Subluxation of the crystalline lens: A no-ring approach

Purpose: To report the surgical technique and outcomes of a no-ring approach to posterior chamber intraocular lens (IOL) implantation in children and young adults with subluxation of the crystalline lens. Methods: A three-piece 5.5-mm hydrophobic acrylic IOL was implanted in which the optic and one haptic were inside the capsular bag and the other haptic was in the ciliary sulcus. The implantations were done in 13 eyes of 10 patients with lens subluxation. One haptic was fixated in the ciliary sulcus by passing it through an operculum-shaped opening in the capsular bag edge, made between 2 and 3 clock hours, clockwise from the middle point of the subluxated area. Results: The mean age of the 10 patients was 12.61 ± 8.04 years and the mean follow-up was 21.38 ± 11.29 months. The mean preoperative and postoperative best-corrected visual acuity (BCVA) was 1.15 ± 0.58 and 0.37 ± 0.17 logarithm of the minimum angle of resolution, respectively, in 11 measured eyes. The postoperative BCVA was 20/40 or better in 4 eyes (36.36%), between 20/40 and 20/60 in 6 eyes (54.54%), and between 20/80 and 20/100 in 1 eye (9.09%). The BCVA improved in all eyes. Postoperative IOL decentration between 1.0 and 1.5 mm occurred in 3 of 13 eyes (23.08%). Conclusion: The results indicate that the no-ring technique allows centration of an IOL implanted into a subluxated capsular bag without using capsule tension rings. © SLACK Incorporated.

Excited fermion contribution to Z(0) physics at one loop

We investigate the effects induced by excited leptons at the one-loop level in the observables measured on the Ζ peak at LEP. Using a general effective Lagrangian approach to describe the couplings of the excited leptons, we compute their contributions to both oblique parameters and Ζ partial widths. Our results show that the new effects are comparable to the present experimental sensitivity, but they do not lead to a significant improvement on the available constraints on the couplings and masses of these states.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Cultural Knowledge for Named Entity Disambiguation: A Graph-Based Semantic Relatedness Approach

One of the ultimate aims of Natural Language Processing is to automate the analysis of the meaning of text. A fundamental step in that direction consists in enabling effective ways to automatically link textual references to their referents, that is, real world objects. The work presented in this paper addresses the problem of attributing a sense to proper names in a given text, i.e., automatically associating words representing Named Entities with their referents. The method for Named Entity Disambiguation proposed here is based on the concept of semantic relatedness, which in this work is obtained via a graph-based model over Wikipedia. We show that, without building the traditional bag of words representation of the text, but instead only considering named entities within the text, the proposed method achieves results competitive with the state-of-the-art on two different datasets.

A machine learning approach for recognising woody plants on railway trackbeds

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

Task profiling : A task-Based approach to measuring the integration of ICT in the classroom

The measurement of ICT (information and communication technology) integration is emerging as an area of research interest with such systems as Education Queensland including it in their recently released list of research priorities. Studies to trial differing integration measurement instruments have taken place within Australia in the last few years, particularly Western Australia (Trinidad, Clarkson, & Newhouse, 2004; Trinidad, Newhouse & Clarkson, 2005), Tasmania (Fitzallen 2005) and Queensland (Finger, Proctor, & Watson, 2005). This paper will add to these investigations by describing an alternate and original methodological approach which was trialled in a small-scale pilot study conducted jointly by Queensland Catholic Education Commission (QCEC) and the Centre of Learning Innovation, Queensland University of Technology (QUT) in late 2005. The methodology described is based on tasks which, through a process of profiling, can be seen to be artefacts which embody the internal and external factors enabling and constraining ICT integration.