ChatGPT, a large language model AI, has the potential to transform higher education by providing students with personalized learning support, assisting in writing tasks, and enhancing their level of engagement. This study examines the factors influencing the acceptance of ChatGPT among university students in Peru, following the extended UTAUT2 model with the addition of a construct called knowledge sharing (KS). A total of 772 students from seven universities in Lambayeque and La Libertad participated in an online survey, providing insights into their perceptions and experiences with using ChatGPT for academic purposes. The results from the structural equation model showed that effort expectancy, behavioral intention, and knowledge sharing positively influenced the actual use of ChatGPT. Furthermore, effort expectancy and performance expectancy were found to be determinants of the behavioral intention to use ChatGPT. The study also revealed that performance expectancy and behavioral intention serve as sequential mediating variables regarding the effect of effort expectancy on actual use. These findings suggest a positive adoption of ChatGPT among students, driven by individual and contextual factors, and highlight the importance of managing effort and performance expectations appropriately. This study represents a significant advancement in understanding the acceptance of ChatGPT in higher education and provides valuable guidance for practical implementation efforts, ensuring that this powerful AI tool is effectively leveraged to support student learning and success.

This systematic review examines digital transformation in post-pandemic supply chains through a bibliometric analysis of literature from 2020 to 2024. Using the PRISMA protocol, we analyzed publications from Scopus, Web of Science, and ScienceDirect databases. Results show that sustainability has become the dominant keyword in digital transformation research, with China, the United States, and India forming the main research triangle. The most influential technologies driving transformation are big data, blockchain, artificial intelligence, and Internet of Things (IoT). Co-citation network analysis revealed three major research clusters: the green cluster led by Gunasekaran and Angappa focusing on supply chain management; the red cluster led by Rahman and Muhammad Saddiq addressing implementation aspects; and the blue cluster led by Calatayud and Rodriguez examining innovation and adaptation. Organizations are shifting from purely operational approaches to more holistic transformations that integrate strategic and organizational dimensions. We identified important research gaps in developing regions and in the integration of emerging technologies with existing systems. This review enhances the understanding of post-pandemic supply chain digitization while providing a framework for future research in this rapidly evolving field.

This systematic review synthesizes evidence on economic instruments that mobilize renewable-energy investment in emerging economies, analyzing 50 peer-reviewed studies published between 2015 and 2025 under PRISMA 2020. We advance an Institutional Capacity Integration Framework that ties instrument efficacy to regulatory, market, and coordination capabilities. Green bonds have mobilized roughly USD 500 billion yet work only where robust oversight and liquid markets exist, offering limited gains for decentralized access. Direct subsidies cut renewable electricity costs by 30–50% and connect 45 million people across varied contexts, but pose fiscal–sustainability risks. Carbon pricing schemes remain rare given their administrative complexity, while multilateral climate funds show moderate effectiveness (coefficients 0.3–0.8) dependent on national coordination strength. Bibliometric mapping with Bibliometrix reveals three fragmented paradigms—market efficiency, state intervention, and international cooperation—and highlights geographic gaps: sub-Saharan Africa represents just 16% of studies despite acute financing barriers. Sixty-eight percent of articles employ descriptive designs, constraining causal inference and reflecting tensions between SDG 7 (affordable energy) and SDG 13 (climate action). Our framework rejects one-size-fits-all prescriptions, recommending phased, context-aligned pathways that progressively build capacity. Policymakers should tailor instrument mixes to institutional realities, and researchers must prioritize causal methods and underrepresented regions through focused initiatives for equitable global progress.

This study evaluated the vegetation recovery following the 2022 fires in Pampa Caballero, Granja Porcon, Cajamarca, using the Normalized Difference Vegetation Index (NDVI). Sentinel-2 satellite images corresponding to pre-fire, during-fire, and post-fire periods were analyzed. The results showed that the average NDVI significantly decreased during the fire, reflecting a considerable impact on the vegetation. However, two years later, the NDVI increased above pre-fire levels, indicating a remarkable ecosystem recovery. This regeneration highlights the resilience of vegetation in the affected area. The use of NDVI for multitemporal analysis proved to be an effective tool for monitoring post-fire regeneration, providing key information for sustainable management and conservation planning in fire-affected areas. This approach allows the identification of recovery patterns and areas that may require additional interventions to promote long-term ecosystem resilience.

In recent years, the academic research landscape in La Libertad, Peru, has been characterized by a limited and uneven scientific output among institutions and faculty members. Factors such as an excessive workload, insufficient resources, and inadequate training in research methodologies have hindered the development of a robust research culture. Understanding the attitudes of faculty members toward research is crucial for enhancing scientific production within the university setting. The aims of this study were (1) to comparatively analyze faculty’s attitudes toward research in La Libertad, considering their sociodemographic characteristics, and (2) to examine these attitudes across five key dimensions: personal perception, capabilities and training, evaluation, challenges, and collaboration and methodology in research. A sample of 110 university teachers from Chepén, Piura, Trujillo, and Chiclayo participated in a structured questionnaire survey. Descriptive statistics were calculated, and comparative analyses were conducted via the Mann–Whitney U test and the Kruskal–Wallis test. The results revealed significant differences in attitudes toward research on the basis of gender, age, and teaching experience across various dimensions. Personal perceptions of research varied significantly across all three sociodemographic factors. Research skills and training showed disparities by gender and teaching experience but not by age. The valuation of research differed by gender and teaching experience but not by age. The challenges in research varied by age but not by gender or experience. Collaboration and methodology differed by gender and teaching experience but not by age. This study contributes to our understanding of the research attitudes in higher education by highlighting the complex interplay of sociodemographic factors. These findings have implications for developing personalized professional development strategies and targeted interventions to enhance research skills, improve the valuation of research, and address age-specific challenges in the research process.

La investigación tiene por objetivo determinar la influencia del amperaje y tiempo de residenciasobre la remoción de metales pesados en el tratamiento de aguas ácidas por electrocoagulación,proceso electroquímico que desestabiliza las cargas contaminantes por medio de electrodos, loque genera un intercambio iónico. Con el tiempo las partículas suspendidas propician una zona desedimentación y flotación debido a la densidad de los metales, dando como resultado la remociónde los metales presentes y agua de mejor calidad. Para comprobar este procedimiento se tomaron80 litros de muestra, de aguas acidas, de los Pad´s 6 y 7 con un pH entre 2.00 y 2.12; se trabajócon un reactor de 10 amperios, 2 electrodos, cátodo y ánodo, en un tiempo promedio de 3h con03 min. Se obtuvieron resultados entre 70- 100% de remoción de metales presentes y un agua deTipo III, debido a las variaciones del amperaje, cantidad de electrodos y tiempo de residencia; seobservaron cambios fisicoquímicos, obteniéndose un pH entre 5.15 y 6.00.Palabras clave: Electrocoagulación, electrodo, remoción de metales, agua ácida.