A dynamic approach to forecast rental growth

Numerous econometric models have been proposed for forecasting property market performance, but limited success has been achieved in finding a reliable and consistent model to predict property market movements over a five to ten year timeframe. This research focuses on office rental growth forecasts and overviews many of the office rent models that have evolved over the past 20 years. A model by DiPasquale and Wheaton is selected for testing in the Brisbane, Australia office market. The adaptation of this study did not provide explanatory variables that could assist in developing a reliable, predictive model of office rental growth. In light of this result, the paper suggests a system dynamics framework that includes an econometric model based on historical data as well as user input guidance for the primary variables. The rent forecast outputs would be assessed having regard to market expectations and probability profiling undertaken for use in simulation exercises. The paper concludes with ideas for ongoing research.

Shrinking neighborhood evolution : a novel stochastic algorithm for numerical optimization

Focusing on the conditions that an optimization problem may comply with, the so-called convergence conditions have been proposed and sequentially a stochastic optimization algorithm named as DSZ algorithm is presented in order to deal with both unconstrained and constrained optimizations. The principle is discussed in the theoretical model of DSZ algorithm, from which we present the practical model of DSZ algorithm. Practical model efficiency is demonstrated by the comparison with the similar algorithms such as Enhanced simulated annealing (ESA), Monte Carlo simulated annealing (MCS), Sniffer Global Optimization (SGO), Directed Tabu Search (DTS), and Genetic Algorithm (GA), using a set of well-known unconstrained and constrained optimization test cases. Meanwhile, further attention goes to the strategies how to optimize the high-dimensional unconstrained problem using DSZ algorithm.

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.

Experimental investigation on extruded snack products from rice and Mung bean : optimization of parameters

Experiments were undertaken to study effect of initial conditions on the expansion ratio of two grains in a laboratory scale, single speed, single screw extruder at Naresuan University, Thailand. Jasmine rice and Mung bean were used as the material. Three different initial moisture contents were adjusted for the grains and classified them into three groups according to particle sizes. Mesh sizes used are 12 and 14. Expansion ratio was measured at a constant barrel temperature of 190oC. Response surface methodology was used to obtain optimum conditions between moisture content and particle size of the materials concerned.

Molecular dynamics study of dynamic buckling properties of nanowires with defects

Based on the embedded atom method (EAM) and molecular dynamics (MD) method, the deformation properties of Cu nanowires with different single defects under dynamic compression have been studied. The mechanical behaviours of the perfect nanowire are first studied, and the critical stress decreases with the increase of the nanowire’s length, which is well agreed with the modified Euler theory. We then consider the effects to the buckling phenomenon resulted from different defects. It is found that obvious decrease of the critical stress is resulted from different defects, and the largest decrease is found in nanowire with the surface vertical defect. Surface defects are found exerting larger influence than internal defects. The buckling duration is found shortened due to different defects except the nanowire with surface horizon defect, which is also found possessing the largest deflection. Different deflections are also observed for different defected nanowires. It is find that due to surface defects, only deflection in one direction is happened, but for internal defects, more complex deflection circumstances are observed.

Diagnosing dry eye with dynamic-area high-speed videokeratoscopy

Dry eye syndrome is one of the most commonly reported eye health conditions. Dynamic-area highspeed videokeratoscopy (DA-HSV) represents a promising alternative to the most invasive clinical methods for the assessment of the tear film surface quality (TFSQ), particularly as Placido-disk videokeratoscopy is both relatively inexpensive and widely used for corneal topography assessment. Hence, improving this technique to diagnose dry eye is of clinical significance and the aim of this work. First, a novel ray-tracing model is proposed that simulates the formation of a Placido image. This model shows the relationship between tear film topography changes and the obtained Placido image and serves as a benchmark for the assessment of indicators of the ring’s regularity. Further, a novel block-feature TFSQ indicator is proposed for detecting dry eye from a series of DA-HSV measurements. The results of the new indicator evaluated on data from a retrospective clinical study, which contains 22 normal and 12 dry eyes, have shown a substantial improvement of the proposed technique to discriminate dry eye from normal tear film subjects. The best discrimination was obtained under suppressed blinking conditions. In conclusion,this work highlights the potential of the DA-HSV as a clinical tool to diagnose dry eye syndrome.

Exploration and optimization of user experience in viewing videos on a mobile phone

Compared with viewing videos on PCs or TVs, mobile users have different experiences in viewing videos on a mobile phone due to different device features such as screen size and distinct usage contexts. To understand how mobile user’s viewing experience is impacted, we conducted a field user study with 42 participants in two typical usage contexts using a custom-designed iPhone application. With user’s acceptance of mobile video quality as the index, the study addresses four influence aspects of user experiences, including context, content type, encoding parameters and user profiles. Accompanying the quantitative method (acceptance assessment), we used a qualitative interview method to obtain a deeper understanding of a user’s assessment criteria and to support the quantitative results from a user’s perspective. Based on the results from data analysis, we advocate two user-driven strategies to adaptively provide an acceptable quality and to predict a good user experience, respectively. There are two main contributions from this paper. Firstly, the field user study allows a consideration of more influencing factors into the research on user experience of mobile video. And these influences are further demonstrated by user’s opinions. Secondly, the proposed strategies — user-driven acceptance threshold adaptation and user experience prediction — will be valuable in mobile video delivery for optimizing user experience.