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One of the major advances in artificial intelligence nowadays is to understand, process and utilize the humans' natural language. This has been achieved by employing the different natural language processing (NLP) techniques along with the aid of the various deep learning approaches and architectures. Using the distributed word representations to substitute the traditional bag-of-words approach has been utilized very efficiently in the last years for many NLP tasks. In this paper, we present the detailed steps of building a set of efficient word embedding models called ArWordVec that are generated from a huge repository of Arabic tweets. In addition, a new method for measuring Arabic word similarity is introduced that has been used in evaluating the performance of the generated ArWordVec models. The experimental results show that the performance of the ArWordVec models overcomes the recently available models on Arabic Twitter data for the word similarity task. In addition, two of the large Arabic tweets datasets are used to examine the performance of the proposed models in the multi-class sentiment analysis task. The results show that the proposed models are very efficient and help in achieving a classification accuracy ratio exceeding 73.86% with a high average F1 value of 74.15. [ABSTRACT FROM AUTHOR]

[Display omitted] • The MAO-MMT-Mn Nano-clay coating was developed on Ti using MAO technique. • The MAO-MMT-Mn coating exhibited excellent bond strength on Ti. • The MAO-MMT-Mn coating enabled the sustained release of Mn, Si, and P. • The MAO-MMT-Mn coating exhibited outstanding osteogenic and angiogenic properties. • The MAO-MMT-Mn coating enhanced local osseointegration and angiogenesis in vivo. Titanium (Ti) is a widely used and favored material for dental implants, with research efforts focusing on enhancing its osseointegration capabilities. Montmorillonite (MMT), a nano-clay, has shown significant potential in drug delivery, wound healing, and other biomedical applications. Implant coatings based on MMT have demonstrated promising results; however, challenges remain in terms of surface bond strength, stability, and uniformity. Studies have indicated that manganese (Mn) can enhance cell viability, proliferation, and adhesion by activating integrin receptors. Here, a KMnO 4 -modified MMT (MAO-MMT-Mn) coating was developed on Ti through the micro-arc oxidation (MAO) technique. The MAO-MMT-Mn coating significantly strengthens its bond, enabling it to withstand greater loads. The incorporation of Mn, silicon (Si) and phosphorus (P) resulted in the formation of uniformly distributed lamellar MMT, facilitating the sustained release of these elements. In vitro experiments demonstrated that the MAO-MMT-Mn coating promoted osteoblast adhesion and endothelial cell migration, and led to a significant increase in the expression of osteogenic and angiogenic genes compared to uncoated Ti. Moreover, in vivo assessments using micro-CT scans and histological staining revealed improved local osseointegration and angiogenesis after 4 weeks of implantation. In conclusion, the MAO-MMT-Mn coating offers a safe, simple, and cost-effective method to enhance the surfaces of Ti implants, thereby potentially enhancing the clinical outcomes of dental implant procedures. [ABSTRACT FROM AUTHOR]