The Syrian crisis has resulted in a devastating impact on refugees’ oral health and data on their oral health is lacking. To explore oral health and dental needs of Syrian refugee children, a cross-sectional study of 484 children was conducted. Caries prevalence, DMFT, SiC, and oral hygiene indices were recorded. Caries prevalence was 96.1%, with mean dmft/DMFT scores of 3.65/1.15, SiC scores were 6.64/2.56, and Hygiene Index was 1.13. Decay was the main component of dmft/DMFT (89%-88%). Most common complaint was pain (98.3%) with 88% of the children do not brush/brush occasionally. Pearson’s correlation displayed a strong association between dental caries and age (P ≤ 0.01), where caries in permanent dentition increases and in deciduous dentition decreases. Syrian refugees showed poor oral health, high caries prevalence, high unmet dental needs, and poor oral hygiene practices, which indicates lack of dental care services, and warranting urgent prevention to reduce the burden of oral disease of this population.

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Social media platforms changed from being socialization platforms to serve businesses through advertisements. This research aims at investigating active young users' experience with social media ads by studying the personalization and the usefulness of the ads, and the role of the host architecture of the used platform. The results prove that users' experience was affected by the designated variables: personalization, perceived usefulness, and the host architecture. Specifically, It was found that social media users find social media ads useful, and personalized, and that the perceived usefulness and personalization significantly affect the usage of host architecture which significantly affects users’ experience. Additionally, a significant difference is found between clusters of student answers in terms of personalization and perceived usefulness effect on user experience.

Abstract. The problem of spam e-mail has gained a tremendous amount of attention. Although entities tend to use e-mail spam filter applications to filter out received spam e-mails, marketing companies still tend to send unsolicited emails in bulk and users still receive a reasonable amount of spam e-mail despite those filtering applications. This work proposes a new method for classifying emails into spam and non-spam. First, several e-mail content features are extracted and then those features are used for classifying each e-mail individually. The classification results of three different classifiers (i.e. Decision Trees, Random Forests and k-Nearest Neighbor) are combined in various voting schemes (i.e. majority vote, average probability, product of probabilities, minimum probability and maximum probability) for making the final decision. To validate our method, two different spam e-mail collections were used.