Effect of Aluminum Oxide Nanoparticles Fuel Additives on the Performance and Emissions of Diesel Engine

The effect of Aluminum Oxide (AL2O3) nanoparticles fuel additives on the performance and emission characteristics of diesel engine was investigates. Diesel fuel was mixed with the (AL2O3) nanoparticles in the mass fractions of 100 and 150 ppm by using ultrasonicator. Direct injection (DI), by using fiat diesel engine that run at fixed speed (1500 rpm) and constant fuel injection pressure (400 bar) with varying the operation load. The gain result was likening with those obtain when the operation the diesel engine with normal fuel. Measurements indicated that there is enhancement in the thermal efficiency and the brake specific fuel consumption with increasing the dosing level of (AL2O3) nanoparticles in the blended fuel. The emission results at all loads showed that NOx and smoke produced by (AL2O3) blended fuels were less than those produced by diesel fuel. Diesel fuel produced CO and HC more than (AL2O3) blended fuels at high load and less at low load

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran