Tissue engineering is a relatively new area of research that combines medical, biological, and engineering fundamentals to create tissue-engineered constructs that regenerate, preserve, or slightly increase the functions of tissues. To create mature tissue, the extracellular matrix should be imitated by engineered structures, allow for oxygen and nutrient transmission, and release toxins during tissue repair. Numerous recent studies have been devoted to developing three-dimensional nanostructures for tissue engineering. One of the most effective of these methods is electrospinning. Numerous nanofibrous scaffolds have been constructed over the last few decades for tissue repair and restoration. The current review gives an overview of attempts to construct nanofibrous meshes as tissue-engineered scaffolds for various tissues such as bone, cartilage, cardiovascular, and skin tissues. Also, the current article addresses the recent improvements and difficulties in tissue regeneration using electrospinning.

Cloud computing is an emerging area of computer technology that benefits form the processing power and the computing resources of many connected, geograph ically distanced computers connected via Internet.Cloud computing eliminates the need of having a complete infrastructure of hardware and software to meetusers requirements and applications. It can be thought of or considered as a complete or a partialoutsourcing of hardware and software resources. Toaccess cloud applications, a good Internet connectionand a standard Internet browser are required. Cloudcomputing has its own drawback from the securitypoint of view; this paper aims to address most of these threats and their possible solutions

Text categorization is the process of grouping documents into categories based on their contents. This process is important to make information retrieval easier, and it became more important due to the huge textual information available online. The main problem in text categorization is how to improve the classification accuracy. Although Arabic text categorization is a new promising field, there are a few researches in this field. This paper proposes a new method for Arabic text categorization using vector evaluation. The proposed method uses a categorized Arabic documents corpus, and then the weights of the tested document's words are calculated to determine the document keywords which will be compared with the keywords of the corpus categorizes to determine the tested document's best category.

<p class="0abstract">Smart mobile devices and cloud computing are widely used today. While mobile and portable devices have different capabilities, architectures, operating systems, and communication channels than one another, government data are distributed over heterogeneous systems. This paper proposes a 3-tier mediation framework providing single application to manage all governmental services. The framework is based on private cloud computing for adapting the content of Mobile-Government (M-Government) services using Role-Based Access Control (RBAC) and Derive Unique Key Per Transaction (DUKPT). The 3-layers in the framework are: presence, integration, and homogenization. The presence layer is responsible for adapting the content with regard to four contexts: device, personal, location, and connectivity contexts. The integration layer, which is hosted in a private cloud server, is responsible for integrating heterogeneous data sources. The homogenization layer is responsible for converting data into XML format. The flexibility of the mediation and XML provides an adaptive environment to stream data based on the capabilities of the device that sends the query to the system.</p>

Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a significant rise in the number of choices of products and services offered on the Internet. This is where recommender systems come into play by providing meaningful recommendations to consumers based on their needs and interests effectively. However, recommender systems are still vulnerable to the scenarios of sparse rating data and cold start users and items. To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. The experimental results have shown that the proposed approach outperforms other benchmark recommendation approaches with regard to recommendation accuracy and coverage.