtering with respect to polytopic uncertainty is designed. The purpose of this paper is to give an illustration of this application and a contrast with traditional Kalman filter. A game theory H(infinity) filter is first reviewed; next we utilize linear matrix inequalities (LMI) approach to design the robust H(infinity) filter. For the special INS/GPS model, unstable model case is considered. We give an explanation for Kalman filter divergence under uncertain dynamic system and simultaneously investigate the relationship between H(infinity) filter and Kalman filter. A loosely coupled INS/GPS simulation system is given here to verify this application. Result shows that the robust H(infinity) filter has a better performance when system suffers uncertainty; also it is more robust compared to the conventional Kalman filter.

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

A CCG virtual system for big data application communication costs analysis

Network topology and routing are two important factors in determining the communication costs of big data applications at large scale. As for a given Cluster, Cloud, or Grid system, the network topology is fixed and static or dynamic routing protocols are preinstalled to direct the network traffic. Users cannot change them once the system is deployed. Hence, it is hard for application developers to identify the optimal network topology and routing algorithm for their applications with distinct communication patterns. In this study, we design a CCG virtual system (CCGVS), which first uses container-based virtualization to allow users to create a farm of lightweight virtual machines on a single host. Then, it uses software-defined networking (SDN) technique to control the network traffic among these virtual machines. Users can change the network topology and control the network traffic programmingly, thereby enabling application developers to evaluate their applications on the same system with different network topologies and routing algorithms. The preliminary experimental results through both synthetic big data programs and NPB benchmarks have shown that CCGVS can represent application performance variations caused by network topology and routing algorithm.

Electrochemical mechanism of eugenol at a Cu doped gold nanoparticles modified glassy carbon electrode and its analytical application in food samples

A simple one-step electrodeposition method was used to construct a glassy carbon electrode (GCE), which has been modified with Cu doped gold nanoparticles (GNPs), i.e. a Cu@AuNPs/GCE. This electrode was characterized with the use of scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. The eugenol was electrocatalytically oxidized at the Cu@AuNPs/GCE. At this electrode, in comparison with the behavior at the GCE alone, the corresponding oxidation peak current was enhanced and the shift of the oxidation potentials to lower values was observed. Electrochemical behavior of eugenol at the Cu@AuNPs/GCE was investigated with the use of the cyclic voltammetry (CV) technique, and additionally, in order to confirm the electrochemical reaction mechanism for o-methoxy phenols, CVs for catechol, guaiacol and vanillin were investigated consecutively. Based on this work, an electrochemical reaction mechanism for o-methoxy phenols was suggested, and in addition, the above Cu@AuNPs/GCE was successfully employed for the analysis of eugenol in food samples.

Grant application: QUT Learning and Teaching scheme 2012 Cracking the Code - A checklist to complement CRAs for first year Justice students.

Widening participation brings with it increasing diversity, increased variation in the level of academic preparedness (Clarke, 2011; Nelson, Clarke, & Kift 2010). Cultural capital coupled with negotiating the academic culture creates an environment based on many assumptions about academic writing and university culture. Variations in staff and student expectations relating to the teaching and learning experience is captured in a range of national and institutional data (AUSSE, CEQ, LEX). Nationally, AUSSE data (2009) indicates that communication, writing, speaking and analytic skills, staff expectations are quite a bit higher than students. The research team noted a recognisable shift in the changing cohort of students and their understanding and engagement with feedback and CRAs, as well as variations in teaching staff and student expectations. The current reality of tutor and student roles is that: - Students self select when/how they access lectures and tutorials. - Shorter tutorial times result in reduced opportunity to develop rapport with students. - CRAs are not always used consistently by staff (different marking styles and levels of feedback). - Marking is not always undertaken by the student’s tutor/lecturer. - Student support services might be recommended to students once a poor grade has been given. Students can perceive this as remedial and a further sense of failure. - CRA sheet has a mark /grade attached to it. Stigma attached to low mark. Hard to focus on the CRA feedback with a poor mark etched next to it. - Limited opportunities for sessionals to access professional development to assist with engaging students and feedback. - FYE resources exist, however academic time is a factor in exploring and embedding these resources. Feedback is another area with differing expectations and understandings. Sadler (2009) contends that students are not equipped to decode the statements properly. For students to be able to apply feedback, they need to understand the meaning of the feedback statement. They also need to identify, the particular aspects of their work that need attention. The proposed Checklist/guide would be one page and submitted with each assessment piece thereby providing an interface to engage students and tutors in managing first year understandings and expectations around CRAs, feedback, and academic practice.

The first application of wastewater-based drug epidemiology in five South Korean cities

Illicit drug consumption in five cities in South Korea was estimated by analyzing 17 drug residues in untreated wastewater samples collected during the Christmas and New Year period of 2012-13. Only methamphetamine, amphetamine, and codeine were detected at concentrations of tens of nanograms per liter or even lower concentrations in more than 90% of the samples. Other illicit drug residues (including cocaine, methadone, and benzoylecgonine) that have been detected frequently in wastewater from other countries were not found in this study. Methamphetamine was found to be the most widely used illicit drug in South Korea, and the estimated average consumption rate was 22 mg d−1 (1000 people)−1. This rate is, for example, 2-5 times lower than the estimated average consumption rates in Hong Kong and other parts of China and 4-80 times lower than the estimated average consumption rates in cities in Western countries. It should be noted that the wastewater samples analyzed in this study were collected during a holiday season, when daily consumption of illicit drugs is often higher than on an average day. The methamphetamine usage rates were calculated for different cities in South Korea, and the usage rates in smaller cities was higher (2-4 times) than the average.

Impacts of tillage and application methods on atrazine and alachlor losses from upland fields: Research paper

The effects of tillage practises and the methods of chemical application on atrazine and alachlor losses through run-off were evaluated for five treatments: conservation (untilled) and surface (US), disk and surface, plow and surface, disk and preplant-incorporated, and plow and preplant-incorporated treatments. A rainfall simulator was used to create 63.5 mm h^-1 of rainfall for 60 min and 127 mm h^-1 for 15 min. Rainfall simulation occurred 24-36 h after chemical application. There was no significant difference in the run-off volume among the treatments but the untilled treatment significantly reduced erosion loss. The untilled treatments had the highest herbicide concentration and the disk treatments were higher than the plow treatments. The surface treatments showed a higher concentration than the incorporated treatments. The concentration of herbicides in the water decreased with time. Among the experimental sites, the one with sandy loam soil produced the greatest losses, both in terms of the run-off volume and herbicide loss. The US treatments had the highest loss and the herbicide incorporation treatments had smaller losses through run-off as the residue cover was effective in preventing herbicide losses. Incorporation might be a favorable method of herbicide application to reduce the herbicide losses by run-off.

Application of power electronics in improving power quality and supply efficiency of AC traction networks

Major advances in power electronics during recent years have prompted considerable interest within the traction community. The capability of new technologies to reduce the AC railway networks' effect on power quality and improve their supply efficiency is expected to significantly decrease the cost of electric rail supply systems. Of particular interest are Static Frequency Converter (SFC), Rail Power Conditioner (RPC), High Voltage Direct Current (HVDC) and Energy Storage Systems (ESS) solutions. Substantial impacts on future feasibility of railway electrification are anticipated. Aurizon, Australia's largest heavy haul railway operator, has recently commissioned the world's first 50Hz/50Hz SFC installation and is currently investigating SFC, RPC, HVDC and ESS solutions. This paper presents a summary of current and emerging technologies with a particular focus on the potential techno-economic benefits.

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Constructing a traditional Chinese medicine data warehouse application

The explosive growth in the development of Traditional Chinese Medicine (TCM) has resulted in the continued increase in clinical and research data. The lack of standardised terminology, flaws in data quality planning and management of TCM informatics are preventing clinical decision-making, drug discovery and education. This paper argues that the introduction of data warehousing technologies to enhance the effectiveness and durability in TCM is paramount. To showcase the role of data warehousing in the improvement of TCM, this paper presents a practical model for data warehousing with detailed explanation, which is based on the structured electronic records, for TCM clinical researches and medical knowledge discovery.

Generalised growth models for aquatic species with an application to blacklip abalone (Haliotis rubra)

This paper presents a maximum likelihood method for estimating growth parameters for an aquatic species that incorporates growth covariates, and takes into consideration multiple tag-recapture data. Individual variability in asymptotic length, age-at-tagging, and measurement error are also considered in the model structure. Using distribution theory, the log-likelihood function is derived under a generalised framework for the von Bertalanffy and Gompertz growth models. Due to the generality of the derivation, covariate effects can be included for both models with seasonality and tagging effects investigated. Method robustness is established via comparison with the Fabens, improved Fabens, James and a non-linear mixed-effects growth models, with the maximum likelihood method performing the best. The method is illustrated further with an application to blacklip abalone (Haliotis rubra) for which a strong growth-retarding tagging effect that persisted for several months was detected