Concrete pavements are essential to modern infrastructure, but their low tensile and flexural strengths can cause cracking and shrinkage. This study evaluates fiber reinforcement with steel and carbon fibers in various combinations to improve rigid pavement performance. Six concrete mixes were tested: a control mix with no fiber, a mix with 1% steel fiber (SF1%), a mix with 1% carbon fiber (CF1%), and three hybrid mixes with 1% fiber content: 0.75% steel /0.25% carbon fiber (SF0.75CF0.25), 0.25% steel /0.75% carbon fiber (SF0.25CF0.75), and 0.5% steel /0.5% carbon fiber ((SF0.5CF0.5). Laboratory experiments including compressive, flexural, and splitting tensile strength tests were conducted at 7, 28, and 90 days, while Finite Element Analys

Ruthenium-Ruthenium and Ruthenium–ligand interactions in the triruthenium "[Ru₃(μ-H)(μ₃-κ²-Hamphox-N,N)(CO)₉]" cluster are studied at DFT level of theory. The topological indices are evaluated in term of QTAIM (quantum theory of atoms in molecule). The computed topological parameters are in agreement with related transition metal complexes documented in the research papers. The QTAIM analysis of the bridged core part, i.e., Ru₃H, analysis shows that there is no bond path and bond critical point (chemical bonding) between Ru(2) and Ru(3). Nevertheless, a non-negligible delocalization index for this non-bonding interaction is calculated

     This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

Information security contributes directly to increase the level of trust between the government’s departments by providing an assurance of confidentiality, integrity, and availability of sensitive governmental information. Many threats that are caused mainly by malicious acts can shutdown the egovernment services. Therefore the governments are urged to implement security in e-government projects.
Some modifications were proposed to the security assessment multi-layer model (Sabri model) to be more comprehensive model and more convenient for the Iraqi government. The proposed model can be used as a tool to assess the level of security readiness of government departments, a checklist for the required security measures and as a commo

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

This paper examines the change in planning pattern In Lebanon, which relies on vehicles as a semi-single mode of transport, and directing it towards re-shaping the city and introducing concepts of "smooth or flexible" mobility in its schemes; the concept of a "compact city" with an infrastructure based on a flexible mobility culture. Taking into consideration environmental, economical and health risks of the existing model, the paper focuses on the four foundations of the concepts of "city based on culture flexible mobility, "and provides a SWOT analysis to encourage for a shift in the planning methodology.

In this paper all possible regressions procedure as well as stepwise regression procedure were applied to select the best regression equation that explain the effect of human capital represented by different levels of human cadres on the productivity of the processing industries sector in Iraq by employing the data of a time series consisting of 21 years period. The statistical program SPSS was used to perform the required calculations.

This paper presents the dynamic responses of generators in a multi-machine power system.  The fundamental swing equations for a multi-machine stability analysis are revisited. The swing equations are solved to investigate the influence of a three-phase fault on the network largest load bus. The Nigerian 330kV transmission network was used as a test case for the study. The time domain simulation approach was explored to determine if the system could withstand a 3-phase fault. The stability of the transmission network is estimated considering the dynamic behaviour of the system under various contingency conditions. This study identifies Egbin, Benin, Olorunsogo, Akangba, Sakete, Omotosho and Oshogbo as the key buses w