• Energy Research
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The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building a ship, through optimisation of ship operations. In recent years, ship efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. The data have been successfully applied to extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview of applying ML techniques to enhance ships’ sustainability. The work covers the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning. Suitable ML approaches are analysed and compared on a scenario basis, with their space for improvements also discussed. Meanwhile, a reminder is given that ML has many inherent uncertainties and hence should be used with caution.

This open access book brings together a collection of cutting-edge insights into how action can and is already being taken against climate change at multiple levels of our societies, amidst growing calls for transformative and inclusive climate action. In an era of increasing recognition regarding climate and ecological breakdown, this book offers hope, inspiration and analyses for multi-level climate action, spanning varied communities, places, spaces, agents and disciplines, demonstrating how the energy and dynamism of local scales are a powerful resource in turning the tide. Interconnected yet conceptually distinct, the book’s three sections span multiple levels of analysis, interrogating diverse perspectives and practices inherent to the vivid tapestry of climate action emerging locally, nationally and internationally. Delivered in collaboration with the UK’s ‘Place-Based Climate Action Network’, chapters are drawn from a wide range of authors with varying backgrounds spread across academia, policy and practice.

The phenomena of climate change transcend all national and regional boundaries. To address this complex challenge, we must determine the areas of the country of interest, in this case, Greece, that have been most adversely affected by climate. Greece is surrounded by water, and a significant part of its GDP is derived from the marine and maritime industries, including tourism. Since the start of the IntelComp project, a Preparatory Living Lab (PLL) has been planned and delivered, feeding into the development of the IntelComp platform and the Living Lab on Climate Change Adaptation. The study's results lead to the conclusion that one of the most important challenges in tackling climate change is the decarbonisation challenge, specifically the shift to renewable energy sources and the investments that must be made. Several EU and national policy frameworks, including the European Green Deal, the Climate Law, the National Long-term Strategy for 2050 (on the Climate and Energy), highlight the decarbonisation as one of the major challenges in the climate change pledge. This will be the primary subject of the IntelComp climate change case study. PLLs also led to the identification of policy questions and useful data sources to aid the IntelComp project's launch. While previous research on co-production has primarily focused on involving citizens through public participation processes in order to gain their support, trust, and insights in structured decision-making processes, our approach opens a new channel for incorporating external knowledge into problem-solving processes. The IntelComp project will aid in policy development by providing pertinent tools co-developed with the final users that will provide insights and analysis in the field of STI (Science, Technology, Innovation) encompassing all of the Energy areas mentioned above.

AbstractThe production of Z boson pairs in proton–proton ($${\mathrm{p}} {\mathrm{p}} $$ p p ) collisions, $${{\mathrm{p}} {\mathrm{p}} \rightarrow ({\mathrm{Z}}/\gamma ^*)({\mathrm{Z}}/\gamma ^*) \rightarrow 2\ell 2\ell '}$$ p p → ( Z / γ ∗ ) ( Z / γ ∗ ) → 2 ℓ 2 ℓ ′ , where $${\ell ,\ell ' = {\mathrm{e}}}$$ ℓ , ℓ ′ = e or $${{\upmu }}$$ μ , is studied at a center-of-mass energy of 13$$\,\text {TeV}$$ TeV with the CMS detector at the CERN LHC. The data sample corresponds to an integrated luminosity of 137$$\,\text {fb}^{-1}$$ fb - 1 , collected during 2016–2018. The $${\mathrm{Z}} {\mathrm{Z}} $$ Z Z production cross section, $$\sigma _{\text {tot}} ({\mathrm{p}} {\mathrm{p}} \rightarrow {\mathrm{Z}} {\mathrm{Z}} ) = 17.4 \pm 0.3 \,\text {(stat)} \pm 0.5 \,\text {(syst)} \pm 0.4 \,\text {(theo)} \pm 0.3 \,\text {(lumi)} \text { pb} $$ σ tot ( p p → Z Z ) = 17.4 ± 0.3 (stat) ± 0.5 (syst) ± 0.4 (theo) ± 0.3 (lumi) pb , measured for events with two pairs of opposite-sign, same-flavor leptons produced in the mass region $${60< m_{\ell ^+\ell ^-} < 120\,\text {GeV}}$$ 60 < m ℓ + ℓ - < 120 GeV is consistent with standard model predictions. Differential cross sections are also measured and agree with theoretical predictions. The invariant mass distribution of the four-lepton system is used to set limits on anomalous $${\mathrm{Z}} {\mathrm{Z}} {\mathrm{Z}} $$ Z Z Z and $${{\mathrm{Z}} {\mathrm{Z}} \gamma }$$ Z Z γ couplings.