The study focused on the influence of the variables seawater proportion (%) and bicarbonate concentration on Spirulina sp. biomass production, using a Central Composite Rotational Design (CCRD) to evaluate the optimal regions of biomass production (ϕ), adaptation phase (λ), specific growth rate (µ) and generation time (G), using for each treatment the Gompertz mathematical model. The highest values of biomass (ϕ) log N/N0: 0.93 were obtained at 168.8 hours; in a culture medium with pH 8.8±0.1; at a temperature of 25°C; with salinity of 1.2% and 1.6 gL of bicarbonate. The lowest value of ϕ was 0.45±0.01; coincidentally obtained in repetitions of the central point, using a salinity of 0.7% and 3.0g/L of bicarbonate; in a medium with pH 9.1±0.4 to 25°C; which shows the importance of salinity provided by the volume of seawater relative to bicarbonate, in the production of Spirulina...

The study focused on the influence of the variables seawater proportion (%) and bicarbonate concentration on Spirulina sp. biomass production, using a Central Composite Rotational Design (CCRD) to evaluate the optimal regions of biomass production (ϕ), adaptation phase (λ), specific growth rate (µ) and generation time (G), using for each treatment the Gompertz mathematical model. The highest values of biomass (ϕ) log N/N0: 0.93 were obtained at 168.8 hours; in a culture medium with pH 8.8±0.1; at a temperature of 25°C; with salinity of 1.2% and 1.6 gL of bicarbonate. The lowest value of ϕ was 0.45±0.01; coincidentally obtained in repetitions of the central point, using a salinity of 0.7% and 3.0g/L of bicarbonate; in a medium with pH 9.1±0.4 to 25°C; which shows the importance of salinity provided by the volume of seawater relative to bicarbonate, in the production of Spirulina...

COVID-19 has caused an economic crisis in the business world, leaving limitations in the continuity of the payment chain, with companies resorting to credit access. This study aimed to determine the optimal machine learning predictive model for the credit risk of companies under the Reactiva Peru Program because of COVID-19. A multivariate regression analysis was applied with four regressor variables (economic sector, granting entity, amount covered, and department) and one predictor (risk level), with a population of 501,298 companies benefiting from the program, under the CRISP-DM methodology oriented especially for data mining projects, with artificial intelligence techniques under the machine learning Lasso and Ridge regression models, with econometric algebraic mathematical verification to compare and validate the predictive models using SPSS, Jamovi, R Studio, and MATLAB software. ...