Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

A comparative study of the semi-elastic and fully-elastic MapReduce models

MapReduce is a computation model for processing large data sets in parallel on large clusters of machines, in a reliable, fault-tolerant manner. A MapReduce computation is broken down into a number of map tasks and reduce tasks, which are performed by so called mappers and reducers, respectively. The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics and it can obtain a better solution in a reasonable time. Furthermore, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement which puts a fixed number of mapper/reducer on each machine. The comparison results show that the computation using our mapper/reducer placement is much cheaper than the computation using the conventional placement while still satisfying the computation deadline.

A peaking and tailing approach to education and curriculum renewal for sustainable development

Contextual factors for sustainable development such as population growth, energy, and resource availability and consumption levels, food production yield, and growth in pollution, provide numerous complex and rapidly changing education and training requirements for a variety of professions including engineering. Furthermore, these requirements may not be clearly understood or expressed by designers, governments, professional bodies or the industry. Within this context, this paper focuses on one priority area for greening the economy through sustainable development—improving energy efficiency—and discusses the complexity of capacity building needs for professionals. The paper begins by acknowledging the historical evolution of sustainability considerations, and the complexity embedded in built environment solutions. The authors propose a dual-track approach to building capacity building, with a short-term focus on improvement (i.e., making peaking challenges a priority for postgraduate education), and a long-term focus on transformational innovation (i.e., making tailing challenges a priority for undergraduate education). A case study is provided, of Australian experiences over the last decade with regard to the topic area of energy efficiency. The authors conclude with reflections on implications for the approach.

Biolistic transformation of elite indica rice (Oryza sativa L.) cultivars through semi-solid and liquid medium selection systems

Following microprojectile mediated delivery of a plasmid construct (pAHC-25) encoding bar (bialophos resistance) gene into five-day-old scutellar calli derived from mature embryos, the effectiveness of selection procedure for bar-gene expressing tissue was compared for two indica rice cultivars (IR-64 and Karnal Local). While IR-64 transformants could be selected through the generally used semi-solid selection medium, the same procedure was not effective in the basmati cultivar Karnal Local. In the latter case, while lower concentrations (2–4 mg 1−1) of the selective agent phosphinothricin (PPT) yielded only escapes, higher concentrations (6–8 mg l−1) inhibited proliferation of transformed as well as untransformed sectors. For Karnal Local, a liquid medium based selection system was successfully utilized for recovering transformed sectors and, eventually, regenerants. The study demonstrates the generation of transformants of two elite indica cultivars using the environment-independent system of mature embryos from seeds.

Profiles of illicit drug use during annual key holiday and control periods in Australia: Wastewater analysis in an urban, a semi-rural and a vacation area

AIMS: To examine changes in illicit drug consumption between peak holiday season (23 December-3 January) in Australia and a control period two months later in a coastal urban area, an inland semi-rural area and an island populated predominantly by vacationers during holidays. DESIGN: Analysis of representative daily composite wastewater samples collected from the inlet of the major wastewater treatment plant in each area. SETTING: Three wastewater treatment plants. PARTICIPANTS: Wastewater treatment plants serviced approximately 350, 000 persons in the urban area, 120,000 in the semi-rural area and 1100-2400 on the island. MEASUREMENTS: Drug residues were analysed using liquid chromatography coupled to a tandem mass spectrometer. Per capita drug consumption was estimated. Changes in drug use were quantified using Hedges' g. FINDINGS: During the holidays, cannabis consumption in the semi-rural area declined (g = -2.8) as did methamphetamine (-0.8), whereas cocaine (+1.5) and ecstasy (+1.6) use increased. In the urban area, consumption of all drugs increased during holidays (cannabis +1.6, cocaine +1.2, ecstasy +0.8 and methamphetamine +0.3). In the vacation area, methamphetamine (+0.7), ecstasy (+0.7) and cocaine (+1.1) use increased, but cannabis (-0.5) use decreased during holiday periods. CONCLUSIONS: While the peak holiday season in Australia is perceived as a period of increased drug use, this is not uniform across all drugs and areas. Substantial declines in drug use in the semi-rural area contrasted with substantial increases in urban and vacation areas. Per capita drug consumption in the vacation area was equivalent to that in the urban area, implying that these locations merit particular attention for drug use monitoring and harm minimisation measures.

Editorial for Journal of Learning Design, 6(3) - "Special issue: Legal education and curriculum renewal"

Welcome to this special edition of the Journal of Learning Design which focuses on legal education and curriculum renewal in law. At the outset ,we would like to thank the editors of the Journal, Margaret Lloyd and Nan Bahr for agreeing to host this special edition. The special edition is timely as legal education in Australia is enjoying a lively period of renewal.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Robots move : bootstrapping the development of object representations using sensorimotor coordination

This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations are refined as multiple instances of sensory data are presented; however, it is uncertain whether two data instances are synonymous with the same object. This process can easily diverge from stability. The premise of the presented work is that a robot's motor information instigates successful generation of visual representations. An understanding of self-motion enables a prediction to be made before performing an action, resulting in a stronger belief of data association. The system is implemented as a data-driven partially observable semi-Markov decision process. Object representations are formed as the process's hidden states and are coordinated with motor commands through state transitions. Experiments show the prediction process is essential in enabling the unsupervised learning method to converge to a solution - improving precision and recall over using sensory data alone.

Condition-based maintenance optimisation without a predetermined strategy structure for a two-component series system

Most existing research on maintenance optimisation for multi-component systems only considers the lifetime distribution of the components. When the condition-based maintenance (CBM) strategy is adopted for multi-component systems, the strategy structure becomes complex due to the large number of component states and their combinations. Consequently, some predetermined maintenance strategy structures are often assumed before the maintenance optimisation of a multi-component system in a CBM context. Developing these predetermined strategy structure needs expert experience and the optimality of these strategies is often not proofed. This paper proposed a maintenance optimisation method that does not require any predetermined strategy structure for a two-component series system. The proposed method is developed based on the semi-Markov decision process (SMDP). A simulation study shows that the proposed method can identify the optimal maintenance strategy adaptively for different maintenance costs and parameters of degradation processes. The optimal maintenance strategy structure is also investigated in the simulation study, which provides reference for further research in maintenance optimisation of multi-component systems.

Error bounds for calculation of the Gittins indices

For a wide class of semi-Markov decision processes the optimal policies are expressible in terms of the Gittins indices, which have been found useful in sequential clinical trials and pharmaceutical research planning. In general, the indices can be approximated via calibration based on dynamic programming of finite horizon. This paper provides some results on the accuracy of such approximations, and, in particular, gives the error bounds for some well known processes (Bernoulli reward processes, normal reward processes and exponential target processes).

Cultivating teachers’ beliefs, knowledge and skills for leading change in schools

Australian policy initiatives and state curriculum reform efforts affirm a commitment to address student disengagement through the development of inclusive school environments, curriculum, and pedagogy. This paper, drawing on critical social theory, describes three Australian projects that support the cultivation of teachers’ beliefs, knowledge and skills for critical reflection and leading change in schools. The first project reports on the valued ethics that emerged in pre-service teacher reflections about a Service-learning Program at a university in Queensland. The second project reports on a school-based collaborative inquiry approach to professional development with a focus on literacy practices. The final project reports on an initiative in another university in Victoria, to operationalise pedagogical change and curriculum renewal in Victoria, through the Principles of Learning and Teaching (PoLT). These case studies illustrate how critical reflection and development of beliefs, knowledge and skills can be acquired to better meet the needs of schools.

Interactions between energy intake and expenditure in the development and treatment of obesity.

Summary There are four interactions to consider between energy intake (EI) and energy expenditure (EE) in the development and treatment of obesity. (1) Does sedentariness alter levels of EI or subsequent EE? and (2) Do high levels of EI alter physical activity or exercise? (3) Do exercise-induced increases in EE drive EI upwards and undermine dietary approaches to weight management and (4) Do low levels of EI elevate or decrease EE? There is little evidence that sedentariness alters levels of EI. This lack of cross-talk between altered EE and EI appears to promote a positive EB. Lifestyle studies also suggest that a sedentary routine actually offers the opportunity for over-consumption. Substantive changes in non exercise activity thermogenesis are feasible, but not clearly demonstrated. Cross talk between elevated EE and EI is initially too weak and takes too long to activate, to seriously threaten dietary approaches to weight management. It appears that substantial fat loss is possible before intake begins to track a sustained elevation of EE. There is more evidence that low levels of EI does lower physical activity levels, in relatively lean men under conditions of acute or prolonged semi-starvation and in dieting obese subjects. During altered EB there are a number of small but significant changes in the components of EE, including (i) sleeping and basal metabolic rate, (ii) energy cost of weight change alters as weight is gained or lost, (iii) exercise efficiency, (iv) energy cost of weight bearing activities, (v) during substantive overfeeding diet composition (fat versus carbohydrate) will influence the energy cost of nutrient storage by ~ 15%. The responses (i-v) above are all “obligatory” responses. Altered EB can also stimulate facultative behavioural responses, as a consequence of cross-talk between EI and EE. Altered EB will lead to changes in the mode duration and intensity of physical activities. Feeding behaviour can also change. The degree of inter-individual variability in these responses will define the scope within which various mechanisms of EB compensation can operate. The relative importance of “obligatory” versus facultative, behavioural responses -as components of EB control- need to be defined.

Asset health prediction using condition indicators and operating environment indicators : a case study

Prognostics and asset life prediction is one of research potentials in engineering asset health management. We previously developed the Explicit Hazard Model (EHM) to effectively and explicitly predict asset life using three types of information: population characteristics; condition indicators; and operating environment indicators. We have formerly studied the application of both the semi-parametric EHM and non-parametric EHM to the survival probability estimation in the reliability field. The survival time in these models is dependent not only upon the age of the asset monitored, but also upon the condition and operating environment information obtained. This paper is a further study of the semi-parametric and non-parametric EHMs to the hazard and residual life prediction of a set of resistance elements. The resistance elements were used as corrosion sensors for measuring the atmospheric corrosion rate in a laboratory experiment. In this paper, the estimated hazard of the resistance element using the semi-parametric EHM and the non-parametric EHM is compared to the traditional Weibull model and the Aalen Linear Regression Model (ALRM), respectively. Due to assuming a Weibull distribution in the baseline hazard of the semi-parametric EHM, the estimated hazard using this model is compared to the traditional Weibull model. The estimated hazard using the non-parametric EHM is compared to ALRM which is a well-known non-parametric covariate-based hazard model. At last, the predicted residual life of the resistance element using both EHMs is compared to the actual life data.

Urban identity and colour : the case of Tirana, Albania

When an artist is put in charge of a city, he will use the town as his canvas. Edi Rama became Mayor of Tirana in 2000 and since them has undertaken an unusual and colourful renewal program for the Albania capital.