El cultivo de café es una de las principales actividades agrícolas en la región Amazonas de Perú. El presente trabajo tuvo como finalidad evaluar el comportamiento agronómico de las variedades Catimor, Maragogipe y Castillo de café (Coffea arabica), establecidas bajo un sistema agroforestal, empleando como sombra árboles de Eucalyptus torreliana, Colubrina glandulosa y Pinus tecunumanii. El estudio de inició en 2017 en el distrito de Huambo, Provincia de Rodríguez de Mendoza, Amazonas-Perú. Los tratamientos se distribuyeron en un diseño experimental en bloques completos al azar, y consistió en tres bloques y nueve tratamientos. Las variables evaluadas fueron: altura de planta, altura hasta la primera hoja, número de hojas, estado general de la planta y diámetro del tallo. La metodología utilizada consistió en análisis individual para cada tiempo, prueba de homogeneidad de varianzas y análisis conjunto en el tiempo para un mismo espacio, considerando la interacción tratamiento x tiempo, en evaluaciones a 30, 60, 90, 120 y 150 días después del establecimiento del cultivo. Los resultados indican que la variedad de café Castillo, tanto bajo sombra de P. tecunumanii como de C. glandulosa, presentó el mejor comportamiento agronómico; mientras que la variedad Maragogipe bajo sombra de E. torreliana o de C. glandulosa mostró un deficiente comportamiento agronómico.

Cracks in concrete cause structural damage, and it is important to identify and classify them. The objective of the research was to describe the behavior and predict the type of failure in concrete cylinders using convolutional neural networks. The methodology consisted of creating a database of 2650 images of failure types in concrete cylinders tested in compression at the Laboratory of Testing and Strength of Materials of the National University of Jaen, Cajamarca, Peru. To identify cracks on the concrete surface, the database was divided into training (60%), validation (20%), and testing (20%), and a transfer learning approach was developed using the MobileNet, DenseNet121, ResNet50, and VGG16 algorithms from the Keras library, programmed in Python. To validate the performance of each model, the following indicators were used: recall, precision, and F1 score. The results show that the models studied correctly classified the type of failure in concrete with accuracies of 96, 91, 86, and 90%, with the MobileNet algorithm being the best predictor with 96%. The novelty of the study was the development of deep learning algorithms with different architectures that can be used in structural health assessment as an automated and reliable method compared to traditional ones. In addition, these trained algorithms can be used as source code in drones for structural monitoring. Doi: 10.28991/CEJ-2023-09-09-01 Full Text: PDF

Influencia de especies arbóreas nativas en sistemas silvopastoriles sobre el rendimiento y valor nutricional de Lolium multiflorum y Trifolium repens

Within the scope of this research, an evaluation was conducted on the problem-based learning (PBL) and project-based learning (PrBL) methods in teaching physics to mechanical and electrical engineering students at a public university in Peru. Two groups, each consisting of 15 and 16 students, were carefully selected to ensure uniformity in academic, socioeconomic, and emotional aspects. The measurement of academic performance was conducted both at the beginning and the end of the period. The statistical analysis using the t-student test revealed that the PrBL method was significantly more effective than PBL. These findings underscore the association between the choice of learning methods and the specific scientific area being taught, emphasizing the superiority of the PrBL approach in this educational context. This discovery suggests that implementing learning strategies that incorporate a project-based approach can be a valuable option to enhance academic performance in scientific disciplines.

Purpose: This article purpose is exposing role of expert in validation of information collection instruments for business purposes. Theoretical framework: The work is based on the multidisciplinary, interdisciplinary, transdisciplinary and pluridisciplinary vision, as relevant aspects in the expert's profile. Design/Methodology/Approach: The coherence, relevance, independence and impact of each of the items that make up the instrument are established as validation criteria, as well as the possible judgments to be issued, specifically, acceptance, modification or rejection, and prepares a validation sheet to facilitate the formalization of the opinion. Findings: The role of the expert in the validation of information gathering instruments for business purposes is of strategic importance, since the confidence in the inferences made depends on the quality of the data obtained. Research, practical & social implications: It is vitally important to verify that the expert candidate meets the profile and thus be convinced that the judgments issued contribute to the proper definition of the instrument. Originality/Value: The expert's profile is proposed to achieve an information collection instrument in accordance with the formulated objectives and a validation sheet is proposed.

Estimación de la radiación solar diaria para la ciudad de Bagua, región Amazonas, Perú