ABSTRACT
The ocean is facing multiple pressures from human activities, including the effects of climate change. Science has a prominent role in identifying problems and communicating these to society. However, scientists are also increasingly taking an active role in developing solutions, including strategies for adapting to and mitigating climate change, increasing food security, and reducing pollution. Transmitting these solutions to society changes our narrative about the ocean and motivates actions. The United Nations triple initiatives for this decade-the Sustainable Development Goals, the Decade on Ocean Science for Sustainable Development, and the Decade of Ecosystem Restoration-provide the momentum for this change in narrative and focus. Here, we reflect on the search for solutions and the need for better ways of communicating science in a positive way. We synthesize insights from a summer school held during the COVID-19 pandemic and present some examples of successes and failures and the lessons learned from these.
ABSTRACT
Presently, several million people suffer from major depressive and bipolar disorders. Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. Electroencephalogram records provide important information to improve clinical diagnosis and are very useful in the scientific community. In this work, electroencephalogram records and patient data from the Hospital Virgen de la Luz in Cuenca (Spain) were processed for a correct classification of bipolar disorders. This work implemented an innovative radial basis function-based neural network employing a fuzzy means algorithm. The results show that the proposed method is an effective approach for discrimination of two kinds of classes, i.e., bipolar disorder patients and healthy persons. The proposed algorithm achieved the best performance compared with other machine learning techniques such as Bayesian linear discriminant analysis, Gaussian naive Bayes, decision trees, K-nearest neighbour, or support vector machine, showing a very high accuracy close to 97%. Therefore, the neural network technique presented could be used as a new tool for the diagnosis of bipolar disorder, considering the possibility of integrating this method into medical software.