International Conference on Applied Technologies

The microservices architecture has gained popularity in software development for its scalability, flexibility, and independent deployment of services. However, there are significant technical challenges in its implementation, particularly concerning the support tools required for efficient operation and maintenance. Previous research has explored the state of microservices architecture, but a comprehensive and categorized analysis of specific support tools for microservices, including those for monitoring, security, automation, and deployment, is still lacking. This study aims to provide a comprehensive view of the current state of support tools for microservices, identifying key technologies and analyzing their applications and limitations. To achieve this goal, a Systematic Mapping Study (SMS) was conducted following the procedures of Petersen and Kitchenham, where 96 primary articles were collected and analyzed. The results include a taxonomy of tools organized into categories such as design and visualization, testing, monitoring, automation, and security, showing the diversity of approaches and their usefulness in specific environments. The research identifies critical areas where support tools require further development, such as the integration of advanced observability and automation tools. This analysis is useful for researchers and practitioners seeking to implement or improve microservices architectures in cloud applications.

This study examines how augmented reality (AR) impacts the understanding of technological concepts and emotions of elementary school students concerning computer generations. AR has established itself as an innovative tool in the educational field, providing more interactive and compelling learning experiences. This research was conducted at the school “Libros & Acuarelas” where an AR prototype called COMPUAR (Computation in Augmented Reality) was implemented in the computer lab with students in the seventh grade. This study examines how augmented reality (AR) impacts the understanding of technological concepts and emotions of elementary school students concerning computer generations. AR has established itself as an innovative tool in the educational field, providing more interactive and compelling learning experiences. This research was conducted at the school “Libros & Acuarelas” where an AR prototype called CompuAR (Computation in Augmented Reality) was implemented in the computer lab with students in the seventh grade. A mixed methodology combining Likert scale-based questionnaires, AEQ emotional evaluation questionnaires, and direct observations was adopted to collect information on students’ academic performance and emotions. The questionnaires were designed to assess both the cognitive and emotional impact of AR. Statistical methods were used for data analysis, including analysis of variance (ANOVA) comparing the effectiveness of AR versus traditional teaching methods. The results showed that applying CompuAR significantly improved participation by improving students’ emotions and cognitive learning, especially those with special educational needs.

This document meticulously details the design and implementation process of a Low-Cost Signal Generator using the AD9833 module. This device can generate sinusoidal, triangular, and square signals with a high degree of precision, making it ideal for applications in electronics laboratories and educational projects. The methodology employed is divided into four main stages: circuit design, simulation, fabrication, and characterization tests of the signal generator. Operational tests demonstrated that the generator meets the design specifications, producing stable signals in a frequency range of 0.1 Hz to 12.5 MHz. The features of the AD9833 module allow for flexible configuration of waveforms and frequencies, facilitating its use in various testing and development applications. This work was developed to provide a solid foundation for the creation of economical and efficient signal generators, thus contributing to the accessibility of advanced tools in educational and research environments.

This document presents the development of a Node-RED application with an interactive graphical interface for the visualization of emotional biosignals, allowing users to monitor and analyze their emotional state. The objective is to record relevant information on the implementation of algorithms in real time and the integration of sensors such as the MAX30100 and AD8232 ECG, together with an ESP32 and electrodes to take the samples, in order to identify emotional patterns using predefined thresholds. The aim is to optimize the efficiency of the system by improving biosignal transmission algorithms, minimizing resource consumption, and guaranteeing fast and fluid visualization of emotions on OLED screens, to improve the overall user experience. In this study, a methodology divided into four phases was used: research, development, integration, and optimization. Once the application was implemented, the results obtained in the identification of emotional patterns were compared and the algorithms that offered the best performance were optimized. Finally, an intuitive graphical interface was created in Node-RED that allows users to efficiently visualize and monitor their emotional biosignals, functioning as a tool to optimize people’s emotional well-being.

Using RStudio’s Bibliometrix software and the PRISMA methodology, a systematic literature review (SLR) and a scientific mapping of the scientific productions of the Scopus database on the prediction of academic success in Higher Education Institutions were developed. The search was narrowed down using the ERIC thesaurus or its approximations and Boolean operators. The main questions related to the meta-analysis (MA) of the data on the located scientific productions were set as follows: 1. In which years were the most publications on the topic published? 2. What are the journals to which the papers belong? 3. From which nations are the research carried out? 4. What is the type of paper being dealt with? 5. In what areas is the research being conducted? 6. What is the predominant language? 7. Key words that relate to research? 8. According to the number of citations, which papers are most relevant? The analysis found that scientific productions in English are the most common in the subject, and the highest number of publications (27.40%) was recorded in 2023, and 2024 promises to exceed this figure. SLR researched to offer a comprehensive and objective view of the available studies of Data Mining (DM) and its contribution of techniques to determine university academic performance like Educational Data Mining (EDM). Finally, Random Forest (RF), Nearest Neighbors (NN), Support Vector Machines (SVM), Decision Trees, Logistic Regression, Closed Recurrent Neural Network (GRU), Naive Bayes and XGBoost are the most important EDM techniques.

The objective of this study was to investigate convection drying using thin slices of Emilia apples (Malus communis). Mathematical modeling is determined using Python between the residual moisture index (PHR), drying time, and temperature, which allows optimal and precise control during the dehydration process without affecting the sensory properties of the product. Experimentally, the behavior of the PHR concerning drying time has been determined, using three temperatures (50, 60, and 70 ºC) at a constant speed corresponding to 2 m/s. The PHR experimental data were fitted to the Newtonian and Midilli-Kacuk drying models. A simple relationship between PHR, drying time, and temperature can be determined using the Newton model. The simplicity of Newton's model methodically illustrates a way to optimally and precisely control the dehydration of slices of Emilia apples (Malus communis).

The proposal of a virtual Internet of Things (IoT) sensor network emerges as a cost-effective solution to enhance laboratory practices within educational environments. This simulation of IoT ecosystems at the software level offers a practical approach to understanding and implementing IoT technologies. This research validates predominant IoT messaging protocols through a virtual IoT laboratory, analyzing the configuration of encrypted and unencrypted messaging protocols. Therefore, this approach establishes a series of laboratory practices focused on Hypertext Transfer Protocol / Hypertext Transfer Protocol Secure (HTTP/HTTPS) and Message Queuing Telemetry Transport / Message Queuing Telemetry Transport Secure (MQTT/MQTTS), which are prevalent within IoT cloud platforms. From a cybersecurity standpoint, the investigation offers a methodology for evaluating the security effectiveness of these protocols, specifically in the context of passive attacks. Undergraduate students of the Universidad Internacional del Ecuador (UIDE, abbreviation in Spanish) tested and validated these laboratory practices in the Emerging Technologies course as part of the Information Technology Engineering Program. Finally, opinion surveys demonstrated the pedagogical impact of these laboratory practices during the academic period (November 2023 - March 2024). The feedback served as a cornerstone for the ongoing curriculum enhancement of the course.

Shift work can adversely affect workers’ health in various ways, including sleep disorders, mental and physical health problems, fatigue, and impaired job performance. It has a significant impact on both the physical and mental health of workers due to the disruption of natural circadian rhythms and changes in lifestyle habits. The aim of this study was to assess sleep quality in relation to different work schedules and job roles among hospitality workers. A quantitative, analytical, non-experimental research design was implemented. The Pittsburgh Sleep Quality Index (PSQI) questionnaire was used to assess 83 hospitality workers at a beach in Ecuador. The findings revealed that those engaged in rotating shifts are three times more likely [OR = 3.53 (95% CI = 1.42–8.80)] to experience sleep problems compared to those who do not work rotating shifts. Among the 42 workers on rotating shifts, 30 reported sleep issues. Poor sleep quality was more prevalent among male workers. The productivity of a sector vital to the economy may be compromised if corrective measures are not implemented, potentially leading to workplace accidents or other complications arising from poor sleep quality.

This study focuses on the design and prototype development of a mobile application that utilizes Internet of Things (IoT) technology to improve the quality of life for individuals with motor disabilities. These individuals often encounter obstacles within their homes that hinder their ability to perform daily activities independently. To address this issue, a mobile application was developed to control a motorized system that automates the opening and closing of residential doors, providing users with increased autonomy. The application’s primary goal was to create an accessible and customizable user interface, allowing users with mobility challenges to manage their home environment efficiently. The application was designed strongly emphasizing accessibility, ensuring ease of use while delivering secure and efficient control over automatic doors. User feedback was integral to the design process, ensuring the final product met the specific needs of the target population. The results suggest that integrating IoT with a user-centered mobile interface can significantly enhance the independence and quality of life for people with motor disabilities.

This study uses the Analytic Hierarchy Process (AHP) methodology to compare three functional testing tools of AI-based software: Selenium, Testim, and Applitools. The goal is to evaluate these tools based on six key criteria: usability, error detection efficiency, integration with other systems, technical support, cost, and flexibility. Each criterion is divided into specific sub-criteria, with weights assigned to quantify the performance of each tool. The evaluation criteria are established through an exhaustive literature review and expert consultations, and a hierarchy is organized to ensure a detailed and quantitative assessment of each tool. The results show that Testim receives the highest scores, excelling in usability, problem-solving efficiency, and technical support with a total score of 0.84. Applitools got a total of 0.82 and its best features were integration and error detection. While Selenium got the lowest score of 0.75, it is recognized for its flexibility and cost but it needs to improve its usability and technical support. This study provides an impartial framework for selecting AI-based software functional testing tools, highlighting the importance of considering multiple criteria in decision-making. The results benefit projects aiming to improve software quality by choosing the tools that best meet their needs.

In recent years, the healthcare sector in Ecuador has experienced an increase in workload and challenging working conditions, especially following the COVID-19 pandemic. This situation has highlighted the importance of assessing and managing psychosocial risks that impact the mental and physical health of healthcare workers. The study aims to conduct a multitemporal analysis of psychosocial risks in a private hospital in Guayaquil between 2020 and 2024 to identify trends and establish preventive measures to reduce adverse effects on healthcare workers in the short and long term. This is a quantitative, observational, retrospective, and longitudinal study that uses a psychosocial risk assessment questionnaire from Ecuador’s Ministry of Labor, administered to 1,500 workers in 2020, 2021, 2023, and 2024. Data were statistically analyzed using ANOVA to identify significant differences between the evaluated years. The results show a decrease in the level of psychosocial risk over the last two years compared to the initial years of the study, particularly in dimensions such as workload, skill development, and workplace harassment. This change is attributed to the preventive measures implemented after the pandemic, which have contributed to better adaptation of workers to workplace conditions. The study highlights the importance of maintaining and strengthening preventive measures for psychosocial risks, as risk levels have decreased in recent years due to improved workplace practices. Future research is suggested to include additional variables and qualitative approaches to better understand the factors influencing workers’ perceptions and working conditions.

The success of aquaculture production relies heavily on the effective monitoring and control of various variables throughout the cultivation process. Traditional data collection and processing methods fall short when handling large volumes of data. Therefore, integrating artificial intelligence (AI) and big data techniques is essential. This study aims to design and evaluate architecture for aquaculture's water quality prediction system, leveraging Big Data to enhance fish farming management. The proposed architecture was developed using deductive and inductive methods and analyzed through synthetic analytical techniques. Validation was performed using the 2-tuple linguistic representation model, with eight criteria evaluated by six experts. The architecture comprises four logical layers: Data Acquisition, Communication, Services, and Interaction. These layers work synergistically, encompassing tasks from parameter measurement to user notifications via web or mobile platforms. The results indicate a high level of acceptance, suggesting that the proposed architecture is highly suitable for improving water quality management in aquaculture systems.

This article aims to explore lean manufacturing principles within the context of Industry 4.0, focusing on how Lean practices drive adoption and enhance operational performance through digitalization and advanced manufacturing technologies in a rebar manufacturing company from Bosnia. Practical insights illustrate the application of Lean 4.0 principles in reducing non-value-added activities in the process by demonstrating tangible benefits in terms of operational efficiency. Specifically, process cycle efficiency for the sample carrying from Mesh factory to the Quality Control department was analyzed. Results showed that the efficiency of the current process was 9.89%, which is in alignment with the literature. By integrating Lean 4.0 strategies, which combine traditional lean methods with advanced digital technologies, significant improvements were achieved by the elimination of some non-value-added activities. As a result, the efficiency of the optimized system was increased to 13.91%, demonstrating the practical benefits of Lean 4.0 in streamlining industrial processes. This case study highlights how adopting lean methodologies can effectively optimize operations, reduce waste, and boost competitiveness in the evolving landscape of Industry 4.0.