Conference Agenda

Abstract-The purpose of this project, entitled “Determining vegetation cover loss through remote sensing in Padre Abad from 2005 to 2025,” is to identify, quantify, and analyze changes in vegetation cover in the province using multi-temporal satellite images processed with GIS tools such as QGIS and Python. This research responds to the alarming loss of primary forests in the region, driven by human activities such as shifting cultivation, logging, illicit crops, and legal agricultural expansion. Through the analysis of satellite images from the years 2005, 2010, 2015, 2019, and 2025, the study seeks to visualize patterns of deforestation and vegetation recovery, generating thematic maps and statistics that serve as a basis for sustainable territorial planning. The project provides scientific evidence for environmental decision-making and promotes the use of accessible technologies for landscape monitoring in vulnerable areas.

This systematic review analyzed the characteristics of e-commerce and its impact on consumer behavior in the retail sector in Latin America during the period 2020-2025. Rigorous methodological criteria were applied in accordance with the PRISMA methodology, considering 40 studies from Mexico, Brazil, Ecuador and Peru, obtained from academic databases such as Scopus, Google Scholar, Redalyc, etc. The findings revealed an expansion of technological infrastructure in the region, evidenced by the increase in Internet access, the use of digital platforms and electronic means of payment. The adoption of digital technologies improved the user experience, highlighting the importance of adaptive design and web usability in loyalty and purchase intent. The influence of social networks, transaction security and trust in payment methods were key factors in the digital consumer's decision. The COVID-19 pandemic accelerated the use of digital channels, changing consumer habits. Despite the growth of e-commerce, challenges related to digital inclusion, payment security and access in rural areas persisted. The study showed a higher concentration of research in Mexico and Brazil.

Arsenic contamination in drinking water poses a critical global public health threat, affecting over 140 million people. Conventional technologies such as coagulation–filtration and activated alumina have been widely applied but show limited effectiveness against trivalent arsenic (As(III)) and generate hazardous waste. This study aimed to conduct a systematic comparative review of emerging and conventional technologies for arsenic removal, evaluating their effectiveness, economic feasibility, and regulatory compliance. A PRISMA-based protocol and PICOC-informed search strategy were applied to Scopus, yielding 22 eligible scientific articles published between 2015 and 2025. Results indicate that emerging technologies such as chitosan-based bioadsorption and electrocoagulation achieve removal efficiencies above 90 % for both As(III) and As(V), with operational costs ranging from 0.10 to 0.60 USD/m³, making them highly adaptable to rural and decentralized contexts. In contrast, technologies like reverse osmosis—although highly effective (>95 %)—face economic and operational limitations, particularly related to brine disposal and energy demands. Bibliometric analysis shows that India and China lead recent scientific output, with “electrocoagulation” and “bioadsorption” identified as dominant themes. The study concludes that there is no universally optimal technology; rather, selection should be context-specific, balancing technical efficiency, affordability, and long-term sustainability.

This analysis delves into the strategic crossroads of artificial intelligence (AI) and the transition toward sustainable business models, an increasingly urgent challenge. With the triple pressure of the climate crisis, tightening regulations, and constantly evolving consumer awareness, an inevitable question arises: what is AI's real role in all of this? The main problem is not the lack of technology, but the lack of a clear roadmap to organize all the scientific knowledge about this convergence. It's like having a state-of-the-art navigation system without knowing where it's going. To map this emerging territory, we used a quantitative, exploratory, and retrospective bibliometric approach using databases such as Scopus. We reviewed 216 publications between 2019-025 and used tools such as VOSviewer and the Bibliometrix package in R to conduct our analysis. This setup helped us identify co-authorship networks, institutional collaborations, and, above all, the thematic clusters that define the field. Our findings show a sharp increase in scientific output after 2020, with journals such as the Journal of Cleaner Production and IEEE Access serving as key platforms. Thematic clusters reveal that AI acts as a digital nervous system for businesses: driving energy efficiency, performing predictive analytics for risk management, and optimizing supply chains.

This article presents the design and implementation of an automated irrigation system for urban crops of Allium fistulosum (chives), based on soil moisture sensors and a low-cost IoT architecture. The system uses an ESP32 microcontroller as the processing and communication unit, which collects real-time data from capacitive sensors and an ultrasonic sensor to estimate the available water level. Data is transmitted via HTTP requests to a Google Sheets spreadsheet, using Google Apps Script as the bridge between the hardware and the cloud.

The system operates autonomously, activating water pumps when humidity levels fall below defined thresholds. Sensor calibration translated the analog readings into humidity percentages, ensuring an accurate and efficient response. The results demonstrate a 92.86% reduction in water use compared to manual irrigation, while maintaining optimal conditions for crop development. This solution represents an accessible, replicable alternative aligned with the principles of sustainability and automation in urban agriculture.

The purpose of this research is to evaluate green innovations in the framework of corporate social responsibility (CSR) in four Peruvian coffee organizations. Using a quantitative method and a transversal, non-experimental design, we applied a 5-point Likert scale survey, yielding a total of 80 responses. The objective of this analysis was to determine the characteristics of green innovation in the organizations.

The results show that 58.75% of the organizations achieve high levels of green innovation, and 66.3% achieve outstanding social results. However, 67.5% present regular corporate images, highlighting opportunities to strengthen sustainability and positive impact. Overall, the study highlights that, although coffee organizations are moving towards greater adoption of green innovations and sustainable practices, there are still areas for improvement, especially in terms of corporate image. These results provide valuable information to develop strategies and improve sustainability and social responsibility in the coffee industry.