This study presents a novel fifth-order iterative method for solving nonlinear systems derived from a modified combination of Jarratt and Newton schemes, incorporating a frozen derivative of the Jacobian. The method is applied to approximate solutions of the nonlinear convection–diffusion equation. A MATLAB script function was developed to implement the approach in two stages: first, discretizing the equation using the Crank–Nicolson Method, and second, solving the resulting nonlinear systems using Newton’s iterative method enhanced by a three-step Jarratt variant. A comprehensive analysis of the results highlights the method’s convergence and accuracy, comparing the numerical solution with the exact solution derived from linear parabolic partial differential transformations. This innovative fifth-order method provides an efficient numerical solution to the nonlinear convection–diffusion equation, addressing the problem through a systematic methodology that combines discretization and nonlinear equation solving. The study underscores the importance of advanced numerical techniques in tackling complex problems in physics and mathematics.

This study explores the application of a bioflocculant derived from poultry eggshell waste for the removal of Chlorella spp. and related contaminants from agricultural wastewater using a statistically guided experimental design. In accordance with circular bioeconomy principles, eggshell residues were repurposed as a low-cost and sustainable biomaterial for water treatment. Chlorella spp. was selected as the target microalga due to its rapid proliferation, tolerance to eutrophic environments, and frequent presence in agricultural effluents. A two-level factorial design with center points was applied to evaluate the individual and interactive effects of key operational parameters, including pH, temperature, initial biomass concentration, and bioflocculant dosage. The highest biomass removal efficiency (94%) was achieved at pH 10, a temperature of 18.5 °C, a bioflocculant dose of 100 mg L⁻¹, and an initial biomass concentration of approximately 3.76 × 10⁷ cells mL⁻¹, with a contact time of 360 min. Under these optimized conditions, notable reductions were also observed in chemical oxygen demand (78%), nitrates (87%), phosphates (21%), and coliform bacteria (99.6%). The developed regression model exhibited strong predictive capability (R² = 0.97), indicating high reproducibility within the investigated experimental conditions. Overall, the findings suggest that eggshell-derived bioflocculants may represent a promising alternative to conventional chemical flocculants for agricultural wastewater treatment. High removal efficiency was achieved at relatively low dosages under operational conditions, supporting the potential of this approach for improving microalgae harvesting and the wastewater treatment processes.

The decarbonization of the transport sector represents one of the main challenges in the global energy transition, particularly in developing countries with a high dependence on fossil fuels. This study evaluates the techno-economic feasibility of green hydrogen (H2V) production for urban mobility in Loja, Ecuador, using wind energy as the primary source. Using HOMER Pro V3.14.2, a hybrid renewable system was modeled incorporating a 23 MW PEM electrolyzer, wind power generation, storage systems, and hydrogen refueling infrastructure. The analysis estimates an annual hydrogen production of 550,181 kg, sufficient to supply a fleet of 50 fuel cell electric buses (FCEVs). The levelized cost of hydrogen (LCOH) was calculated at 6.25 USD/kg, demonstrating competitive potential compared to regional benchmarks. The results indicate that system viability strongly depends on capital investment and renewable energy costs. The study concludes that Loja presents favorable geographical and energy conditions for implementing green hydrogen solutions, although regulatory, infrastructure, and financial challenges persist. These findings contribute to the development of sustainable urban transport models in intermediate cities in Latin America.

En esta investigación, se utilizó el algoritmo de programación lineal (OptKnock) en Python en el modelo GEM iYali4 de Yarrowia lipolytica, para predecir estrategias de eliminación de genes y optimizar la producción de licopeno. Como la levadura no produce naturalmente el licopeno, se utilizó adicionalmente rutas heterólogas. Para el análisis de ingeniería metabólica, se emplearon los paquetes Cameo y COBRApy, y el modelo basado en restricciones metabólicas: FBA para la optimización. Se generaron visualizaciones gráficas de los planos de fase de fenotipo (production envelopes), cuyo fin es representar las diferentes etapas de crecimiento óptimo al utilizar de manera distinta dos sustratos: oxígeno y nitrógeno; analizando la optimización de la eliminación del gen PGM2 (fosfoglucomutasa) que resultó de la predicción de knockouts usando OptKnock. Los resultados del FBA mostraron una tasa de producción de licopeno de 0.0567 (mmol/gdcw/h) al eliminar el gen PGM2, lo que indicó que la predicción y el posterior ensayo de eliminación de genes resulta adecuado para simular y aumentar la producción de licopeno usando el modelo iYali4 de esta levadura; aunque, por otra parte se comprometió la producción de biomasa, disminuyendo el crecimiento del microorganismo a una tasa de crecimiento cercana al cero.