Integrating knowledge from scientific literature is essential in biomedical research. However, the rapid growth of scientific literature makes staying up to date increasingly challenging. Retrieval-Augmented Generation (RAG) offers a promising framework, but its effectiveness in specialized biomedical domains remains unclear. In this work, we propose a two-stage retrieval pipeline for RAG, with a focus on Bordetella pertussis as a case study. Our method first applies hard filtering with synonym expansion to eliminate irrelevant passages, and then performs hybrid search, followed by reranking. We evaluate our approach using a dataset of 58 pertussis-related queries with automatic relevance judgments from multiple large language models (LLMs). Experimental results show that our pipeline improves MAP@10 by 13.4-20.4 points compared with existing methods and achieves the highest MRR@10. Furthermore, consistent improvements across different LLMs highlight the effectiveness of our approach.

For non-native English speakers, learning programming presents unique challenges, particularly in languages like Python, which heavily rely on English syntax and documentation. This study explores the challenges faced by non-native English-speaking Japanese university students enrolled in introductory Python programming courses. A survey was held to examined various aspects of their learning experience, including their programming proficiency, English fluency, use of translation tools, and the impact of bilingual resources on their learning. Clustering analysis was employed to group the participants into three distinct clusters: beginners, intermediate learners, and advanced learners. Cluster 0 (beginners) had low programming experience and English fluency, relying on non-English materials and translation tools. Cluster 1 (intermediate) showed moderate skills, using bilingual resources and translation aids. Cluster 2 (advanced) had higher proficiency but faced challenges with debugging and code optimization. Our findings highlight the need for tailored resources to support students navigating English-based programming environments.