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RISS 인기검색어
- 검색키워드
- 저자 : Serkan Ayvaz (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Evaluation of rigid-end offset effect on seismic behavior of a structure subjected to Van earthquake
Serkan Bekiroğlu,Abdurrahman Şahin,Barış Sevim,Yusuf Ayvaz 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.47 No.6
Numerical damage assessment of Van train station building consisting of three RC blocks due to 2011 Van Earthquakes by nonlinear dynamic analysis is presented. The structural model is created with rigid-end offsets and plastic hinges for nonlinear analysis. Rigid-end offsets are considered for connection areas and proposed for wall-supported elements. In wall-supported elements, walls take place in a limited part of the columns. Nonlinear dynamic analysis of the building with and without rigid-end offsets is performed by using real earthquake records and results are compared. The results show that rigid-end offsets have significant effects on the seismic behavior of the structures.
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Evaluation of rigid-end offset effect on seismic behavior of a structure subjected to Van earthquake
Bekiroglu, Serkan,Sahina, Abdurrahman,Sevima, Baris,Ayvaz, Yusuf Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.47 No.6
Numerical damage assessment of Van train station building consisting of three RC blocks due to 2011 Van Earthquakes by nonlinear dynamic analysis is presented. The structural model is created with rigid-end offsets and plastic hinges for nonlinear analysis. Rigid-end offsets are considered for connection areas and proposed for wall-supported elements. In wall-supported elements, walls take place in a limited part of the columns. Nonlinear dynamic analysis of the building with and without rigid-end offsets is performed by using real earthquake records and results are compared. The results show that rigid-end offsets have significant effects on the seismic behavior of the structures.
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Prediction of Drug–Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities
Selma Dere,Serkan Ayvaz 대한의료정보학회 2020 Healthcare Informatics Research Vol.26 No.1
Objectives: Drug–drug interaction (DDI) is a vital problem that threatens people’s health. However, the prediction of DDIs through in-vivo experiments is not only extremely costly but also difficult as many serious side effects are hard to detect in in-vivo and in-vitro settings. The aim of this study was to assess the effectiveness of similarity-based in-silico computational DDI prediction approaches and to provide a cost effective and scalable solution to predict potential DDIs. Methods: In this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs. Results: Using the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs. Conclusions: In this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.
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