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  1. Haidar AM, Mohamed A, Al-Dabbagh M, Hussain A, Masoum M
    Int J Neural Syst, 2009 Dec;19(6):473-9.
    PMID: 20039470
    Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper.
  2. Borromeo GL, Ahmad MS, Buckley S, Bozanic M, Cao A, Al-Dabbagh M, et al.
    Eur J Dent Educ, 2018 Aug;22(3):e321-e326.
    PMID: 29024268 DOI: 10.1111/eje.12296
    INTRODUCTION: The role of dental auxiliaries in collaborative care of patients with special needs is compelling. This study was undertaken to investigate the perceptions of Special Needs Dentistry (SND) education and practice amongst students enrolled in Australian programmes in dental auxiliary, namely dental hygiene, dental therapy and oral health therapy (DH/DT/OHT).

    MATERIALS AND METHODS: All Australian institutions offering DH/DT/OHT programmes (n = 14) were invited to participate in a self-administered questionnaire survey, conducted online, involving students across all academic years. Twelve institutions agreed to participate, but only five institutions were included in the final analysis, with a student response rate of 31.1%. Answers to open-ended questions were coded and grouped for measurement of frequencies. Quantitative data were analysed via chi-squared and Fisher's exact tests (significance taken as P 

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