Biofouling is a major problem shared among all maritime sectors employing submerged structures where it leads to substantially increased costs and lowered operational lifespans if poorly addressed. Insight into the ongoing processes at the relevant marine locations is key to effective management of biofouling. Of specific concern for the marine renewable energy (MRE) sector is the fact that information on biofouling composition and magnitude across geographies is dispersed throughout published papers and consulting reports. To enable rapid access to relevant key biofouling events the present work describes a European biofouling database to support the MRE sector and other maritime industries. The database compiles in one document qualitative and quantitative data for challenging biofouling groups, including non-native species associated with MRE and related marine equipment, in different European Ecoregions. It provides information on the occurrence of fouling species and data on key biofouling parameters, such as biofouling thickness and weight. The database aims to aid the MRE sector and offshore industries in understanding which biofouling communities their devices are more susceptible to at a given site, to facilitate informed decisions. In addition, the biofouling mapping is useful for the development of biosecurity risk management plans as well as academic research.

Due to the Web expansion, the prediction of online news popularity is becoming a trendy research topic. In this paper, we propose a novel and proactive Intelligent Decision Support System (IDSS) that analyzes articles prior to their publication. Using a broad set of extracted features (e.g., keywords, digital media content, earlier popularity of news referenced in the article) the IDSS first predicts if an article will become popular. Then, it optimizes a subset of the articles features that can more easily be changed by authors, searching for an enhancement of the predicted popularity probability. Using a large and recently collected dataset, with 39,000 articles from the Mashable website, we performed a robust rolling windows evaluation of five state of the art models. The best result was provided by a Random Forest with a discrimination power of 73%. Moreover, several stochastic hill climbing local searches were explored. When optimizing 1000 articles, the best optimization method obtained a mean gain improvement of 15 percentage points in terms of the estimated popularity probability. These results attest the proposed IDSS as a valuable tool for online news authors.

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