In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

   The petroleum industry, which is one of the pillars of the national economy, has the potential to generate vast wealth and employment possibilities. The transportation of petroleum products is complicated and changeable because of the hazards caused by the corrosion consequences. Hazardous chemical leaks caused by natural disasters may harm the environment, resulting in significant economic losses. It significantly threatens the aim for sustainable development. When a result, determining the likelihood of leakage and the potential for environmental harm, it becomes a top priority for decision-makers as they develop maintenance plans. This study aims to provide an in-depth understanding of the risks associated with oil and gas pipeli

A modified water injection technique has organized by this study to improve oil recovery of the Mishrif reservoirs using polymerized alkaline surfactant water (PAS-Water) injection. It is planned to modify the existing water injection technology, first to control and balance the hazardous troublemaker reservoir facies of fifty-micron pore sizes with over 500 millidarcies permeability, along with the non-troublemaker types of less than twenty micron pore sizes with 45 to 100 millidarcies permeability. Second to control Mishrif reservoirs rock-wettability. Special core analysis under reservoir conditions of 2250 psi and 90 °C has carried out on tens of standard core plugs with heterogeneous buildup, using the proposed renewal water f

Health and safety problem can be described by statistics it can only be understood by knowing and feeling the pain, suffering, and depression. Health and safety has a legal responsibility to protect it for everyone who can affect in the workplace. This includes manufacturers, suppliers, designers and controllers of work places and employees. Work injury is one of the major problems in manufacturing and production systems industries; it is reduced production efficiency and affects the cost. To gain flexibility from a traditional manufacturing system and production efficiency, this paper is about the application of estimating technology to preview and synthesis of Lost Time of Work Injuries in industry systems aims to provide a safe workin

Purpose:  the purpose of study is estimate the Risk premium, Interest rate, Inflation and FDI in the through of Coronavirus in the MENA countries.   Theoretical framework: The theoretical framework included the study of the main variables, which are risk premium, interest rate, inflation, and foreign direct investment during the Corona virus pandemic.   Design/methodology/approach:  Concentrating on “COVID-19”, as an effective factor on the Foreign direct investment (FDI), I employ data of “MENA (Middle East and Northern Africa)” countries from 2000 to 2021 to investigate the impact of COVID-19, financial and macroeconomic indicators on FDI relying on the analytic research approach of Static panel data regression, includ

Many researchers tried to prevent or reduce moisture damage and its sensitivity to temperature to improving the performance of hot mix asphalt because it is decreasing the functional and structural life of fixable pavement due to the moisture damage had exposed to it.

The main objective of this study is to inspect the effect of (fly ash “3%, 6%, 12%”, hydrated lime”5%, 10%, 20%” and silica fumes”1%, 2%, 4%) referring to previous research by the net weight asphalt cement as a modified material on the moisture and temperature sensitivity of hot mix asphalt. This was done using asphalt from AL-Nasiria refinery with penetration grade 40-50, nominal maximum size (12.5) mm (surface course) of aggregate and on

This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett