This paper investigates an effective computational method (ECM) based on the standard polynomials used to solve some nonlinear initial and boundary value problems appeared in engineering and applied sciences. Moreover, the effective computational methods in this paper were improved by suitable orthogonal base functions, especially the Chebyshev, Bernoulli, and Laguerre polynomials, to obtain novel approximate solutions for some nonlinear problems. These base functions enable the nonlinear problem to be effectively converted into a nonlinear algebraic system of equations, which are then solved using Mathematica®12. The improved effective computational methods (I-ECMs) have been implemented to solve three applications involving nonli

This study investigates the results of electrocoagulation (EC) using aluminum (Al) electrodes as anode and stainless steel (grade 316) as a cathode for removing silica, calcium, and magnesium ions from simulated cooling tower blowdown waters. The simulated water contains (50 mg/l silica, 508 mg/l calcium, and 292 mg/l magnesium). The influence of different experimental parameters, such as current density (0.5, 1, and 2 mA/cm²), initial pH(5,7, and 10), the temperature of the simulated solution(25⁰C and 35 ⁰C), and electrolysis time was studied. The highest removal efficiency of 80.183%, 99.21%, and 98.06% for calcium, silica, and magnesium ions, respectively, were obtained at a current de

With the increasing reliance on microgrids as flexible and sustainable solutions for energy distribution, securing decentralized electricity grids requires robust cybersecurity strategies tailored to microgrid-specific vulnerabilities. The research paper focuses on enhancing detection capabilities and response times in the face of coordinated cyber threats in microgrid systems by implementing advanced technologies, thereby supporting decentralized operations while maintaining robust system performance in the presence of attacks. It utilizes advanced power engineering techniques to strengthen cybersecurity in modern power grids. A real-world CPS testbed was utilized to simulate the smart grid environment and analyze the impact of cyberattack

Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Over the past few decades, the surveying fieldworks were usually carried out based on classical positioning methods for establishing horizontal and vertical geodetic networks. However, these conventional positioning techniques have many drawbacks such as time-consuming, too costly, and require massive effort. Thus, the Global Navigation Satellite System (GNSS) has been invented to fulfill the quickness, increase the accuracy, and overcome all the difficulties inherent in almost every surveying fieldwork. This research assesses the accuracy of local geodetic networks using different Global Navigation Satellite System (GNSS) techniques, such as Static, Precise Point Positioning, Post Processing Kinematic, Session method, a

<span>As a result of numerous applications and low installation costs, wireless sensor networks (WSNs) have expanded excessively. The main concern in the WSN environment is to lower energy consumption amidst nodes while preserving an acceptable level of service quality. Using multi-mobile sinks to reduce the nodes' energy consumption have been considered as an efficient strategy. In such networks, the dynamic network topology created by the sinks mobility makes it a challenging task to deliver the data to the sinks. Thus, in order to provide efficient data dissemination, the sensor nodes will have to readjust the routes to the current position of the mobile sinks. The route re-adjustment process could result in a significant m