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There has been major progress over the last two decades in digitising historical knowledge of biodiversity and in making biodiversity data freely and openly accessible. Interlocking efforts bring together international partnerships and networks, national, regional and institutional projects and investments and countless individual contributors, spanning diverse biological and environmental research domains, government agencies and non-governmental organisations, citizen science and commercial enterprise. However, current efforts remain inefficient and inadequate to address the global need for accurate data on the world's species and on changing patterns and trends in biodiversity. Significant challenges include imbalances in regional engagement in biodiversity informatics activity, uneven progress in data mobilisation and sharing, the lack of stable persistent identifiers for data records, redundant and incompatible processes for cleaning and interpreting data and the absence of functional mechanisms for knowledgeable experts to curate and improve data. Recognising the need for greater alignment between efforts at all scales, the Global Biodiversity Information Facility (GBIF) convened the second Global Biodiversity Informatics Conference (GBIC2) in July 2018 to propose a coordination mechanism for developing shared roadmaps for biodiversity informatics. GBIC2 attendees reached consensus on the need for a global alliance for biodiversity knowledge, learning from examples such as the Global Alliance for Genomics and Health (GA4GH) and the open software communities under the Apache Software Foundation. These initiatives provide models for multiple stakeholders with decentralised funding and independent governance to combine resources and develop sustainable solutions that address common needs. This paper summarises the GBIC2 discussions and presents a set of 23 complementary ambitions to be addressed by the global community in the context of the proposed alliance. The authors call on all who are responsible for describing and monitoring natural systems, all who depend on biodiversity data for research, policy or sustainable environmental management and all who are involved in developing biodiversity informatics solutions to register interest at https://biodiversityinformatics.org/ and to participate in the next steps to establishing a collaborative alliance. The supplementary materials include brochures in a number of languages (English, Arabic, Spanish, Basque, French, Japanese, Dutch, Portuguese, Russian, Traditional Chinese and Simplified Chinese). These summarise the need for an alliance for biodiversity knowledge and call for collaboration in its establishment.

The digitization era for electric utilities is tightly coupled with the development of interdisciplinary software (S/W) platforms aiming at establishing much more efficient communication pathways with their clientele and other smart grid market stakeholders. This paper presents the SOCIALENERGY S/W platform, which is funded by the EU H2020 work programme. SOCIALENERGY is a user engagement, social networking, gamification, and business management platform aiming at evolving energy markets' operation and educating virtual energy communities. The proposed business model is targeted on electric utilities' customer segment. The proposed system is modular by design incorporating several subsystems from various disciplines, such as ICT, energy efficiency, behavioral economics, education, and gamification. The proposed system facilitates the easy, rich, and deep communication among individual energy consumers, virtual energy communities, utilities, and other less direct stakeholders (such as electric appliance retailers and building renovators). Finally, the paper provides important research/innovation insights and challenges to be addressed towards the proposed Green Social Response Network concept and the exploitation of SOCIALENERGY system as part of more complex systems for the 2030 smart grid era and beyond.

The study is concerned with a suitable heating model for the prediction of the pre-explosion time of a single bio-oil droplet. Diffusion and distillation limit models of the internal droplet liquid transport are considered.. The diffusion limit model only allows diffusion, while the distillation limit model implies the existence of an infinitely fast transport rate, therefore it represents the fastest possible transport limit. It is most likely that after the droplet ignition, the water and the lighter fuel fractions evaporate and burns firstly at almost constant droplet temperature in accordance with the distillation limit model. The increase of the liquid viscosity leads to domination of the diffusion limit model. According to the diffusion limit model, shortly after initiation of gasification the droplet surface becomes more concentrated of high-boiling point components, so the droplet surface reaches very high temperature, while the droplet core has a higher concentration of low-boiling components, accumulating a substantial amount of heat at temperatures near the superheat limit. Low thermal diffusivity values of bio-oil allow simplification of the solution of the equations that govern the diffusion limit model. This, in turns, allows estimating of the effective value of the bio-oils superheat limit and prediction of the droplets pre-explosion lifetime. Introduction Bio-oil a liquid fuel produced by the pyrolysis of biomass. It is potential alternative liquid fuel source for both power generation and transport. In general, bio-oils are combustible, but not flammable due to the high content of moisture and non-volatile components that require high energy for ignition. Bio-oils are multi-component mixtures comprised of different size molecules derived primarily from depolymerisation and fragmentation reactions of three key biomass building blocks: cellulose, hemicellulose, and lignin. Moisture content of biooil varies over a wide range (15-35%) depending on the feedstock and process conditions [1]. The presence of moisture can promote the onset of micro-explosions in the bio-oil droplets [2]. These micro-explosions can have an important impact on the combustion behaviour of bio-oils. Wornat et al. [3] observed a multistep process: ignition, quiescent burning emitting blue radiation, droplet microexplosion, disruptive sooty burning of droplet fragments emitting bright yellow radiation, formation and burnout of cenosphere particles. A recent scale analysis [4] employed a radiant heating of a water bubble combustible membrane model. However, a further development of this model requires more specific details of droplet combustion behaviour to be revealed. Classical models Consider a fuel droplet that contains two components of different volatility. With the contact between atomised droplets and hot air, heat is transferred to the droplet by convection from the air and by radiation from the feedback of the flame, and converted to latent heat during liquid evaporation. The vapours are transported into the air by convection through the boundary layer that surrounds each droplet. The initially uniformly mixed liquid components separate into a core with expanding radius and growing pressure, and a membrane (shell). The less volatile component covers the surface because of the finite diffusion velocity of the liquid [5]. Core liquid enters a metastable state in which the temperature rises above the usual boiling temperature and an instantaneous transition of the liquid into a vapour phase via the formation of miniscule bubbles occurs [6]. Hallet et al. [7] reported that initially the temperature of a multi-component droplet remains practically constant because the mixture is thermally dominated by evaporation of water and other volatile components (i.e. alcohols and acid groups). Then, the droplet temperature increases sharply and the pyrolysis of the residual factions begins. At the end of the transient heating period, the droplet begins to vibrate with intensity decreasing in time, evidently hindered by viscosity, until it degenerates via a micro-explosion. Hallet et al. [7] expected that the internal boiling could be accompanied by superheating of the liquid, although this was not experimentally verified. According to the classical model of Law [8, 9] the energy conservation equation at the droplet surface is

Abstract The performance is presented of the reconstruction and identification algorithms for electrons and photons with the CMS experiment at the LHC. The reported results are based on proton-proton collision data collected at a center-of-mass energy of 13 TeV and recorded in 2016–2018, corresponding to an integrated luminosity of 136 fb^-1. Results obtained from lead-lead collision data collected at √(sNN)=5.02 TeV are also presented. Innovative techniques are used to reconstruct the electron and photon signals in the detector and to optimize the energy resolution. Events with electrons and photons in the final state are used to measure the energy resolution and energy scale uncertainty in the recorded events. The measured energy resolution for electrons produced in Z boson decays in proton-proton collision data ranges from 2 to 5%, depending on electron pseudorapidity and energy loss through bremsstrahlung in the detector material. The energy scale in the same range of energies is measured with an uncertainty smaller than 0.1 (0.3)% in the barrel (endcap) region in proton-proton collisions and better than 1 (3)% in the barrel (endcap) region in heavy ion collisions. The timing resolution for electrons from Z boson decays with the full 2016–2018 proton-proton collision data set is measured to be 200 ps.