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Titlebook: 18th International Probabilistic Workshop; IPW 2020 José C. Matos,Paulo B. Louren?o,Hélder S. Sousa Conference proceedings 2021 The Editor(

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期刊全稱(chēng)18th International Probabilistic Workshop
期刊簡(jiǎn)稱(chēng)IPW 2020
影響因子2023José C. Matos,Paulo B. Louren?o,Hélder S. Sousa
視頻videohttp://file.papertrans.cn/101/100381/100381.mp4
發(fā)行地址Contains latest research developments in probability and statistics for engineering applications.Provides methods and tools for uncertainty quantification and modeling.Illustrates the application of r
學(xué)科分類(lèi)Lecture Notes in Civil Engineering
圖書(shū)封面Titlebook: 18th International Probabilistic Workshop; IPW 2020 José C. Matos,Paulo B. Louren?o,Hélder S. Sousa Conference proceedings 2021 The Editor(
影響因子This volume presents the proceedings of the 18th International Probabilistic Workshop (IPW), which was held in Guimar?es, Portugal in May 2021. Probabilistic methods are currently of crucial importance for research and developments in the field of engineering, which face challenges presented by new materials and technologies and rapidly changing societal needs and values. Contemporary needs related to, for example, performance-based design, service-life design, life-cycle analysis, product optimization, assessment of existing structures and structural robustness give rise to new developments as well as accurate and practically applicable probabilistic and statistical engineering methods to support these developments. These proceedings are a valuable resource for anyone interested in contemporary developments in the field of probabilistic engineering applications..
Pindex Conference proceedings 2021
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o tackle those ethical challenges as they were addressed by an expert ethics team in the EC-funded datAcron project. We start by explaining the efforts made at the EU level to ensure the ethical development of publicly funded technologies and the framework and risks all projects need take into accou
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Probabilistic Seismic Risk Assessment of School Buildings data repositories that can be utilized to search for therapeutic targets for cancer treatment. We then introduce software tools frequently used for genomic data mining. Finally, we summarize working algorithms for the discovery of therapeutic biomarkers.
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d and nonlinear learning for Big Data are also covered.?.In turn,the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deuts
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