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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Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.

Over a one-year period, 2990 patients attended a primary health care practice in urban Riyadh, Saudi Arabia. Of these, 33.5% had chronic disorders. Clinically significant obesity (BMI > 29.9 Kg/m2) was present in 24.5% of those with chronic disorders. Musculoskeletal disorders, diabetes mellitus (DM), digestive disorders and cardiovascular disease accounted for 38%, 36%, 24% and 22% of encounters respectively. Uncontrolled DM was encountered in 7.1% while uncontrolled systolic hypertension was present in 28.8% of patients with these disorders. A significant proportion (42%) of patients with bronchial asthma required emergency management. Symptomatic relief was obtained in 57% of patients with irritable bowel and 87% of patients with osteoarthritis of the knees. The results point to a trend of morbidity similar to that encountered in developed nations with affluence and sedentary life style. There is a need to focus on obesity, life style measures that reduce weight would be expected to positively influence diabetes, hypertension and osteoarthritis of the knees. Monitoring of outcome measures would help identify areas of improvement and preventive measures.

A numerical study of the 3-D double-diffusive natural convection in an inclined solar distiller was established. The flow is considered laminar and caused by the interaction of thermal energy and the chemical species diffusions. The governing equations of the problem, are formulated using vector potential-vorticity formalism in its 3-D form, then solved by the finite volumes method. The Rayleigh number is fixed at Ra = 105 and effects of the buoyancy ratio and inclination are studied for opposed temperature and concentration gradients. The main purpose of the study is to find the optimum inclination angle of the distiller which promotes the maximum mass and heat transfer.