Learning Management Systems – LMSs are tools that use Information and Communication Technologies – ICT to implement Virtual Learning Environments – VLEs to support institutions dedicated to the teaching-learning process. Using an LMS in virtual classrooms can improve the quality of educational products and offer a wide range of benefits; however, if not properly planned, implementation can fail. This research presents a Systematic Literature Review – SLR on LMS implementation in Higher Education Institutions – HEIs, based on 33 studies retrieved from six scientific databases. The selected articles respond to three research questions proposed in this paper. First, how is the process of implementing an LMS in HEIs carried out? Second, what are the frameworks for software implementation? Third, what are the critical success and failure factors in implementing an LMS in HEIs? The results highlight the implementation of LMS with emerging technologies and software implementation frameworks, as well as the factors that lead to the failure of LMS implementation in HEIs.
Computer security is delving into new fields, and short-distance communication by light is one of them due to its advantages over traditional means that use radio frequency. A few advantages are not reading from a close distance, operating only when the user requires it, and short-range communication that is ideal for security applications and access control. This paper presents the development in the access control prototype with smartphones using light communication. Diverse sensors and flash response speeds were evaluated with physical tests in several cell phones; the same minimum response frequency of 60 Hz was obtained for different devices, and depending on the device, a maximum response of 140 Hz was obtained. Using this information, an app was developed on the Flutter platform to send data frames implementing protocols like infrared, where the signal is encoded with the flash camera’s pulse-width modulation. Using an embedded system known as ESP8266 WeMos D1 Mini, data bytes could be sent, received, synchronized, and verified from the cell phone at a frequency of 30 Hz modulation per bit.
Predicting financial budgets remains an open challenge. Most studies focus on high-frequency markets, such as stock price prediction, leaving the budget domain aside. This work addresses that gap. We evaluate four artificial intelligence techniques for budget prediction: decision trees, random forests, linear regression, and multilayer perceptron. Additionally, we implement a hybrid version, MLP_GA, where the network hyperparameters are optimized using a genetic algorithm. For training and validation, we use two original datasets not publicly available: quarterly financial statements from Mexican entities (2010-2024) and monthly financial statements from Ecuadorian organizations (2019-2022). Both present characteristics typical of emerging economies, including episodes of high inflation, currency devaluations, fiscal policy changes, and the COVID-19 crisis. The results show that MLP_GA achieves superior predictive performance compared to the standard multilayer perceptron and the other techniques. Genetic algorithm optimization enables exploring multimodal and non-convex error surfaces, finding hyperparameter configurations (number of hidden neurons, learning rate, L2 regularization coefficient, maximum iterations, and early stopping tolerance) that significantly reduce overfitting, a critical problem when dealing with short time series of only forty to sixty quarterly observations. Statistical tests confirm that the differences are significant. The contribution is threefold. First, regarding the application domain, we present the first systematic and comparative evaluation of hybrid models combining genetic algorithms with multilayer perceptrons specifically applied to financial budget prediction, distinguishing ourselves from existing studies that focus almost exclusively on high-frequency stock market prediction. Second, regarding the geographical context, we validate our models in two Latin American economies that the literature has systematically ignored: Mexico and Ecuador, a region representing less than one percent of datasets used in the literature according to recent meta-analyses. Third, regarding the methodological justification, we demonstrate that genetic optimization is particularly effective for moderate-sized data, where more complex architectures like LSTMs or transformers suffer from overfitting due to their high parameter count. The combination of monthly and quarterly analysis improves model robustness, allowing accurate performance in both short-term scenarios and broader projections. These findings suggest that the MLP_GA approach can improve financial budgeting in data-constrained contexts. The paper concludes with a discussion of limitations and future research lines.
Los semilleros de investigación universitarios combinan objetivos pedagógicos con desarrollo real de software, introduciendo tensiones organizacionales que el SDLC estándar no contempla. Este trabajo presenta un análisis retrospectivo del desarrollo de la versión 1.0 del Aplicativo del Banco de Ideas (ABI), desarrollado por el semillero Master Digital de la Universidad de Investigación y Desarrollo (UDI), Bucaramanga, Colombia. A partir de evidencia documental —reportes semanales, artefactos de diseño, registros Git y actas— se identificaron cinco dimensiones de desviación recurrente: gobernanza y dirección académica, gestión de proyecto y coordinación, coherencia metodológica y definición temprana del stack tecnológico, documentación y gestión del conocimiento, y responsabilidad individual. La ausencia de mecanismos formales de gobernanza generó un atraso acumulado de entre siete y nueve meses, concentrando la carga operativa en un subconjunto reducido de integrantes. Se proponen recomendaciones organizacionales adaptadas a entornos formativos para fortalecer la trazabilidad, continuidad técnica y estabilidad operativa de proyectos similares en semilleros universitarios latinoamericanos.
Actualmente, hospitales en Huila-Colombia necesitan sistemas de información hospitalaria para garantizar la integridad y disponibilidad de la información de pacientes; además, tener disponible la información de pacientes en línea. Aunque, los sistemas son necesarios para muchas actividades, los hospitales del Huila no cuentan con sistemas de información. En consecuencia, personal de salud utiliza métodos no estructurados para acceder a los datos; que ocasiona la toma de decisiones inadecuadas. Por lo tanto, este trabajo presenta el diseño e implementación de un sistema de información para optimizar el proceso de gestión de pruebas de análisis de orina en hospitales del Huila, que mejore el acceso a la información. El sistema implementado utiliza el estándar HL7-FHIR, sobre plataformas Open-Source, y basado en la Web, escalable y compatible con otros sistemas de información hospitalaria. Finalmente, la solución propuesta minimiza tiempos de acceso y gestión de las pruebas integrales realizadas por el equipo médicos.
