Innovation & Development in Engineering and Applied Science
Innovation & Development in Engineering and Applied Science an open-access technical-scientific electronic journal (Open Journal System) published by the Technical University of the North, with the purpose of disseminating the results obtained in research and technical studies. IDEAS has a permanent call for papers written by professors, professionals, university students, graduates, national and international experts, in areas related to applied sciences, science and engineering. There is no cost or fee associated with publishing in the journal. It is published every six months (January, July); in addition, it has the ISSN 2600-5573 that allows to identify the articles presented in the international database ISSN Portal.
Editorial de la Universidad Tecnica del Norte
Authors retain the copyright of their works and grant the journal IDEAS the right of first publication. Articles are published under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), which allows reading, downloading, copying, distributing, and sharing the content for non-commercial purposes, provided that proper credit is given to the author(s) and the original publication in the journal, without making modifications or creating derivative works. The journal IDEAS does not charge fees for submission, processing, or publication of manuscripts and guarantees open access to its contents.
Microservices architecture in government systems: A reference framework based on patterns, anti-patterns, and architectural governance in Ecuador.
Government information systems are undergoing a progressive transition from monolithic architectures to microservices-based approaches, driven by the need to enhance scalability, resilience, and continuous delivery capabilities. However, in emerging economy contexts such as Ecuador, this transition is constrained by deficiencies in architectural governance, the coexistence of legacy systems, and the lack of institutional standardization. This study proposes a microservices-based reference architecture for Ecuadorian government systems, complemented by a systematic taxonomy of architectural patterns and anti-patterns derived from a systematic literature review, comparative analysis of distributed architectures, and socio-technical modeling. A mixed-method approach is adopted, integrating: (i) systematic literature review (SLR), (ii) architectural decomposition of government domains, (iii) design pattern classification, and (iv) risk assessment of anti-patterns in distributed environments. The results indicate that, while microservices significantly improve scalability and deployment frequency, they also introduce substantial operational complexity in the absence of robust mechanisms for governance, observability, and distributed data management. As a primary contribution, this study identifies architectural governance as the critical factor for the sustainable adoption of microservices in the public sector, and proposes structured guidelines for its implementation in the Ecuadorian context.
Copyright (c) 2026 Fabián Caicedo Goyes https://creativecommons.org/licenses/by-nc-nd/4.0
Model Based on the Integration of Agile Approaches and PMBOK Guide Practices and Its Relationship with Quality and Customer Satisfaction in WFM Telecom SAC
The study aimed to determine the relationship between a model based on the integration of agile approaches and PMBOK guide practices, and quality and customer satisfaction in WFM Telecom SAC. A quantitative, applied, descriptive correlational, and cross sectional non experimental design was used. The population and sample consisted of 31 clients selected through census sampling. Three Likert type questionnaires were applied, validated through expert judgment and with adequate reliability. The results showed a positive and significant correlation between the integrated model and quality (r = 0.682; p = 0.000), as well as between the integrated model and customer satisfaction (r = 0.802; p = 0.000). It is concluded that the articulation between agile practices and PMBOK guide practices strengthens service quality and improves customer satisfaction.
Copyright (c) 2026 Andres Marthans, Franklin Flores-Ayala, Cayo Leon, Leon Vera Sandra https://creativecommons.org/licenses/by-nc-nd/4.0
Process model for a software testing service and its influence on final product quality
The research aimed to determine how a process model for software testing services affects the quality of the final product. This study is justified by the lack of a unified approach that integrates frameworks such as CMMI, TMMI, ITIL, and PMBOK to manage testing services under outsourcing schemes. A Design Science methodological approach was applied, combining literature review, model development, and validation through a case study in a Peruvian specialized consultancy. The results showed substantial improvements in delivery time efficiency, reduction of incidents, and increased customer satisfaction. Implementing the model enabled more rigorous process control, demonstrating that adopting international best practices has a positive impact on the quality and reliability of the delivered software. The study concludes that the proposal contributes to standardizing critical processes in testing organizations.
Copyright (c) 2026 Cayo León, Alejandro Bartra, Fany Sobero, Marcelo Cisneros Rúales https://creativecommons.org/licenses/by-nc-nd/4.0
Systematic Review of Artificial Intelligence-Based Techniques for Extracting Information from Unstructured Documents
En la actualidad, muchas organizaciones gestionan o conservan sus documentos físicos en formato digital, guardando la información en archivos no estructurados como los PDF, sin embargo, al tratarse de documentos escaneados, la extracción y el análisis de la información resultan difíciles de obtener. Cuando se utilizan de forma adecuada, estos datos pueden contribuir significativamente a mejorar la eficiencia operativa. Aunque los métodos tradicionales de extracción de información presentan ciertas limitaciones, las soluciones basadas en inteligencia artificial (IA) se perfilan como una alternativa más eficaz. No obstante, persiste una clara falta de estudios académicos que ofrezcan una evaluación exhaustiva de las técnicas de IA aplicadas a la extracción de información en contenidos no estructurados. Esta Revisión Sistemática de la Literatura se lleva a cabo con la metodología (PRISMA) que tiene como objetivo identificar y examinar los estudios existentes relacionados con las técnicas aplicadas a la extracción de texto a partir de documentos no estructurados, así como ofrecer orientaciones que puedan guiar investigaciones futuras en este ámbito. A través de un análisis detallado de 40 estudios seleccionados publicados entre 2020 a 2025 se identifican los principales modelos que sirven para la extracción de texto de documentos. La revisión revela que las técnicas impulsadas por IA tienen un gran potencial para permitir la extracción automática de información en documentos no estructurados, ya sean impresos o manuscritos. No obstante, trabajar con formatos variados de documentos plantea ciertos desafíos. Como respuesta, se propone un marco basado en enfoques híbridos de IA, orientado al procesamiento eficaz de conjuntos de datos de alta calidad para facilitar dicha extracción. Asimismo, se destaca la necesidad de una colaboración activa entre organizaciones e investigadores, clave para enfrentar las múltiples complejidades que implica el análisis de datos no estructurados.
Printed and Handwritten Text Recognition
Copyright (c) 2026 Johanna Guamán Granda https://creativecommons.org/licenses/by-nc-nd/4.0
Performance Analysis of SDN Controllers in an Emulated Network for Streaming Traffic
Software-defined networking (SDN) requires efficient controllers to manage time-sensitive traffic, yet there is limited evidence on their comparative performance under demanding load conditions. This study evaluates three widely used SDN controllers: OpenDaylight (ODL), ONOS, and Ryu, under three load scenarios (low, medium, and high) in an emulated Mininet environment. Latency, jitter, throughput, packet loss, availability, and memory consumption were measured during multimedia streaming transmission. The results show that Ryu maintains the best balance between service continuity and effective delivery as concurrency increases, exhibiting lower degradation in availability and throughput. ONOS delivers competitive performance with an emphasis on high availability, whereas ODL is more sensitive to increased load, particularly in delivery-related metrics. The study concludes that controller selection should prioritize service continuity and delivery reliability in high-concurrency scenarios.
Copyright (c) 2026 Santiago Meneses Narvaez, Emilio Zhuma Mera, Kevin Cedeño, Steveen Quiros https://creativecommons.org/licenses/by-nc-nd/4.0
Evaluation of the Maturity of Information Technology Governance in an Ecuadorian RuralDecentralised Autonomous Government Using COBIT 2019
Public institutions face significant challenges in ensuring adequate Information Technology (IT) governance, which is essential for institutional efficiency. This study aimed to propose and validate a methodological model based on COBIT 2019 to measure and improve the maturity of IT processes in the Rural Parish Decentralised Autonomous Government of Tufiño. The methodology employed followed a mixed-methods approach structured into five phases: institutional diagnosis, data collection, maturity assessment, application of design factors, and validation of improvement proposals. The results were validated through the Delphi method in two rounds with ten experts. The findings indicate an initial maturity level of 0.6, revealing significant gaps in relation to the target level of 2.4. The improvement proposal was validated in two Delphi rounds, achieving acceptance rates of 92 % and 100 %. The combined model of COBIT 2019 and the Delphi method made it possible to evaluate the effectiveness of COBIT 2019 in strengthening IT governance in GADP-Tufiño. Its application proved to be effective and replicable in both public and private institutions.
Copyright (c) 2026 Jenny Iñiguez, Marco Pusdá–Chulde https://creativecommons.org/licenses/by-nc-nd/4.0
Mobile app implementation with E-Invoicing to optimize E-Commerce in retail smes
Retail SMEs in developing markets often face significant operational inefficiencies due to reliance on manual processing. This research addresses the critical problem of excessive transaction times (over 30 minutes) and the lack of automated fiscal compliance at "Men’s Locker Clothing Ec," a retail store in Ecuador. A descriptive-constructive methodology was applied using the Mobile-D agile framework to design and deploy a cross-platform mobile application with electronic invoicing (Flutter + Firebase). The evaluation followed a mixed-methods approach with 81 users. Results demonstrate a high level of user satisfaction, supporting the Technology Acceptance Model (TAM): 97.5% of users affirmed brand identity consistency, and 96.3% found the app easy and intuitive to use. Crucially, the application improved operational efficiency by reducing the average total purchasing workflow time by 74.07%, shifting from over 30 minutes in the traditional physical process to 15–20 minutes in the digital session. This study validates that lightweight agile methodologies and serverless architectures are effective strategies for modernizing retail SMEs.
Copyright (c) 2026 Luis Fernando Palacios Ochoa, Diego Javier Trejo España https://creativecommons.org/licenses/by-nc-nd/4.0
User Experience-Based Mobile App for Dementia Caregivers
This work involves the development and implementation of a user-experience-based mobile application designed to support family caregivers of patients with dementia, a disease that affects the cognitive system or mental processes of people, especially those of older age. The participatory action method is used, which involves end users, in this case, caregivers. This method was applied in two phases: initial data collection through interviews and surveys, and subsequently, a prototype of the application was created with iterative feedback sessions from caregivers. In this phase, the Kanban methodology was used, which allowed for changes to be made based on the criteria of 30 people, collected through surveys. This allowed for defining the caregivers' specific needs and building a user-friendly, scalable application with dynamic and easy-to-use interfaces.
Copyright (c) 2026 Carlos Pinargote Navarrete; Fabricio Santana Campoverde; Hernan Vargas Nolivos, Mauricio Quimiz Moreira, Merly Mendoza Palma https://creativecommons.org/licenses/by-nc-nd/4.0
Environmental Monitoring with IoT and Big Data: A Systematic Review of Advances and Challenges.
The integration of the Internet of Things (IoT) and Big Data analytics has revolutionized environmental monitoring, enabling quantitative and qualitative advances in natural resource management. This systematic review, based on 55 studies published between 2019 and 2024 and following the PRISMA protocol, identifies trends, achievements and challenges in the application of these technologies. In agriculture, the adoption of IoT sensors has reduced water consumption by 35% and increased productivity by 12%. In water management, IoT nanosensors have increased the detection capacity of contaminants fivefold over conventional methods. Deep learning models, such as LSTM networks fed by data from more than 15,000 IoT nodes, have achieved greater than 92% accuracy in predicting extreme weather events. In addition, the integration of IoT and Big Data platforms in cities has helped reduce CO₂ emissions by 15% by optimizing traffic and energy consumption. Innovations such as edge computing architecture have decreased processing latency by 68%, and digital twins have improved the accuracy of predictive models by 40%. Globally, the environmental IoT market is projected to be worth $21.5 billion by 2028, with more than 152 cities implementing IoT networks for air quality and 35 million hectares under precision agriculture. However, challenges remain, such as interoperability (23% of deployments), initial deployment cost and the technology gap between regions. The IoT-Big Data convergence drives a transition towards proactive environmental management systems, although its scalability depends on overcoming these challenges.
Copyright (c) 2025 Gema Isabel Medranda Cobeña https://creativecommons.org/licenses/by-nc-nd/4.0
An Econometric Analysis of Unemployment in Ecuador: Classical Approaches and a Machine Learning-Based Proposal
This study focuses on modeling and predicting unemployment in Ecuador using neural networks, comparing their effectiveness with traditional econometric methods. The dependent variable, unemployment (), defined as the percentage of the working population without employment, is analyzed based on seven macroeconomic predictors: Real gross domestic product (GDP, x₁), inflation (x₂, percentage change in the consumer price index), exports (x₃) and imports (x₄, valued in millions of USD), real minimum wage (x₅), public spending (x₆, percentage of GDP), and interest rate (x₇). Previous research, based on Okun's Law, identified linear correlations between GDP growth and unemployment reduction using Ordinary Least Squares (OLS) and cointegration models. However, these conventional techniques, including multiple regressions, showed limitations in capturing the nonlinear dynamics inherent in unemployment. In contrast, neural networks (NN), especially sequential architectures, demonstrated superiority in modeling complex interactions between variables. The study evaluates deep learning models with one and two hidden layers, identifying an optimal configuration of one hidden layer (16 neurons), achieving a loss of 0.2846, 75% accuracy, and Mean Square Error (MSE) of 0.2064 on unseen test data. The results highlight the ability of neural networks to model unemployment more accurately than linear methods, attributing this to their adaptability to learn nonlinear patterns in multivariate data. This study demonstrates that variables such as GDP, inflation, public spending, and interest rates significantly influence unemployment. Neural networks (NN) effectively capture these complex relationships. Their usefulness in predicting socioeconomic phenomena in developing countries such as Ecuador is highlighted, overcoming the limitations of traditional models.
Copyright (c) 2025 Iván Andrés Narváez Pistala, Erick Patricio Herrera Granda https://creativecommons.org/licenses/by-nc-nd/4.0
Data security and privacy in the information age in Ecuador.
Digitalization in Ecuador has increased in recent years, and with it the exposure of both personal and organizational data. This exposure increases the risk of cyberattacks. It is necessary to be aware of the risks that currently exist and recommendations for avoiding them. This study presents a systematic literature review with the aim of providing recommendations and recognizing risks in an increasingly digitalized Ecuador, using the PRISMA methodology. It demonstrates that the main threats are sophisticated cyberattacks and a lack of user awareness regarding cybersecurity. Although the Organic Law on Personal Data Protection (LOPDP) exists, there are challenges to its effective implementation. In conclusion, important strategies are presented, such as regulatory frameworks, ongoing staff training on cyberattacks, and the use of new technologies to prevent such cyberattacks. These measures, aligned with international standards, are important for strengthening Ecuador's technological resilience against threats from the digital environment.
Copyright (c) 2025 Cristopher Quijije, Wilmer Moreira https://creativecommons.org/licenses/by-nc-nd/4.0
Analysis of gastric cancer using artificial intelligence: An innovative approach in oncological research.
Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, primarily due to the challenges of early diagnosis, which often relies on invasive procedures and manual image analysis. This study presents the development of a web-based application powered by artificial intelligence, using convolutional Neural Network for the automatic detection of anomalies in endoscopic images. The methodology was structured into four phases: data collection, preprocessing, model implementation, and system evaluation. The algorithm achieved a classification accuracy of 97.4% and an average analysis time of 0.10 seconds per image, exceeding clinical performance benchmarks. The system proved to be effective, fast, and accessible to healthcare professionals, including those in resource-constrained environments. Its user-friendly interface and centralized database facilitated seamless integration into clinical practice. This research highlights the potential of deep neural networks in supporting early diagnosis of gastric cancer. Future work will focus on validating the tool in real-world clinical settings and exploring additional deep learning architectures to further enhance system performance.
Copyright (c) 2025 Michel Plaza Granoble, Oscar López Gorozabel https://creativecommons.org/licenses/by-nc-nd/4.0
AI-Based tool to support emotional diagnosis in children through drawings
This article presents the development of a technological tool based on artificial intelligence for the analysis of children's drawings, in order to support therapists in the detection of emotions in children aged 5 to 7 years. The solution is implemented through a mobile application developed in MIT App Inventor, which captures images of the drawings and sends them to a Flask server hosted on a Raspberry Pi for processing. The tool uses the HSV color model to analyze the colors used by children and, through an artificial intelligence API, generate preliminary diagnoses about their emotional state. The system is designed to optimize the early detection of emotional patterns, facilitating the work of therapists and parents in understanding children's psychological well-being. This project represents a contribution to the field of multimedia communication and data processing applied to children's mental health.
Copyright (c) 2026 Pablo Cevallos–Cadena, Pamela Imbaquingo–Carlosama, Becker Velalcazar–Villota, Dennis Morales–Quespaz, Fabián Cuzme–Rodríguez https://creativecommons.org/licenses/by-nc-nd/4.0
Predictive models of energy consumption in IoT networks using advanced neural networks
The study focused on predicting energy consumption in wireless sensor networks, an integral part of the Internet of Things (IoT), using advanced predictive models. For the agricultural environment, a dataset obtained from the Institute of Electrical and Electronics Engineers (IEEE) repository was used, corresponding to a prototype energy harvesting system based on a thermoelectric generator (TEG). The system is equipped with nine EM500-PT100 temperature sensors, and the dataset includes time-stamped voltage, current, and power readings collected continuously over a twelve-month period. Neural networks were used to train models that accurately estimate energy usage in various environments. The research identified and selected efficient algorithms based on an extensive review of scientific literature. Recurrent neural networks (RNNs), long-short-term memory units (LSTMs), and gated recurrent units (GRUs) were measured based on their ability to predict energy consumption, resulting in satisfactory performance with a coefficient of determination (R2) above 92 %. The results demonstrated that the developed models can efficiently predict energy consumption, enabling optimized energy management and reduce operating costs in IoT applications. Furthermore, the research highlighted the importance of energy efficiency in environmental sustainability and extending the lifespan of IoT devices. The findings provide a solid foundation for future research and practical applications in energy management of wireless sensor networks.
Copyright (c) 2026 Angel Torres Quijije, Emilio Zhuma Mera, Diego Intriago Rodríguez, Fernando Arraes Fuertes https://creativecommons.org/licenses/by-nc-nd/4.0
Application of multivariate techniques to productivity indicators derived from the simulation of production processes at a dairy company
The objective of this research is to model and analyze production processes in the food industry, specifically in the manufacture of dairy products, using multivariate statistical techniques. The multivariate techniques MANOVA and PERMANOVA compare groups with multiple dependent variables. Productivity indicators evaluate efficiency and performance, facilitating improvements and strategic decisions. Simulation optimizes processes, reducing costs and improving efficiency through accurate analysis and predictions. To this end, the unit operations of the fruit yogurt production process were identified and documented, developing a structured database with the processing times for each process. The integrity of the database was validated, imputing 3.3% of the missing data using the KNN method, and the absence of outliers was verified using Mahalanobis distance. The analysis of the execution times determined the most appropriate probability distributions for each process, with validation using the Chi-square goodness-of-fit test. The simulation process was validated using a Student's t-test, comparing the actual process with the simulated one, which ensured its representativeness. Subsequently, improvement scenarios were simulated using FlexSim software and Lean Manufacturing techniques (TPM, 5S, Poka Yoke, and muda elimination), evaluating the impact on production times and the quantity of finished products. The results were analyzed using the PERMANOVA method, given that the assumptions of normality, homoscedasticity, and independence for MANOVA were not met. The analyses revealed statistically significant differences between the simulation scenarios, with the 5S scenario standing out as the most efficient in reducing production time and increasing the number of products. The model explained 44% of the variability in the results, highlighting the effectiveness of the 5S methodology in optimizing the process.
Copyright (c) 2025 Cristian Andrés Flores Cadena https://creativecommons.org/licenses/by-nc-nd/4.0
Analysis of Resistance to Bactericera cockerelli in Potato Germplasm: A Sustainable Approach
Bactericera cockerelli (Sulc) is the main vector of plant phloem-associated pathogens responsible for the potato purple top complex, a disease that can cause losses of up to 100% in potato crops. Chemical control methods, mainly based on insecticides, have proven to be inefficient due to the development of insect resistance. This problem highlights the need to implement sustainable strategies to mitigate its impact. One promising strategy is the genetic resistance of plants, specifically antibiosis, this type of resistance implies that plants negatively affect the insect's biology. In this context, the present study aimed to identify potato genotypes conserved by the National Roots and Tubers Program of the National Institute of Agricultural Research with antibiosis resistance characteristics against B. cockerelli, by means of a statistical analysis based on a complete factorial design. The evaluation considered the number of eggs oviposited on different days of evaluation in 9 wild species, 18 improved varieties, 15 native varieties and 18 promising clones. Genotypes with a marked resistance due to antibiosis were identified. These materials can contribute valuable genetic resources for breeding programs and the implementation of integrated crop management strategies, thus reducing dependence on pesticides that endanger human health and biodiversity.
Copyright (c) 2025 Jessica Marlene Amagua Bautista https://creativecommons.org/licenses/by-nc-nd/4.0
Software-Defined Networks (SDN) have emerged to enhance flexibility and agility in network environments by enabling management through the separation of the control plane and the forwarding plane. This separation allows networks to quickly adapt to changing environments. Meanwhile, Multiprotocol Label Switching (MPLS) networks manage traffic using a label-switching mechanism, providing key advantages in enterprise environments. This research comparatively analyzes the performance of Software-Defined Wide Area Networks (SD-WAN) and Multiprotocol Label Switching (MPLS) networks, focusing on critical parameters such as latency, jitter, bandwidth, and packet loss. For the analysis, different simulation scenarios have been designed, each subjected to a controlled traffic load. The results of each generated load are sent to a database, which is then connected to Grafana, allowing for easy data visualization through graphs.
Copyright (c) 2026 Santiago Meneses Narvaez, Emilio Zhuma Mera, Ángel Torres Quijije, Marco Chimbo Fogacho https://creativecommons.org/licenses/by-nc-nd/4.0
Tribological Analysis of a MEP engine in the Cities of Quito and Santo Domingo with Different Oil Viscosity Grades
This project focuses on analyzing the wear of SIE engines with oils of different viscosity in the cities of Quito (2850 m.a.s.l.) and Santo Domingo (655 m.a.s.l.), to identify the wear of the internal elements of the engine through tribological tests. A 2002 Nissan Sentra B15 vehicle was used and extra 87 octane gasoline, several oil filters for each test, lubricants with viscosity grades SAE 10W30 and 20W50. Likewise, measurements were made at different engine speeds, such as 1500 and 2500 rpm. The data obtained in reference to polluting gases allowed us to observe that 10W30 oil generates higher polluting emissions and higher maintenance costs. Finally, the tribological tests and trials show that oils with the same specification and different atmospheric pressure and viscosity grade, generate different wear on the internal components of SIE engines.
Mechanical, Mechatronics, Vehicle and Materials Engineering
Copyright (c) 2025 INNOVATION & DEVELOPMENT IN ENGINEERING AND APPLIED SCIENCES
A Methodology to develop a strategic plan for Fair Trade Organizations in Ecuador. FEPP CAMARI case study
In Ecuador there are fair trade organizations that provide support to small producers who continually work empirically; FEPP CAMARI is a social organization that promotes equitable, fair and supportive marketing in society. Therefore, this study shows a methodological proposal of the strategic plan focused on small producers with fundamental and friendly strategic axes for efficient and effective execution.
Implementation of clean technologies as a strategy to improve environmental sustainability in industrial plants
This essay examines the impact of implementing clean technologies in industrial plants to enhance environmental sustainability. Case studies of companies that have adopted clean technologies, such as renewable energies, efficient waste management, and reduction of pollutant emissions, are analyzed. The results demonstrate that the implementation of clean technologies has shown to reduce greenhouse gas emissions and decrease the consumption of natural resources in the studied industrial plants. Furthermore, optimization of production processes and reduction of operational costs are observed. These findings support the importance of adopting clean technologies in the industrial sector to promote more sustainable production. The successful implementation of clean technologies not only benefits the environment but can also generate economic savings and improve the corporate image of companies.
Risk factors associated with burnout syndrome in teachers in the province of Carchi.
Summary. Burnout syndrome is widely known as a work-related problem that afflicts teachers worldwide. This problem related to risk factors that generate stress arises due to the work environment and various situations related to dealing with people who demand a high level of dedication and involvement. The present study focused on identifying risk factors associated with burnout syndrome in teachers in the province of Carchi. The main objective was to validate the adaptation of the Maslach Burnout Inventory (MBI) to the specific educational context, and to propose a model of analysis of results based on artificial intelligence. When validating the instrument, it was identified that it was not completely adapted to the local context, so the instrument was modified, having to remove 5 questions that were identified as not relevant by the Confirmatory Factor Analysis, and demographic information of the participants considered of interest to the participating institutions was incorporated. Thus, for the extraction of results from the test, a new results processing model was designed based on multivariate techniques, contemplating 37 input variables. For this, multiple alternatives were considered, including binary classifiers, shallow neural networks and five deep learning models, finding that deep learning, especially a five-layer model developed with TensorFlow and Keras, offered the most accurate predictions of emotional exhaustion, a key dimension of burnout syndrome. This model achieved an accuracy of 86% and an MSE of 0.1193604, demonstrating its reliability for automatic detection of the syndrome without the need for professional diagnosis. The research validates the adaptation of the instrument to the target context and highlights the importance of establishing new models of analysis when using instruments modified from the MBI, ensuring their relevance and applicability in specific contexts.
Detection of Chronic Malnutrition in Children Under Five Years of Age Applying Multivariate Techniques
This article shows the design of five classifiers for the detection of chronic malnutrition in children under five years of age, for which a database was processed with five quantitative and 26 qualitative variables that were subjected to a dimension decrease employing a multiple correspondence analysis, subsequently, by using neural networks the first classifier was generated. The following three designs used deep neural network techniques, starting from a principal component analysis that determined an architecture with a hidden layer that houses 96. 3% of the cumulative variance, the difference between these classifiers lies in the technique that determined the number of neurons in the hidden layer. The last designed classifier employs logistic regression as a traditional statistical technique. The study ended with tests performed with the best-performing classifier that allowed for establishing the most influential variables.
Copyright (c) 2024 INNOVATION & DEVELOPMENT IN ENGINEERING AND APPLIED SCIENCES
Study of associativity in MSMEs in the province of Imbabura-Ecuador, a case study of manufacturing industries.
The research carried out on MSMEs in the province of Imbabura through the application of a survey focused on knowing if MSMEs are aware of associativity and the benefits it generates, seeks to identify if companies are interested in managing an associative consortium model based on the economic sector in which they are classified according to the work activity carried out, for this the research was divided into phases. Phase I. identification of the population and selection of the sample using statistical formulas and a greater number of companies according to the economic sector and canton, obtaining a sample of 328 for the purposes of this investigation, phase II, information gathering was carried out through the application of a survey where information was collected about the strategic planning managed in each MSME and it was identified that a low number of companies have a strategic planning and organizational structure, phase III, with the purpose of analyzing whether companies are interested in forming associative consortia, six questions were developed, from which it was obtained that there is a lack of knowledge of the benefits that an associative consortium provides and that they are interested in managing activities to improve their positioning. both nationally and internationally
The implementation of an "Innovation in Public Services: Web Application for Citizen Revenue Collection with ISO/IEC 25010 Focus" arises as an essential measure to address the challenges inherent in the manual collection process of citizen services: drinking water, drinking water service installation, connection maintenance, reconnection of connections, garbage collection, sewerage, in society. The adoption of SCRUM, recognized for its agile and adaptable approach, is key to the execution of this project. By employing SCRUM, effective process management is facilitated, with iterative deliveries and close collaboration between developers and end users. This iterative approach allows for continuous adjustments and improvements during development, ensuring that the final platform meets the specific needs of the Santo Domingo N°1 community in the province of Pichincha, Cayambe canton. By integrating SCRUM and usability metrics in accordance with ISO/IEC 25010, the aim is not only to speed up payment processes but also to provide high-quality software that meets the expectations of partners and local authorities.
Cytochrome P450-mediated bioactivation of the chemotherapeutic cyclophosphamide for use in cancer gene therapy
This review examines the role of cytochrome P450 enzymes in the bioactivation of the chemotherapeutic agent cyclophosphamide (CPA) for use in cancer gene therapy. The study explores specific cytochrome P450 enzymes, such as CYP2B6, CYP2C9, and CYP3A4, responsible for metabolizing CPA into its active form, highlighting its potential in suicide gene therapy or GDEPT. The review also discusses genetic modifications made to improve the catalytic efficiency of these enzymes and addresses the challenges and future directions of integrating GDEPT with other cancer therapies to improve treatment specificity and efficacy.
Design and implementation of a portable modular platform for the teaching of microcontrollers.
The research focuses on the design and construction of a modular teaching platform for microcontroller and embedded systems laboratories at a university. The platform consists of 20 modules with silkscreen printed components, communication ports and power supply. It communicates with the PC through a USB port, processing the information of the entire modular platform. The design and components used were selected in response to the real needs of teachers and students, gathered through surveys. In addition, the platform allows the integration of these subjects in the simulation of technological processes and the intelligent control of a house. The results show that the modular platform has improved the teaching-learning process, applying theoretical concepts in real environments. It works with Arduino IDE software and free hardware. Its design is ergonomic and didactic for each prototype station of each circuit. Its dimensions are 555 mm long by 355 mm wide, which facilitates its mobilization. The power supply of the boards is included and is indicated with a voltage of 5 V and 3.3 V. It is optimal in practice development time, circuit design and reduction of unnecessary expenses for students. The effectiveness of the project was evaluated through performance evaluations, satisfaction surveys and development of laboratory guides in areas such as automation, control and power.
The strategic process and competitiveness of a consulting company in the year 2022, case: JC Ingeniería y Consultoría S.A.C.
This study aims to establish the level of relationship between the strategic process and the competitiveness of a consulting company in the year 2022. The results obtained from the research will allow us to determine how the strategic process is being developed in the consulting company J.C. Engineering and Consulting S.A.C. and how this impacts its competitiveness. The research will help build an effective framework to analyze, review, and measure the impact of the strategic process on business competitiveness. It is concluded that the variables 'strategic process' and 'competitiveness of a consulting company' are positively related with a correlation coefficient of 0.696. This leads to the decision to reject the null hypothesis; that is, a significant positive relationship exists between the strategic process and the competitiveness of a consulting company in 2022.
Review of the curricular design applied to mechatronics in relation to the labor field.
This article presents a review of the curricular design in mechatronics, aligned with the needs of the educational sector and the industrial field. Key components such as the integration of pedagogical knowledge and the development of technical competencies in fundamental areas such as educational robotics, mechanics, programming, design, electricity and electronics are examined, which facilitates the teaching of these disciplines in educational institutions. The research highlights the importance of training professionals capable of educating future generations in technical areas, as well as performing effectively in industrial sectors, strengthening the link between the university and contemporary social and technological demands. The study emphasizes the need to continually update curricular content, integrating essential pedagogical aspects such as didactic planning, formative evaluation and the use of active and collaborative methodologies, together with emerging technologies such as artificial intelligence (AI), advanced manufacturing and virtual simulation. It is emphasized that the flexibility of the curriculum, both pedagogically and technically, is crucial for students to adapt to the educational field of mechatronics and the dynamic industrial environment. This approach ensures effective interaction between the university, society and industry, promoting comprehensive education that not only responds to the needs of the productive sector, but also to the growing demand for educators specialized in technology.
Copyright (c) 2025 Héctor Barros Castro https://creativecommons.org/licenses/by-nc-nd/4.0
The impact of educational robotics on the development of soft skills and abilities in students in Ecuador
The present research reviews the impact of educational robotics on the development of competencies of students in Ecuador. In a context where education seeks to adapt to the demands of the 21st century, robotics is presented as an innovative tool to improve learning. The objective of this review is to analyze how the implementation of robotics in the classroom contributes to the development of technical and soft skills. A documentary methodology was used, reviewing previous studies and educational experiences in different institutions. The analysis techniques included literature review and case study. The main results indicate that students participating in educational robotics activities improved significantly in areas such as problem solving, teamwork and creativity [2]. In addition, an increase in motivation and interest in core subjects was observed. In conclusion, educational robotics not only enhances the learning of technical skills, but also fosters interpersonal skills, preparing students to face the challenges of future employment in an increasingly technological world.
Copyright (c) 2025 Marco Chiluisa https://creativecommons.org/licenses/by-nc-nd/4.0
Wildlife species distribution modeling with unmanned aircraft
Aquí tienes el texto con la ortografía y el estilo académico pulidos (no cambié el significado, solo mejoré fluidez y correcciones menores): The use of drones and open-source software in Species Distribution Models (SDMs) represents an innovative approach to the study of wildlife. This article reviews the current state of the art regarding their integration, identifying trends, challenges, and opportunities. The methodology followed the PRISMA guidelines and included a literature search in databases such as Scopus, Web of Science, and Google Scholar. Articles published within the last decade were selected if they addressed the use of drones in wildlife monitoring and the application of open-source tools in spatial data analysis. The results show a significant increase in the use of drones to collect precise geospatial data, improving the identification of habitats and species distribution patterns. Tools such as QGIS, R, and MaxEnt enable data processing without licensing costs, promoting accessibility and scientific reproducibility. However, challenges remain in terms of methodological standardization, the integration of heterogeneous data sources, and limited detection capabilities for certain species. Variability in image quality and environmental conditions also affects the accuracy of the results. In conclusion, the combination of drones and open-source software offers clear benefits: it enhances efficiency, improves model accuracy, and reduces costs. Nevertheless, greater standardization and technical validation are required to optimize its application in ecology and conservation.
Information and communication engineering
Copyright (c) 2026 Marco David Revelo, Luis Rivera, Cristian Revelo https://creativecommons.org/licenses/by-nc-nd/4.0