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In the current setting, marked by the major challenges of growing food demand and climate change impacts, the ability of sub-Saharan agriculture to meet population needs depends on the resilience and adaptation capacity of this system. Using agrobiodiversity to promote agricultural sustainability is a strategy that has garnered much attention lately. Research suggests that mixing species or varieties within crop fields could increase the yield and/or stability. This mixing is also geared towards the conservation of crop diversity while ensuring that the various associated products and services will be available at the farm level.Few recent research studies have highlighted the benefits of varietal mixtures for Africa. This lack of research is a concern, given that this continent is considered to be highly vulnerable to climate change. This study was carried out to test whether plots with varietal mixtures would outperform monovarietal plots in terms of yield and pathogen regulation under smallholder farming conditions in Senegal. Together with farmers, we conducted 30 experiments in which mixtures of early- and late-flowering pearl millet landraces were grown in these farmers’ fields, while monitoring their low input management. We noted a significant positive effect of varietal mixtures on grain yield (mean gain of 63 ± 31.5 kg ha−1 for mixture plots, p = 0.046) with a relative yield total (RYT) averaging 1.87 ± 0.94. Both early- and late-flowering landraces benefited from mixtures, with a greater impact on late-flowering landraces. Higher fertility in terms of the seed number, percentage of fertile tillers and number of panicles per tiller, was documented in mixture plots. We did not find a significant effect of mixture on fodder, striga or weed infestation.In water and nutrient resource limiting conditions, such as in Sahelian agroecosystems, growing mixtures of early- and late-flowering landraces appeared to be an efficient way to increase productivity while ensuring agrobiodiversity conservation. Perhaps even more importantly, mixtures allowed farmers to harvest multiple products with different uses in an agrosocioecosystem context with constantly increasing land pressure.

Large eddy simulation of a flameless combustion furnace is discussed with comparison against experimental measurements of the mean thermochemical quantities. The focus is on the introduction in flow simulations of complex chemistry through the training of neural networks, in order to simulate the oxidation of a gaseous fuel representative of recycled gases available in the steel industry. A canonical problem, based on a non-adiabatic stochastic micro-mixing model and combined with a detailed description of chemistry, is setup to train the neural networks prior to the flow simulation. For these networks to be predictive, the thermochemical composition space is decomposed into sub-domains from a partitioning algorithm. A neural network is trained in every sub-domain to return the increments in time of the most influential thermochemical quantities, from the knowledge of temperature and species mass fractions solved with the flow. Implemented in an open-source low-Mach number fluid mechanics code, the neural networks complex chemistry is shown to be very efficient in terms of CPU time, with an overhead of only 60% compared to the non-reactive multi-species simulation of the furnace.

Objectives: This study measured the impact of the first wave of COVID-19 pandemic (COVID-19) (March–April 2020) on the incidence of bloodstream infections (BSIs) at Assistance Publique – Hôpitaux de Paris (APHP), the largest multisite public healthcare institution in France. Methods: The number of patient admission blood cultures (BCs) collected, number of positive BCs, and antibiotic resistance and consumption were analysed retrospectively for the first quarter of 2020, and also for the first quarter of 2019 for comparison, in 25 APHP hospitals (ca. 14 000 beds). Results: Up to a fourth of patients admitted in March–April 2020 in these hospitals had COVID-19. The BSI rate per 100 admissions increased overall by 24% in March 2020 and 115% in April 2020, and separately for the major pathogens (Escherichia coli, Klebsiella pneumoniae, enterococci, Staphylococcus aureus, Pseudomonas aeruginosa, yeasts). A sharp increase in the rate of BSIs caused by microorganisms resistant to third-generation cephalosporins (3GC) was also observed in March–April 2020, particularly in K. pneumoniae, enterobacterial species naturally producing inducible AmpC (Enterobacter cloacae...), and P. aeruginosa. A concomitant increase in 3GC consumption occurred. Conclusions: The COVID-19 pandemic had a strong impact on hospital management and also unfavourable effects on severe infections, antimicrobial resistance, and laboratory work diagnostics.