Search

Since COVID-19 was declared as a pandemic by World Health Organization in March 2020, 169,682,828 cases have been reported worldwide, with 151,416,570 recovered, and 3,526,647 deaths by May 28, 2021. Oxygen gas cylinders demand is booming globally due to its need for COVID-19's for intensive care. Thus, it is critical for hospitals to know exactly the time of receiving oxygen gas cylinders since this will help in minimizing the fatality rate. In this regards, this paper proposes a Multilayer Perceptron Neural Network-based model to predict the delivery time of oxygen gas cylinders for a real-life logistics data from a company that delivers oxygen gas cylinders to all cities around Saudi Arabia. Besides, Multilayer Perceptron Neural Network is benchmarked to supported vector machine and multiple linear regression. Although all the considered models have the ability to provide accurate prediction results, the findings indicate that the proposed supported vector machine and Multilayer Perceptron Neural Network model provide better prediction results. The analysis was achieved through a methodology to identify factors with the highest impact and build a neural network model. The model was further optimized to identify the best order and select the best subset of input variables. The analysis showed that the neural network model can be used effectively to estimate the delivery time of oxygen gas cylinders. The model illustrated high accuracy of prediction by comparing the predicted values to the actual values. [ABSTRACT FROM AUTHOR]

Climate change impact reports by the Intergovernmental Panel on Climate Change (IPCC) are well established throughout the world. By now many local, regional, and central authorities are aware of these impacts and they try to take necessary precautions according to their environmental circumstances. Preliminary decisions cannot be taken unless there is a model for future climate change impacts, especially for rainfall occurrence, amount, and frequency estimations. The main purpose is to combine the general circulation (climate) model (GCM) outputs with the local meteorology station records by a suitable downscaling procedure. In this paper, a statistical quadrangle downscaling model (QDM) is developed for rainfall estimations up to 2100. This model helps to downscale the nearby four scenario rainfall amounts at GCM grids to a desired point. Two main steps in the model structure are the use of a regional dependence function (RDF) for transferring the GCM output data from the four nearest grid points to the location. The second step is to adjust the downscaled scenario monthly rainfalls with the local rainfall records. The GCM monthly rainfall outputs are downloaded from the National Center for Atmospheric Research (NCAR) with special report emission scenario A2. The application of the methodology is presented for Riyadh City, Kingdom of Saudi Arabia (KSA). The RDF is obtained from the most reliable meteorology records at 27 locations that are irregularly scattered all over the country. Then it is used for scenario data transfer from the regular grid points. The adjustment is based on the average statistical properties of concurrent monthly rainfall amounts between 1985 and 1996. The trend component in the GCM remains intact. It has been observed that there is a major increasing trend in the monthly rainfalls after 2050. In the meantime, each decade starting from 2011 gives estimates that indicate steady rainfall increases up to 2050. The QDM indicates a 40% increase in the annual total rainfall amounts, with a 15% standard deviation increase. [ABSTRACT FROM AUTHOR]

The tilt angle of photovoltaic (PV) panels is a major factor affecting the amount of solar radiation falling on the panel surface. The optimum tilt angle depends on the position of the sun, latitude and local geographical characteristics. This paper investigates PV panel optimum tilt angles for various cities in the Kingdom of Saudi Arabia. For simulation purposes, horizontal solar radiation data for the study cities was obtained from NASA. MATLAB software was used to optimize tilt angle by maximizing solar radiation. Experiments were conducted to validate the theoretical requirement for negative tilt angles during summer. Results demonstrate that adjusting tilt angles six times per year harvests 99.5% of the solar radiation that could be attained with daily PV panel adjustment. [ABSTRACT FROM AUTHOR]