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Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs), the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs) were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario) were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (RMSE5 day) not used during ANN training. For BGL predictions of up to 1 hour aRMSE5 dayof (±SD)0.15±0.04 mmol/L was observed. For BGL predictions up to 10 hours, aRMSE5  dayof (±SD)0.14±0.16 mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.

The biomechanical interaction between the residual limb and the prosthetic socket determines the quality of fit of the socket in lower limb prosthetics. An understanding of this interaction and the development of quantitative measures to predict the quality of fit of the socket are important for optimal socket design. Finite-element modeling is used widely for biomechanical modeling of the limb/socket interaction and requires information on the internal and external geometry of the residual limb. Volumetric imaging methods such as X-ray computed tomography, magnetic resonance imaging, and ultrasound have been used to obtain residual limb shape information. Of these modalities, ultrasound has been introduced most recently and its development for visualization in prosthetics is the least mature. This paper reviews ultrasound image acquisition and processing methods as they have been applied in lower limb prosthetics.

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Conformal therapy offers considerable advantages over conventional radiotherapy treatment, because it has the potential for matching almost exact/y the delivered dose distribution to the prescribed dose distribution. Associated (inverse) treatment planning methods address a constrained linear optimization problem. In this article, a method based on maximizing the total entropy of the beam profiles is developed. Maximum entropy optimization constrains the computed dose to be within well-defined tolerances of the prescribed dose, and h a s advantages of robustness, fast convergence, and high accuracy. For the work reported here, it is assessed using clinically prescribed irregular target dose volumes based on magnetic resonance imaging and computed tomography images. Results are shown for a twodimensional, homogeneous absorption, primary dose computation model, to illustrate the feasibility of the approach; however, the method may be extended to accommodate a more general threedimensional model, including inhomogeneities and scatter dose contributions. Optimization of beam offset for a regular angular displacement of beams is also considered, with particular regard to implications on total beam energy, entropy, and computation time.

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