Formulation of Locally Reacting Surfaces in FDTD/K-DWM Modelling of Acoustic Spaces

In this paper, we present new methods for constructing and analysing formulations of locally reacting surfaces that can be used in finite difference time domain (FDTD) simulations of acoustic spaces. Novel FDTD formulations of frequency-independent and simple frequency-dependent impedance boundaries are proposed for 2D and 3D acoustic systems, including a full treatment of corners and boundary edges. The proposed boundary formulations are designed for virtual acoustics applications using the standard leapfrog scheme based on a rectilinear grid, and apply to FDTD as well as Kirchhoff variable digital waveguide mesh (K-DWM) methods. In addition, new analytic evaluation methods that accurately predict the reflectance of numerical boundary formulations are proposed. numerical experiments and numerical boundary analysis (NBA) are analysed in time and frequency domains in terms of the pressure wave reflectance for different angles of incidence and various impedances. The results show that the proposed boundary formulations structurally adhere well to the theoretical reflectance. In particular, both reflectance magnitude and phase are closely approximated even at high angles of incidence and low impedances. Furthermore, excellent agreement was found between the numerical boundary analysis and the experimental results, validating both as tools for researching FDTD boundary formulations.

Locally regularised two-stage learning algorithm for RBF network centre selection

Nonlinear models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually network dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsomous network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise.

General practitioners' habits and knowledge in relation to the management of H. pylori-associated dyspepsia and their views about a locally available 13-carbon urea breath test

We report the results of general practitioners' views on Helicobacter pylori-associated dyspepsia and use of screening tests in the community. The use of office serology tests in screening is of concern as independent validation in specialist units has been disappointing.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

Local recurrence in patients with large and locally advanced breast cancer treated with primary chemotherapy

Background: Primary chemotherapy is being given in the treatment of large and locally advanced breast cancers, but a major concern is local relapse after therapy. This paper has examined patients treated with primary chemotherapy and surgery (either breast-conserving surgery or mastectomy) and has examined the role of factors which may indicate those patients who are subsequently more likely to experience local recurrence of,disease.

Methods: A consecutive series of 173 women, with data available for 166 of these, presenting with large and locally advanced breast cancer (T2 >4 cm, T3, T4, or N2) were treated with primary chemotherapy comprising cyclophosphamide, vincristine, doxorubicin, and prednisolone and then surgery (either conservation or mastectomy with axillary surgery) followed by radiotherapy were examined.

Results: The clinical response rate of these patients was 75% (21% complete and 54% partial), with a complete pathological response rate of 15%. A total of 10 patients (6%) experienced local disease relapse, and the median time to relapse was 14 months (ranging from 3 to 40). The median survival in this group was 27 months (ranging from 13 to 78). In patients having breast conservation surgery, local recurrence occurred in 2%, and in those undergoing mastectomy 7% experience local relapse of disease. Factors predicting patients most likely to experience local recurrence were poor clinical response and residual axillary nodal disease after chemotherapy.

Conclusions: Excellent local control of disease can be achieved in patients with large and locally advanced breast cancers using a combination of primary chemotherapy, surgery and radiotherapy. However, the presence of residual tumor in the axillary lymph nodes after chemotherapy is a predictor of local recurrence and patients with a better clinical response were also less likely to experience local disease recurrence. The size and degree of pathological response did not predict patients most likely to experience recurrence of disease. (C) 2003 Excerpta Medica, Inc. All rights reserved.