Conference Agenda

La inteligencia artificial (IA), puede contribuir a la mejora de la logística de insumos en empresas del sector de las empresas de repostería, las cuales enfrentan desafíos operativos debido a la perecibilidad de sus productos, la baja digitalización de procesos y la necesidad de precisión en la planificación y control de inventarios. Para ello, nuestro propósito es llevar a cabo una investigación guiada por el modelo PICO, considerando artículos indexados en la base de datos Scopus entre el año 2019 y 2024. En la cual, se usará el uso de metodologías aplicando criterios de inclusión y exclusión enfocados en la pertinencia temática, revisión por pares y el idioma inglés. La búsqueda utilizó combinaciones de palabras clave como “Artificial Intelligence”, “Inventory Management” y “Operational Efficiency”. Los resultados del documento a seguido las cuatro etapas del protocolo PRISMA: identificación, cribado, elegibilidad y selección final. Asimismo, nos arroja como resultados de un total de 1,015 documentos, se filtraron 15 estudios que cumplieron con todos los criterios definidos. Se identificó un crecimiento sostenido en la producción científica sobre IA en logística, con un pico en 2023. La conclusión nos menciona que la IA permite mejorar significativamente la eficiencia operativa en la logística de repostería.

The growing exposure of adolescents to digital risks—such as cyberbullying, loss of privacy, information manipulation, and contact with strangers—has prompted the development of specific technological strategies to protect them in virtual environments. This Systematic Literature Review (SLR) aims to identify, classify, and analyze cybersecurity strategies designed to safeguard adolescents, evaluating their characteristics, underlying technologies, effectiveness levels, and application contexts. Method: Following the PICOC framework and PRISMA guidelines, a systematic search was conducted in the Scopus database for articles published between 2020 and 2025. After applying inclusion and exclusion criteria, 30 relevant studies were selected and analyzed. Results: The strategies identified leveraged technologies such as artificial intelligence, automated parental control, educational gamification, and adaptive authentication systems. Conclusions: The most effective solutions combined pedagogical and technological approaches, emphasizing personalization, real‑time monitoring, and active engagement of both adolescents and responsible adults. Future research should adopt longitudinal designs with broader geographic diversity, and public policies should reinforce digital literacy and child safety in online environments

This review examines phase I and II bacteriophage therapy clinical trials in MDR bacteria, analyzing their safety and efficacy in clinical studies conducted over the past seven years, from 2018 to 2024, assessing the safety and efficacy of bacteriophage therapy. The inclusion criteria for this review are detailed in the methodology. Likewise, evaluation criteria such as: type of phages used (single or bacteriophage cocktails), valency (monovalent or polyvalent), routes of administration (oral, intravenous, intraperitoneal, among others), dose and duration of treatment, follow-up times, efficacy assessment (in vitro, clinical studies), safety, and tolerability are also included. In conclusion, bacteriophage therapy reports clinical improvement in patients with severe infections, tolerability, and a lack of serious adverse events, especially in those patients who do not respond to conventional antibiotics. The results suggest that bacteriophage therapies have enormous potential for treating bacterial infections resistant to traditional antibiotics. However, the lack of clinical studies in this field hampers its standardization, highlighting the need for a multidisciplinary approach for successful clinical implementation in future studies.

The progress of Industry 4.0 has led to significant transformations in the perception and functioning of industrial environments, placing particular emphasis on maintenance tactics. In this context, predictive maintenance (PdM), supported by machine learning (ML) methods, has established itself as an essential tool for anticipating operational errors, minimizing unexpected interruptions, and enhancing process efficiency. This analysis offers a systematic literature review with the aim of examining the application of machine learning algorithms to predict critical errors in industrial processes. To achieve this, the PRISMA methodology was used in combination with the PICOC approach. The first query in the Scopus database yielded 328 results. Twenty-seven articles were obtained that met requirements such as the use of algorithmic models, industrial environments, implementation of PdM and availability of evaluation tools. The main findings showed that the deep learning models with the best predictive performance included LSTM networks, hybrid CNN architectures and small IoT solutions. The most frequent failures were found to be mechanical, electrical and environmental, especially in industries such as manufacturing, energy and transportation. This report assesses the current state of the art and makes recommendations on possible directions for the development of intelligent industrial maintenance.

Between 2000 and 2019, underground mining operations reported 494 fatal accidents, with 27% caused by rockfalls—making them the leading cause of death in these environments. With an average of 5.2 such incidents per year, implementing critical controls and effective risk management is crucial to improving safety. Understanding the causes and consequences of these accidents is essential for developing preventive strategies to reduce future incidents. The Bow Tie methodology, integrated with Bayesian networks, offers a comprehensive framework for managing and mitigating risks associated with rockfall accidents. This approach structures risk management by identifying potential hazards, analyzing their causes and consequences, and implementing preventive and mitigative controls.

This research contributes to the SDG of decent work and economic growth and aims to determine the relationship between labor inclusion and social responsibility. The research was applied, with a quantitative approach, a non-experimental cross-sectional design, a correlational level, and a hypothetical-deductive method. The total population consisted of 60 employees from a service company. The descriptive and inferential results for each grouped variable and general and specific hypothesis tests provide evidence that allows us to conclude that the variable labor inclusion is related to social responsibility.

Keywords: work importance, social well-being, social adaptation

The article analyzes the sustainability of Peru's frozen squid exports between 2019 and 2023, evaluating market and exporting company diversification using the Herfindahl-Hirschman Index (HHI). Globally, a trend toward diversification was observed, with the HHI decreasing from 1379 to 957. In Peru, exports showed significant recovery after the COVID-19 pandemic, albeit with high concentration in the Chinese market, which experienced a 361.5% growth between 2020 and 2023. The research highlights challenges related to dependence on specific markets and companies, emphasising the need for innovative strategies to diversify destinations and foster a more resilient business ecosystem, promoting practices that ensure economic and environmental sustainability. Methodologically, a descriptive analysis was conducted using export data from VERITRADE, revealing that Piura leads national exports, while other regions such as Ica and Lambayeque face significant declines. The study concludes that, although initial diversification is positive, recent concentration poses a risk. It recommends strategies to foster market diversification, support new exporting companies, and strengthen the sector's environmental and economic sustainability.