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Chemical, manufacturing, and control development timelines occupy a significant part of vaccine end-to-end development. In the on-going race for accelerating timelines, in silico process development constitutes a viable strategy that can be achieved through an artificial intelligence (AI)-driven or a mechanistically oriented approach. In this opinion, we focus on the mechanistic option and report on the modeling competencies required to achieve it. By inspecting the most frequent vaccine process units, we identify fluid mechanics, thermodynamics and transport phenomena, intracellular modeling, hybrid modeling and data science, and model-based design of experiments as the pillars for vaccine development. In addition, we craft a generic pathway for accommodating the modeling competencies into an in silico process development strategy. The development of mechanistically oriented in silico processes would be useful for vaccines. Fluid mechanics, thermodynamics and transport phenomena, intracellular modeling, hybrid modeling and data science, and model-based design of experiments are pivotal competences for in silico process development. A generic pathway can accommodate these modeling competencies into an in silico process development strategy. [ABSTRACT FROM AUTHOR]

In membrane reactors, the interaction of reaction and membrane separation can be exploited to achieve a "process intensification", a key objective of sustainable development. In the present work, the properties that the membrane must have to obtain this result in a pervaporation reactor are analyzed and discussed. Then, the methods to enhance these properties are investigated for the photocatalytic synthesis of vanillin, which represents a case where the recovery from the reactor of vanillin by means of pervaporation while it is produced allows a substantial improvement of the yield, since its further oxidation is thus prevented. To this end, the phenomena that control the permeation of both vanillin and the reactant (ferulic acid) are analyzed, since they ultimately affect the performances of the membrane reactor. The results show that diffusion of the aromatic compounds takes place in the presence of low concentration gradients, so that the process is controlled by other phenomena, in particular by the equilibrium with the vapor at the membrane-permeate interface. On this basis, it is demonstrated that the performances are enhanced by increasing the membrane thickness and/or the temperature, whereas the pH begins to limit the process only at values higher than 6.5. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 10 3 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at 10.5281/zenodo.7034956 and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (300k+ km²) covering a transitional continental-to-Mediterranean region where snow plays an important, but still poorly constrained societal and ecological role. IT-SNOW provides ∼500-m, daily maps of Snow Water Equivalent (SWE), snow depth, bulk-snow density, and liquid water content for the 5 period 01/09/2010 - 31/08/2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 350+ on-the-ground snow-depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in-situ snow data from three focus regions (Aosta Valley, Lombardia, and Molise) shows little to none mean bias compared to the former, and Root Mean Square Errors on the order of 30 to 60 cm and 90 to 300 mm for in-situ, measured snow depth and Snow Water Equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak SWE and annual streamflow that are in line with expectations for this mixed rain-snow region (22% on average). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of SWE across the Italian landscape at peak accumulation, which on average occurs on the 4th of March. Nearly 52% of mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23%), and central Apennines (5%). IT-SNOW is freely available with the following DOI: https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources - a crucial endevor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

One crucial mechanism in the spread of wildfires is the so-called fire-spotting: a random phenomenon that occurs when embers are transported over large distances. Fire-spotting speeds up the rate of spread and starts new ignitions that can jeopardise firefighting operations. Unfortunately, operational fire-spread simulators may not account for spotting events, thus overlooking the harmful consequences associated with this phenomenon. In this work, three fire spotting parametrisations are integrated in the operational wildfire simulator PROPAGATOR based on Cellular Automata (CA). RandomFront, a physics-based parametrisation of fire-spotting, is tested for the first time in the context of CA simulators. RandomFront is compared with other two parametrisations already adopted in CA based simulators, those by Alexandridis and co-authors and by Perryman and collaborators. A wildfire occurred in the summer of 2021 in the municipality of Campomarino (Molise, Italy), and where spotting effects were clearly reported, is used as a case study. This case study, featuring evident airborne transport of firebrands, paves the way for a framework for comparing parameterised spotting models used in operational scenarios. RandomFront produced a more complex burning probability pattern than the other parametrisations and it predicted a higher probability of burning in the zone mainly affected by the fire-spotting. • Comparison study among fire-spotting parametrisations. • Implementation of RandomFront fire-spotting routine in PROPAGATOR. • A real wildfire event is reproduced in operational time. • Results are analysed through probabilistic scores. [ABSTRACT FROM AUTHOR]