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109. The Accessibility and Effect of Cardiac Rehabilitation in COVID-19 Pandemic Era.

Author

Kim, Chul, Song, Jun Hyeong, and Kim, Seung Hyoun

Subjects
COVID-19 pandemic, CARDIAC rehabilitation, COVID-19, PHYSICAL activity, EXERCISE tests
Abstract

Objective: To prospectively compare the efficacy of conventional center-based cardiac rehabilitation (CBCR) and home-based cardiac rehabilitation (HBCR) during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Ninety patients were divided into HBCR and CBCR groups based on cardiovascular risk stratification and individual preference. The CBCR group performed supervised in-hospital exercise training 2–3 times/week and subsequent self-exercise at home. The HBCR group performed self-exercise at home after one or two sessions of exercise education. The cardiopulmonary exercise test results at baseline and those at the 3-, 6-, and 12-month follow-ups were analyzed as primary outcome. Results: The peak oxygen consumption (peak VO2, mL/kg/min) in the CBCR group was 20.1 and 24.0 at baseline and 12 months, respectively, showing significant improvement (p=0.006). In the HBCR group, it only increased from 24.4 to 25.5, showing suboptimal improvement. A significant increase in the Korean activity scale/index was confirmed only in the CBCR group (p=0.04). The cardiovascular outcome did not differ between the two groups, nor did the dropout rate or demographic factors. Conclusion: During the COVID-19 pandemic, only CBCR was associated with a significant improvement in peak VO2 and physical activity levels, a finding that differs from those of other studies and seems to be affected by COVID-19. Therefore, in situations where the importance of HBCR is emphasized, it is essential to introduce measures to monitor and enhance exercise adherence among participants. [ABSTRACT FROM AUTHOR]

Published
2024
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110. Development of a Risk Prediction Model for Adverse Skin Events Associated with TNF-α Inhibitors in Rheumatoid Arthritis Patients.

Author

Kim, Woorim, Oh, Soo-Jin, Kim, Hyun-Jeong, Kim, Jun-Hyeob, Gil, Jin-Yeon, Ku, Young-Sook, Kim, Joo-Hee, Kim, Hyoun-Ah, Jung, Ju-Yang, Choi, In-Ah, Kim, Ji-Hyoun, Kim, Jinhyun, Han, Ji-Min, and Lee, Kyung-Eun

Subjects
MACHINE learning, DISEASE risk factors, TUMOR necrosis factors, GENOME-wide association studies, LOGISTIC regression analysis, RHEUMATOID arthritis
Abstract

Background: Rheumatoid arthritis (RA) is a chronic inflammatory disorder primarily targeting joints, significantly impacting patients' quality of life. The introduction of tumor necrosis factor-alpha (TNF-α) inhibitors has markedly improved RA management by reducing inflammation. However, these medications are associated with adverse skin reactions, which can vary greatly among patients due to genetic differences. Objectives: This study aimed to identify risk factors associated with skin adverse events by TNF-α in RA patients. Methods: A cohort study was conducted, encompassing patients with RA who were prescribed TNF-α inhibitors. This study utilized machine learning algorithms to analyze genetic data and identify markers associated with skin-related adverse events. Various machine learning algorithms were employed to predict skin and subcutaneous tissue-related outcomes, leading to the development of a risk-scoring system. Multivariable logistic regression analysis identified independent risk factors for skin and subcutaneous tissue-related complications. Results: After adjusting for covariates, individuals with the TT genotype of rs12551103, A allele carriers of rs13265933, and C allele carriers of rs73210737 exhibited approximately 20-, 14-, and 10-fold higher incidences of skin adverse events, respectively, compared to those with the C allele, GG genotype, and TT genotype. The machine learning algorithms used for risk prediction showed excellent performance. The risk of skin adverse events among patients receiving TNF-α inhibitors varied based on the risk score: 0 points, 0.6%; 2 points, 3.6%; 3 points, 8.5%; 4 points, 18.9%; 5 points, 36.7%; 6 points, 59.2%; 8 points, 90.0%; 9 points, 95.7%; and 10 points, 98.2%. Conclusions: These findings, emerging from this preliminary study, lay the groundwork for personalized intervention strategies to prevent TNF-α inhibitor-associated skin adverse events. This approach has the potential to improve patient outcomes by minimizing the risk of adverse effects while optimizing therapeutic efficacy. [ABSTRACT FROM AUTHOR]

Published
2024
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111. Comparison of Maximum Heat Transfer Rate of Thin Vapor Chambers with Different Wicks under Multiple Heat Sources and Sinks.

Author

Kim, Sung Hyoun, Kang, Seo Yeon, Park, Sung Jun, and Jang, Seok Pil

Subjects
*HEAT transfer, *HEAT sinks, *HEAT flux, *TEMPERATURE distribution, *VAPORS
Abstract

In this paper, we present a new analytical model to investigate the maximum heat transfer rate of a thin vapor chamber (TVC) with multiple heat sources and sinks. The model can specifically consider different heat flux conditions for each heat source. Both capillary limitations and allowable maximum temperature constraints were employed to determine the maximum heat transfer rate. The liquid and vapor pressure distributions within the TVC were analytically derived using the Brinkman-extended Darcy equation and the Hagen–Poiseuille equation, respectively. Additionally, the theoretical wall temperature distribution was calculated based on the 3D energy equation, considering different heat flux conditions for multiple heat sources with a weighting factor. Our results demonstrate that the heat flux conditions applied to the heat sources significantly impact the internal flow pattern of the TVC. These changes in flow patterns influence the pressure distributions of the liquid and vapor, thereby affecting the maximum heat transfer rate. Furthermore, the effects of wick parameters on the maximum heat transfer rate under various heat flux conditions were examined. [ABSTRACT FROM AUTHOR]

Published
2024
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112. A Test Data Generation for Performance Testing in Massive Data Processing Systems

Author

Kim, Sunkyung, Park, JiSu, Kim, Kang Hyoun, Shon, Jin Gon, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Park, James J., editor, Loia, Vincenzo, editor, Choo, Kim-Kwang Raymond, editor, and Yi, Gangman, editor

Published
2019
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136. 14.1 A Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC

Author

Kar, Monodeep, primary, Silberman, Joel, additional, Venkataramani, Swagath, additional, Srinivasan, Viji, additional, Fleischer, Bruce, additional, Rubin, Joshua, additional, Lancaster, JohnDavid, additional, Lee, Saekyu, additional, Cohen, Matthew, additional, Ziegler, Matthew, additional, Cao, Nianzheng, additional, Woodward, Sandra, additional, Agrawal, Ankur, additional, Zhou, Ching, additional, Chatarasi, Prasanth, additional, Gooding, Thomas, additional, Guillorn, Michael, additional, Hekmatshoartabari, Bahman, additional, Jacob, Philip, additional, Jain, Radhika, additional, Jain, Shubham, additional, Jung, Jinwook, additional, Kim, Kyu-Hyoun, additional, Koswatta, Siyu, additional, Lutz, Martin, additional, Mannari, Alberto, additional, Mathew, Abey, additional, Nair, Indira, additional, Ranjan, Ashish, additional, Ren, Zhibin, additional, Rider, Scot, additional, Roewer, Thomas, additional, Satterfield, David, additional, Schaal, Marcel, additional, Sen, Sanchari, additional, Tellez, Gustavo, additional, Tran, Hung, additional, Wang, Wei, additional, Zalani, Vidhi, additional, Zhang, Jintao, additional, Zhang, Xin, additional, Shah, Vinay, additional, Senger, Robert, additional, Kumar, Arvind, additional, Lu, Pong-Fei, additional, and Chang, Leland, additional

Published
2024
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