Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low-or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI).Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression.

Practical recommendations are given that researchers, traffic police, medical authorities, nongovernmental organizations (NGOs), educational institutions, and municipalities can adopt to lower the risk of pedestrian crashes.

Image corner detection is very important in the fields of image analysis and computer vision. Curvature calculation techniques are used in many contour-based corner detectors. We identify that existing calculation of curvature is sensitive to local variation and noise in the discrete domain and does not perform well when corners are closely located. In this paper, discrete curvature representations of single and double corner models are investigated and obtained. A number of model properties have been discovered which help us detect corners on contours. It is shown that the proposed method has a high corner resolution (the ability to accurately detect neighbouring corners) and a corresponding corner resolution constant is also derived. Meanwhile, this method is less sensitive to any local variations and noise on the contour; and false corner detection is less likely to occur. The proposed detector is compared with seven state-of-the-art detectors. Three test images with ground truths are used to assess the detection capability and localization accuracy of these methods in noise-free and cases with different noise levels. Twenty-four images with various scenes without ground truths are used to evaluate their repeatability under affine transformation, JPEG compression, and noise degradations. The experimental results show that our proposed detector attains a better overall performance.

Illumination changes in outdoor environments under non-ideal weather conditions have a negative impact on automotive scene understanding and segmentation performance. In this paper, we present an evaluation of illuminationinvariant image transforms applied to this application domain. We compare four recent transforms for illumination invariant image representation, individually and with colour hybrid images, to show that despite assumptions to contrary such invariant pre-processing can improve the state of the art in scene understanding performance. In addition, we propose a robust approach based on using an illumination-invariant image representation, combined with the chromatic component of a perceptual colour-space to improve contemporary automotive scene understanding and segmentation. By using an illumination invariant pre-process, to reduce the impact of environmental illumination changes, we show that the performance of deep convolutional neural network based scene understanding and segmentation can yet be further improved. This illuminating result enforces the need for invariant (unbiased) training sets within such deep network training and shows that even a welltrained network may still not offer truly optimal performance (if we ignore any prior data transforms attributable to a priori insight). Our approach is demonstrated over a range of example imagery where we show a notable improvement in performance using pre-processed, illumination invariant, automotive scene imagery.

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