https://dspace.ewha.ac.kr/handle/2015.oak/267951 Fri, 10 Apr 2026 08:58:07 GMT 2026-04-10T08:58:07Z https://dspace.ewha.ac.kr/handle/2015.oak/275140 Title: Improving patellofemoral pain assessment with weight-bearing computed tomography and machine learning using three-dimensional knee joint metrics Ewha Authors: 최장환; 이효빈 Abstract: Objectives: To evaluate whether three-dimensional (3D) knee metrics derived from weight-bearing computed tomography (WBCT) with machine learning predict patellofemoral pain severity more accurately compared with two-dimensional (2D) metrics. Methods: Diagnostic accuracy was assessed using the mean absolute error (MAE) as the primary endpoint. A five-fold cross-validation was performed for each model (random forest, gradient boosting, convolutional neural networks (CNNs), with hyperparameters tuned via grid search. The reference standard was the anterior knee pain scale (AKPS). Paired t-tests with Bonferroni correction compared with MAE differences among models. 3D knee alignment features (tilt, rotation, translations) were extracted from WBCT; 2D metrics were obtained from oblique-axial slices. Retrospective data were acquired from January to June 2022. Results: In cross-validation, random forest using 3D metrics yielded an MAE of 7.8 (95 % confidence interval (CI): 7.3–8.2), significantly lower than 8.6 (95 % CI: 8.1–9.1) in 2D-based regression (P = 0.02). CNN predictions from distal slices had an MAE of 7.5 (95 % CI: 7.0–8.0), outperforming proximal slices (8.3 (95 % CI: 7.7–8.9), P = 0.03). AKPS improved from 72 ± 10 (pretreatment) to 82 ± 6 (post-treatment) (P < 0.001). Conclusion: 3D WBCT metrics combined with machine learning significantly improved diagnostic accuracy for patellofemoral pain severity compared with conventional 2D imaging. This approach provides an objective, reproducible framework for clinical assessment and treatment planning in orthopedic practice. © 2025 Elsevier B.V. Thu, 01 Jan 2026 00:00:00 GMT https://dspace.ewha.ac.kr/handle/2015.oak/275140 2026-01-01T00:00:00Z https://dspace.ewha.ac.kr/handle/2015.oak/275006 Title: Insights for Curriculum-Oriented Instruction of Programming Paradigms for Non-Computer Science Majors: Survey and Public Q&A Evidence Ewha Authors: 박현석 Abstract: This study examines how different programming paradigms are associated with learning experiences and cognitive challenges as encountered by non-computer science novice learners. Using a case-study approach situated within specific instructional contexts, we integrate survey data from undergraduate students with large-scale public question-and-answer data from Stack Overflow to explore paradigm-related difficulty patterns. Four instructional contexts—C, Java, Python, and Prolog—were examined as pedagogical instantiations of imperative, object-oriented, functional-style, and logic-based paradigms using text clustering, word embedding models, and interaction-informed complexity metrics. The analysis identifies distinct patterns of learning challenges across paradigmatic contexts, including difficulties related to low-level memory management in C-based instruction, abstraction and design reasoning in object-oriented contexts, inference-driven reasoning in Prolog-based instruction, and recursion-related challenges in functional-style programming tasks. Survey responses exhibit tendencies that are broadly consistent with patterns observed in public Q&A data, supporting the use of large-scale community-generated content as a complementary source for learner-centered educational analysis. Based on these findings, the study discusses paradigm-aware instructional implications for programming education tailored to non-major learners within comparable educational settings. The results provide empirical support for differentiated instructional approaches and offer evidence-informed insights relevant to curriculum-oriented teaching and future research on adaptive learning systems. © 2026 by the authors. Thu, 01 Jan 2026 00:00:00 GMT https://dspace.ewha.ac.kr/handle/2015.oak/275006 2026-01-01T00:00:00Z https://dspace.ewha.ac.kr/handle/2015.oak/274658 Title: SeqDA-HLA: Language Model and Dual Attention-Based Network to Predict Peptide-HLA Class I Binding Ewha Authors: 최장환 Abstract: Accurate prediction of peptide-HLA class I binding is crucial for immunotherapy and vaccine development, but existing methods often struggle to capture the intricate biological relationships between peptides and diverse HLA alleles. Here, we introduce SeqDA-HLA, a pan-specific prediction model that combines language model-based embeddings (ELMo) with a dual attention mechanism-self-aligned cross-attention and self-attention-to capture rich contextual features and pairwise interactions. Evaluations against 14 state-of-the-art methods on multiple benchmark datasets demonstrate that SeqDA-HLA consistently outperforms competing approaches, achieving an AUC value up to 0.9856 and accuracy as high as 0.9408. Notably, SeqDA-HLA maintains robust performance across peptide lengths (8-14) and HLA alleles, showcasing its generalizability. Beyond predictive accuracy, SeqDA-HLA offers interpretability by highlighting essential anchor residues and revealing key binding motifs, thereby aligning with experimentally validated biological insights. As a further demonstration of practical impact, we fine-tune SeqDA-HLA on an Influenza virus dataset, successfully predicting binding changes induced by single amino acid mutations. Overall, SeqDA-HLA serves as a powerful and interpretable tool for peptide-HLA binding prediction, with potential applications in epitope-based vaccine design and precision immunotherapy. Wed, 01 Jan 2025 00:00:00 GMT https://dspace.ewha.ac.kr/handle/2015.oak/274658 2025-01-01T00:00:00Z https://dspace.ewha.ac.kr/handle/2015.oak/274605 Title: UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching Ewha Authors: 민동보 Abstract: Unlike other vision tasks where Transformer-based approaches are becoming increasingly common, stereo depth estimation is still dominated by convolution-based models. This is mainly due to the limited availability of real-world ground truth for stereo matching, which hinders the performance improvement of transformer-based stereo approaches. In this paper, we propose UniTT-Stereo, a method to maximize the potential of Transformer-based stereo architectures by unifying self-supervised learning for pre-training with stereo matching framework based on supervised learning. Specifically, we design a dual-task learning scheme that reconstructs masked regions of an input image while simultaneously predicting corresponding points in the paired image. We demonstrate that this approach encourages the model to learn locality-aware representations, which are critical to overcoming the data inefficiency of Transformers. Moreover, to address these challenging tasks of reconstruction-and-prediction, we propose a variable masking ratio strategy that promotes robustness to varying levels of visual information. Additionally, we introduce losses that exploit stereo geometry and correspondence at the appearance, feature, and disparity levels. To further validate the effectiveness of our design, we conduct frequency decomposition and attention map visualization, which reveal how the model effectively captures fine-grained structures and cross-view correspondences. State-of-the-art performance of UniTT-Stereo is validated on various benchmarks such as the ETH3D, KITTI 2012, and KITTI 2015 datasets. Code is available at: https://github.com/00kim/UniTT-Stereo Wed, 01 Jan 2025 00:00:00 GMT https://dspace.ewha.ac.kr/handle/2015.oak/274605 2025-01-01T00:00:00Z