A Combined Approach of Process Mining and Rule-based AI for Study Planning and Monitoring in Higher Education

This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from expected plans. These insights are combined with recommendations and requirements of the corresponding study programs extracted from examination regulations. Here, event calculus and answer set programming are used to provide models of the study programs which support planning and conformance checking while providing feedback on possible study plan violations. In its combination, process mining and rule-based artificial intelligence are used to support study planning and monitoring by deriving rules and recommendations for guiding students to more suitable study paths with higher success rates. Two applications will be implemented, one for students and one for study program designers.Comment: 12 pages, 4 figures, conference, 30 reference

The KCNQ1 channel - remarkable flexibility in gating allows for functional versatility

The KCNQ1 channel (also called Kv7.1 or KvLQT1) belongs to the superfamily of voltage-gated K(+) (Kv) channels. KCNQ1 shares several general features with other Kv channels but also displays a fascinating flexibility in terms of the mechanism of channel gating, which allows KCNQ1 to play different physiological roles in different tissues. This flexibility allows KCNQ1 channels to function as voltage-independent channels in epithelial tissues, whereas KCNQ1 function as voltage-activated channels with very slow kinetics in cardiac tissues. This flexibility is in part provided by the association of KCNQ1 with different accessory KCNE β-subunits and different modulators, but also seems like an integral part of KCNQ1 itself. The aim of this review is to describe the main mechanisms underlying KCNQ1 flexibility