Andrés Valenzuela-Keller

El modelo económico actual, denominado modelo económico lineal, se enfoca en la industrialización y en lograr objetivos económicos, lo que ha desencadenado diversos problemas ambientales, como crisis hídrica y altas temperaturas. Por ello, se han tomado medidas para mitigar el daño, y es en este contexto donde empresas de diversas industrias han implementado el modelo de Economía Circular (EC). Una economía circular (EC) puede definirse como un modelo económico orientado al uso eficiente de los recursos mediante la minimización de residuos, la retención de valor a largo plazo, la reducción de recursos primarios y la creación de ciclos cerrados de productos, componentes y materiales, dentro de los límites de la protección ambiental y los beneficios socioeconómicos (Morseletto, 2020). Por esta razón, el presente estudio busca identificar el impacto económico/financiero en la adopción de EC en empresas mediante una revisión sistemática, utilizando la metodología PRISMA, reuniendo hallazgos de los últimos diez años. El estudio identificó 193 artículos científicos, de los cuales solo 22 cumplieron con los criterios de inclusión y fueron analizados de forma descriptiva con una síntesis narrativa. Los principales hallazgos del estudio muestran que su implementación genera una mejor imagen corporativa mediante innovación verde, ecoinnovación, ecodiseño e I+D. Sin embargo, implica un alto costo de inversión, generando impactos económicos-financieros negativos en el corto plazo. Por lo tanto, se requiere apoyo gubernamental para mitigar los costos asociados a la implementación del modelo de EC, siendo necesaria una legislación que refuerce la conexión entre las empresas y su entorno.

The objective of this study is to analyze the determinants of the implementation of innovation actions with environmental impact in Chilean companies. A logistic regression model is used based on data from the National Innovation Survey with a sample of 1,143 companies. The results show that innovating in processes and products determines innovation practices with environmental impact. Also, social innovation, networks with universities, quality improvement, cost reduction, and incorporation of companies are determinants of innovation with environmental impact. Keywords: innovation, green innovation, environmental impact

Este estudio examina la percepción de estudiantes de Ingeniería Comercial sobre la influencia de factores personales e institucionales en su rendimiento académico. Se analizaron variables como la interacción con pares y profesores, el compromiso conductual, la motivación del aprendizaje y la percepción del desempeño. La investigación se realizó mediante una encuesta a 124 estudiantes de la Universidad Católica del Maule en Chile durante el segundo semestre de 2022. Los resultados, basados en un análisis de regresión, indicaron que todas las variables estudiadas fueron percibidas como significativas para el rendimiento académico. No obstante, la interacción con los profesores se destacó como el factor más influyente, dando relevancia a la necesidad de que la unidad académica refuerce el diseño de estrategias pedagógicas que fortalezcan la actuación de los profesores en el proceso educativo, mediante conocimientos, habilidades didácticas y formas de interacción en el aula.

En los últimos años, los estudios sobre compras impulsivas en la población joven y adulta joven han sumado importancia principalmente por los efectos en las finanzas personales de los individuos. El presente estudio tuvo por finalidad determinar las variables que afectan el impulso de compra en un caso de jóvenes y adultos jóvenes utilizando una muestra de 243 individuos chilenos. Para ello, se aplicó una encuesta que captura variables demográficas y dos escalas asociadas al locus de control y la impulsividad en la compra. Se estimó un modelo de regresión lineal y logística para obtener los determinantes de la impulsividad y prevalencia en la compra mediante plataformas online. Los resultados muestran que los determinantes se encuentran relacionados al locus de control externo, el ingreso y los métodos de pago asociados al crédito. El aporte principal del trabajo se centra en estudiar los riesgos de la población joven de incurrir en prácticas financieras riesgosas mediante las compras impulsivas en un contexto de creciente oleada de estímulos que favorecen la compra online. Este estudio aporta a la discusión, analizando la composición de perfiles de riesgo de los jóvenes en torno a la impulsividad y la prevalencia de compra impulsiva en esta población.

La pandemia por COVID-19 ha traído grandes consecuencias económicas en los mercados. Este trabajo analiza la relación entre el avance de programas de vacunación y mercados financieros latinoamericanos. Se utiliza un enfoque de análisis de coherencia Wavelet para evaluar el movimiento conjunto de los mercados y el avances de estrategias de inoculación en base a datos diarios de Argentina, Brasil, Chile y México. Los resultados muestran que el avance de los programas de vacunación en los países latinoamericanos tienen efectos positivos y significativos en los rendimientos de sus mercados financieros.

La pandemia por Covid-19 generó consecuencias enormes en la economía mundial. Durante 2020, Chile fue un país enormemente afectado por el número de contagios con una evolución desfavorable de la pandemia a nivel nacional. Esto llevó a que la autoridad sanitaria decretada en reiteradas oportunidades el confinamiento de la población, lo que en consecuencia significó que muchas empresas tuvieran que paralizar sus funciones. Este trabajo tiene como objetivo analizar la existencia de heterogeneidad espacial en los determinantes de la variación de las ventas de microempresas a nivel municipal en Chile, con especial énfasis en los efectos del confinamiento y otras variables sociodemográficas. Para ello, se utilizó una aproximación a través del método de regresión ponderada geográficamente con kernel adaptativo. Los resultados muestran que, existen efectos negativos tanto de la cantidad de casos como del confinamiento a nivel municipal con zonas especialmente afectadas en el centro y norte del país. Los resultados son una contribución a la comprensión de cómo la pandemia afectó durante 2020 a las microempresas y a la generación de estrategias a nivel municipal.

This work presents a free new database designed from a real industrial process to recognize, identify, and classify the quality of the red raspberry accurately, automatically, and in real time. Raspberry trays with recently harvested fresh fruit enter the industry’s selection and quality control process to be categorized and subsequently their purchase price is determined. This selection is carried out from a sample of a complete batch to evaluate the quality of the raspberry. This database aims to solve one of the major problems in the industry: evaluating the largest amount of fruit possible and not a single sample. This major dataset enables researchers in various disciplines to develop practical machine-learning (ML) algorithms to improve red raspberry quality in the industry, by identifying different diseases and defects in the fruit, and by overcoming limitations by increasing the performance detection rate accuracy and reducing computation time. This database is made up of two packages and can be downloaded free from the Laboratory of Technological Research in Pattern Recognition repository at the Catholic University of the Maule. The RGB image package contains 286 raw original images with a resolution of 3948 × 2748 pixels from raspberry trays acquired during a typical process in the industry. Furthermore, the labeled images are available with the annotations for two diseases (86 albinism labels and 164 fungus rust labels) and two defects (115 over-ripeness labels, and 244 peduncle labels). The MATLAB code package contains three well-known ML methodological approaches, which can be used to classify and detect the quality of red raspberries. Two are statistical-based learning methods for feature extraction coupled with a conventional artificial neural network (ANN) as a classifier and detector. The first method uses four predictive learning from descriptive statistical measures, such as variance, standard deviation, mean, and median. The second method uses three predictive learning from a statistical model based on the generalized extreme value distribution parameters, such as location, scale, and shape. The third ML approach uses a convolution neural network based on a pre-trained fastest region approach (Faster R-CNN) that extracts its features directly from images to classify and detect fruit quality. The classification performance metric was assessed in terms of true and false positive rates, and accuracy. On average, for all types of raspberries studied, the following accuracies were achieved: Faster R-CNN 91.2%, descriptive statistics 81%, and generalized extreme value 84.5%. These performance metrics were compared to manual data annotations by industry quality control staff, accomplishing the parameters and standards of agribusiness. This work shows promising results, which can shed a new light on fruit quality standards methodologies in the industry.

Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant, and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.

INTRODUCTION. Training in entrepreneurship and its impact on the intention to undertake in university students is a topic that has become relevant in recent years. The objective of this study is to analyze the relationship between entrepreneurial training and the intention to undertake. METHOD. To this end, a measurement instrument was applied to a sample of 286 Chilean university students. In the statistical processing, the construct based on the Theory of Planned Behavior (TPB) was psychometrically validated and inferential statistics were used to analyze the existence of differences between entrepreneurial intention and the modalities of entrepreneurship training programs. RESULTS. The results show that there are positive differences attributable to entrepreneurial education. Thus, having taken subjects related to entrepreneurship shows significant differences in entrepreneurial intention, entrepreneurial attitudes and the perceived ability of students. Also, it was found that participating in practical experiences in entrepreneurship shows significant differences in all dimensions of the TPB so they seem to be effective in modifying entrepreneurship behavior in students.. DISCUSSION. The implications of the results are discussed, in terms of the importance of practical training and offering additional courses to the mandatory curriculum in entrepreneurship. Finally, as future lines of research it is considered relevant to explore the dimensions of entrepreneurial attitude and self-efficacy, considering the impact that entrepreneurship training has on these variables.

Agriculture has always been an important economic and social sector for humans. Fruit production is especially essential, with a great demand from all households. Therefore, the use of innovative technologies is of vital importance for the agri-food sector. Currently artificial intelligence is one very important technological tool widely used in modern society. Particularly, Deep Learning (DL) has several applications due to its ability to learn robust representations from images. Convolutional Neural Networks (CNN) is the main DL architecture for image classification. Based on the great attention that CNNs have had in the last years, we present a review of the use of CNN applied to different automatic processing tasks of fruit images: classification, quality control, and detection. We observe that in the last two years (2019–2020), the use of CNN for fruit recognition has greatly increased obtaining excellent results, either by using new models or with pre-trained networks for transfer learning. It is worth noting that different types of images are used in datasets according to the task performed. Besides, this article presents the fundamentals, tools, and two examples of the use of CNNs for fruit sorting and quality control.