Bayesian Network Model for Accessing Safety and Security of Offshore Wind Farms

The offshore wind industry experience a rising importance for the worldwide energy production, which is accompanied by increasing amount of wind turbines and Offshore Wind Farms (OWFs). However, due to its harsh environment and its role for energy provision, OWFs are confronted with several threats that are impacting its safety and security. Consequently, decision making at design as well as run time plays an important role for providing safe and secure operation in OWFs. We propose in this work the application of a Bayesian Network (BN) for a high-level representation of the safety and security state of an OWF. The developed BN-model is based on the safety and security goals and related functions defined in Kopke et al. (2019). The derived model enables a user to analyze the overall importance of high impact functions, like compliance, environmental protection, supply reliability or accident prevention. Obtained results indicate that the proposed BN-model enables decision makers to explore cross-system interrelations, and thus, to define requirements for implementations on lower design levels

Nine Love Songs and Carols

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An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms

Offshore wind farms (OWFs) are important infrastructure which provide an alternative and clean means of energy production worldwide. The offshore wind industry has been continuously growing. Over the years, however, it has become evident that OWFs are facing a variety of safety and security challenges. If not addressed, these issues may hinder their progress. Based on these safety and security goals and on a Bayesian network model, this work presents a methodological approach for structuring and organizing expert knowledge and turning it into a probabilistic model to assess the safety and security of OWFs. This graphical probabilistic model allowed us to create a high-level representation of the safety and security state of a generic OWF. By studying the interrelations between the different functions of the model, and by proposing different scenarios, we determined the impacts that a failing function may have on other functions in this complex system. Finally, this model helped us define the performance requirements of such infrastructure, which should be beneficial for optimizing operation and maintenance

Measurement of the Lifetime Difference Between B_s Mass Eigenstates

We present measurements of the lifetimes and polarization amplitudes for B_s --> J/psi phi and B_d --> J/psi K*0 decays. Lifetimes of the heavy (H) and light (L) mass eigenstates in the B_s system are separately measured for the first time by determining the relative contributions of amplitudes with definite CP as a function of the decay time. Using 203 +/- 15 B_s decays, we obtain tau_L = (1.05 +{0.16}/-{0.13} +/- 0.02) ps and tau_H = (2.07 +{0.58}/-{0.46} +/- 0.03) ps. Expressed in terms of the difference DeltaGamma_s and average Gamma_s, of the decay rates of the two eigenstates, the results are DeltaGamma_s/Gamma_s = (65 +{25}/-{33} +/- 1)%, and DeltaGamma_s = (0.47 +{0.19}/-{0.24} +/- 0.01) inverse ps.Comment: 8 pages, 3 figures, 2 tables; as published in Physical Review Letters on 16 March 2005; revisions are for length and typesetting only, no changes in results or conclusion

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació