Compliance with the basic conditions of quality in higher education implies the design of strategies to reduce student dropout, and Information and Communication Technologies (ICT) in the educational field have allowed directing, reinforcing, and consolidating the process of professional academic training. We propose an academic and emotional tracking model that uses data mining and machine learning to group university students according to their level of dropout risk. We worked with 670 students from a Peruvian public university, applied 5 valid and reliable psychological assessment questionnaires to them using a chatbot-based system, and then classified them using 3 density-based unsupervised learning algorithms, DBSCAN, K-Means, and HDBSCAN. The results showed that HDBSCAN was the most robust option, obtaining better validity levels in two of the three internal indices evaluated, where the performance of the Silhouette index was 0.6823, the performance of the Davies–Bouldin index was 0.6563, and the performance of the Calinski–Harabasz index was 369.6459. The best number of clusters produced by the internal indices was five. For the validation of external indices, with answers from mental health professionals, we obtained a high level of precision in the F-measure: 90.9%, purity: 94.5%, V-measure: 86.9%, and ARI: 86.5%, and this indicates the robustness of the proposed model that allows us to categorize university students into five levels according to the risk of dropping out.

Introduction: Migration is a social phenomenon that affects the structure and distribution of the population, driven by the search for better opportunities and living conditions. In this regard, irregular migration poses a challenge for host countries, as it involves the entry of individuals without the appropriate documentation, potentially compromising national security and border control. Objective: To evaluate the application of the unsupervised DBSCAN algorithm to classify foreigners based on the level of risk of irregular immigration at the National Migration Superintendence of Peru. Methods: We use the DBSCAN algorithm on a dataset from the National Immigration Superintendence, classifying foreigners into clusters according to their level of risk of irregular immigration. In addition, we use the Silhouette, Davies-Bouldin, and Calinski-Harabasz coefficients to evaluate the quality of the classification. Results: DBSCAN classified foreigners into four clusters based on the level of risk of irregular immigration: high, medium-high, medium-low, and low. The performance of the Silhouette index was 0.5338, the Davies-Bouldin index was 0.6213, and the Calinski-Harabasz index was 3680.2359. Conclusions: We show that the use of DBSCAN in the National Immigration Superintendence effectively classified foreigners according to the level of risk of irregular immigration. This tool supports informed decisions of immigration inspectors, favoring Peruvian immigration regulation.

Orange waste, generally discarded, is a source of many bioactive compounds that could be used for the development of high-value-added products in the food, cosmetic, and pharmaceutical industries. The objective of this study was to evaluate the influence of extraction method (automated Soxhlet extraction and temperature-controlled maceration), solvent system, and extraction time on the recovery of bioactive compounds from Valencia orange (Citrus sinensis) by-products. Proximate characterization of the dried orange residue (DOR) was performed prior to extraction. The type of solvent (ethanol and methanol), solvent:water ratio (50, 75, and 100%), and extraction time (60 and 120 min) were evaluated in terms of total extraction yield (TEY), total phenolic content (TPC), and antioxidant capacity determined by ABTS and DPPH assays, for each extraction method. ASE generally provided higher extraction yield and total phenolic content, particularly with 75:25 ethanol:water at 120 min, whereas TCM combined with methanol produced the highest antioxidant capacity. Extracts with up to 46.32% TEY, 5.57 mg GAE/g dm, and antioxidant capacities of 66.49 and 11.10 µmol TE/g dm determined by ABTS and DPPH assays, respectively, were obtained. The results demonstrated that Valencia orange by-products are a source of phenolic compounds and antioxidants with potential for product development across different industrial sectors.

La compra de teléfonos celulares depende de múltiples factores de comportamiento, no obstante, se desconoce la forma precisa de los aspectos que influyen en dicha decisión. Se buscó determinar la relación del factor más influyente del comporta-miento del consumidor con la decisión de compra; para ello se revisaron argumentos teóricos de Kotler y Armstrong, y Koma y Jha. Se utilizó un diseño no experimental correlacional de corte transversal. La población muestral se conformó por 362 participantes a quienes se aplicaron un cuestionario de encuesta. Los resultados demostraron que el factor cultural (marca del último celular), el factor social (deseo del futuro celular), el factor personal (edad) y el psicológico (aprendizaje que me dejó el actual celular) tuvieron un p – valor menor a 0.050, por eso se consideran como los factores más influentes en la decisión de compra; además, indicaron cierto vínculo significativo con características, innovación y precio como aspecto funda-mental dentro de los factores. Se concluyó que los factores influyentes del comportamiento del consumidor se relacionan significativamente con la decisión de compra, donde el coeficiente de contingencia (C), evidenció una dependencia con la innovación (C = 0.318), mientras que el segundo fue la edad con la V de Cramer igual a 0.223.

Medicinal plants play an essential role in the food, pharmaceutical, and cosmetic industries due to their ability to prevent and treat diseases. In this study, a three-factor, three-level Box–Behnken experimental design (BBD) with response surface methodology (RSM) was used to maximize the conditions of ultrasound-assisted extraction (UAE) of bioactive compounds from matico and chacruna leaves in terms of total extraction yield (TEY), total phenolic content (TPC) and antioxidant activity (AA) using ABTS and DPPH assays. The effect of methanol concentration (X1: 25%, 50%, and 75%), time (X2: 3, 6, and 9 min), and power (X3: 90, 270, and 450 W) was evaluated as independent variables. The experimental results were fitted to second-order polynomial models, and multiple regression analysis and analysis of variance were used to determine the suitability of the models, using which 3D response surface plots were generated. Considering the multivariable optimization, the best extraction conditions were 73.68% v/v methanol, 9 min, 269.32 W for matico, and 64.84% v/v methanol, 3 min, 344.44 W for chacruna. Under these conditions, the maximum value of 18.33 and 20.83% for TEY, 7.16 and 40.86 mg GAE/g dm for TPC, 56.88 and 526.38 µmol TE/g dm for DPPH were predicted for matico and chacruna, respectively. Practical Applications: This research focused on the modeling by response surface methodology (RSM) of Ultrasound-Assisted Extraction of bioactive compounds from matico and chacruna, Peruvian plants used in traditional medicine. The methodologies used allow the maximization of bioactive extraction, which presented a high recovery of phenolics with high antioxidant activity. These results highlight the use of Amazon plants in traditional medicine and their possible use in other industries such as cosmetic or food safety.