Oral cancer knowledge, attitudes, and practices among dentists in Khartoum State, Sudan

The dental professions hold an important responsibility in the control of oral cancer and the early diagnosis highly depends on their knowledge. The present study was developed to assess the knowledge, attitude, and practice of dentists in Khartoum State regarding oral cancer prevention and early detection. An administered questionnaire was structured and sent to all licensed 130 dentists working in public dental clinics in Khartoum State. Responses to the questionnaire were analyzed using descriptive and analytical statistics. Although the majority of the dentists were knowledgeable about the major risk factors of oral cancer, more than half of the dentists reported they do not carry out any special examination to detect oral cancer in age 40 and above in asymptomatic patients. Dentists indicated their lack of training as the main barrier for conducting a comprehensive oral cancer examination. Interestingly, the vast majority of the dentists express their interest to have further oral cancer educational and training sessions. The findings of the present study suggested strongly that educational and training interventions are necessary to enhance preventive measures which may lead to reduce mortality and morbidity from oral cancer

Improved RMR rock mass classification using artificial intelligence algorithms

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures