This study evaluates the impact of pre-harvest wilting treatments (90, 75, 60, 45, and 30 days before harvest) on sugarcane quality in Ecuador, utilizing PCA Biplot and MANOVA Biplot to identify key factors affecting quality. The experiment was conducted on the CC-8592 sugarcane variety, with five treatments and four replications per treatment. Results showed a strong correlation between quality variables such as Brix, Pol, and juice purity, but no significant differences were observed among the wilting treatments. These findings suggest that pre-harvest wilting time does not significantly affect sugarcane quality under the studied conditions, indicating that other agronomic practices may have a greater impact on quality. Data on agronomic traits and quality parameters like weight, stem diameter, height, yield, brix, and juice purity were collected and analyzed. PCA Biplot findings revealed a strong correlation among quality variables, accounting for 98.5% of the cumulative variance, suggesting a significant interrelation with overall cane quality. However, MANOVA Biplot showed that wilting treatments did not significantly alter these quality metrics, indicating that other management practices may have a more substantial impact on sugarcane quality than the timing of wilting. The study highlights that while wilting is commonly used in sugarcane management, its effect is limited under the studied conditions in Ecuador. This research provides a basis for refining cultivation strategies and underscores the need for informed agronomic practices to enhance both the productivity and sustainability of sugarcane production.

Background: This paper investigates the impact of Ecuador’s Ley Orgánica de Educación Superior (LOES), enacted in 2010, which mandated comprehensive reforms in higher education. Objective: The objective of this study is to determine whether the implementation of the LOES has led to a significant increase in academic publications in Ecuador, thereby contributing to sustainable educational practices and aligning with the Sustainable Development Goals (SDGs). In this context, sustainability refers to the ongoing capacity of higher education institutions to generate research that contributes to the long-term development of knowledge, aligns with global sustainability goals, and ensures educational practices that are responsive to societal needs. Methods: Using a synthetic control method, this study evaluates the impact of the LOES on the number of academic publications recorded in Scopus. A synthetic control consisting of data from countries with similar educational and economic profiles serves as a counterfactual to Ecuador’s situation. Results: Before the LOES, the average annual number of publications was stable at 253.4 per year. Post-2010, this number surged, reaching an average of 3645.84 publications per year, representing a 358.41% increase. By 2023, the actual number of publications in Ecuador rose to approximately 6584, significantly surpassing the synthetic control estimate of 1431 publications. Conclusions: The findings confirm that the LOES has been effective in increasing research output and advancing educational sustainability in Ecuador. This analysis not only contributes to the literature on the impact of educational policies but also demonstrates the utility of synthetic control methods in assessing policy effectiveness, underscoring the potential of targeted interventions to promote academic excellence and sustainability in education.

The objective of this study was to analyze social perception, trust, and vaccine hesitancy through a combined approach of bibliometric analysis and qualitative synthesis, based on the most cited articles in the recent scientific literature. A systematic search was conducted in indexed databases, identifying patterns of production, collaboration, citation, thematic networks, and conceptual trends associated with the study of public trust in vaccines. The results reveal a marked geographic concentration of scientific production, dominated by the United States and the United Kingdom, as well as a strong articulation of thematic clusters linked to digital disinformation, health communication, risk perception, and psychosocial determinants of vaccine acceptance. The qualitative synthesis of the most influential studies reveals that vaccine hesitancy is a multidimensional phenomenon, determined by sociocultural, cognitive, emotional, and structural factors that interact dynamically according to each context. Disinformation, institutional trust, community narratives, and the credibility of sources emerge as central components in individual decision-making. Together, the integrated results enable a deeper understanding of vaccine hesitancy beyond traditional cognitive models, highlighting the need for contextualized communication strategies, intercultural approaches, and health policies based on trust and social participation. This study provides an integral view of the scientific landscape and establishes priority lines for future research and the design of effective public health interventions.

The goal of this study was to investigate how AI-powered adaptive learning systems are developed and applied in educational environments, with an emphasis on how these systems improve student results and learning customization. A thorough review of the literature was conducted to examine significant research that uses AI algorithms such as neural networks and support vector machines to modify course materials and evaluate student performance in real-time. This review shows that AI-driven adaptive learning systems enhance student engagement and academic performance, especially in online and STEM learning environments. However, insufficient infrastructure, computational biases, and technological constraints have hindered wider adoption. This study suggests that to improve the integration of AI in education, there should be more international collaboration and the creation of ethical frameworks to address existing restrictions.

This study presents a bibliometric analysis of the intersection between Corporate Social Responsibility (CSR) and community health, aiming to identify research trends, key contributors, and thematic developments in the field. Using data from Scopus, this study maps the evolution of CSR literature with a focus on public health and sustainability. The findings indicate a marked increase in publications over the past decade, with significant contributions from institutions in the United States, the United Kingdom, and China. Key themes identified include workplace health promotion, ethical concerns in corporate practices, and the role of CSR in mitigating public health risks, particularly during global crises such as the COVID-19 pandemic. This study highlights gaps in the literature and suggests directions for future research, including the need for interdisciplinary approaches and policy-driven CSR strategies. The results provide a valuable reference for academics and policymakers seeking to align corporate responsibility efforts with global health objectives.

This research examines the application of artificial intelligence (AI) in urban solid waste management, focusing on innovative solutions that optimize processes and promote environmental sustainability. Through a systematic literature review, recent studies employing machine learning techniques, neural networks, and IoT sensors to transform traditional waste management systems are analyzed. The review covers research published between 2018 and 2025, selected from reputable databases such as Web of Science and Scopus, following PRISMA protocol guidelines to ensure the quality and relevance of the included works. The analysis highlights various areas of AI application, including waste collection route optimization, landfill site selection, supply chain improvement, and automated waste classification. These applications not only reduce operational costs and pollutant emissions but also foster the circular economy through more efficient recycling and material reuse. Additionally, real-time data integration facilitates strategic decision-making for infrastructure planning and resource allocation in complex urban environments. The study synthesizes current technological advances while identifying limitations such as dependence on data quality and availability and the need to expand access to diverse information sources. In conclusion, the incorporation of AI in solid waste management represents a transformative tool that can drive the development of smarter, more resilient, and sustainable cities.