A ‘cost-per-wear’ model based on contact lens replacement frequency

Purpose: The aim was to construct and advise on the use of a cost-per-wear model based on contact lens replacement frequency, to form an equitable basis for cost comparison. ---------- Methods: The annual cost of professional fees, contact lenses and solutions when wearing daily, two-weekly and monthly replacement contact lenses is determined in the context of the Australian market for spherical, toric and multifocal prescription types. This annual cost is divided by the number of times lenses are worn per year, resulting in a ‘cost-per-wear’. The model is presented graphically as the cost-per-wear versus the number of times lenses are worn each week for daily replacement and reusable (two-weekly and monthly replacement) lenses.---------- Results: The cost-per-wear for two-weekly and monthly replacement spherical lenses is almost identical but decreases with increasing frequency of wear. The cost-per-wear of daily replacement spherical lenses is lower than for reusable spherical lenses, when worn from one to four days per week but higher when worn six or seven days per week. The point at which the cost-per-wear is virtually the same for all three spherical lens replacement frequencies (approximately AUD$3.00) is five days of lens wear per week. A similar but upwardly displaced (higher cost) pattern is observed for toric lenses, with the cross-over point occurring between three and four days of wear per week (AUD$4.80). Multifocal lenses have the highest price, with cross-over points for daily versus two-weekly replacement lenses at between four and five days of wear per week (AUD$5.00) and for daily versus monthly replacement lenses at three days per week (AUD$5.50).---------- Conclusions: This cost-per-wear model can be used to assist practitioners and patients in making an informed decision in relation to the cost of contact lens wear as one of many considerations that must be taken into account when deciding on the most suitable lens replacement modality.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

A taxonomy of model-based testing approaches

Model-based testing (MBT) relies on models of a system under test and/or its environment to derive test cases for the system. This paper discusses the process of MBT and defines a taxonomy that covers the key aspects of MBT approaches. It is intended to help with understanding the characteristics, similarities and differences of those approaches, and with classifying the approach used in a particular MBT tool. To illustrate the taxonomy, a description of how three different examples of MBT tools fit into the taxonomy is provided.

Reasoning about component similarity in building product models from the construction perspective

Identifying the design features that impact construction is essential to developing cost effective and constructible designs. The similarity of building components is a critical design feature that affects method selection, productivity, and ultimately construction cost and schedule performance. However, there is limited understanding of what constitutes similarity in the design of building components and limited computer-based support to identify this feature in a building product model. This paper contributes a feature-based framework for representing and reasoning about component similarity that builds on ontological modelling, model-based reasoning and cluster analysis techniques. It describes the ontology we developed to characterize component similarity in terms of the component attributes, the direction, and the degree of variation. It also describes the generic reasoning process we formalized to identify component similarity in a standard product model based on practitioners' varied preferences. The generic reasoning process evaluates the geometric, topological, and symbolic similarities between components, creates groupings of similar components, and quantifies the degree of similarity. We implemented this reasoning process in a prototype cost estimating application, which creates and maintains cost estimates based on a building product model. Validation studies of the prototype system provide evidence that the framework is general and enables a more accurate and efficient cost estimating process.

Approximate Bayesian computation using auxiliary model based estimates

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms using indirect infer- ence. We embed this approach within a sequential Monte Carlo algorithm that is completely adaptive. This methodological development was motivated by an application involving data on macroparasite population evolution modelled with a trivariate Markov process. The main objective of the analysis is to compare inferences on the Markov process when considering two di®erent indirect mod- els. The two indirect models are based on a Beta-Binomial model and a three component mixture of Binomials, with the former providing a better ¯t to the observed data.

Beyond rule based reasoning - the meaning and use of cases

Commercial legal expert systems are invariably rule based. Such systems are poor at dealing with open texture and the argumentation inherent in law. To overcome these problems we suggest supplementing rule based legal expert systems with case based reasoning or neural networks. Both case based reasoners and neural networks use cases-but in very different ways. We discuss these differences at length. In particular we examine the role of explanation in existing expert systems methodologies. Because neural networks provide poor explanation facilities, we consider the use of Toulmin argument structures to support explanation (S. Toulmin, 1958). We illustrate our ideas with regard to a number of systems built by the authors

Fluid-structure interaction analysis by coupled FE-SPH model based on a novel searching algorithm

Fluid–Structure Interaction (FSI) problem is significant in science and engineering, which leads to challenges for computational mechanics. The coupled model of Finite Element and Smoothed Particle Hydrodynamics (FE-SPH) is a robust technique for simulation of FSI problems. However, two important steps of neighbor searching and contact searching in the coupled FE-SPH model are extremely time-consuming. Point-In-Box (PIB) searching algorithm has been developed by Swegle to improve the efficiency of searching. However, it has a shortcoming that efficiency of searching can be significantly affected by the distribution of points (nodes in FEM and particles in SPH). In this paper, in order to improve the efficiency of searching, a novel Striped-PIB (S-PIB) searching algorithm is proposed to overcome the shortcoming of PIB algorithm that caused by points distribution, and the two time-consuming steps of neighbor searching and contact searching are integrated into one searching step. The accuracy and efficiency of the newly developed searching algorithm is studied on by efficiency test and FSI problems. It has been found that the newly developed model can significantly improve the computational efficiency and it is believed to be a powerful tool for the FSI analysis.

Unbalance identification in large steam turbo-generator unit using a model-based method

Fault identification in industrial machine is a topic of major importance under engineering point of view. In fact, the possibility to identify not only the type, but also the severity and the position of a fault occurred along a shaft-line allows quick maintenance and shorten the downtime. This is really important in the power generation industry where the units are often of several tenths of meters long and where the rotors are enclosed by heavy and pressure-sealed casings. In this paper, an industrial experimental case is presented related to the identification of the unbalance on a large size steam turbine of about 1.3 GW, belonging to a nuclear power plant. The case history is analyzed by considering the vibrations measured by the condition monitoring system of the unit. A model-based method in the frequency domain, developed by the authors, is introduced in detail and it is then used to identify the position of the fault and its severity along the shaft-line. The complete model of the unit (rotor – modeled by means of finite elements, bearings – modeled by linearized damping and stiffness coefficients and foundation – modeled by means of pedestals) is analyzed and discussed before being used for the fault identification. The assessment of the actual fault was done by inspection during a scheduled maintenance and excellent correspondence was found with the identified one by means of authors’ proposed method. Finally a complete discussion is presented about the effectiveness of the method, even in presence of a not fine tuned machine model and considering only few measuring planes for the machine vibration.

Stress relaxation analysis of single chondrocytes using porohyperelastic model based on AFM experiments

The aim of this study is to investigate the stress relaxation behavior of single chondrocytes using the Porohyperelastic (PHE) model and inverse Finite Element Analysis (FEA). Firstly, based on Atomic Force Microscopy (AFM) technique, we have found that the chondrocytes exhibited stress relaxation behavior. We explored the mechanism of this stress relaxation behavior and concluded that the intracellular fluid exuding out from the cells during deformation plays the most important role in the stress relaxation. Next, we have applied the inverse FEA technique to determine necessary material parameters for PHE model to simulate this stress relaxation behavior as this model is proven capable of capturing the non-linear behavior and the fluid-solid interaction during the stress relaxation of the single chondrocytes. It is observed that this PHE model can precisely capture the stress relaxation behavior of single chondrocytes and would be a suitable model for cell biomechanics.

Adoption of accountable-eHealth systems by future healthcare professionals: An empirical research model based on the Australian context

This paper provides a first look at the acceptance of Accountable-eHealth systems, a new genre of eHealth systems, designed to manage information privacy concerns that hinder the proliferation of eHealth. The underlying concept of AeH systems is appropriate use of information through after-the-fact accountability for intentional misuse of information by healthcare professionals. An online questionnaire survey was utilised for data collection from three educational institutions in Queensland, Australia. A total of 23 hypothesis relating to 9 constructs were tested using a structural equation modelling technique. A total of 334 valid responses were received. The cohort consisted of medical, nursing and other health related students studying at various levels in both undergraduate and postgraduate courses. The hypothesis testing disproved 7 hypotheses. The empirical research model developed was capable of predicting 47.3% of healthcare professionals’ perceived intention to use AeH systems. A validation of the model with a wider survey cohort would be useful to confirm the current findings.