Linear text segmentation plays an important role in many natural language processing tasks. Many algorithms have been proposed and shown to improve the performance of linear text segmentation. However, the previous studies often suffer from either lower segmentation accuracy or higher computational complexity. Moreover, parameter setting is another critical problem in some algorithms. Although manual assignment is an approach to solve this problem, it may increase the user's burden, and the parameters provided may not always be suitable to reflect the real metadata of a text. In this paper, a hybrid algorithm, TSHAC-DPSO, is proposed to tackle these problems. A novel linear Text Segmentation algorithm based on Hierarchical Agglomerative Clustering (TSHAC) is proposed to rapidly generate a satisfactory solution without an auxiliary knowledge base, parameter setting, or user involvement; then an efficient evolutional algorithm, Discrete Particle Swarm Optimization (DPSO), is adopted to generate the global optimal solution by refining the solution created by TSHAC. TSHAC-DPSO fully utilizes the merits of both algorithms which not only improve the accuracy of linear text segmentation, but also make the execution more efficient and flexible. The experimental results show that TSHAC-DPSO provides comparable segmentation accuracy with several well-known linear text segmentation algorithms.

Objective: Pharmaceutical chemistry plays a key role in pharmaceutical speciality, which is aiming at making students master basic concepts, basic theory and basic skills of pharmaceutical chemistry. The teachers, however, often encounter some challenges in their teaching process, such as insufficient learning motivation for students, lack of effective means to achieve teaching goals, non-uniformity of program outcomes and ability to satisfy industry needs. It is necessary to seek for an efficient way to improve teaching level by applying new mode. Methods: In view of the great success of Problem Based Learning (PBL) mode in medical courses, this mode is also attempted in pharmaceutical chemistry. We applied PBL method in teaching of pharmaceutical chemistry with the procedure as follows: Under the guidance of teachers, students carry out an active learning around the core knowledge or problem in each module, undergoing a logic process of "asking questions, establishing hypothesis, self-study to disambiguation, demonstrating hypothesis" to acquire knowledge. Results: Well-design of curriculum program and reasonable setting up of assessment system are both emphasized in teaching reform. Through teaching reform of PBL mode in pharmaceutical chemistry, complicated materials are easily to understand and basic knowledge is easily connected to practice. In addition, students' enthusiasm of autonomous learning is inspired and good teaching effects are found. Conclusion: Only more efforts in teaching practice in pharmaceutical chemistry are unceasingly made, will the goal of teaching reform with PBL method be achieved.

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In this paper, a knowledge exploration assistant system for inquiry-based learning is proposed to support students when inquiry-based learning is conducted. The proposed system provides students with instant assistance with proper knowledge materials and knowledge exploration functions during their learning process. In addition, students' learning history is automatically recorded by the proposed system. A field experiment was undertaken on the freshman course "Computer Science". The experimental results showed that the proposed system improved the students' learning achievement.

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