RESUMEN
The article presents the results of original and relevant tests from the point of view of using self-compacting concrete admixtures, especially their compatibility with the cement and mutual compatibility in the case of using several admixtures in one mixture. The research contributes to the recognition of the effect of an unintentionally air-entraining superplasticiser (SP), anti-foam (AFA), viscosity-modifying (VMA) and air-entraining (AEA) admixtures on the internal frost resistance and compressive strength of self-compacting concrete. Positive and undesirable effects of the combined use of several admixtures in this area have not been the subject of extensive analyses and publications so far. Superplasticiser, which unintentionally introduced a large amount of air to the concrete mixture, had a negative effect on the strength of the concrete and a positive effect on frost resistance. The addition of AFA to such concrete did not change the strength but worsened the values of the parameters estimating frost resistance. The AEA admixture resulted in a decrease in the strength of concrete but contributed to a change in the tendency to weaken the frost resistance observed in non-air-entrained concrete. The article also deals with the problem of compliance of the frost resistance criteria estimated upon various measures. It may be disturbing that finding frost resistance based on one criterion does not always mean frost resistance on another criterion. The discrepancies can be significant and misleading.
RESUMEN
Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.