A Real-Time Optimization (RTO) strategy incorporating the fuzzy sets theory is developed, where the problem constraints obtained from process considerations are treated in fuzzy environment. Furthermore, the objective function is penalized by a fuzzified form of the key process constraints. To enable using conventional optimization techniques, the resulting fuzzy optimization problem is then reformulated into a crisp programming problem. The crisp programming problem is solved using both Sequential Quadratic Programming (SQP) and Heuristic Random Optimization (HRO) techniques for comparison purposes. The proposed fuzzy RTO strategy is demonstrated via the Tennessee Eastman benchmark process, and is also compared with a crisp RTO strategy from the literature. Remarkable economical improvement is found over the crisp RTO. In spite of the fuzzified constraints, the proposed strategy yields smooth operation of the process, while maintaining the product quality within the acceptable range.

In this paper a novel process monitoring scheme for reducing the type I and type II error rates in the monitoring phase is proposed. First, the proposed approach uses an augmented data matrix to implement the process dynamic. Then, we apply independent component analysis (ICA) transformation to the augmented data matrix, and eliminate the outliers using the local outlier factor (LOF) algorithm. Finally, the control limit based on the LOF value of the cleaned data are obtained. In the monitoring phase, if the LOF value of each sample exceeds the control limit, fault has occurred, otherwise, data is normal. The proposed method is applied to fault detection in both a simple multivariate dynamic process and the Tennessee Eastman process. In both processes, type I and type II error rates are witnessed to reduce by considering the process dynamic and performing the LOF algorithm. Results clearly indicate better performance of the proposed scheme compared to the alternative methods.

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Children in the U.S. suffer from high rates of cavities, dental caries, and other childhood oral diseases. Pediatric oral healthcare in the United States is a very important issue; however, despite funding for pediatric oral healthcare from federal, state, and private organizations it remains an often neglected issue.  Expanded preventive care services, strengthened relationships between policymakers, dental care providers and public health professionals are needed to improve the oral healthcare delivery for children. 

Background: The importance of improving quality and performance in higher education has led various universities to turn to eff, ective methods of educational evaluation, such as auditing. Objectives: The present study evaluated the academic performance of the Tabriz Faculty of Management and Medical Informatics postgraduate students, an Iranian Center of Excellence in Health Management based on the Tennessee Academic Audit Model. Methods: This descriptive-cross sectional study was conducted in 2019 with the participation of educational managers and faculty members of the same faculties in two phases consisting of self-assessment and external evaluation. After contextualization, the Tennessee comprehensive higher education audit checklist was used. Data were studied descriptively, and the results were reported as frequency (percentage) and mean ±,standard deviation. Analysis of variance (ANOVA) and Tukey’, s post hoc tests were used to evaluate the signifi, cance of the diff, erence in academic performance between the educational groups. t-test was also used to evaluate the diff, erence in performance scores in self-assessment and external evaluation phases. A P-value < 0. 05 was considered signifi, cant. Results: The participants’,performance in the self-assessment phase was moderate (total score: 5. 32), and their performance in the external evaluation phase was weak (total score: 2. 75). The best and the worst self-assessment scores were in the dimensions of “, overall assessment”,and “, follow-up of previous academic audits, ”,respectively. In the external evaluation, the dimensions of “, contributions to the program and university goals”,and “, follow-up of previous academic audits”,had the best and worst performance scores, respectively. Conclusions: The results demonstrated that the Tabriz Faculty of Management and Medical Informatics of the Medical School needs to improve in terms of international standards. Therefore, managers and policymakers are required to implement interventions to address this gap.