Response surface methodology (RSM) is a widely used tool for modeling and optimization for food processes. The objective of this review is to evaluate recent findings on the use of RSM in the extraction of compounds from agri-food products. First, the steps for the application of RSM were briefly detailed. According to the analysis performed, RSM is suitable because it evaluates the effects of the independent variables and their interactions on the responses, which is ideal for the optimization of different techniques for the extraction of multiple bioactive compounds and therefore, in the various studies, has allowed to significantly increase the yield and even the biological activities of the extracts; however, RSM has limitations and considering the complexity and dynamics of foods, the challenge is much greater. In this sense, it was determined that simultaneous use with other techniques is necessary in order to optimally describe the process and obtain more accurate results.

The timely diagnosis of brain tumors is currently a complicated task. The objective was to build an image classification model to detect the existence or not of brain tumors by adding a classification header to a ResNet-50 architecture. The CRISP-DM methodology was used for data mining. A dataset of 3847 brain MRI images was used, 2770 images for training, 500 for validation, and 577 for testing. The images were resized to a 256 × 256 scale and then a data generator is created that is responsible for dividing pixels by 255. The training was performed and then the evaluation process was carried out, obtaining an accuracy percentage of 92% and a precision of 94% in the evaluation process. It is concluded that the proposed CNN model composed of a head with a ResNet50 architecture and a seven-layer convolutional network achieves adequate accuracy, becoming an efficient and complementary proposal to other models developed in previous works.

This demonstration of study focalizes the melting transport and inclined magnetizing effect of cross fluid with infinite shear rate viscosity along the Skan-Falkner wedge. Transport of energy analysis is brought through the melting process and velocity distribution is numerically achieved under the influence of the inclined magnetic dipole effect. Moreover, this study brings out the numerical effect of the process of thermophoresis diffusion and Brownian motion. The infinite shear rate of viscosity model of cross fluid reveals the set of partial differential equations (PDEs). Similarity transformation of variables converts the PDEs system into nonlinear ordinary differential equations (ODEs). Furthermore, a numerical bvp4c process is imposed on these resultant ODEs for the pursuit of a numerical solution. From the debate, it is concluded that melting process cases boost the velocity of fluid and velocity ratio parameter. The augmentation of the minimum value of energy needed to activate or energize the molecules or atoms to activate the chemical reaction boosts the concentricity.

In the current global context, there has been a significant increase in respiratory system diseases, particularly pneumonia. This disease has a higher incidence of mortality in children under five years old and adults over 60 years old because it leads to complications if not treated in time. This research leverages convolutional neural networks (CNNs) to classify images, specifically to detect the presence of pneumonia. The data processing methodology utilized in this study is CRISP-DM. The dataset consists of 5,856 images of anteroposterior chest X-rays downloaded from the open repository “Kaggle,” divided into 5,216 images for training, 16 for validation, and 624 for testing. Preprocessing involved image augmentation through modifications to the original images, scaling, and batch division in tensor format. A comparative analysis was conducted among the transfer models: DenseNet, VGG19, and ResNet50 version 2. Each transfer model was the header of a CNN with four subsequent layers. The models underwent training, validation, and testing phases. The test’s results showed that DenseNet achieved an accuracy of 0.87, VGG19 achieved 0.86, and ResNet50 achieved 0.91. These results affirm the effectiveness of ResNet50 in image classification, considering that the model’s output is binary, where 0 represents that the patient does not have pneumonia and 1 indicates that the patient has pneumonia.

La evaluación para los aprendizajes suele ser un tema engorroso en la práctica pedagógica, necesita ser comprendido desde un enfoque que desarrolle habilidades de reflexión, en consecuencia el objetivo principal de la presente investigación fue proponer un modelo didáctico para promover la evaluación formativa en los docentes de educación básica regular, presenta un diseño no experimental de tipo descriptivo proposicional, la población estuvo constituida por 30 estudiantes de educación secundaria de la institución educativa Ricardo Palma –Sullana. El instrumento utilizado fue un cuestionario y fue validado a través de juico de expertos, su confiabilidad, según el Alfa de Cronbach, fue de 0,973. El instrumento aplicado tuvo como propósito recoger información respecto al proceso de evaluación que utilizan los docentes en su práctica habitual. Los resultados descriptivos indica que el 63, 3% se encuentra en el nivel de inicio, el 33,3% en proceso y solo el 3,3% en logrado Se concluye que uno de los aspectos fundamentales para promover la evaluación formativa en el proceso de enseñanza aprendizaje es considerar la retroalimentación reflexiva como una oportunidad para que el estudiante pueda identificar sus logros y dificultades en relación al nivel esperado de la competencia, ante ello surge la necesidad de proponer un modelo didáctico que orientare a los docentes a desarrollar una retroalimentación de calidad .

El rendimiento académico se evaluó desde la aptitud de los alumnos, en esta línea de investigación se han desarrollado trabajos en diferentes ámbitos que tienen la limitación para acceder a instrumentos que evalúen el rendimiento académico desde la percepción del alumno. Esta investigación tuvo como objetivo analizar la validez y confiabilidad de la escala de rendimiento académico desde la percepción del alumno, según la perspectiva de los educandos en universidades privadas en Perú. Para tal efecto, se empleó un diseño instrumental, con una muestra de 282 alumnos seleccionados mediante un muestreo no probabilístico por criterios. Los índices de ajuste comparativo presentaron valores aceptables (CFI, 0,97; TLI, 0,97), los índices de bondad de ajuste también son aceptables (RMSEA, 0.088; SRMR, 0.072), el análisis factorial solo ha validado 18 ítems con las tres dimensiones; la confiabilidad ha presentado valores superiores a 0.80. Por lo tanto, se concluye que la escala puede ser utilizada como una herramienta de diagnóstico y evaluación integral, para mejorar los hábitos de estudio de los alumnos, y replantear nuevas estrategias de enseñanza considerando las necesidades de los docentes.