This study presents a scientometric analysis of renewable energy applications in low-temperature regions, focusing on green hydrogen production, carbon storage, and emerging trends. Using bibliometric tools such as RStudio and VOSviewer, the research evaluates publication trends from 1988 to 2024, revealing an exponential growth in renewable energy studies post-2021, driven by global policies promoting carbon neutrality. Life cycle assessment (LCA) plays a crucial role in evaluating the environmental impact of energy systems, underscoring the need to integrate renewable sources for emission reduction. Hydrogen production via electrolysis has emerged as a key solution in decarbonizing hard-to-abate sectors, while carbon storage technologies, such as bioenergy with carbon capture and storage (BECCS), are gaining traction. Government policies, including carbon taxes, fossil fuel phase-out strategies, and renewable energy subsidies, significantly shape the energy transition in cold regions by incentivizing low-carbon alternatives. Multi-objective optimization techniques, leveraging artificial intelligence (AI) and machine learning, are expected to enhance decision-making processes, optimizing energy efficiency, reliability, and economic feasibility in renewable energy systems. Future research must address three critical challenges: (1) strengthening policy frameworks and financial incentives for large-scale renewable energy deployment, (2) advancing energy storage, hydrogen production, and hybrid energy systems, and (3) integrating multi-objective optimization approaches to enhance cost-effectiveness and resilience in extreme climates. It is expected that the research will contribute to the field of knowledge regarding renewable energy applications in low-temperature regions.

Chemically demethoxylated and Ca-cross-linked orange-peel waste was engineered as a biosorbent for Mn(II) removal from water. A three-factor Box–Behnken design (biosorbent dose 3–10 g L⁻¹, initial Mn²⁺ 100–300 mg L⁻¹, contact time 3–8 h; pH 5.5 ± 0.1, 25 °C) required only 16 runs to locate the optimum (10 g L⁻¹, 100 mg L⁻¹, 8 h), at which the material removed 94.8% ± 0.3% manganese removal under the optimized conditions (10 g L⁻¹, 100 mg L⁻¹, 8 h, pH 5.5) of dissolved manganese and reached a Langmuir capacity of 29.7 mg g⁻¹. Equilibrium data fitted the Freundlich (R² = 0.968) and Sips (R² = 0.969) models best, indicating a heterogeneous surface, whereas kinetic screening confirmed equilibrium within 6 h. FTIR and SEM–EDX verified abundant surface –COO⁻/–OH groups and showed Mn deposits that partially replaced residual Ca, supporting an ion-exchange component in the uptake mechanism. A preliminary cost analysis ( 90% regeneration efficiency over three cycles highlight the economic and environmental promise of this modified agro-waste for polishing Mn-laden effluents.

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 presents a scientometric analysis of renewable energy applications in low-temperature regions, focusing on green hydrogen production, carbon storage, and future trends. The research covers three distinct periods from 1988 to 2024, using bibliometric tools such as R-Studio and VOSviewer to evaluate scientific publications and keyword co-occurrence. The study highlights an exponential growth in renewable energy research post-2021, driven by global initiatives to achieve carbon neutrality. Life Cycle Assessment (LCA) plays a crucial role in identifying the environmental impacts of energy systems, emphasizing the need to integrate renewable energy sources to reduce carbon emissions. The analysis reveals that hydrogen production via electrolysis has emerged as a key topic in reducing carbon footprints, particularly in hard-to-abate sectors. Carbon storage technologies, including bioenergy with carbon capture and storage (BECCS), have gained traction as essential components of future energy systems. The results indicate a growing interest in optimizing renewable energy systems for low-temperature environments, with significant progress in solar, wind, and hydrogen technologies. The study underscores the importance of local and regional adaptations, integrating energy storage solutions, and diversifying energy matrices to enhance energy security. Future trends suggest a focus on multi-objective optimization using deep learning and AI to improve energy system performance. The findings contribute to understanding the global research landscape on renewable energy applications in cold regions, offering valuable insights into sustainable solutions for extreme climates.

The purpose of this study is to measure the level of air pollution in the district of Ventanilla and Mi Peru, in Peru. The concentrations of suspended particles range from 2.5 g to 10 g, also known as PM10, and the concentrations of suspended particles less than 2.5 g, also known as PM2.5. The task is to measure CO2, PM2.5 and PM10 pollution to protect the health of people living in the region under study. A system was implemented to measure the concentrations of PM10 and PM2.5 pollutants in CO2 polluted air. The measurement system includes a dust and CO2 sensor, as well as an ambient temperature and humidity sensor, a DHT11 sensor for these measurements, and an ESP8266 module for wireless recording and cloud recording. An Arduino Uno R3 and ESP8266 board use wifi to process the sensor values. a Google Sheets spreadsheet and a PaaS cloud computing service provided by Google. An ANN was chosen because it has proven to be effective in air quality predictions. Compared to other similar works, only one network was created, but several prototypes were developed and evaluated to avoid arbitrariness in design decisions. Data normalization, architecture selection, and activation function selection were three specific components of the NR design that were examined. Finally, artificial neural networks are used to predict PM10 and PM2.5 particulate matter concentrations.

Emerging economies face problems in the administration and enforcement of intellectual property protection in their countries, especially at the university level. Given that patents are an important part of intellectual property rights, their study allows us to understand the role of universities in the diffusion of technology in society; however, the characterization and production of patents in Latin American universities, especially in Peru, has been little studied. The objective of this article is to fully characterize the patents granted and applied for, taking into account the current context to determine their progress, the lines of technological interest and the collaboration networks at the National University of Callao. The research is descriptive, documentary and legal information in the field of intellectual property has been tracked, establishing a concept contextualized to the current situation. The patent statistics provided in the article show that, despite the continuous growth that has been seen from 2018 to the present in patent production, constant patent guidance should be given to students, graduates, and faculty of the National University of Callao to increase their participation. In addition, to promote the creation of patents through multidisciplinary research that helps them to understand in a practical way the procedure they must follow to patent.