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Abstract In recent years many researches have been focused on active magnetic bearing (AMB) systems. AMB offers advantages such as, contact less and friction free operation, excellent performance over a wide range of temperature, no need for lubricant and longer life. Technology of magnetic bearings can be considered as a new field of research in Iran. This paper presents design, manufacturing and control of a magnetically levitation system with successful operation. This research concluded with documentation of the AMB technology which is prerequisite for earning the technology of active magnetic systems (AMS) and paves the way to develop it.

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Backgrounds: Reinfection among COVID-19 patients is still a challenging issue in the medical literature. Therefore, the current meta-analysis was conducted to estimate the pooled incidence rate of reinfection among COVID-19 patients.
Materials & Methods: A comprehensive search was conducted in PubMed, Web of Science, and Scopus databases from July 1 to October 1, 2021. Original studies which estimated the incidence rate of COVID-19 reinfection were included. CASP (Critical Appraisal skills program) was used to assess the quality of studies. Data were analyzed by STATA statistical software Version 15 (StataCorp, College Station, TX, USA).
Findings: A total of 3803 articles were found, of which 16 articles remained after title, abstract, and full text screening. The minimum and maximum incidence rates of reinfection were 0.001 and 0.73%, respectively. The pooled estimated incidence rate of COVID-19 reinfection was 0.11% (95% confidence interval: 0.02-0.20, p< .001, I² = 100.0). The highest pooled estimated incidence rate of reinfection was observed in people <50 years old (0.14%) (95% CI: 0.001-0.34, p<.001, I² = 100). Regarding the time elapsed after the first infection, the highest reinfection rate occurred four months after the first infection (0.12%) (95% CI: 0.001-0.27, p< .001, I² = 100).
Conclusion: The incidence rate of reinfection among COVID-19 patients is expected to be high. However, it seems that the influence of factors including the age of patients and the time elapsed after the first infection must be considered.

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Detection of land use/cover changes in many different studies is one of the basic needs for environmental monitoring and management. Conversion of agricultural lands is one of the main issues related to urban planning. In this study an attempt has been made to study land use/cover changes through image processing techniques. Two landsat TM images of Isfahan area provided in 1990 and 1998 were atmospherically rectified and registered on each other. Images were then classified to ten different land use/cover classes using Bayesian classification algorithm. Training sites were generated using fuzzy logic approach. A post classification comparison approach was then used to create a change map. The results show a dramatic change on agricultural lands in this area during this period.