A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

In this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

Bridging theory and practice in ecosystem services mapping: a systematic review

The mapping and assessment of Ecosystem Services (ES) aims at better connecting environmental conservation, economic development, and human well-being. However, 60 years after the development of the ES concept, a persistent gap remains between the production of scientific knowledge on ES and its use in support of policy and management. Here, we report on a systematic review of the scientific literature that helps better understand key challenges and offers potential solutions to bridge this gap. The review considered four criteria: (1) how stakeholders participate to studies; (2) how usable ES maps are for decision-making; (3) what policy recommendations were made; and (4) what research recommendations were made. The analysis of 135 papers published between 2008 and 2020 revealed diverse technical and conceptual challenges that could prevent the effective use of ES concepts and methods outside the academic realm. The main challenges identified in the literature were the uncertainty levels of ES mapping outputs, issues of spatial scales, the understanding of ES interactions, and the need for temporal analysis. Many policies rely on mapped outcomes, creating a window of opportunity for the uptake of ES mapping into policy-making. However, it remains key to involve stakeholders early in the co-design of ES studies and to better understand their preferences and motivation to adopt ES mapping in their practices. The study shows that higher levels of learnability of ES mapping practices, further popularization to foster public awareness, and increased capacity building would facilitate the ES concept uptake into decision and policy-making.

A systemic approach to tackling ocean plastic debris

Growing evidence of the prevalence of plastics in the oceanic environment is causing increasing concern. In this study we consider the possible impact of major changes to the supply and consumption of plastics. While we recognize that tackling the problem of plastic debris in the ocean calls for integrated solutions, our analysis contributes to filling the gap in knowledge about the effectiveness of intervention strategies. Our results find that strategies addressing plastics demand have a greater impact than those focussing on plastics supply. However, only measures aimed at plastics supply are effective in the short run. These considerations can help to inspire the prioritization of intervention strategies and to contribute to the design of integrated interventions aimed at tackling the problem of marine plastic debris through a systemic approach.