Zeitschriftenartikel zum Thema „Hot spot prediction“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung zum Thema "Hot spot prediction" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
Zhang, Sijia, Le Zhao, Chun-Hou Zheng und Junfeng Xia. „A feature-based approach to predict hot spots in protein–DNA binding interfaces“. Briefings in Bioinformatics 21, Nr. 3 (08.04.2019): 1038–46. http://dx.doi.org/10.1093/bib/bbz037.
Kenneth Morrow, John, und Shuxing Zhang. „Computational Prediction of Protein Hot Spot Residues“. Current Drug Metabolism 18, Nr. 9 (01.03.2012): 1255–65. http://dx.doi.org/10.2174/138920012799362909.
Kenneth Morrow, John, und Shuxing Zhang. „Computational Prediction of Protein Hot Spot Residues“. Current Pharmaceutical Design 18, Nr. 9 (01.03.2012): 1255–65. http://dx.doi.org/10.2174/138161212799436412.
Nair B.J, Bipin, und Lijo Joy. „A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions“. International Journal of Engineering & Technology 7, Nr. 1.9 (01.03.2018): 145. http://dx.doi.org/10.14419/ijet.v7i1.9.9752.
Tuncbag, N., O. Keskin und A. Gursoy. „HotPoint: hot spot prediction server for protein interfaces“. Nucleic Acids Research 38, Web Server (05.05.2010): W402—W406. http://dx.doi.org/10.1093/nar/gkq323.
Liu, Siyu, Chuyao Liu und Lei Deng. „Machine Learning Approaches for Protein–Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment“. Molecules 23, Nr. 10 (04.10.2018): 2535. http://dx.doi.org/10.3390/molecules23102535.
Roll, Uri, Lewi Stone und Shai Meiri. „Hot-Spot Facts and Artifacts-Questioning Israel's Great Biodiversity“. Israel Journal of Ecology and Evolution 55, Nr. 3 (06.05.2009): 263–79. http://dx.doi.org/10.1560/ijee.55.3.263.
Wang, Ao, und Yimin Xuan. „Multiscale prediction of localized hot-spot phenomena in solar cells“. Renewable Energy 146 (Februar 2020): 1292–300. http://dx.doi.org/10.1016/j.renene.2019.07.073.
Grosdidier, Solene, und Juan Fernandez-Recio. „Protein-protein Docking and Hot-spot Prediction for Drug Discovery“. Current Pharmaceutical Design 18, Nr. 30 (23.08.2012): 4607–18. http://dx.doi.org/10.2174/138161212802651599.
Zhang, Ming, und Wei Chen. „Hot Spot Data Prediction Model Based on Wavelet Neural Network“. Mathematical Problems in Engineering 2018 (30.10.2018): 1–10. http://dx.doi.org/10.1155/2018/3719564.
Rommel, D. P., D. Di Maio und T. Tinga. „Transformer hot spot temperature prediction based on basic operator information“. International Journal of Electrical Power & Energy Systems 124 (Januar 2021): 106340. http://dx.doi.org/10.1016/j.ijepes.2020.106340.
Skillen, Alex, Alistair Revell, Hector Iacovides und Wei Wu. „Numerical prediction of local hot-spot phenomena in transformer windings“. Applied Thermal Engineering 36 (April 2012): 96–105. http://dx.doi.org/10.1016/j.applthermaleng.2011.11.054.
Zhang, Yue, Lianfei Shan, Jianming Yu und Hongwei Lv. „Transformer winding hot spot temperature prediction based on ε -fuzzy tree“. IOP Conference Series: Earth and Environmental Science 300 (09.08.2019): 042034. http://dx.doi.org/10.1088/1755-1315/300/4/042034.
Díaz-Valle, Armando, José Marcos Falcón-González und Mauricio Carrillo-Tripp. „Hot Spots and Their Contribution to the Self-Assembly of the Viral Capsid: In Silico Prediction and Analysis“. International Journal of Molecular Sciences 20, Nr. 23 (27.11.2019): 5966. http://dx.doi.org/10.3390/ijms20235966.
Jin, Jae Sik, und Joon Sik Lee. „Electron–Phonon Interaction Model and Prediction of Thermal Energy Transport in SOI Transistor“. Journal of Nanoscience and Nanotechnology 7, Nr. 11 (01.11.2007): 4094–100. http://dx.doi.org/10.1166/jnn.2007.010.
Jin, Jae Sik, und Joon Sik Lee. „Electron–Phonon Interaction Model and Prediction of Thermal Energy Transport in SOI Transistor“. Journal of Nanoscience and Nanotechnology 7, Nr. 11 (01.11.2007): 4094–100. http://dx.doi.org/10.1166/jnn.2007.18084.
Higa, Roberto Hiroshi, und Clésio Luis Tozzi. „Prediction of binding hot spot residues by using structural and evolutionary parameters“. Genetics and Molecular Biology 32, Nr. 3 (2009): 626–33. http://dx.doi.org/10.1590/s1415-47572009000300029.
Deng, Lei, Yuanchao Sui und Jingpu Zhang. „XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting“. Genes 10, Nr. 3 (21.03.2019): 242. http://dx.doi.org/10.3390/genes10030242.
Chen, Peng, Jinyan Li, Limsoon Wong, Hiroyuki Kuwahara, Jianhua Z. Huang und Xin Gao. „Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences“. Proteins: Structure, Function, and Bioinformatics 81, Nr. 8 (23.07.2013): 1351–62. http://dx.doi.org/10.1002/prot.24278.
Shao, Yong-Bo, Zhi-Fu Du und Seng-Tjhen Lie. „Prediction of hot spot stress distribution for tubular K-joints under basic loadings“. Journal of Constructional Steel Research 65, Nr. 10-11 (Oktober 2009): 2011–26. http://dx.doi.org/10.1016/j.jcsr.2009.05.004.
Chen, Zixi, Fuqiang Liu, Bin Li, Xiaoqing Peng, Lin Fan und Aijing Luo. „Prediction of hot spot areas of hemorrhagic fever with renal syndrome in Hunan Province based on an information quantity model and logistical regression model“. PLOS Neglected Tropical Diseases 14, Nr. 12 (21.12.2020): e0008939. http://dx.doi.org/10.1371/journal.pntd.0008939.
Deng, Yongqing, Jiangjun Ruan, Yu Quan, Ruohan Gong, Daochun Huang, Cihan Duan und Yiming Xie. „A Method for Hot Spot Temperature Prediction of a 10 kV Oil-Immersed Transformer“. IEEE Access 7 (2019): 107380–88. http://dx.doi.org/10.1109/access.2019.2924709.
Mohamadi, Bahaa, Timo Balz und Ali Younes. „Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt“. Remote Sensing 12, Nr. 20 (12.10.2020): 3307. http://dx.doi.org/10.3390/rs12203307.
Kim, Jeong Guk, Byeong Choon Goo, Sung Cheol Yoon und Sung Tae Kwon. „Thermographic Investigation of Hot Spots in Railway Brake Discs“. Key Engineering Materials 385-387 (Juli 2008): 669–72. http://dx.doi.org/10.4028/www.scientific.net/kem.385-387.669.
Zhao, Yueyao, Jiawei Zhang und Haojie Li. „Deformation prediction analysis of vertical displacement of deep foundation pit based on LIBSVM“. E3S Web of Conferences 206 (2020): 01021. http://dx.doi.org/10.1051/e3sconf/202020601021.
Xu, Yan, Kai Zhang, Hong Liang Zheng, Yu Cheng Sun und Xue Lei Tian. „An Improved Geometric Model to Predict Hot Spots of Castings“. Materials Science Forum 689 (Juni 2011): 29–32. http://dx.doi.org/10.4028/www.scientific.net/msf.689.29.
Zhang, Yiyi, Xingxiao Wei, Xianhao Fan, Ke Wang, Ran Zhuo, Wei Zhang, Shuo Liang, Jian Hao und Jiefeng Liu. „A Prediction Model of Hot Spot Temperature for Split-Windings Traction Transformer Considering the Load Characteristics“. IEEE Access 9 (2021): 22605–15. http://dx.doi.org/10.1109/access.2021.3056529.
Shabarek, Abdullah, Steven Chien und Soubhi Hadri. „Deep Learning Framework for Freeway Speed Prediction in Adverse Weather“. Transportation Research Record: Journal of the Transportation Research Board 2674, Nr. 10 (27.08.2020): 28–41. http://dx.doi.org/10.1177/0361198120947421.
Xia, Linyuan, Qiumei Huang und Dongjin Wu. „Decision Tree-Based Contextual Location Prediction from Mobile Device Logs“. Mobile Information Systems 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/1852861.
Pruncu, C. I., Z. Azari, C. Casavola und C. Pappalettere. „Characterization and Prediction of Cracks in Coated Materials: Direction and Length of Crack Propagation in Bimaterials“. International Scholarly Research Notices 2015 (31.01.2015): 1–13. http://dx.doi.org/10.1155/2015/594147.
Freitas e Silva, Kleber Santiago, Raisa Melo Lima, Patrícia de Sousa Lima, Lilian Cristiane Baeza, Roosevelt Alves da Silva, Célia Maria de Almeida Soares und Maristela Pereira. „Interaction of Isocitrate Lyase with Proteins Involved in the Energetic Metabolism in Paracoccidioides lutzii“. Journal of Fungi 6, Nr. 4 (23.11.2020): 309. http://dx.doi.org/10.3390/jof6040309.
Matijosaitiene, Irina, Peng Zhao, Sylvain Jaume und Joseph Gilkey Jr. „Prediction of Hourly Effect of Land Use on Crime“. ISPRS International Journal of Geo-Information 8, Nr. 1 (31.12.2018): 16. http://dx.doi.org/10.3390/ijgi8010016.
Kim, Sung-Min, Yosoon Choi und Hyeong-Dong Park. „New Outlier Top-Cut Method for Mineral Resource Estimation via 3D Hot Spot Analysis of Borehole Data“. Minerals 8, Nr. 8 (11.08.2018): 348. http://dx.doi.org/10.3390/min8080348.
Kunicki, Borucki, Cichoń und Frymus. „Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet“. Energies 12, Nr. 18 (17.09.2019): 3561. http://dx.doi.org/10.3390/en12183561.
Guan, Mingxiang, Le Wang und Liming Chen. „Channel allocation for hot spot areas in HAPS communication based on the prediction of mobile user characteristics“. Intelligent Automation & Soft Computing 22, Nr. 4 (07.04.2016): 613–20. http://dx.doi.org/10.1080/10798587.2016.1152771.
Zhu, Xiaolei, und Julie C. Mitchell. „KFC2: A knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features“. Proteins: Structure, Function, and Bioinformatics 79, Nr. 9 (06.07.2011): 2671–83. http://dx.doi.org/10.1002/prot.23094.
Araújo, J. A., L. Susmel, D. Taylor, J. C. T. Ferro und J. L. A. Ferreira. „On the prediction of high-cycle fretting fatigue strength: Theory of critical distances vs. hot-spot approach“. Engineering Fracture Mechanics 75, Nr. 7 (Mai 2008): 1763–78. http://dx.doi.org/10.1016/j.engfracmech.2007.03.026.
Sun, Yuanyuan, Gongde Xu, Na Li, Kejun Li, Yongliang Liang, Hui Zhong, Lina Zhang und Ping Liu. „Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression“. Symmetry 13, Nr. 8 (22.07.2021): 1320. http://dx.doi.org/10.3390/sym13081320.
SHI, GUANGLIN, LIN ZHU und DONGBIN WEI. „A NEW PREDICTION APPROACH FOR THE STRUCTURAL FATIGUE LIFE BASED ON MULTI-FACTOR CORRECTION“. Surface Review and Letters 25, Nr. 05 (Juli 2018): 1850095. http://dx.doi.org/10.1142/s0218625x18500956.
Mo, Shu Min, Ke Feng Zeng und Chao Liu. „Early Warning Mechanism of Huangshan World Geopark to Divert Passenger Traffic“. Advanced Materials Research 1030-1032 (September 2014): 2014–18. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2014.
SRINIVASAN, M., und A. KRISHNAN. „ASSESSING THE RELIABILITY OF TRANSFORMER TOP OIL TEMPERATURE MODEL“. International Journal of Reliability, Quality and Safety Engineering 19, Nr. 05 (Oktober 2012): 1250024. http://dx.doi.org/10.1142/s0218539312500246.
Ruzicka, Filip, und Tim Connallon. „Is the X chromosome a hot spot for sexually antagonistic polymorphisms? Biases in current empirical tests of classical theory“. Proceedings of the Royal Society B: Biological Sciences 287, Nr. 1937 (21.10.2020): 20201869. http://dx.doi.org/10.1098/rspb.2020.1869.
Matijosaitiene, Irina, Anthony McDowald und Vishal Juneja. „Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data“. Sustainability 11, Nr. 10 (19.05.2019): 2848. http://dx.doi.org/10.3390/su11102848.
Lu, Jian Hui, Meng Bing Wei und Kai Yuan Zheng. „Multiaxial Fatigue Life Prediction of the CII Platform Leg Based on Critical Plane Energy Method“. Applied Mechanics and Materials 624 (August 2014): 255–61. http://dx.doi.org/10.4028/www.scientific.net/amm.624.255.
Feuerstein, Stefanie, und Kerstin Schepanski. „Identification of Dust Sources in a Saharan Dust Hot-Spot and Their Implementation in a Dust-Emission Model“. Remote Sensing 11, Nr. 1 (20.12.2018): 4. http://dx.doi.org/10.3390/rs11010004.
Orozco, G. A., J. R. Gomez, O. F. Sanchez, I. D. Gil und A. Duran. „Effect of kinetic models on hot spot temperature prediction for phthalic anhydride production in a multitubular packed bed reactor“. Canadian Journal of Chemical Engineering 88, Nr. 2 (April 2010): 224–31. http://dx.doi.org/10.1002/cjce.20276.
Schlee, Sandra, Kristina Straub, Thomas Schwab, Thomas Kinateder, Rainer Merkl und Reinhard Sterner. „Prediction of quaternary structure by analysis of hot spot residues in protein‐protein interfaces: the case of anthranilate phosphoribosyltransferases“. Proteins: Structure, Function, and Bioinformatics 87, Nr. 10 (10.06.2019): 815–25. http://dx.doi.org/10.1002/prot.25744.
Chen, Ya Bo, Yue Sun, Xu Ri Sun, Ge Hao Sheng und Xiu Chen Jiang. „Real-Time Temperature On-Line Monitoring and Analysis System for Transformers“. Applied Mechanics and Materials 521 (Februar 2014): 409–13. http://dx.doi.org/10.4028/www.scientific.net/amm.521.409.
Ding, Guangyu, und Liangxi Qin. „Study on the prediction of stock price based on the associated network model of LSTM“. International Journal of Machine Learning and Cybernetics 11, Nr. 6 (30.11.2019): 1307–17. http://dx.doi.org/10.1007/s13042-019-01041-1.
Shi, H. L., und G. W. Lan. „A GREY MODEL FOR SHORT-TERM PREDICTION OF THE IONOSPHERIC TEC“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (08.02.2020): 1161–67. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1161-2020.