Conference Agenda

The study examines an underexplored area of research: the awareness gap between commercial and private brands among university students. Through a quantitative non-probabilistic survey of 154 university centennial students at private universities, the research aimed to understand how branding elements influenced their purchasing decisions. The findings emphasize the urgent need to establish a stronger bond between centennial consumers and products under private labels due to the prevailing lack of awareness and the challenges faced by private brands in attracting university students. Addressing these issues requires a comprehensive approach involving educational initiatives and strategic marketing efforts to establish a deeper connection between private labels and the centennial consumer base.

The nucleotide market, essential for biotechnological and medical applications, is highly concentrated, with a small number of companies controlling production and commercialization. Entry barriers include technological, regulatory, and commercial restrictions that hinder new entrants. This study analyzes business models that could facilitate entry into this industry, focusing on nucleotide production from biomass as an innovative and sustainable alternative. Through an exploratory literature review and a comparative case analysis in high-tech markets, five strategic approaches are identified: 1) Niche strategy, targeting highly specialized segments with differentiated products; 2) Strategic collaborations, leveraging partnerships with universities and research centers to reduce costs and risks; 3) Value chain integration, aiming to minimize dependence on dominant suppliers by internalizing key processes; 4) Production innovation, emphasizing biomass-based methods to lower costs and enhance sustainability; and 5) Ingredient brand model, positioning nucleotides as a key input in final products without directly competing with established manufacturers. The study compares these approaches, highlighting their opportunities and threats, and discusses their applicability within the biotechnology sector. Finally, it underscores the need to assess the risks associated with entering oligopolistic markets and develop strategies to mitigate these challenges.

The rapid expansion of Internet of Things (IoT) environments has brought transformative advancements alongside significant cybersecurity challenges. This study conducts a comprehensive assessment of IoT cybersecurity, analyzing current vulnerabilities and anticipating future threats. Key issues such as data privacy, integrity, and resilience against sophisticated attacks are explored, emphasizing the need for robust strategies to secure IoT ecosystems. The research highlights the layered IoT architecture and its unique security demands at the perception, network, and application levels. Furthermore, it examines the role of emerging technologies like 5G in mitigating these challenges and enabling more secure, scalable infrastructures. Through a detailed evaluation of existing countermeasures, vulnerabilities, and case studies—such as Tesla’s IoT-enabled systems—this work underscores the critical need for proactive approaches in cybersecurity. By integrating innovative methodologies and predictive strategies, this study aims to contribute to a more secure and resilient IoT landscape, fostering advancements in smart cities, healthcare, and industrial applications.

Supply chains are essential for business competitiveness and their transformation towards sustainable and efficient models. In the context of Industry 4.0, it is crucial to develop technologies that allow traceability of processes. This work aims to identify the challenges and technologies that are most relevant in the supply chain based on a bibliometric review, using a qualitative approach by searching for articles in the Scopus database. The main challenge facing supply chains in Latin America is the lack of specialization, since most studies on the subject address the issue in a general way, followed by the diversity of technologies that are implemented without any type of regulation. This review shows how Industry 4.0 contributes to the fulfillment of the Sustainable Development Goals (SDG) and the challenge of Latin America for the implementation of emerging technologies.

Challenge-Based Learning (CBL) and Game-Based Learning (GBL) have emerged as effective pedagogical strategies to enhance student engagement, motivation, and deep learning in engineering education. This study explores the impact of integrating CBL and GBL in an undergraduate integral calculus course within an engineering program, using a space exploration narrative to contextualize mathematical concepts. The intervention was structured around an eight-week mission where students applied definite and indefinite integrals, areas, volumes of revolution, optimization, and differential equations to solve real-world space navigation challenges. A total of 104 first-year engineering students, divided into an experimental group (CBL + GBL) and a control group (traditional learning), participated in the study.

Quantitative results indicate a significant increase in motivation (+48%), interest in the course (+45%), and teamwork perception (+42%) in the experimental group compared to the control group. The most notable effect was a 70% increase in interest in space and orbital mechanics, demonstrating the effectiveness of thematic and problem-driven learning. Although students found the game-based approach more challenging, they also rated it as significantly more enjoyable. Qualitative feedback highlighted greater engagement, deeper conceptual understanding, and improved collaboration among peers.

These findings suggest that embedding interactive, problem-driven learning experiences into engineering mathematics instruction enhances motivation, academic interest, and real-world application competencies. The study provides evidence supporting the integration of game-based challenges and thematic narratives in STEM education, reinforcing the role of active and immersive learning strategies in shaping future engineers.

The article addresses predictive maintenance (PdM) applied to underground mining equipment using artificial intelligence, a crucial approach for improving efficiency and reducing operating costs. The objective is to optimize the equipment's lifespan through early fault detection, avoiding costly repairs and unplanned downtime. The challenge lies in the extreme conditions and intensive use of the equipment, which makes it difficult to predict failures using traditional methods. The methodology includes continuous monitoring of key parameters (temperature, pressure, oil analysis, thickness measurement) through sensors and real-time data analysis. This data is processed using artificial intelligence and machine learning techniques to identify patterns that precede failures. The results show that PdM can reduce maintenance costs by 8% and increase equipment availability by 10%, leading to greater productivity and safety in underground mining operations