http://repository.sungshin.ac.kr/handle/2025.oak/148 2026-05-19T14:07:24Z http://repository.sungshin.ac.kr/handle/2025.oak/8866 Title: Risk of anxiety disorders after epilepsy diagnosis: A nationwide retrospective cohort study Author(s): 정호현; 이승원; 강채윤; 최운비; 배영오 Abstract: Objective To evaluate the long-term psychiatric consequences of an epilepsy diagnosis on the incidence of anxiety disorders among patients in South Korea. Method This study utilized data from the Korean National Health Insurance Service spanning 2002–2013 to analyze longitudinal risks and contributing factors for anxiety disorders among 2109 patients with epilepsy compared to 21,090 matched controls. Results Patients with epilepsy demonstrated a significantly higher risk of developing anxiety disorders, with an incidence rate of 65.38 per 1000 person-years (95 % CI, 59.61–71.28) versus 33.13 per 1000 person-years (95 % CI, 31.89–34.38) for controls. The incidence rate ratio (IRR) was 1.97 (95 % CI, 1.79–2.18), indicating nearly double the risk relative to the control group. This risk was particularly pronounced in males and individuals under 60, underscoring age and male sex as key risk factors for anxiety post-epilepsy diagnosis. Conclusion The findings underscore the critical need for prompt psychological evaluations and interventions in the management of epilepsy. Addressing these psychological impacts early can significantly enhance outcomes and quality of life for patients, particularly among those at greater risk such as males under the age of 60. 2025-07-17T15:00:00Z http://repository.sungshin.ac.kr/handle/2025.oak/8855 Title: Gene behaviors-based network enrichment analysis and its application to reveal immune disease pathways enriched with COVID-19 severity-specific gene networks Author(s): 박희원; Seiya Imoto; Satory Miyano Abstract: Motivation Gene network analysis is essential for understanding the complex mechanisms underlying diseases, which often involve disruptions in molecular networks rather than individual genes. Despite the availability of large-scale omics datasets and computational tools for gene network analysis, interpretation of the biological relevance of these extensive networks remains challenging.Results We propose a novel computational strategy, gene behaviors-based network enrichment analysis, which systematically identifies functional pathways enriched in phenotype-specific gene networks. Our novel method incorporates comprehensive network characteristics, i.e. gene expression levels, edge strengths, and structural patterns of edges, to rank genes based on activity and assess pathway enrichment, effectively identifying functional pathways enriched within these networks. Through simulation studies, our strategy demonstrated superior performance compared with that of existing methods in identifying enriched pathways. We applied this strategy to whole-blood RNA-seq data from 1102 COVID-19 samples provided by the Japan COVID-19 Task Force. The analysis revealed immune disease pathways enriched with COVID-19 severity-specific gene networks, including "Systemic lupus erythematosus" in asymptomatic and severe samples and "Infl 2025-06-30T15:00:00Z http://repository.sungshin.ac.kr/handle/2025.oak/8849 Title: Efficient curve fitting with penalized B-splines for oceanographic and ecological applications Author(s): 박관영; Dong-Young Lee; Ju-Seong Lee; Hee-Jung Jee; R. Jisung Park; Ja-Yong Koo; Jae-Hwan Jhong Abstract: This study introduces a penalized B-spline approach for estimating smooth curves, incorporating a total variation penalty to balance flexibility and interpretability. By leveraging group penalties and the Alternating Direction Method of Multipliers (ADMM) algorithm, the method ensures consistency across response variables and computational efficiency. We applied this approach to two real-world datasets: oceanographic drifter data in the Nino 4 region and Demoiselle Crane migration data. The fitted trajectories closely captured both large-scale trends and localized variations, demonstrating robustness against noise and irregularly sampled data. This framework is particularly advantageous for analyzing spatiotemporal data, as it effectively removes unnecessary knots and adapts to the complexity of underlying patterns. The total variation penalty controls curve smoothness by penalizing abrupt changes in the estimated function, while the group penalty ensures that all response variables share a consistent set of knots, enhancing interpretability. Although this study focused on two-dimensional spatial trajectories, the methodology is designed for general p-dimensional data and can be extended to three-dimensional datasets, such as avian flight paths or marine animal diving behaviors. Future research could refine the 2025-06-30T15:00:00Z http://repository.sungshin.ac.kr/handle/2025.oak/8845 Title: Improved Frobenius FFT for Code-Based Cryptography on Cortex-M4 Author(s): 김수리 Abstract: Polynomial multiplication over finite fields is one of the most significant operations in code-based cryptography, including HQC, which has been selected as a standardized algorithm in the NIST PQC round 4 process. In the standardization process, the performance of an algorithm is important not only in general-purpose systems but also in embedded systems. In particular, NIST has recommended the ARM Cortex-M4 as the benchmark platform for embedded systems. In CHES2021, Chen et al. optimized BIKE on the ARM Cortex-M4, using the Frobenius Additive FFT as the polynomial multiplication algorithm. However, although HQC was finally selected as a standard algorithm in March 2025, an efficient implementation for the ARM Cortex-M4 platform, which NIST recommends as the benchmark for embedded systems, has not yet been reported. In this paper, we propose an optimized implementation of the Frobenius Additive FFT to accelerate polynomial multiplication in BIKE and HQC on the ARM Cortex-M4 platform. Our approach exploits the fact that one operand of field multiplications in the Frobenius Additive FFT is fixed, allowing the transformation of these operations into binary matrix-vector products. We then apply XOR-efficient linear layer techniques combined with a register scheduling strategy specifically designed for the constrain 2025-06-24T15:00:00Z