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Mingyu Hwang,Yeon Jung Lee,Minji Lee,Byungjoo Kang,Yun Sung Lee,Jaeuk Hwang,Sung-il Woo,Sang-Woo Hahn 대한정신약물학회 2021 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.19 No.2
Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suici-dality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited sig-nificantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion: LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.
Windows 10 환경의 Box 클라우드 아티팩트 분석
윤혜민(Hyemin Yun),김재욱(Jaeuk Kim),황은비(Eunbi Hwang),김해니(Haeni Kim),권태경(Taekyoung Kwon) 한국정보보호학회 2019 情報保護學會誌 Vol.29 No.6
클라우드 서비스는 사용자에게 데이터 저장, 공유, 다운로드 등을 용이하게 해주는 성질과 시, 공간적 특성과 관계없이 사용할 수 있는 특성으로 최근 IT 산업에서 많이 사용되고 있다. 그러나 서버가 공격당할 경우 데이터와 사용자 개인 정보를 포함한 민감 정보가 유출되는 등 다양한 범죄에 노출되어 있다. 이러한 이유로 클라우드의 포렌식 분석 필요성이 증가하였으며, 현재 많이 상용화되어 있는 응용 프로그램에 대해서는 많은 연구가 진행되었다. 본 논문에서는 Box 클라우드에 대해 포렌식 분석을 진행하고 수집한 아티팩트에 관해 설명한다.