Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students' learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students' learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.
Lewis antibodies, such as anti-Lea and anti-Leb, are commonly encountered in routine immunohematology. They are typically IgM in nature and are generally considered clinically insignificant, as they rarely cause hemolytic transfusion reactions (HTRs) or hemolytic disease of the fetus and newborn (HDFN). However, rare cases have been reported where anti-Lewis antibodies caused mild transfusion reactions. In this case report, we describe a 69-year-old male with sepsis secondary to a neck carbuncle who was found to have clinically significant anti-Lewis antibodies. These antibodies presented a challenge during crossmatching, as only two out of seven units of packed red blood cells were compatible. This case underscores the importance of thorough pre-transfusion testing to ensure safe and effective blood transfusion practices.