This study provides a comprehensive overview of vertical-axis wind turbines (VAWTs) for emerging energy applications by combining a bibliometric analysis and a thematic mini-review. Scopus-indexed publications from 1979 to 2025 were analyzed using PRISMA guidelines and bibliometric tools (Bibliometrix, CiteSpace, and VOSviewer) to map global research trends, and a parallel mini-review distilled recent advances into five thematic areas: aerodynamic strategies, advanced materials, urban integration, hybrid systems, and floating offshore platforms. The results reveal that VAWT research output has surged since 2006, led by China with strong contributions from Europe and North America, and is concentrated in leading renewable energy journals. Dominant topics include computational fluid dynamics (CFD) simulations, performance optimization, wind–solar hybrid integration, and adaptation to turbulent urban environments. Technologically, active and passive aerodynamic innovations have boosted performance albeit with added complexity, remaining mostly at moderate technology readiness (TRL 3–5), while advanced composite materials are improving durability and fatigue life. Emerging applications in microgrids, building-integrated systems, and offshore floating platforms leverage VAWTs’ omnidirectional, low-noise operation, although challenges persist in scaling up, control integration, and long-term field validation. Overall, VAWTs are gaining relevance as a complement to conventional turbines in the sustainable energy transition, and this study’s integrated approach identifies critical gaps and high-priority research directions to accelerate VAWT development and help transition these turbines from niche prototypes to mainstream renewable solutions.

Photovoltaic (PV) power forecasting combined with energy storage systems (ESS) is critical for grid stability and renewable energy optimization. Machine learning (ML) techniques have shown promise in improving PV forecast accuracy and ESS operation. However, the intersection of PV forecasting and ESS control remains underexplored, warranting a systematic review of recent advances and evaluation of ML effectiveness in PV–ESS integration. To assess research trends in ML-based PV forecasting with ESS (scientometric analysis), synthesize state-of-the-art ML approaches for PV–ESS forecasting (systematic review), and quantify their overall predictive performance via meta-analysis of the coefficient of determination (R²). A comprehensive search of Scopus (2010–2025) was conducted following PRISMA 2020 guidelines. Studies focusing on ML-based PV power forecasting integrated with ESS were included. Multiple reviewers screened the records and extracted data. Study quality was appraised using Joanna Briggs Institute checklists. A random-effects meta-analysis of R² was performed to aggregate model performance across studies. The search identified 227 records; 50 studies were included in the review and 5 in the meta-analysis. Publications grew rapidly after 2018, indicating increased interest in PV–ESS forecasting. Deep learning models and hybrid architectures were the most frequently studied and outperformed traditional methods, while integrating PV forecasts with ESS control consistently improved operational outcomes. Common methodological limitations were noted, such as limited external validation and non-standardized evaluation metrics. The meta-analysis found a pooled R² ~0.95 (95% CI) with no heterogeneity (I² = 0), and no evidence of publication bias. ML-based forecasting significantly improves PV–ESS performance, underscoring AI as a key enabler for effective PV–ESS integration. Future research should address remaining gaps and explore advanced approaches to further enhance PV–ESS outcomes.

This study aims to map the evolution of pico-hydropower and Michell–Banki (cross-flow) turbine research from 2000 to 2025 through a combined bibliometric analysis and qualitative mini-review. In total, 1036 Scopus-indexed records were initially identified and refined to 922 relevant publications for analysis. Bibliometric mapping with CiteSpace, VOSviewer, and Bibliometrix identified publication trends and seven major thematic clusters (dominated by topics such as cross-flow turbine design, renewable energy integration, and asynchronous generators), while a qualitative mini-review of key studies provided contextual depth. The analysis detected 25 keywords with strong citation bursts, indicating a shift in focus over the last decade from traditional electrical regulation toward digitalization and additive manufacturing. The mini-review distilled three dominant lines of inquiry geometric design optimization, hydraulic performance characterization, and socio-economic evaluation and highlighted critical knowledge gaps, including the absence of standardized flow–head–efficiency (Q–H–η) performance data, sparse reporting of economic metrics like levelized cost of energy (LCOE), and limited high-altitude (above 3000 m) validation of pico-hydro systems. This study’s integrative approach is unique compared to prior bibliometric or technical reviews, providing a comprehensive overview of the pico-hydropower landscape and outlining a future research agenda to standardize experimental protocols, integrate economic analysis, and extend cross-flow turbine deployments to high-Andean regions.

Currently, the use of photovoltaic systems as a source of energy has increased. However, its installation requires hiring specialized professionals to perform complex calculations for the sizing of photovoltaic systems. The objective of this research is to develop a mobile application that receives and analyzes different physical parameters to size the photovoltaic system in different districts of the city of Chepén. For this reason, portable bases were developed to detect irradiation and geolocation, which were placed within the districts of Chepén and wirelessly sent the data through Firebase to a mobile application that performed the sizing calculations. The result was obtained when comparing the districts of Chepén and Pacanga that the district with better radiation and that required the use of fewer solar panels was Chepén. It is concluded that a larger area can be analyzed using the portable bases to identify the location with the most convenient sizing and that the application can help people who are not related to the field of photovoltaic energy.

The present study aimed to determine the type of correlation between the use of Information and Communication Technologies (ICTs) as virtual tutors and e-learning in the context of the Covid-19 health emergency. A group of students from the National University of Callao (UNAC) was taken as a case study. The study group consisted of 60 students of the statistics course and the flexibility, didactics and motivation provided by the virtual tutors were taken as important factors, as well as the conceptual, attitudinal and procedural learning of the course. The result by means of Spearman's Rho obtained a value of r = 0.688 which concludes that there is a positive and significant correlation indicating that the use of Virtual Tutors is related to the learning level of the students of a course and generates an impact on the improvement of the knowledge of the subject.

This scientific article focuses on the progress of research management in the context of pandemic due to the Sars-Cov2 virus between 2021 and 2022. It shows the problems encountered with respect to research training and scientific production of the research centers of the National University of Callao. The experimental group consisted of 15 centers. The method followed in the research was based on a series of 4 stages, the analysis of the reality of research centers, from which two variables were obtained: Scientific production (X2) and Levels of knowledge in research (X1). Secondly, the choice of agile methodology, which explains why the choice of Scrum. In the third phase design and implementation of platform based on Scrum. After the implementation, the results obtained through an evaluation of the trial period were favorable compared to the initial measurement, concluding that the proposed management increased not only the level of training by the research centers.