Fernandez Rojas, Joel Hugo
Preferred name
Fernandez Rojas, Joel Hugo
Email
hugof@upeu.edu.pe
ORCID
Scopus Author ID
13 resultados
Mostrando 1 - 10 de 13
- Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Antifungal effect from Zingiber officinale, Aloe vera and Trichoderma sp. for control of Moniliophthora roreri in Theobroma cacao in Huánuco, Peru(2022-01-01); ; Alex Rubén Huamán De La CruzTheobroma cacao is the main raw material to produce chocolate, as well as for use in the food, cosmetic, and pharmaceutical industries. However, Moniliophthora roreri is one of the most destructive fungal diseases and the main limiting of cacao production worldwide. Thus, this work aimed to assess the inhibitory effect of extracts of Zingiber officinale (T1) and Aloe vera (T2), and Trichoderma harzianum + Bacillus subtillis. (T3) on Moniliophthora roreri infection in Theobroma cacao; in addition, a control (T4) was also evaluated. Each treatment was applied to six plants of cacao. Incidence of monilia infection and fruit weight were monitored every 15 days (in total four periods) after the application of the treatment by spray. Significant differences (P<0.05) were found among treatments for incidence. It was observed that spraying entire cacao trees after two times (approximately 30 days) showed a reduction of monilia infection. After all periods, T1, T2, and T3 showed an incidence of monilia infection by 20.5, 17.7, and 14.9% respectively, compared to cultural control of 41.1%. This reduction of moniliasis infection translates into an increase in fruit weight average for T3 (8.4 kg), T2 (7.3 kg), and T1 (6.9 kg). In contrast, in the control (T3), the fruit weight average decreased by 5.3 kg. Biological control showed efficient management of pathogens as M. roreri. It is recommended to use such antifungal (Aloe vera) spray over at least 120 days which would decrease infection incidence even more. - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Biochemical differences based on sex and clusters of biomarkers in patients with COVID-19: analysis from the CARDIO COVID 19–20 registry(2025-03-05); - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Predictive Model with Machine Learning for Environmental Variables and PM2.5 in Huachac, Junín, Perú(2025-03-12); PM2.5 pollution is increasing, causing health problems. The objective of this study was to model the behavior of PM2.5AQI (air quality index) using machine learning (ML) predictive models of linear regression, lasso, ridge, and elastic net. A total of 16,543 records from the Huachac, Junin area in Peru were used with regressors of humidity in % and temperature in °C. The focus of this study is PM2.5AQI and environmental variables. Methods: Exploratory data analysis (EDA) and machine learning predictive models were applied. Results: PM2.5AQI has high values in winter and spring, with averages of 52.6 and 36.9, respectively, and low values in summer, with a maximum value in September (spring) and a minimum in February (summer). The use of regression models produced precise metrics to choose the best model for the prediction of PM2.5AQI. Comparison with other research highlights the robustness of the chosen ML models, underlining the potential of ML in PM2.5AQI. Conclusions: The predictive model found was α = 0.1111111 and a Lambda value λ = 0.150025, represented by PM2.5AQI = 83.0846522 − 10.302222000 (Humidity) − 0.1268124 (Temperature). The model has an adjusted R2 of 0.1483206 and an RMSE of 25.36203, and it allows decision making in the care of the environment. - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru(2021-09-16); ; ; Maycol O. Echevarria - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Multiple Linear Regression Model of Environmental Variables, Predictors of Global Solar Radiation in the Area of East Lima, Peru(2022-04-01); ; ; Sulamita Marinela Ramos ChipaAbstract Multiple regression models are very relevant to predict values using predictor variables. The objective of this study was to predict the global solar radiation in the year 2019 in the area of East Lima, Peru. Three continuous quantitative predictor variables were analyzed: temperature, humidity, wind speed and the response variable was global solar radiation, resulting in a model with excellent significance p<0.001 that shows the prediction is effective. The multiple linear regression method was used, finding an average global radiation of 175 W/m2 and predictor variables with average temperature of 19.2 °C, humidity 23.9% and wind speed 1.77 m/s, with the highest temperature in summer recorded at 24.6°C, the highest humidity of 51.2% in autumn, the highest wind speed in summer at 2.63 m/s and the highest maximum global solar radiation in spring with 183 W/m 2 . - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Observations of Electric Fields during two Partial Solar Eclipses at the Geomagnetic Equator(2025-07-08); - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Biorremediación de suelos salinos con enmiendas orgánicas de estiércol de cuy y vacuno, Cusco-Perú(2023-01-20) - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Biodegradation of Low-Density Polyethylene—LDPE by the Lepidopteran Galleria Mellonella Reusing Beekeeping Waste(2022-09-09); - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Fuzzy Model with Meteorological Variables for the Determination of the THSW Index and the Electric Field in the Area of East Lima, Peru(2021-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publicación, Observations of electric fields during two partial solar eclipses at the geomagnetic equator(2025-12-03);