ArcHydro is a model developed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). Digital Elevation Model (DEM) data have been used to derive hydrological features, which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions. Detailed delineation of drainage networks is the first step for many natural resource management studies. Compared with interpretation from aerial photographs or topographic maps, auto

Nuclear emission rates for nucleon-induced reactions are theoretically calculated based on the one-component exciton model that uses state density with non-Equidistance Spacing Model (non-ESM). Fair comparison is made from different state density values that assumed various degrees of approximation formulae, beside the zeroth-order formula corresponding to the ESM. Calculations were made for 96Mo nucleus subjected to (N,N) reaction at Emax=50 MeV. The results showed that the non-ESM treatment for the state density will significantly improve the emission rates calculated for various exciton configurations. Three terms might suffice a proper calculation, but the results kept changing even for ten terms. However, five terms is found to give

Visualization of subsurface geology is mainly considered as the framework of the required structure to provide distribution of petrophysical properties. The geological model helps to understand the behavior of the fluid flow in the porous media that is affected by heterogeneity of the reservoir and helps in calculating the initial oil in place as well as selecting accurate new well location. In this study, a geological model is built for Qaiyarah field, tertiary reservoir, relying on well data from 48 wells, including the location of wells, formation tops and contour map. The structural model is constructed for the tertiary reservoir, which is an asymmetrical anticline consisting of two domes separated by a saddle. It is found that

Horizontal wells have revolutionized hydrocarbon production by enhancing recovery efficiency and reducing environmental impact. This paper presents an enhanced Black Oil Model simulator, written in Visual Basic, for three-dimensional two-phase (oil and water) flow through porous media. Unlike most existing tools, this simulator is customized for horizontal well modeling and calibrated using extensive historical data from the South Rumaila Oilfield, Iraq. The simulator first achieves a strong match with historical pressure data (1954–2004) using vertical wells, with an average deviation of less than 5% from observed pressures, and is then applied to forecast the performance of hypothetical horizontal wells (2008–2011). The result

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

In this paper, the deterministic and the stochastic models are proposed to study the interaction of the Coronavirus (COVID-19) with host cells inside the human body. In the deterministic model, the value of the basic reproduction number   determines the persistence or extinction of the COVID-19. If   , one infected cell will transmit the virus to less than one cell, as a result,  the person carrying the Coronavirus will get rid of the disease .If   the infected cell  will be able to infect  all  cells that contain ACE receptors. The stochastic model proves that if  are sufficiently large then maybe  give  us ultimate disease extinction although ,  and this  facts also proved by computer simulation.

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co

The relationship between government revenues and the fiscal balance represents a central pillar in the analysis of fiscal sustainability. However, its modeling faces a fundamental challenge in the form of structural uncertainty, which is not captured by point estimates in traditional models such as ARDL, as these models assume structural stability that is inconsistent with the nature of rentier economies. The current study aims to develop a fuzzy framework by constructing a Fuzzy Autoregressive Distributed Lag (FARDL) model. This is achieved through integrating the Autoregressive Distributed Lag (ARDL) approach with fuzzy logic theory, thereby enabling the incorporation of uncertainty into the inherent structure of the economic rela

Aerial manipulation of objects has a number of advantages as it is not limited by the morphology of the terrain. One of the main problems of the aerial payload process is the lack of real-time prediction of the interaction between the gripper of the aerial robot and the payload. This paper introduces a digital twin (DT) approach based on impedance control of the aerial payload transmission process. The impedance control technique is implemented to develop the target impedance based on emerging the mass of the payload and the model of the gripper fingers. Tracking the position of the interactional point between the fingers of gripper and payload, inside the impedance control, is achieved using model predictive control (MPD) approach.

TWMS Journal of Applied and Engineering Mathematics | Volume: 16 Issue: 9