This paper presents a study to investigate the behavior of post-tensioned segmental concrete beams that exposed to high-temperature. The experimental program included fabricating and testing twelve simply supported beams that divided into three groups depending on the number of precasting concrete segments. All specimens were prepared with an identical length of 3150 mm and differed in the number of the incorporated segments of the beam (9, 7, or 5 segments). To simulate the genuine fire disasters, nine out of twelve beams were exposed to a high-temperature flame for one hour. Based on the standard fire curve (ASTM – E119), the temperatures of 300◦C (572◦F), 500◦C (932◦F), and 700◦C (1292◦F) were adopted. Consequently,

Abstract:

It is essential to provide health care directly or indirectly based on laboratory tests in order of diagnosing diseases. There is an urgent need for the results of such tests to be accurate and reliable. Therefore, the International Organization for Standardization has prepared special standards for medical laboratories to prove their efficiency, presented in standards 15189: 2012. The aim of the research is to determine the possibility of the Al-alwiya Women's Hospital in meeting the requirements of the standards 15189: 2012, which includes administrative requirements consisting of fifteen items and technical requirements consisting of ten items. The research is important because laboratories shou

It is important that real time stability in smart grids is ensured as the integration of renewables and the complexity of the systems grows. In this paper, we provide a solid architecture, which combines a Residual CNNLSTM deep neural network predictor, FPGA-accelerated Model Predictive Control (MPC), and SHAP-based explainability. The proposed method predicted with 99.8% accuracy using the Electrical grid Stability Simulated Dataset (UCI) and minimized the instability rates surpassing 85 percent in all operating conditions. Meeting real-time operating needs, FPGA deployment on a Xilinx Zynq UltraScale+ provided 3.1 ms latency and 5 times reduced energy consumption against CPU processing. By emphasizing bus voltage and frequency as major in

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi

The purpose of this article was to identify and assess the importance of risk factors in the tendering phase of construction projects. The construction project cannot succeed without the identification and categorization of these risk elements. In this article, a questionnaire for likelihood and impact was designed and distributed to a panel of specialists to analyze risk factors. The risk matrix was also used to research, explore, and identify the risks that influence the tendering phase of construction projects. The probability and impact values assigned to risk are used to calculate the risk's score. A risk matrix is created by combining probability and impact criteria. To determine the main risk elements for the tender phase of

The purpose of this article was to identify and assess the importance of risk factors in the tendering phase of construction projects. The construction project cannot succeed without the identification and categorization of these risk elements. In this article, a questionnaire for likelihood and impact was designed and distributed to a panel of specialists to analyze risk factors. The risk matrix was also used to research, explore, and identify the risks that influence the tendering phase of construction projects. The probability and impact values assigned to risk are used to calculate the risk's score. A risk matrix is created by combining probability and impact criteria. To determine the main risk elements for the tend

Abstract:

 The underlying objective of the international standard No. (6) to assist in accounting applications for the extractive industries, taking into consideration the goals and objectives contained in the sixteenth of the private International Accounting Standards criterion accounting for land, machinery and equipment, as well as Standard No. axes (38) relating to intangible assets, and in order to create a vision of a comprehensive development needs oil in order to exact evaluation of policies related to the particular needs and draw a comprehensive frameworks with respect to treatment of expenditures and revenues in the oil production industry, is also interested in Standard No. (6) within the primary objectiv

Smear zone is usually formed around the prefabricated vertical drains (PVD’s) due to mandrel driving. The geotechnical properties of the soil in this zone exhibit significant changes that affect the performance of the PVD’s. The most relevant property in this respect is the coefficient of permeability. So far, no serious attention is paid to investigate the effects of shearing under large shear strains on the geotechnical properties of the soft soil in Fao region. In this study, an extensive laboratory testing program was conducted to assess the characteristics of the smear zone with an emphasis on the permeability coefficient of Fao soft soil. The results show that the permeability of the smear zone is about 70% of

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le