Conference Agenda

Human resource management in micro-enterprises in the food service industry suffers from operational deficiencies due to the use of manual processes for attendance tracking, shift scheduling, and payroll calculations. This leads to administrative errors, delays, and limited decision-making. To address this problem, this study aims to propose and validate a conceptual model for digital attendance and shift management designed to improve operational efficiency. The research adopted a quantitative approach with a pre-experimental pretest-posttest design. The model was developed following the Rational Unified Process (RUP) and modeled using UML diagrams, integrating technological and process components. To evaluate its impact, indicators related to shift incidents, attendance records, and payroll calculation errors were used. Since the data did not follow a normal distribution, the Wilcoxon signed-rank test was applied with a significance level of 0.05. The results showed statistically significant operational improvements after the model's implementation, reflected in a reduction of administrative incidents, optimized operating time, and increased record accuracy. It is concluded that the proposed model constitutes a viable and replicable alternative for digitizing attendance and shift management in small food service businesses.

The integration of Artificial Intelligence (AI) into higher business education has generated an unprecedented transformation in scientific production and academic ethics. The objective of this study is to analyze the impact of AI on the research output of business students through a Systematic Literature Review (SLR). Following the PRISMA protocol, a final sample of 40 thematic articles was selected, grounded in a theoretical framework of teaching competencies (TPACK). The results were structured into three dimensions: (i) Output Optimization (45% of the sample), (ii) Adoption and Ethics (37.5%), and (iii) Pedagogical Transformation (17.5%). The study concludes that AI acts as a catalyst for productivity, but its success depends on a curricular redesign that prioritizes the research process over the result. This study provides a strategic roadmap for business schools to integrate AI ethically and effectively.

The study develops and implements a predictive system based on machine learning techniques for managing orchid pollination and germination processes at the Ecuadorian company Ecuagenera. These processes exhibit high biological and seasonal variability, which has historically forced the company to rely on manual records and empirical experience, generating operational uncertainty and production losses. The research adopts a quantitative and experimental approach, using historical data collected in the laboratory and applying feature engineering techniques to incorporate temporal, biological, and operational variables. Different supervised learning algorithms were evaluated, including Random Forest, XGBoost, and LightGBM, selecting the models with the best predictive performance according to metrics such as MAE, RMSE, and coefficient of determination (R²). The results show that Random Forest offers a high level of accuracy in predicting germination time, while XGBoost performs better in predicting the times associated with the pollination and germination process.

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Accurate measurement of angular velocity in DC motors is essential for industrial and educational applications; however, commercial tachometers present high costs that limit their accessibility. This work presents the design and implementation of a low-cost DC motor monitoring and diagnostic system based on the HC020K optical encoder and ESP32 microcontroller, with an embedded web server for remote data visualization. The system measures angular velocity through interrupt-based pulse counting, calculating revolutions per minute (RPM) from the frequency of the 20-slot encoder signal. Experimental tests conducted at 3V, 5V, and 6V showed relative errors between 3.8% and 5.8% compared to the manufacturer's specifi cations for the DC TT motor with 1:90 gear ratio, validating the system's accuracy (>94%). The implemented web server enables real-time remote control and monitoring via WiFi, presenting perfect agreement (0% error) with direct acquisition under 3V and 5V conditions, and 3.6% at 6V. The results demonstrate that the proposed system constitutes a viable, accessible, and accurate alternative for velocity measurement applications in industrial, educational, and research environments, with a cost reduction greater than 85% compared to traditional commercial solutions.

Blockchain has emerged as a promising technology to address long-standing challenges in industrial supply chains, particularly those related to transparency, traceability, and trust among multiple stakeholders. Conventional procurement systems often rely on centralized architectures and intermediaries, resulting in data fragmentation, limited visibility, and increased operational costs. The objective of this study is to analyze the application of blockchain technology in industrial procurement processes and to assess its impact on improving reliability, efficiency, and transparency across supply chains. A systematic literature review (SLR) was conducted using the PICOC framework to define research questions and the PRISMA protocol for study selection. A total of 67 peer-reviewed articles published between 2019 and 2025 were retrieved from the SCOPUS database and classified by industry sector, identified problems, blockchain architectures, and reported outcomes. Findings show a growing research interest in blockchain-enabled procurement, with the food, agriculture, and pharmaceutical sectors exhibiting the highest adoption. Ethereum and Hyperledger are the most used platforms, often combined with hybrid on-chain/off-chain architectures to improve scalability. Reported benefits include enhanced traceability, reduced fraud and errors, improved operational efficiency, and increased automation through smart contracts. However, many implementations remain limited to pilot or controlled environments. Blockchain can significantly enhance procurement performance and supply chain reliability, but challenges related to scalability, interoperability, regulatory alignment, and high implementation costs (particularly for SME’s) remain key barriers to widespread adoption.

Digital filter design is a core topic in digital signal processing (DSP) education; however, many students experience difficulties when linking mathematical concepts with their practical implementation. This paper presents a methodological framework for digital filter design based on Python, Proteus, and Arduino, which combines mathematical modeling, system simulation, and embedded implementation within a structured workflow. As a case study, a second-order Chebyshev Type I IIR low-pass filter is designed, simulated, and implemented. The results show stable system behavior, appropriate frequency selectivity, and low computational requirements, making the proposed approach suitable for low-cost embedded platforms. In addition, the interactive nature of the framework allows parameter exploration and visualization of filter behavior, contributing to a clearer understanding of DSP concepts in educational contexts.

Microenterprises often face difficulties interpreting financial information derived from accounting reports, despite having access to basic financial statements. This interpretative gap constrains managerial decision-making, particularly in contexts with limited financial expertise. Existing intelligent financial systems tend to emphasize predictive analytics and computational complexity, frequently neglecting interpretability and usability requirements relevant to microenterprise environments. To address this gap, this paper presents the design of an intelligent agent for financial statement interpretation in microenterprises, developed using a Design Science Research approach. The proposed artifact transforms simplified financial data into interpretable, decision-oriented insights through the integration of financial ratio analysis, rule-based reasoning, and contextual aggregation. The design emphasizes transparency, traceability, and human-centered outputs, enabling non-expert users to understand key financial conditions without relying on opaque analytical models. The intelligent agent is demonstrated through an illustrative scenario based on simplified financial statements representative of microenterprise contexts. The results show the internal coherence and feasibility of the proposed design, highlighting how explicit rules and narrative explanations can support financial understanding while maintaining methodological rigor. This study contributes an interpretable intelligent artifact tailored to microenterprise financial interpretation and illustrates the applicability of Design Science Research to a persistent practical problem.