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Effective societal responses to rapid climate change in the Arctic rely on an accurate representation of region-specific ecosystem properties and processes. However, this is limited by the scarcity and patchy distribution of field measurements. Here, we use a comprehensive, geo-referenced database of primary field measurements in 1,840 published studies across the Arctic to identify statistically significant spatial biases in field sampling and study citation across this globally important region. We find that 31% of all study citations are derived from sites located within 50 km of just two research sites: Toolik Lake in the USA and Abisko in Sweden. Furthermore, relatively colder, more rapidly warming and sparsely vegetated sites are under-sampled and under-recognized in terms of citations, particularly among microbiology-related studies. The poorly sampled and cited areas, mainly in the Canadian high-Arctic archipelago and the Arctic coastline of Russia, constitute a large fraction of the Arctic ice-free land area. Our results suggest that the current pattern of sampling and citation may bias the scientific consensuses that underpin attempts to accurately predict and effectively mitigate climate change in the region. Further work is required to increase both the quality and quantity of sampling, and incorporate existing literature from poorly cited areas to generate a more representative picture of Arctic climate change and its environmental impacts.

The number of natural disasters is growing. A considerable number of them derives directly from climate changes and generates a deep impact on cultural heritages. As a result, the need for solutions able to cope with uncertain weather conditions and increased natural disasters is getting urgent. STORM is a European Research and Innovation Action co-funded in the H2020 framework that is aimed at creating intelligent tools which will gather data from libraries, sensors and crowd-sensing techniques in order to enable cultural heritage stakeholders (e.g., the organisations which have to manage the sites) to implement the most appropriate actions in the prevention, response and recovery phases of emergencies which could impact on cultural heritages. More specifically, the project aims at addressing those risks deriving from climate change, which in the near future is going to worsen most of the present hazards, as flash floods, heat waves and forest fires. As such, the approach adopted by the project is particularly fit for the purpose of being integrated with smart city systems which are increasingly growing in number and variety. Even if safety issues are not considered central in the overall concept of smart city, it’s plain that the huge quantity of data that can be gathered and processed in any future STORM-like urban cultural heritage compound should feed into the available smart city systems. On the other way round, the mass of data produced or processed by smart city systems have the potential to impact dramatically on emergency management and prevention activities to be implemented by cultural heritage stakeholders. The paper will illustrate the approach that the STORM project is adopting to mitigate the impact of climate changes on cultural heritages and the mutual benefits which could derive from an integration of the STORM outcomes with smart cities systems through the use of standard emergency data exchange protocols and an integrated framework aimed at improving existing processes related to the three identified areas: Prevention, Response and Policy

Climate change impacts on vertebrates have consequences for marine ecosystem structures and services. We review marine fish, mammal, turtle, and seabird responses to climate change and discuss their potential for adaptation. Direct and indirect responses are demonstrated from every ocean. Because of variation in research foci, observed responses differ among taxonomic groups (redistributions for fish, phenology for seabirds). Mechanisms of change are (i) direct physiological responses and (ii) climate-mediated predator-prey interactions. Regional-scale variation in climate-demographic functions makes range-wide population dynamics challenging to predict. The nexus of metabolism relative to ecosystem productivity and food webs appears key to predicting future effects on marine vertebrates. Integration of climate, oceanographic, ecosystem, and population models that incorporate evolutionary processes is needed to prioritize the climate-related conservation needs for these species.

Abstract The main objective of the Central Design Team (CDT) project is to establish an engineering design of a Fast Spectrum Transmutation Experimental Facility (FASTEF) that is the pilot plant of an experimental-scale of both an Accelerator Driven System (ADS) and a Lead Fast Reactor (LFR), based on the MYRRHA reactor concept, planned to be built during the next decade. The MYRRHA reactor concept is devoted to be a multi-purpose irradiation facility aimed at demonstrating the efficient transmutation of long-lived and high radiotoxicity minor actinides, fission products and the associated technology. An important issue regarding the reactor design of the MYRRHA/FASTEF experiment is the In-Vessel Fuel Storage Facilities (IVFSFs), both for fresh and spent fuel, as it might have an impact on the criticality of the overall system that must be quantified. In this work, the neutronic analysis of the in-vessel fuel storage facility and its coupling with the critical core was performed, using the state of the art Monte Carlo program MCNPX 2.6.0 and ORIGEN 2.2 computer code system for calculating the buildup and decay heat of spent fuel. Several parameters were analyzed, like the criticality behavior (namely the Keff), the neutron fluxes and their variations, the fission power production and the radiation damage (the displacements per atom). Finally, also the heat power generated by the fission products decay in the spent fuel was assessed.

This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.

The optical properties of strongly doped semiconductor nanocrystals depend strongly on the carrier density of the nanocrystals. These characteristics can be exploited for the design of innovative optical devices based on ultrafast switching potentially in the THz modulation bandwidth. In this study, the optical response of one‐dimensional photonic crystals incorporating colloidal nanoparticles of a highly doped semiconductor such as indium tin oxide (ITO) is investigated, taking into consideration the angular dependence of the photonic band gap and the position dependence of the photonic band gap on the light‐induced tunability of the ITO doping.