SUMMARYGoldmann applanation tonometry (GAT) is a method used to estimate the intraocular pressure by measuring the indentation resistance of the cornea. A popular approach to investigate the sensitivity of GAT results to material and geometry variations is to perform numerical modelling using the finite element method, for which a calibrated material model is required. These material models are typically calibrated using experimental inflation data by solving an inverse problem. In the inverse problem, the underlying material constitutive behaviour is inferred from the measured macroscopic response (chamber pressure versus apical displacement). In this study, a biomechanically motivated elastic fibre-reinforced corneal material model is chosen. The inverse problem of calibrating the corneal material model parameters using only experimental inflation data is demonstrated to be ill-posed, with small variations in the experimental data leading to large differences in the calibrated model parameters. This can result in different groups of researchers, calibrating their material model with the same inflation test data, drawing vastly different conclusions about the effect of material parameters on GAT results. It is further demonstrated that multiple loading scenarios, such as inflation as well as bending, would be required to reliably calibrate such a corneal material model.

Glaucoma is the second leading cause of irreversible blindness. The primary indicator for glaucoma is an elevated intraocular pressure, which is estimated by means of contact or non-contact tonometry. However, these techniques do not accurately account for the cornea properties that deviate from the norm, thus leading to the inaccurate estimation of the intraocular pressure. This work builds on a previous study, in which a combination of an artificial neural network and a genetic algorithm was used to estimate the intraocular pressure and cornea properties. This paper proposes to use proper orthogonal decomposition to accurately estimate the intraocular pressure independent of the cornea properties. The results indicate that proper orthogonal decomposition is able to estimate the intraocular pressure, and that the cornea properties have a slight influence on the estimation. For thicker corneas, however, the intraocular pressure prediction is influenced. This study concluded that this deterministic technique avoids the ambiguity that could result from a method relying on a stochastic optimization routine.

Fingerprint recognition systems are prevalent in high-security applications. As a result, the act of spoofing these systems with artificial fingerprints is of increasing concern. This research presents an automatic means for spoof-detection using optical coherence tomography (OCT). This technology is able to capture a 3D representation of the internal structure of the skin and is thus not limited to a 2D surface scan. The additional information afforded by this representation means that accurate spoof-detection can be achieved. Two features were extracted to detect the presence of (1) an additional thin layer on the surface of the skin and (2) a thicker additional layer or a complete artificial finger. An analysis of these features showed that they are highly separable, resulting in 100% accuracy regarding spoof-detection, with no false rejections of real fingers. This is the first attempt at fully automated spoof-detection using OCT.

<div>This paper analyses a set of previously obtained experimental results on various clay fillers added to high density polyethylene (HDPE). The composite material was compounded using an extrusion process and manufactured into tensile test samples by means of hot pressing. Various manufacturing parameters (number of extrusions, press time, sample cooling method), material (polymer grade, clay type, clay weight loading) and testing parameters (strain rate) were investigated to determine their influence on the mechanical properties of the composite system. <br></div><div>Exploratory data analysis was first employed by graphically representing the data using scatter plots to identify any main characteristics, patterns or anomalies. The statistical analysis was used to quantify the effects of the ultimate tensile strength by first conducting a one way ANOVA analysis before developing a linear model for the response variable analysis. For the percentage elongation to failure, the observations was first grouped into three groups, Brittle, Intermediate or Ductile. A linear discriminant analysis was performed to classify the groups considering a training set of 80% randomly selected observations and testing on the remaining 20%. <br></div><br>

Polymers are used in various industrial applications due to their ease of production, light weight, and ductility. Fillers such as clays are added to polymers to improve a range of factors such as material processing, thermal properties, fire retardance and cost. However, adding clays may negatively impact the mechanical performance of the composite. In addition, manufacturing parameters, for example, number of extrusions, press time, and so forth may also have an influence on the resulting composite system. This study performs a statistical analysis on a set of previously obtained experimental results, which investigated the influence of various manufacturing, material, and testing parameters on the composite mechanical properties. Exploratory data and statistical analysis techniques are applied to the historical tensile test data to gain insight into the influence on mechanical properties as well as the relationships and interactions between the parameters. Specifically, it is shown that clay loading does not have a statistically significant effect on the composite mechanical properties, which is contrary to literature. Another surprising result is the poor performance of the clay that is compatible with high‐density polyethylene compared to the clay that is compatible with poly vinyl chloride. The contribution of this paper is to demonstrate the usefulness of applying statistical analysis on a large volume of data to understand the diverse correlations between the different variables.