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Advanced Engineering Technology and Application
Number of Followers: 9

This is an Open Access Journal

Open Access journal
ISSN (Print) 2090-9535 - ISSN (Online) 2090-9543
Published by Fayoum University

[2 journals]

Flexural Behavior of Lightweight Ferrocement Composite Beams
- Abstract: : Ferrocement is a construction material that specializes at crack management, impact resistance, and durability. The primary goal of this study is to look into the effect of different metallic mesh reinforcing materials on the flexural behaviour of lightweight ferrocement composite beams as a suitable replacement for traditional reinforced concrete beams. To strengthen the ferrocement skin layers, two types of steel mesh (Welded wire mesh and expanded metal mesh) were used. Fourteen beams with dimensions of 1000 mm length, 100 mm width, and 150 mm depth were cast and tested to failure under flexural loadings. At all levels of loading, the deformation characteristics and cracking behaviour of each beam were observed and monitored. The findings showed that the ferrocement composite beams could achieve high ultimate and serviceability loads, fracture resistance management, high ductility, lighter and high-energy absorption compared with conventional beam. It might have genuine building advantages for developing countries.  
Improved Emotion Detection Framework for Arabic Text using Transformer
Models *
- Abstract: : Emotion detection in text is a challenging task with various applications in natural language processing and psychology. In recent years, there has been increasing interest in developing algorithms for detecting emotions in Arabic text, given the importance of this language and the lack of resources in this domain. This paper proposes the use of transformer-based models for Arabic emotion detection in text. We use the emotone_ar dataset, a resource for the development and evaluation of algorithms and techniques for emotion detection in Arabic. The proposed model is based on transformers for encoding contextual information in text and classify emotions based on this encoded representation. We evaluate the performance of our model on the emotone_ar dataset and compare our results to previous methods for emotion detection. Our model achieves an accuracy of 74.16% and an F1 score of 0.7406 on the test set, outperforming previous methods for emotion detection on this dataset. We also compare our results to the performance of a Naïve Bayes classifier and show that our approach significantly outperforms this baseline. These results demonstrate the effectiveness of transformer-based models for emotion detection in Arabic text and highlight the potential for further improvements in this area.