Exposure of reinforced concrete buildings to an accidental fire may result in cracking and loss in the bearing capacity of their major components, columns, beams, and slabs. It is a challenge for structural engineers to develop efficient retrofitting techniques that enable RC slabs to restore their structural integrity, after being exposed to intense fires for a long period of time. Experimental
investigation was carried out on twenty one slab specimens made of self compacting concrete, eighteen of them are retrofitted with CFRP sheets after burning and loading till failure while three of them (which represent control specimens) are retrofitted with CFRP sheet after loading till failure without burning. All slabs had been tested in a

Experimental research was carried out to investigate the performance of CFRP wrapping jackets used for retrofitting twelve square reinforced concrete (CR) column specimens damaged by exposure to fire flame, at different temperatures of  300, 500 and 700ºC, except for two specimens that were not burned. The specimens were then loaded axially till failure after gradual or sudden cooling. The specimens were divided into two groups containing two main reinforcement ratios, ρ= 0.0314 and ρ= 0.0542. This was followed by the retrofitting procedure that included wrapping all the specimens with two layers of CFRP fabric sheets. The test results of the retrofitted specimens showed that the fire damaged RC

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

Background: Scientific education aims to be inclusive and to improve students learning achievements, through appropriate teaching and learning. Problem Based Learning (PBL) system, a student centered method, started in the second half of the previous century and is expanding progressively, organizes learning around problems and students learn about a subject through the experience of solving these problems.Objectives:To assess the opinions of undergraduate medical students regarding learning outcomes of PBL in small group teaching and to explore their views about the role of tutors and methods of evaluation. Type of the study: A cross-sectional study.Methods: This study was conducted in Kerbala Medical Colleges among second year students

The purpose of the study is to identify the teaching techniques that mathematics' teachers use due to the Brain-based learning theory. The sample is composed of (90) teacher: (50) male, (40) female. The results have shown no significant differences between male and female responses' mean. Additionally, through the observation of author, he found a lack of using Brain-based learning techniques. Thus, the researcher recommend that it is necessary to involve teachers in remedial courses to enhance their ability to create a classroom that raise up brain-based learning skills.  

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes