Abstract

Abstract

Abstract

Abstract
The aim of this study was to evaluate the factors that determine esthetic outcome after breast cancer conservative treatment, based on a consensual classification obtained with an international consensus panel. Photographs were taken from 120 women submitted to conservative unilateral breast cancer surgery (with or without axillary surgery) and radiotherapy. The images were sent to a panel of observers from 13 different countries and consensus on the classification of esthetic result (recorded as excellent, good, fair or poor) was obtained in 113 cases by means of a Delphi method. For each patient, data were collected retrospectively regarding patient characteristics, tumor, and treatment factors. Univariate and multivariate analysis were used to evaluate the correlation between these factors and overall cosmetic results. On univariate analysis, younger and thinner patients as well as patients with lower body mass index (BMI) and premenopausal status obtained better cosmetic results. In the group of tumor- and treatment-related factors, larger removed specimens, clearly visible scars, the use of chemotherapy and longer follow-up period were associated with less satisfactory results. On multivariate analysis, only BMI and scar visibility maintained a significant association with cosmesis. BMI and scar visibility are the only factors significantly associated with cosmetic results of breast cancer conservative treatment, as evaluated by an international consensus panel.

Abstract
The correct estimation of the friction coefficient in automotive applications is of paramount importance in the design of effective vehicle safety systems. In this article a new parametrization for estimating the peak friction coefficient, in the tire-road interface, is presented. The proposed parametrization is based on a feedforward neural network (FFNN), trained by the Extreme Learning Machine (ELM) method. Unlike traditional learning techniques for FFNN, typically based on backpropagation and inappropriate for real time implementation, the ELM provides a learning process based on random assignment in the weights between input and the hidden layer. With this approach, the network training becomes much faster, and the unknown parameters can be identified through simple and robust regression methods, such as the Recursive Least Squares. Simulation results, obtained with the CarSim program, demonstrate a good performance of the proposed parametrization; compared with previous methods described in the literature, the proposed method reduces the estimation errors using a model with a lower number of parameters.

Abstract
The preservation of musical works produced in the past requires their digitalization and transformation into a machine-readable format. The processing of handwritten musical scores by computers remains far from ideal. One of the fundamental stages to carry out this task is the staff line detection. We investigate a general-purpose, knowledge-free method for the automatic detection of music staff lines based on a stable path approach. Lines affected by curvature, discontinuities, and inclination are robustly detected. Experimental results show that the proposed technique consistently outperforms well-established algorithms.