Water, energy, food, land and climate form a tightly-connected nexus in which actions on one sector impact other sectors, creating feedbacks and unanticipated consequences. This is especially because at present, much scientific research and many policies are constrained to single discipline/sector silos that are often not interacting (e.g., water-related research/policy). However, experimenting with the interaction and determining how a change in one sector could impact another may require unreasonable time frames, be very difficult in practice and may be potentially dangerous, triggering any one of a number of unanticipated side-effects. Current modelling often neglects knowledge from practice. Therefore, a safe environment is required to test the potential cross-sectoral implications of policy decisions in one sector on other sectors. Serious games offer such an environment by creating realistic ‘simulations’, where long-term impacts of policies may be tested and rated. This paper describes how the ongoing (2016–2020) Horizon2020 project SIM4NEXUS will develop serious games investigating potential plausible cross-nexus implications and synergies due to policy interventions for 12 multi-scale case studies ranging from regional to global. What sets these games apart is that stakeholders and partners are involved in all aspects of the modelling definition and process, from case study conceptualisation, quantitative model development including the implementation and validation of each serious game. Learning from playing a serious game is justified by adopting a proof-of-concept for a specific regional case study in Sardinia (Italy). The value of multi-stakeholder involvement is demonstrated, and critical lessons learned for serious game development in general are presented.

Industry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results.

Currently, management tools are designed to solve specific water issues, they cannot interoperate and are losing decisional dependencies along the water supply distribution chain. This article reports a DSS, which uses decisional knowledge to improve daily operations. A specially designed Water Management Ontology (WMO) is used to encompass the water cycle from water management perspective and combined with two inference engines which provide recommendations to the water manager. This knowledge characterization permits to semantically represent human and natural paths interactions in order to discover hidden knowledge, hence founding new IWRM strategies to improve resource management and energy efficiency

We define the MaxSAT problem for many-valued CNF formulas, called many-valued MaxSAT, and establish its complexity class. We then describe a basic branch and bound algorithm for solving many-valued MaxSAT, and an exact many-valued MaxSAT solver we have implemented. Finally, we report the experimental investigation we have performed to compare our solver with Boolean MaxSAT solvers on graph coloring instances. The results obtained indicate that many-valued CNF formulas can become a competitive formalism for representing and solving combinatorial optimization problems.