.
Background: The university social responsibility (USR) goes beyond the traditional extension and solidary social projection of the universities, but the professional must improve and develop proposals in improvement of the country. The objective is to carry out a systematic review of the scientific production related to USR in Peru and to analyse the most important findings of this production. Method: A systematic review of articles in English and Spanish in Scopus, Redalyc, and SciELO was carried out, searching for research related to USR in the Peruvian context. Articles on USR in Latin America or the world were excluded. The search and filtering of articles was carried out until February 2023. Several filters were applied, starting with the search for titles according to the search equation. Then, articles that did not deal with USR were eliminated. Subsequently, abstracts were read and those that did not meet the inclusion criteria were discarded. Finally, the remaining research was analysed to obtain the necessary information for the research. Results: A total of 20 articles were analysed. The main results showed that university social responsibility in the Peruvian context seeks to benefit society and to form ethical and responsible students. However, more policies and actions are needed to encourage the participation of all universities in USR. It was found that 65% of the literature had a quantitative approach, 30% was qualitative and only 5% was mixed. Conclusion: University Social Responsibility (USR) seeks to benefit society, being students the key actors to improve their country with the professional development acquired in higher institutions. The implication for future research is to carry out more studies on USR but within the national university centres in the highlands and jungle areas of Peru, where it is possible to show the state of this topic in other areas of Peru.
Background The use of active methodologies in virtual environments has gained prominence in Latin American higher education, particularly following the expansion of remote learning. However, there is still limited comparative evidence on how students perceive their implementation in different contexts. Methods An exploratory qualitative study with a comparative focus was conducted. Fourteen first-year university students participated, seven from Peru and seven from Chile, selected through purposive sampling. Data collection was carried out via semi-structured interviews. The analysis was conducted using the Reflective Thematic Analysis approach, following the six phases proposed by Braun and Clarke. Results Four main themes were identified: (1) virtual platforms and ways of engaging with them, (2) perceived benefits of virtual learning, (3) challenges and tensions in the learning experience, and (4) students’ recommendations for improving technology-mediated teaching. Participants particularly valued flexibility, autonomy and permanent access to materials, especially when active methodologies such as the flipped classroom, gamification and collaborative work were integrated. However, they also highlighted limitations related to connectivity issues, gaps in digital skills and non-pedagogical uses of the platforms. Conclusions Active methodologies supported by virtual platforms can foster meaningful learning and student engagement, provided they are accompanied by adequate infrastructure, ongoing teacher training and institutional policies aimed at technological equity. The findings provide contextualized evidence for the design of active teaching strategies in Latin American higher education, beyond the context of emergency remote learning.
This study analyzed the phenotypic characteristics of white Huacaya alpaca fiber in Antabamba and Cotaruse, Apurímac, Peru. The relationships between sex and age with fiber quality were evaluated, considering fiber diameter (FD), coefficient of variation (CVFD), comfort factor (CF), and curvature index (CI). A total of 180 alpacas (90 males and 90 females) were randomly selected and distributed into four age categories. Fiber samples were collected from the mid-rib region and analyzed using the Optical Fibre Diameter Analyzer 2000 (Robotic Vision, 2024). Student’s t-test and correlation analysis were applied to determine differences and relationships between variables (p < 0.05). Results showed that alpacas from Antabamba had finer and more homogeneous fibers than those from Cotaruse (FD: 17.50 μm vs. 20.53 μm; CVFD: 30.88% vs. 45.97%). CF was higher in Antabamba (91.69 vs. 83.51%), suggesting softer fibers with better textile quality. No significant differences were found between sexes in FD (19.04 μm vs. 18.99 μm) or CI (12.23 °/mm vs. 10.76 °/mm). In conclusion, fiber quality is influenced by location, likely due to environmental and management factors. These findings may help improve fiber production and commercialization in Apurímac, benefiting rural communities and supporting local economic growth.
According to recent reports from the World Health Organization (WHO), myopia now affects 30% of the global population and continues to rise steadily. Given that early detection is critical for timely and effective treatment. This study evaluates and compares the performance of two prominent convolutional neural network (CNN) architectures—ResNet50 and InceptionV3—for the automated classification of myopia using blue-light fundus imagery. Employing a cross-sectional, non-experimental design, the research utilized a large-scale dataset of 124,794 images (63,294 Myopia; 61,500 Normal). The data were partitioned into training (70%), validation (20%), and testing (10%) sets. Models were implemented in Python using TensorFlow and Keras, leveraging the Google Colab Pro environment with A100 GPU acceleration. To mitigate overfitting and enhance generalization, rigorous preprocessing and data augmentation techniques were applied. Experimental results indicate that both architectures achieved exceptional performance; notably, InceptionV3 outperformed ResNet50, achieving a validation accuracy of 99.97% and a significantly lower loss of 0.0075. These results confirm the robustness of deep learning models for clinical myopia screening in response to the growing global prevalence.
