This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

This research utilized natural asphalt (NA) deposits from sulfur springs in western Iraq. Laboratory tests were conducted to evaluate the performance of an asphalt mixture incorporating NA and verify its suitability for local pavement applications. To achieve this, a combination of two types of NA, namely soft SNA and hard HNA, was blended to create a binder known as Type HSNA. The resulting HSNA exhibited a penetration grade that adhered to Iraqi specifications. Various percentages of NA (20%, 40%, 60%, and 80%) were added to petroleum asphalt. The findings revealed enhanced physical properties of HSNA, which also satisfied the requirements outlined in the Iraqi specifications for asphalt cement.

Consequently, HS

This research utilized natural asphalt (NA) deposits from sulfur springs in western Iraq. Laboratory tests were conducted to evaluate the performance of an asphalt mixture incorporating NA and verify its suitability for local pavement applications. To achieve this, a combination of two types of NA, namely soft SNA and hard HNA, was blended to create a binder known as Type HSNA. The resulting HSNA exhibited a penetration grade that adhered to Iraqi specifications. Various percentages of NA (20%, 40%, 60%, and 80%) were added to petroleum asphalt. The findings revealed enhanced physical properties of HSNA, which also satisfied the requirements outlined in the Iraqi specifications for asphalt cement. Consequently, HSNA can serve as an

This work presents an innovative approach to enhancing the performance of concrete with reclaimed asphalt pavement (RAP) aggregates using titanium dioxide (TiO2) nanoparticles. Traditional limestone coarse aggregates were partially replaced with 30% and 50% RAP aggregates; a subset of mixtures containing RAP aggregates was treated with TiO2 nanoparticles. The rheological, mechanical, and long-term properties of concrete, along with changes in its chemical composition following the addition of RAP and TiO2, were evaluated. Results revealed that using 30% and 50% RAP in concrete mixtures reduced their compressive strength by 18% and 27%, respectively. However, using TiO2 in those mixtures enhanced their compressive strength by 8.7% an

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

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.

Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classifica

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modifie

High performance work systems and general industrial enterprise performance