Morphometric, weight, viability, and germination analysis of castor bean seeds (Ricinus communis) under two temperature and relative humidity conditions

Objective: Morphometric evaluation, weight, viability and germination under two conditions of temperature and relative humidity in sixteen local varieties of castor beans (Ricinus communis) from various states of Mexico (E1-E16) including two commercial varieties (k75B and k93B) were analyzed. Design/methodology/approach: The morphometric characteristics were: area, Elongation Index (IE) and Feret's diameter (DF), by means of a vision system. The viability and germination tolerance (germination percentage (% germination), germination speed (VG) and emergence speed index (IVE)), were evaluated under two conditions of relative humidity and temperature (T1- HR 80%/T20ºC ; T2- HR 30%/T40ºC), under a randomized complete block experiment design with four replicates of 75 seeds. Results: There are morphometric differences (IE, area and DF) between and within the study varieties. There is a significant difference between T1 and T2 in days of radicle emergence (11.6 in T2 and 44.71 in T1), germination percentage (T1: 48.37 and T2: 56%), IVE (T1:34.07+12.72 and T2: 77.02+ 23.78) and VG (T1: 9.93 and T2: 24.60). The results obtained show that there is a positive correlation between the morphometric properties and the germination percentage under T1; but in T2 no correlation was observed. The Limitations on study/implications: The study did not imply limitations. Findings/conclusions: Two local varieties E4 and E15 with productive potential higher than 93% under T1 were found; and under T2 the local varieties E3 and E16 with a productive potential greater than 78%, with respect to the commercial variety k75B showed a better performance under T1 (89.75%) than under T2 (78.67%).Objective: To analyze the morphometric, weight, viability, and germination evaluation under two temperature and relative humidity conditions, in sixteen local varieties of castor bean (Ricinus communis) from several states of Mexico (E1-E16), as well as two commercial varieties (k75B and k93B). Design/Methodology/Approach: The following morphometric characteristics were analyzed using a vision system: area, elongation index (EI), and Feret's diameter (FD). Viability and germination tolerance (germination percentage (GP)), germination speed (GS), and emergence speed index (ESI)) were evaluated under two conditions of relative humidity and temperature (T1 - RH 80%/T 20ºC; T2 - RH 30%/T 40ºC), using a completely randomized block experiment design, with four replicates of 75 seeds. Results: There are morphometric differences (EI, area, and FD) between and within the study varieties. There are significant differences between T1 and T2 regarding the following variables: days of radicle emergence (T1: 44.71 and T2:11.6), germination percentage (T1: 48.37 and T2: 56%), ESI (T1: 34.07+12.72 and T2: 77.02+ 23.78), and GS (T1: 9.93 and T2: 24.60). The results obtained show a positive correlation between the morphometric properties and the germination percentage in T1; however, there was no correlation in T2. Study Limitations/Implications: There were no limitations to carry out this study. Findings/Conclusions: The E4 and E15 local varieties obtained a >93% productive potential in T1, while the E3 and E16 local varieties obtained a >78% productive potential in T2. Meanwhile, the k75B commercial variety had a better performance in T1 (89.75%) than in T2 (78.67%)

Modelado de la Satisfacción Laboral de Docentes Peruanos de Educación Básica utilizando técnicas de aprendizaje automático

Teacher job satisfaction is an important aspect of academic performance, student retention, and teacher retention. We propose to determine the predictive model of job satisfaction of basic education teachers using machine learning techniques. The original data set consisted of 15,087 instances and 942 attributes from the national survey of teachers from public and private educational institutions of regular basic education (ENDO-2018) carried out by the Ministry of Education of Peru. We used the ANOVA F-test filter and the Chi-Square filter as feature selection techniques. In the modeling phase, the logistic regression algorithms, Gradient Boosting, Random Forest, XGBoost and Decision Trees-CART were used. Among the algorithms evaluated, XGBoost and Random Forest stand out, obtaining similar results in 4 of the 8 metrics evaluated, these are: balanced accuracy of 74%, sensitivity of 74%, F1-Score of 0.48 and negative predictive value of 0.94. However, in terms of the area under the ROC curve, XGBoost scores 0.83, while Random Forest scores 0.82. These algorithms also obtain the highest true-positive values (479 instances) and lowest false-negative values (168 instances) in the confusion matrix. Economic income, satisfaction with life, self-esteem, teaching activity, relationship with the director, perception of living conditions, family relationships; health problems related to depression and satisfaction with the relationship with colleagues turned out to be the most important predictors of job satisfaction in basic education teachers.La satisfacción laboral de los maestros es un aspecto importante del rendimiento académico, la retención de estudiantes y la retención de maestros. Proponemos determinar el modelo predictivo de satisfacción laboral de docentes de educación básica utilizando técnicas de aprendizaje automático. El conjunto de datos original constaba de 15.087 instancias y 942 atributos de la encuesta nacional a docentes de instituciones educativas públicas y privadas de educación básica regular (ENDO-2018) realizada por el Ministerio de Educación de Perú. Utilizamos el filtro ANOVA F-test y el filtro Chi-Square como técnicas de selección de características. En la fase de modelado se utilizaron los algoritmos de regresión logística, Gradient Boosting, Random Forest, XGBoost y Decision Trees-CART. Entre los algoritmos evaluados se destacan XGBoost y Random Forest, obteniendo resultados similares en 4 de las 8 métricas evaluadas, estas son: precisión equilibrada del 74 %, sensibilidad del 74 %, F1-Score de 0,48 y valor predictivo negativo de 0,94. Sin embargo, en términos del área bajo la curva ROC, XGBoost obtiene una puntuación de 0,83, mientras que Random Forest obtiene una puntuación de 0,82. Estos algoritmos también obtienen los valores positivos verdaderos más altos (479 instancias) y los valores negativos falsos más bajos (168 instancias) en la matriz de confusión. Ingresos económicos, satisfacción con la vida, autoestima, actividad docente, relación con el director, percepción de las condiciones de vida, relaciones familiares; los problemas de salud relacionados con la depresión y la satisfacción con la relación con los compañeros resultaron ser los predictores más importantes de la satisfacción laboral en los docentes de educación básica