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Although the reinforcement of concrete and brick masonry with fiber-reinforced polymer (FRP) has been extensively researched, its application and impact on natural stone, especially in historic preservation, have received less attention. This study aims to examine the bond-slip characteristics of carbon fiber-reinforced polymer (CFRP) with two types of natural stone masonry, aiming to enhance their effectiveness in reinforcing historic structures. The stones studied include one from the Chouf-Lekdad region (A) and another from a historic structure in Sétif City (B). Both stones were strengthened using CFRP and carbon fiber fabric (CFF) through near-surface mount (NSM) and external bonding (EBR) techniques.

The interaction was assessed during the pull-out test by analyzing the stress transfer mechanisms, adhesion and deformation. This study also examines the effects of the following parameters on the bond between CFRP and stone: type of stone (A and B), type of reinforcement (plat CFRP and CFF), various notch shapes and sizes (bp, tp and Lb), and reinforcement techniques (NSM and EBR).

This study demonstrated the practicality and effectiveness of enhancing natural stone masonry of old buildings by integrating NSM and EBR techniques with CFRP. With a bond length of 30 mm, the pull-out force correlates with the strength of the stone. This indicates the importance of stone strength in obtaining better adhesion. The CFF–resin interface is more cohesive than the CFRP plate–resin interface because the resin penetrates the flexible CFF strip, ensuring better adhesion. In contrast, the CFRP plate interface is rigid and smooth. The results suggest that natural stone–CFRP adhesion is more effective than CFRP bonded to concrete and brick masonry due to the stone's strong resistance.

This experimental investigation provides new study into the bond-slip behavior of CFRP-reinforced natural stone masonry, filling the gap in existing research. The findings offer useful direction for creating FRP strengthening solutions that are specifically adapted to the properties of natural stone used in historic constructions. This study helps to improve preservation procedures by guiding the selection of reinforcing techniques, such as NSM versus EBR, and finding ideal bond lengths. This work's novelty stems from its ability to improve the structural integrity of culturally significant buildings while preserving their historical authenticity.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.