Direct writing of chitosan scaffolds using a robotic system

Purpose – This paper aims to present a novel rapid prototyping (RP) fabrication methods and preliminary characterization for chitosan scaffolds. Design – A desktop rapid prototyping robot dispensing (RPBOD) system has been developed to fabricate scaffolds for tissue engineering (TE) applications. The system is a computer-controlled four-axis machine with a multiple-dispenser head. Neutralization of the acetic acid by the sodium hydroxide results in a precipitate to form a gel-like chitosan strand. The scaffold properties were characterized by scanning electron microscopy, porosity calculation and compression test. An example of fabrication of a freeform hydrogel scaffold is demonstrated. The required geometric data for the freeform scaffold were obtained from CT-scan images and the dispensing path control data were converted form its volume model. The applications of the scaffolds are discussed based on its potential for TE. Findings – It is shown that the RPBOD system can be interfaced with imaging techniques and computational modeling to produce scaffolds which can be customized in overall size and shape allowing tissue-engineered grafts to be tailored to specific applications or even for individual patients. Research limitations/implications – Important challenges for further research are the incorporation of growth factors, as well as cell seeding into the 3D dispensing plotting materials. Improvements regarding the mechanical properties of the scaffolds are also necessary. Originality/value – One of the important aspects of TE is the design scaffolds. For customized TE, it is essential to be able to fabricate 3D scaffolds of various geometric shapes, in order to repair tissue defects. RP or solid free-form fabrication techniques hold great promise for designing 3D customized scaffolds; yet traditional cell-seeding techniques may not provide enough cell mass for larger constructs. This paper presents a novel attempt to fabricate 3D scaffolds, using hydrogels which in the future can be combined with cells.

An efficient approach for the direct synthesis of lithium niobate powders

Lithium niobate powders from the raw powders of Li2 O5 are directly synthesized by a combustion method with urea fuel. The synthesis parameters (e.g. the calcination temperature, calcination time, and urea-to-(Li2 CO3 + Nb2 O5) quantity ratio) are studied to reveal the optimized synthesis conditions for preparing high-quality lithium niobate powders. In our present work, it is found that a urea-to-(Li2 CO3 + Nb2 O5) ratio close to 3, calcination temperature at 550-600 degrees and reaction time around 2.5h may lead to high-quality lithium niobate powsers. The microstructure of synthesized powders is further studied; a possible mechanism of the involved reactions is also proposed.

Direct instruction in mathematics : issues for schools with high Indigenous enrolments : a literature review

Direct instruction, an approach that is becoming familiar to Queensland schools that have high Aboriginal and Torres Strait Islander populations, has been gaining substantial political and popular support in the United States of America [USA], England and Australia. Recent examples include the No Child Left Behind policy in the USA, the British National Numeracy Strategy and in Australia, Effective Third Wave Intervention Strategies. Direct instruction, stems directly from the model created in the 1960s under a Project Follow Through grant. It has been defined as a comprehensive system of education involving all aspects of instruction. Now in its third decade of influencing curriculum, instruction and research, direct instruction is also into its third decade of controversy because of its focus on explicit and highly directed instruction for learning. Characteristics of direct instruction are critiqued and discussed to identify implications for teaching and learning for Indigenous students.

Interactive image manipulation for surgical planning

The Australian e-Health Research Centre in collaboration with the Queensland University of Technology's Paediatric Spine Research Group is developing software for visualisation and manipulation of large three-dimensional (3D) medical image data sets. The software allows the extraction of anatomical data from individual patients for use in preoperative planning. State-of-the-art computer technology makes it possible to slice through the image dataset at any angle, or manipulate 3D representations of the data instantly. Although the software was initially developed to support planning for scoliosis surgery, it can be applied to any dataset whether obtained from computed tomography, magnetic resonance imaging or any other imaging modality.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

A multi-resolution image alignment technique based on direct methods for pose estimation of aerial vehicles

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi- Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles’ state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle’s state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle’s state for more than one minute, at real-time frame rates based, only on visual information.