This research is devoted to investigate relationship between both Ultrasonic Pulse Velocity and Rebound Number (Hammer Test) with cube compressive strength and also to study the effect of steel reinforcement on these relationships.
A study was carried out on 32 scale model reinforced concrete elements. Non destructive testing campaign (mainly ultrasonic and rebound hammer tests) made on the same elements. About 72 concrete cubes (15 X 15 X15) were taken from the concrete mixes to check the compressive strength.. Data analyzed.Include the possible correlations between non destructive testing (NDT) and compressive strength (DT) Statistical approach is used for this purpose. A new relationships obtained from correlations results is give

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

In this study, a novel application of lab-scale dual chambered air-cathode microbial fuel cell (MFC) has been developed for simultaneous bio-treatment of real pharmaceutical wastewater and renewable electricity generation. The microbial fuel cell (MFC) was provided with zeolite-packed anodic compartment and a cation exchange membrane (CEM) to separate the anode and cathode. The performance of the proposed MFC was evaluated in terms of COD removal and power generation based on the activity of the bacterial consortium in the biofilm mobilized on zeolite bearer. The MFC was fueled with real pharmaceutical wastewater having an initial COD concentration equal to 800 mg/L and inoculated with anaerobic aged sludge. Results demo

Active learning is a teaching method that involves students actively participating in activities, exercises, and projects within a rich and diverse educational environment. The teacher plays a role in encouraging students to take responsibility for their own education under their scientific and pedagogical supervision and motivates them to achieve ambitious educational goals that focus on developing an integrated personality for today’s students and tomorrow’s leaders. It is important to understand the impact of two proposed strategies based on active learning on the academic performance of first-class intermediate students in computer subjects and their social intelligence. The research sample was intentionally selected, consis

Coronavirus 2019 (COVID-19) pandemic led to a massive global socio-economic tragedy that has impacted the ecosystem. This paper aims to contextualize urban and rural environmental situations during the COVID-19 pandemic in the Middle East and North Africa (MENA) Region.

Baghdad city has been faced numerous issues related to freshwater environment deteriorations due to many reasons, mainly was the discharge of wastewater without adequate treatment. Al- Rustamiya Wastewater Treatment Plant (WWTP) have been constructed among many plants in Baghdad city to reduce the amount of wastewater discharged into natural environment and its subsequent adverse effects. This study was conducted to evaluate the performance of the plant which consist of a conventional activated sludge (CAS) and sequencing batch reactors (SBR) systems as secondary treatment units and its ability to meet Iraqi specifications. A reliability level determination and analysis also were conducted to find the plant's stability and its capabi

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when